diff --git a/docs/index.html b/docs/index.html index 3e12cdd..bd17538 100644 --- a/docs/index.html +++ b/docs/index.html @@ -302,6 +302,18 @@ Views of Americans about robot vs. human intelligence, All adults” + +
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  • Cost of space launches to low Earth orbit
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  • Share of United States households using specific technologies
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  • diff --git a/docs/machinelearning/1-classfication-comparison.html b/docs/machinelearning/1-classfication-comparison.html index 3954217..0e2fd1d 100644 --- a/docs/machinelearning/1-classfication-comparison.html +++ b/docs/machinelearning/1-classfication-comparison.html @@ -336,6 +336,18 @@ Views of Americans about robot vs. human intelligence, All adults”
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  • diff --git a/docs/materials.html b/docs/materials.html index 6718c09..2c8914d 100644 --- a/docs/materials.html +++ b/docs/materials.html @@ -302,6 +302,18 @@ Views of Americans about robot vs. human intelligence, All adults”
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  • Cost of space launches to low Earth orbit
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  • Share of United States households using specific technologies
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  • Cost of space launches to low Earth orbit
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  • Share of United States households using specific technologies
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  • Cost of space launches to low Earth orbit
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  • diff --git a/docs/math/4-one-variable-integral.html b/docs/math/4-one-variable-integral.html index e2d30cc..8412080 100644 --- a/docs/math/4-one-variable-integral.html +++ b/docs/math/4-one-variable-integral.html @@ -336,6 +336,18 @@ Views of Americans about robot vs. human intelligence, All adults”
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  • Cost of space launches to low Earth orbit
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  • Share of United States households using specific technologies
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  • Cost of space launches to low Earth orbit
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"vis/american-view-on-ai.html#load-data-dataframe", - "title": "Views of Americans about robot vs. human intelligence, All adults”", - "section": "2. load data->dataframe", - "text": "2. load data->dataframe\n\n\nCode\n df=@pipe CSV.File(\"./data/views-of-americans-robot-vs-human-intelligence.csv\")|>DataFrame|>coalesce.(_, 0.0)\n\nfirst(df,5)\n\n\n5×8 DataFrame\n\n\n\nRow\nEntity\nCode\nDay\nNone of these\nDon't know\nMost robots have already developed higher levels of intelligence than humans\nRobots will never be able to develop higher levels of intelligence than humans\nRobots will be able to develop higher levels of intelligence than humans in the future\n\n\n\nString31\nFloat64\nDate\nInt64\nInt64\nInt64\nInt64\nInt64\n\n\n\n\n1\n18-29 years\n0.0\n2021-01-07\n6\n18\n14\n14\n47\n\n\n2\n18-29 years\n0.0\n2021-07-01\n8\n22\n19\n12\n39\n\n\n3\n18-29 years\n0.0\n2022-01-06\n3\n20\n11\n17\n49\n\n\n4\n18-29 years\n0.0\n2022-06-30\n9\n18\n20\n18\n35\n\n\n5\n18-29 years\n0.0\n2023-01-05\n9\n14\n16\n15\n46", + "objectID": "vis/cost-of-space-launch.html#load-package", + "href": "vis/cost-of-space-launch.html#load-package", + "title": "Cost of space launches to low Earth orbit", + "section": "load package", + "text": "load package\n\n\nCode\ninclude(\"utils.jl\")\n\n\n[ Info: loading success\n\n\n\n\nCode\nmake_title(\"Part One : Data Processing\")\n\n\n \n Part One : Data Processing", "crumbs": [ "Data Viz", - "Views of Americans about robot vs. human intelligence, All adults\"" + "Cost of space launches to low Earth orbit" ] }, { - "objectID": "vis/american-view-on-ai.html#data-processing", - "href": "vis/american-view-on-ai.html#data-processing", - "title": "Views of Americans about robot vs. human intelligence, All adults”", - "section": "data processing", - "text": "data processing\n\n\nCode\n cats=names(df)\n rename!(df,\"None of these\"=>:none,\"Don't know\"=>:dtkonw,\n \"Most robots have already developed higher levels of intelligence than humans\"=>:better,\n \"Robots will never be able to develop higher levels of intelligence than humans\"=>:nobetter,\n \"Robots will be able to develop higher levels of intelligence than humans in the future\"=>:future\n \n )\n\n df2=@chain df begin\n @group_by(Day)\n @summarize(none=sum(none),dtkonw=sum(dtkonw), better=sum(better),nobetter=sum(better),future=sum(future))\n \n end\n\n\n6×6 DataFrame\n\n\n\nRow\nDay\nnone\ndtkonw\nbetter\nnobetter\nfuture\n\n\n\nDate\nInt64\nInt64\nInt64\nInt64\nInt64\n\n\n\n\n1\n2021-01-07\n136\n482\n263\n263\n958\n\n\n2\n2021-07-01\n139\n560\n345\n345\n793\n\n\n3\n2022-01-06\n146\n538\n277\n277\n933\n\n\n4\n2022-06-30\n243\n441\n346\n346\n883\n\n\n5\n2023-01-05\n240\n494\n303\n303\n906\n\n\n6\n2023-06-29\n181\n478\n410\n410\n832\n\n\n\n\n\n\n\n\nCode\naxis = (width = 1000, height =500,xticklabelrotation = pi/8,title=\"Attitude about AI development\",yticks=(range(100,1000,7),[\"$i %\" for i in 5:5:35]))\ndata_layer = data(df2)\nmapping_layer=mapping(:Day,[:none,:dtkonw,:better,:nobetter,:future].=> \"value\", color=dims(1) => renamer(cats[4:8]) => \"opinion\")\nvisual_layer = visual(ScatterLines;stroke=1,strokecolor=(:red,0.8),markersize=10,linewidth=3)\ndraw(data_layer * mapping_layer * visual_layer,axis=axis,\n legend=(position=:right, titleposition=:top, framevisible=true, padding=5)\n)\n\n\n┌ Warning: Keyword argument `bgcolor` is deprecated, use `backgroundcolor` instead.\n└ @ Makie ~/.julia/packages/Makie/RgxaV/src/makielayout/blocks/legend.jl:23", + "objectID": "vis/cost-of-space-launch.html#load-datadataframe", + "href": "vis/cost-of-space-launch.html#load-datadataframe", + "title": "Cost of space launches to low Earth orbit", + "section": "1.1 load data=>dataframe", + "text": "1.1 load data=>dataframe\n\n\nCode\n df=@pipe CSV.File(\"./data/cost-space-launches-low-earth-orbit.csv\")|>DataFrame\ndf =@chain df begin\n @select(1,3,4,5)\n @arrange(Year)\n @filter(Entity ∉ [\"Delta E\",\"Scout\",\"Minotaur I\",\"Space Shuttle\"])\n end\nfirst(df,5)\n\n\n5×4 DataFrame\n\n\n\nRow\nEntity\nYear\ncost_per_kg\nlaunch_class\n\n\n\nString31\nInt64\nInt64\nString7\n\n\n\n\n1\nKosmos\n1962\n14900\nSmall\n\n\n2\nAtlas Centaur\n1963\n29500\nMedium\n\n\n3\nTitan II\n1964\n30600\nMedium\n\n\n4\nProton\n1965\n8200\nHeavy\n\n\n5\nTitan III+\n1965\n21000\nMedium\n\n\n\n\n\n\n\n\nCode\nmake_title(\"Part Two : Plots\")\n\n\n \n Part Two : Plots\n \n\n\n\n\nCode\n axis = (width = 600, height =1200)\n data_layer=data(df) \n mapping_layer1=mapping(:Year,:cost_per_kg,color=:launch_class)\n visual_layer1 = visual(Scatter;strokewidth=2,strokecolor=:black)\n mapping_layer2=mapping(:Year,:cost_per_kg, text=:Entity => verbatim)\n visual_layer2 = visual(Makie.Text;color=:black)\n plt=data_layer*mapping_layer1*visual_layer1\n text_pipe=data_layer*mapping_layer2*visual_layer2\ndraw(plt+text_pipe,axis=axis)\n\n\n┌ Warning: Keyword argument `bgcolor` is deprecated, use `backgroundcolor` instead.\n└ @ Makie ~/.julia/packages/Makie/RgxaV/src/makielayout/blocks/legend.jl:23", "crumbs": [ "Data Viz", - "Views of Americans about robot vs. human intelligence, All adults\"" + "Cost of space launches to low Earth orbit" + ] + }, + { + "objectID": "vis/child-mortality-rate-1960to2021.html", + "href": "vis/child-mortality-rate-1960to2021.html", + "title": "Child mortality rate, 1960 to 2021", + "section": "", + "text": "ref\n\n\n\n\nref : Child mortality rate, 1960 to 2021\npackage :The newggslopegraph function from the CGPfunctions package\nCode\n include(\"utils.jl\")\n\n\n[ Info: loading success\nCode\nmake_title(\"Part one : Data processing\")\n\n\n \n Part one : Data processing", + "crumbs": [ + "Data Viz", + "Child mortality rate, 1960 to 2021" + ] + }, + { + "objectID": "vis/child-mortality-rate-1960to2021.html#load-csv", + "href": "vis/child-mortality-rate-1960to2021.html#load-csv", + "title": "Child mortality rate, 1960 to 2021", + "section": "load csv", + "text": "load csv\n\n\nCode\ndf=@pipe CSV.File(\"./data/child-mortality-1960-vs-latest-slope.csv\")|>DataFrame|>rename(_,\"Observation value - Unit_of_measure: Deaths per 100 live births - Indicator: Under-five mortality rate - Sex: Both sexes - Wealth_quintile: All wealth quintiles\"=>:rate)|>select(_,Not(:Code,:Continent,\"Population (historical estimates)\"))|>dropmissing\n\n\n13472×3 DataFrame13447 rows omitted\n\n\n\nRow\nEntity\nYear\nrate\n\n\n\nString\nInt64\nFloat64\n\n\n\n\n1\nAfghanistan\n1957\n37.5905\n\n\n2\nAfghanistan\n1958\n36.9628\n\n\n3\nAfghanistan\n1959\n36.3437\n\n\n4\nAfghanistan\n1960\n35.7301\n\n\n5\nAfghanistan\n1961\n35.1658\n\n\n6\nAfghanistan\n1962\n34.584\n\n\n7\nAfghanistan\n1963\n34.0159\n\n\n8\nAfghanistan\n1964\n33.4848\n\n\n9\nAfghanistan\n1965\n32.9421\n\n\n10\nAfghanistan\n1966\n32.3938\n\n\n11\nAfghanistan\n1967\n31.8366\n\n\n12\nAfghanistan\n1968\n31.2825\n\n\n13\nAfghanistan\n1969\n30.7243\n\n\n⋮\n⋮\n⋮\n⋮\n\n\n13461\nZimbabwe\n2010\n8.61062\n\n\n13462\nZimbabwe\n2011\n8.05877\n\n\n13463\nZimbabwe\n2012\n7.21463\n\n\n13464\nZimbabwe\n2013\n6.62226\n\n\n13465\nZimbabwe\n2014\n6.26086\n\n\n13466\nZimbabwe\n2015\n6.04718\n\n\n13467\nZimbabwe\n2016\n5.78704\n\n\n13468\nZimbabwe\n2017\n5.61751\n\n\n13469\nZimbabwe\n2018\n5.36877\n\n\n13470\nZimbabwe\n2019\n5.26659\n\n\n13471\nZimbabwe\n2020\n5.17696\n\n\n13472\nZimbabwe\n2021\n4.9522", + "crumbs": [ + "Data Viz", + "Child mortality rate, 1960 to 2021" + ] + }, + { + "objectID": "vis/child-mortality-rate-1960to2021.html#tidy-data", + "href": "vis/child-mortality-rate-1960to2021.html#tidy-data", + "title": "Child mortality rate, 1960 to 2021", + "section": "tidy data", + "text": "tidy data\n\n\nCode\n query_years=[1960,1980,2000,2020]\n query_countries=[\"Abkhazia\",\"Uganda\",\"Kenya\",\"Egypt\",\"Spain\",\"Japan\",\"France\"]\n @eval(Main, input_years=query_years)\n @eval(Main, input_countries =query_countries)\n @eval(Main,input_points=length(query_years))\n df=@chain df begin\n @filter(Year ∈ !!input_years)\n @group_by(Entity)\n #filter(d->nrow(d)==4,_) \n @filter(length(Year)==!!input_points) \n @arrange(Year)\n @ungroup\n coerce(_, :Year=>OrderedFactor)\n @mutate(rate=round(rate,digits=1))\n @filter(Entity ∈ !!input_countries)\n end\n\n\n24×3 DataFrame\n\n\n\nRow\nEntity\nYear\nrate\n\n\n\nString\nCat…\nFloat64\n\n\n\n\n1\nEgypt\n1960\n31.8\n\n\n2\nEgypt\n1980\n16.8\n\n\n3\nEgypt\n2000\n4.7\n\n\n4\nEgypt\n2020\n2.0\n\n\n5\nFrance\n1960\n2.8\n\n\n6\nFrance\n1980\n1.2\n\n\n7\nFrance\n2000\n0.5\n\n\n8\nFrance\n2020\n0.4\n\n\n9\nJapan\n1960\n4.0\n\n\n10\nJapan\n1980\n1.0\n\n\n11\nJapan\n2000\n0.5\n\n\n12\nJapan\n2020\n0.2\n\n\n13\nKenya\n1960\n19.7\n\n\n14\nKenya\n1980\n11.1\n\n\n15\nKenya\n2000\n9.9\n\n\n16\nKenya\n2020\n3.9\n\n\n17\nSpain\n1960\n5.5\n\n\n18\nSpain\n1980\n1.5\n\n\n19\nSpain\n2000\n0.5\n\n\n20\nSpain\n2020\n0.3\n\n\n21\nUganda\n1960\n22.3\n\n\n22\nUganda\n1980\n21.9\n\n\n23\nUganda\n2000\n14.6\n\n\n24\nUganda\n2020\n4.4\n\n\n\n\n\n\n\n\nCode\nmake_title(\"Part 2: Plot\")\n\n\n \n Part 2: Plot\n \n\n\n\n\nCode\n @rput df\n R\"\"\"\n # install.packages(\"CGPfunctions\")\n library(CGPfunctions)\n newggslopegraph(dataframe = df,\n Times = Year,\n Measurement = rate,\n Grouping = Entity,\n Title = \"Child mortality rate\",\n SubTitle = \"1960-2020\",\n Caption = \"By R CHARTS\",\n LineThickness = 0.5,\n # DataLabelPadding =0.2,\n # DataLabelLineSize = 0.5,\n # DataLabelFillColor = \"lightblue\",\n ThemeChoice = \"wsj\"\n )\n \"\"\"\n\n\n\n\n\n\n\n\n\nRObject{VecSxp}", + "crumbs": [ + "Data Viz", + "Child mortality rate, 1960 to 2021" ] }, { @@ -174,6 +207,50 @@ "f1 2019 drivers statistics" ] }, + { + "objectID": "vis/fishing-with-net-type.html", + "href": "vis/fishing-with-net-type.html", + "title": "Wild fish catch by gear type, World", + "section": "", + "text": "info\n\n\n\n\nref:Wild fish catch by gear type, World\nfishing gear type : Fish and Overfishing - Our World in Data\nload pacakge\nCode\ninclude(\"utils.jl\")\n\n\n[ Info: loading success", + "crumbs": [ + "Data Viz", + "Wild fish catch by gear type, World" + ] + }, + { + "objectID": "vis/fishing-with-net-type.html#load-data-dataframe", + "href": "vis/fishing-with-net-type.html#load-data-dataframe", + "title": "Wild fish catch by gear type, World", + "section": "2. load data->dataframe", + "text": "2. load data->dataframe\n\n\nCode\ndf=@pipe CSV.File(\"./data/fish-catch-gear-type.csv\")|>DataFrame|>DataFrame|>coalesce.(_, 0.0)|>rename!(_,\"pelagic trawl\"=>:pelagic_trawl)\n\n\n13730×11 DataFrame13705 rows omitted\n\n\n\nRow\nEntity\nCode\nYear\nunknown_gear\nother_gear\nlongline\ngillnet\nsmall_scale\npurse_seine\npelagic_trawl\nbottom_trawl\n\n\n\nString\nAny\nInt64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\n\n\n\n\n1\nAlbania\nALB\n1995\n1.21382\n135.643\n0.0\n0.0\n39.7734\n54.0707\n104.631\n1120.05\n\n\n2\nAlbania\nALB\n1996\n16.5429\n209.023\n0.0\n0.0\n50.4084\n84.432\n163.963\n1804.86\n\n\n3\nAlbania\nALB\n1997\n14.2151\n113.616\n0.0\n0.0\n13.3192\n38.8843\n75.6516\n810.648\n\n\n4\nAlbania\nALB\n1998\n14.2151\n219.064\n0.0\n0.0\n10.9106\n88.8256\n173.728\n1897.43\n\n\n5\nAlbania\nALB\n1999\n14.2151\n227.661\n0.0\n0.0\n11.3388\n92.3117\n180.907\n1972.6\n\n\n6\nAlbania\nALB\n2011\n634.994\n0.0\n0.0\n0.0\n21.7128\n143.804\n0.0\n3144.62\n\n\n7\nAlbania\nALB\n2012\n1499.1\n0.0\n0.0\n0.0\n45.1773\n339.495\n0.0\n7423.87\n\n\n8\nAlbania\nALB\n2013\n1561.1\n0.0\n0.0\n0.0\n47.0043\n353.535\n0.0\n7730.9\n\n\n9\nAlbania\nALB\n2014\n1869.72\n0.0\n0.0\n0.0\n71.146\n282.188\n0.0\n6170.72\n\n\n10\nAlbania\nALB\n2015\n1149.99\n0.0\n0.0\n0.0\n33.2955\n273.578\n0.0\n5982.43\n\n\n11\nAlbania\nALB\n2016\n1447.69\n0.0\n0.0\n0.0\n51.6242\n251.592\n0.0\n5501.66\n\n\n12\nAlbania\nALB\n2017\n1566.67\n0.0\n0.0\n0.0\n58.2399\n249.588\n0.0\n5457.85\n\n\n13\nAlbania\nALB\n2018\n899.878\n0.0\n0.0\n0.0\n17.0815\n299.842\n0.0\n6556.76\n\n\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n\n\n13719\nYemen\nYEM\n1999\n0.0\n10150.0\n0.0\n0.0\n1.73005e5\n0.0\n0.0\n89861.3\n\n\n13720\nYemen\nYEM\n2000\n0.0\n11239.5\n0.0\n0.0\n1.47774e5\n0.0\n0.0\n89681.5\n\n\n13721\nYemen\nYEM\n2001\n0.0\n10217.8\n0.0\n0.0\n1.85588e5\n0.0\n0.0\n1.02895e5\n\n\n13722\nYemen\nYEM\n2002\n0.0\n9503.93\n0.0\n0.0\n2.49449e5\n0.0\n0.0\n1.14498e5\n\n\n13723\nYemen\nYEM\n2003\n0.0\n26166.0\n0.0\n0.0\n3.29698e5\n0.0\n0.0\n1.03919e5\n\n\n13724\nYemen\nYEM\n2004\n0.0\n32574.7\n0.0\n0.0\n372192.0\n0.0\n0.0\n1.0894e5\n\n\n13725\nYemen\nYEM\n2005\n0.0\n30072.7\n0.0\n0.0\n351902.0\n0.0\n0.0\n95306.5\n\n\n13726\nYemen\nYEM\n2006\n0.0\n30707.2\n0.0\n0.0\n3.45725e5\n0.0\n0.0\n86286.5\n\n\n13727\nYemen\nYEM\n2007\n0.0\n27540.5\n0.0\n0.0\n2.61181e5\n0.0\n0.0\n74653.9\n\n\n13728\nYemen\nYEM\n2008\n0.0\n20552.1\n0.0\n0.0\n1.80339e5\n0.0\n0.0\n43331.4\n\n\n13729\nYemen\nYEM\n2009\n0.0\n24330.6\n0.0\n0.0\n2.29675e5\n0.0\n0.0\n57511.8\n\n\n13730\nYemen\nYEM\n2010\n0.0\n28806.0\n0.0\n0.0\n2.64376e5\n0.0\n0.0\n93367.3", + "crumbs": [ + "Data Viz", + "Wild fish catch by gear type, World" + ] + }, + { + "objectID": "vis/fishing-with-net-type.html#data-processing", + "href": "vis/fishing-with-net-type.html#data-processing", + "title": "Wild fish catch by gear type, World", + "section": "3. data processing", + "text": "3. data processing\n\n\nCode\ndf=@chain df begin\n @group_by(Year)\n @summarize(unknown_gear=sum(unknown_gear),\n other_gear=sum(other_gear),\n longline=sum(other_gear),\n gillnet=sum(gillnet),\n small_scale=sum(small_scale),\n purse_seine=sum(purse_seine),\n pelagic_trawl=sum(pelagic_trawl),\n bottom_trawl=sum(bottom_trawl)\n )\n @ungroup\n @pivot_longer(_, unknown_gear:bottom_trawl,names_to = gear_type, values_to =weight)\nend\n\n\n552×3 DataFrame527 rows omitted\n\n\n\nRow\nYear\ngear_type\nweight\n\n\n\nInt64\nString\nFloat64\n\n\n\n\n1\n1995\nunknown_gear\n1.06338e7\n\n\n2\n1996\nunknown_gear\n1.0397e7\n\n\n3\n1997\nunknown_gear\n1.1286e7\n\n\n4\n1998\nunknown_gear\n1.08899e7\n\n\n5\n1999\nunknown_gear\n1.15815e7\n\n\n6\n2011\nunknown_gear\n1.05883e7\n\n\n7\n2012\nunknown_gear\n1.07134e7\n\n\n8\n2013\nunknown_gear\n1.0431e7\n\n\n9\n2014\nunknown_gear\n1.19992e7\n\n\n10\n2015\nunknown_gear\n1.54834e7\n\n\n11\n2016\nunknown_gear\n1.20867e7\n\n\n12\n2017\nunknown_gear\n1.6716e7\n\n\n13\n2018\nunknown_gear\n1.57526e7\n\n\n⋮\n⋮\n⋮\n⋮\n\n\n541\n1994\nbottom_trawl\n5.43778e7\n\n\n542\n2000\nbottom_trawl\n5.18786e7\n\n\n543\n2001\nbottom_trawl\n5.23496e7\n\n\n544\n2002\nbottom_trawl\n5.20457e7\n\n\n545\n2003\nbottom_trawl\n5.00434e7\n\n\n546\n2004\nbottom_trawl\n5.05469e7\n\n\n547\n2005\nbottom_trawl\n4.82271e7\n\n\n548\n2006\nbottom_trawl\n4.84797e7\n\n\n549\n2007\nbottom_trawl\n4.7881e7\n\n\n550\n2008\nbottom_trawl\n4.76333e7\n\n\n551\n2009\nbottom_trawl\n4.80774e7\n\n\n552\n2010\nbottom_trawl\n4.89898e7", + "crumbs": [ + "Data Viz", + "Wild fish catch by gear type, World" + ] + }, + { + "objectID": "vis/fishing-with-net-type.html#plot", + "href": "vis/fishing-with-net-type.html#plot", + "title": "Wild fish catch by gear type, World", + "section": "4. plot", + "text": "4. plot\n\n\nCode\n@rput df\nfunction plot_arestack_r()\n R\"\"\"\n library(ggplot2)\n library(ggthemes)\n library(viridis)\n ggplot(df, aes(x=Year, y=weight, fill=gear_type)) + \n geom_area(alpha=0.6 , size=.5, colour=\"white\")+scale_fill_viridis(discrete = T)+theme_wsj()\n \"\"\"\nend\nplot_arestack_r()\n\n\n┌ Warning: RCall.jl: Warning: package ‘ggthemes’ was built under R version 4.2.3\n└ @ RCall ~/.julia/packages/RCall/gOwEW/src/io.jl:172\n┌ Warning: RCall.jl: Loading required package: viridisLite\n└ @ RCall ~/.julia/packages/RCall/gOwEW/src/io.jl:172\n┌ Warning: RCall.jl: Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.\n│ ℹ Please use `linewidth` instead.\n└ @ RCall ~/.julia/packages/RCall/gOwEW/src/io.jl:172\n\n\n\n\n\n\n\n\n\n\n\n(a) different gear fihsing amount\n\n\n\n\n\n\n\nRObject{VecSxp}\n\n\n(b)\n\n\n\n\n\nFigure 1", + "crumbs": [ + "Data Viz", + "Wild fish catch by gear type, World" + ] + }, { "objectID": "vis/world-electricity-generation.html", "href": "vis/world-electricity-generation.html", @@ -196,6 +273,39 @@ "Total electricity generation\"" ] }, + { + "objectID": "vis/annual-industrial-robots-installed.html", + "href": "vis/annual-industrial-robots-installed.html", + "title": "Annual industrial robots installed", + "section": "", + "text": "info\n\n\n\n\nref:Annual industrial robots installed\nload pacakge\nCode\ninclude(\"utils.jl\")\n\n\n[ Info: loading success", + "crumbs": [ + "Data Viz", + "Annual industrial robots installed" + ] + }, + { + "objectID": "vis/annual-industrial-robots-installed.html#load-data-dataframe", + "href": "vis/annual-industrial-robots-installed.html#load-data-dataframe", + "title": "Annual industrial robots installed", + "section": "2. load data->dataframe", + "text": "2. load data->dataframe\n\n\nCode\ndf=@pipe CSV.File(\"./data/annual-industrial-robots-installed.csv\")|>DataFrame|>coalesce.(_, 0.0)|>rename!(_,\"Total number of industrial robots installed by contry, 2021\"=>:total)|>sort(_,:total)\n\nfirst(df,5)\n\n\n5×4 DataFrame\n\n\n\nRow\nEntity\nCode\nYear\ntotal\n\n\n\nString15\nString3\nInt64\nInt64\n\n\n\n\n1\nPoland\nPOL\n2021\n3300\n\n\n2\nSpain\nESP\n2021\n3400\n\n\n3\nSingapore\nSGP\n2021\n3500\n\n\n4\nThailand\nTHA\n2021\n3900\n\n\n5\nCanada\nCAN\n2021\n4300", + "crumbs": [ + "Data Viz", + "Annual industrial robots installed" + ] + }, + { + "objectID": "vis/annual-industrial-robots-installed.html#plot", + "href": "vis/annual-industrial-robots-installed.html#plot", + "title": "Annual industrial robots installed", + "section": "3. plot", + "text": "3. plot\n\n\nCode\n row,col=size(df)\n fig=Figure()\n ax=Axis(fig[1,1])\n ax.title=\"Annual industrial robots installed\"\n ax.subtitle=\"2021\"\n ax.yticks=(1:row,df.Entity)\n ax.xgridvisible=false\n ax.ygridvisible=false\n xs=df.Entity\n ys=round.(Int,df.total)\n barplot!(ax,1:row,ys;direction=:x,color=ys,bar_labels=:y,#flip_labels_at=20,\n #color_over_background=:dodgerblue3,\n #color_over_bar=:white,\n )\n \n fig", + "crumbs": [ + "Data Viz", + "Annual industrial robots installed" + ] + }, { "objectID": "vis/usa-household-use-technology.html", "href": "vis/usa-household-use-technology.html", @@ -219,299 +329,355 @@ ] }, { - "objectID": "math/2-function.html", - "href": "math/2-function.html", - "title": "2-Two Variables Function corss-section", + "objectID": "vis/irish-whiskey-sales.html", + "href": "vis/irish-whiskey-sales.html", + "title": "Irish Whiskey Sales", "section": "", - "text": "二元函数的分解理解\n\n\n\n二元函数可以从两个变量的方向分解开来研究\n固定一个变量, 改变另一个变量,得到一组一元函数\n在下列函数图里:\n\n第一列固定 y值(取一组离散值),改变 x值(连续变量)\n第二列x,y 都是连续值,二元函数自身\n第三列固定 x值(取一组离散值),改变 y值(连续变量)\n\n这里的方法和概率概念的边缘概率(marginal probability)一样, 意思是给定一个条件(固定一个值), 改变另一个变量的取值. 二元分布的概率密度函数就是二元函数,只不过曲线下积分等于 1\nCode\nusing GLMakie\nxs = ys = -3:0.1:3\nxs2 = ys2 = -2:1:2\nlen = length(xs)\nf1(x, y) = x^2 - y^2\nf2(x, y) = ℯ^(−(x^2) − (y^2))\nf3(x, y) = y^2\nf4(x, y) = 2 + 2x - y\nf5(x, y) = x + 2y + 3\nf6(x, y) = x^2 + y^2\nf7(x, y) = -x^2 - y^2\nf8(x, y) = x^2 \n\nfunc_arr = [f1,f2,f3,f4,f5,f6,f7,f8]\nlatexstr_arr=[L\"f(x, y) = x^2 - y^2\",L\"f(x,y)=ℯ^{−x^2 −y^2}\",L\"f(x,y)=y^2\",\nL\"f(x,y)=2 + 2x - y\",L\"f(x,y)=x + 2y + 3\",L\"f(x,y)=x^2 + y^2\",L\"f(x,y)=-(x^2+y^2)\",L\"f(x,y)=x^2\"]\n\n\"\"\"\n make_z(index::Int)\n 根据函数索引返回 julia 函数表达式和\n 函数 latex string\n\"\"\"\nfunction make_z(index::Int)\n return ( (x,y)->func_arr[index](x,y),latexstr_arr[index])\nend\n\n\nmake_z\nCode\nfunction plot_func(func_z,latexstr)\n f = Figure(resolution=(1800, 1000))\n ax1 = Axis3(f[1, 1], title=L\"fixed y\", azimuth=0.3pi)\n ax2 = Axis3(f[1, 2], title=L\"surface\", azimuth=0.3pi)\n ax3 = Axis3(f[1, 3], title=L\"fixed x\", azimuth=0.3pi)\n ax4 = Axis(f[2, 1])\n ax5 = Axis(f[2, 3])\n ax6 = Axis3(f[2, 2], azimuth=0.3pi)\n Label(f[0, :], latexstr, fontsize=40)\n zs = [func_z(x, y) for x in xs, y in ys]\n surface!(ax2, xs, ys, zs, color=:lightblue)\n for y in ys2\n yspan = fill(y, len)\n lines!(ax1, xs, yspan, func_z.(xs, yspan), color=:black, lw=3)\n lines!(ax4, xs, func_z.(xs, y), color=:black, lw=3)\n lines!(ax6, xs, yspan, func_z.(xs, yspan), color=:black, lw=3)\n end\n for x in xs2\n xspan = fill(x, len)\n lines!(ax3, xspan, ys, func_z.(xspan, ys), color=:red, lw=3)\n lines!(ax5, ys, func_z.(x, ys), color=:red, lw=3)\n lines!(ax6, xspan, ys, func_z.(xspan, ys), color=:red, lw=3)\n\n end\n\n surface!(ax6, xs, ys, zs, color=:lightblue)\n\n\n return f\nend\n\n\nplot_func (generic function with 1 method)\nCode\nlet \nmake_z(1)|>d->plot_func(d[1],d[2]) \nend", + "text": "info\n\n\n\n\nref:Saint Patricks Irish Whiskey Sales\nsource :/Users/lunarcheung/Downloads/The Twisted Nature of Irish Whiskey Sales.png\nCode\ninclude(\"utils.jl\")\n\n\n[ Info: loading success", "crumbs": [ - "Math", - "2-Two Variables Function corss-section" + "Data Viz", + "Irish Whiskey Sales" ] }, { - "objectID": "math/2-function.html#section", - "href": "math/2-function.html#section", - "title": "2-Two Variables Function corss-section", + "objectID": "vis/irish-whiskey-sales.html#load-data-dataframe", + "href": "vis/irish-whiskey-sales.html#load-data-dataframe", + "title": "Irish Whiskey Sales", + "section": "2. load data->dataframe", + "text": "2. load data->dataframe\n\n\nCode\n df=@pipe CSV.File(\"./data/irish.csv\")|>DataFrame|>dropmissing\n first(df,5)\n\n\n5×5 DataFrame\n\n\n\nRow\ncategory\nquality\ncountry\nyear\ncases\n\n\n\nString15\nString15\nString\nInt64\nInt64\n\n\n\n\n1\nIrish Whiskey\nStandard\nUnited States\n1990\n243000\n\n\n2\nIrish Whiskey\nStandard\nUnited States\n1991\n214000\n\n\n3\nIrish Whiskey\nStandard\nUnited States\n1992\n219500\n\n\n4\nIrish Whiskey\nStandard\nUnited States\n1993\n225000\n\n\n5\nIrish Whiskey\nStandard\nUnited States\n1994\n239500", + "crumbs": [ + "Data Viz", + "Irish Whiskey Sales" + ] + }, + { + "objectID": "vis/irish-whiskey-sales.html#data-preprocessing", + "href": "vis/irish-whiskey-sales.html#data-preprocessing", + "title": "Irish Whiskey Sales", + "section": "3. data preprocessing", + "text": "3. data preprocessing\n\n\nCode\n df1=@chain df begin\n @select(country,year, cases)\n #@filter(country in [\"Ireland\",\"Germany\",\"Russia\",\"France\"])\n @group_by(country,year)\n @summarize(country,year,sales=sum(cases)/1000)\n @ungroup\n #@mutate(cases=cases/1000)\n # @filter(country in [\"Ireland\",\"Germany\",\"Russia\"])\n # @group_by(country)\n # #@arrange(year)\n # @mutate(cases =cases.-cases[1])\n # @ungroup\n unique!(_)\n @group_by(country)\n @arrange(year)\n @mutate(sales =sales.-sales[1])\n @ungroup\n\n end\n all_country_data=@chain df1 begin @filter(country ∉ [\"Ireland\",\"Germany\",\"Russia\",\"France\"])\n end\n Ireland_data=@chain df1 begin @filter(country == \"Ireland\")\n end\n four_country_data=@chain df1 begin @filter(country ∈ [\"Ireland\",\"Germany\",\"Russia\",\"France\",\"Spain\"])\n end\n\n\n134×3 DataFrame109 rows omitted\n\n\n\nRow\ncountry\nyear\nsales\n\n\n\nString\nInt64\nFloat64\n\n\n\n\n1\nIreland\n1990\n0.0\n\n\n2\nIreland\n1991\n9.5\n\n\n3\nIreland\n1992\n-26.5\n\n\n4\nIreland\n1993\n-13.0\n\n\n5\nIreland\n1994\n-12.5\n\n\n6\nIreland\n1995\n-22.5\n\n\n7\nIreland\n1996\n-2.4\n\n\n8\nIreland\n1997\n7.8\n\n\n9\nIreland\n1998\n9.25\n\n\n10\nIreland\n1999\n49.15\n\n\n11\nIreland\n2000\n52.1\n\n\n12\nIreland\n2001\n53.35\n\n\n13\nIreland\n2002\n32.8\n\n\n⋮\n⋮\n⋮\n⋮\n\n\n123\nRussia\n2005\n39.75\n\n\n124\nRussia\n2006\n63.25\n\n\n125\nRussia\n2007\n102.75\n\n\n126\nRussia\n2008\n146.75\n\n\n127\nRussia\n2009\n132.0\n\n\n128\nRussia\n2010\n182.0\n\n\n129\nRussia\n2011\n307.25\n\n\n130\nRussia\n2012\n365.25\n\n\n131\nRussia\n2013\n387.25\n\n\n132\nRussia\n2014\n445.75\n\n\n133\nRussia\n2015\n290.5\n\n\n134\nRussia\n2016\n293.5\n\n\n\n\n\n\n\n\nCode\nran=-100:50:350\naxis = (width = 500, height =500,xticks=(ran,[\"$(i)k\" for i in ran]), xticklabelrotation = pi/8,\ntitlealign = :center,titlesize=24,\ntitle=\"The Twisted Nature of Irish Whiskey Sales\",\n subtitle = rich(\n \"Within Europe sales have increased 225% since 1990. While\" , rich(\"Ireland's\",color =:blue), \"sales have decreased\",rich(\"Russia, France \\n and Germany\", color =:green),\"sales to have than made up the difference.\")\n\n)\n\ndata_layer1=data(four_country_data)\ndata_layer2=data(Ireland_data)\n#data_layer3=data(all_country_data)\nmapping_layer1=mapping(:sales,:year,color=:country)\nmapping_layer2=mapping(:sales,:year)\nvisual_layer1 = visual(Lines,linewidth=3)\nvisual_layer2 = visual(Lines, linewidth=8,color=:blue)\n#visual_layer3 = visual(Lines, linewidth=1,color=(:gray,0.3))\nfour_lines=data_layer1* mapping_layer1 * visual_layer1\nireland_line=data_layer2* mapping_layer2 * visual_layer2\n#all_lines=data_layer3* mapping_layer2 * visual_layer3\ndraw(four_lines+ ireland_line ,axis=axis)\n\n\n┌ Warning: Keyword argument `bgcolor` is deprecated, use `backgroundcolor` instead.\n└ @ Makie ~/.julia/packages/Makie/RgxaV/src/makielayout/blocks/legend.jl:23", + "crumbs": [ + "Data Viz", + "Irish Whiskey Sales" + ] + }, + { + "objectID": "math/4-one-variable-integral.html", + "href": "math/4-one-variable-integral.html", + "title": "4-single-variable-integral", "section": "", - "text": "Figure 1: 1\n\n\n\n\n\n\n\n\n\n\nFigure 2: 3\n\n\n\n\n\n\n\n\n\n\nFigure 3: 4\n\n\n\n\n\n\n\n\n\n\nFigure 4: 5\n\n\n\n\n\n\n\n\n\n\nFigure 5: 6\n\n\n\n\n\n\n\n\n\n\nFigure 6: 7\n\n\n\n\n\n\n\n\n\n\nFigure 7: 8\n\n\n\n\n\n\n\n\n\n\nFigure 8: 9\n\n\n\n\n\n\n\n\n\n\nFigure 9: 10", + "text": "一元函数积分示意\n\n\n\n欧拉方法, 改变Δx, 提高近似精度\n使用 rect = Rect(start-x,start-y,width,height)方法 绘制线性近似的矩形\n\nstart-x: 初始的点的 x 坐标\nstart-y 初始的点的 y 坐标 这里为 0 矩形初始点都在 x轴上\nwidth Δx\nheight 函数值\n\nfig[fldmod1(idx,3)...] fldmod1 方法来生成 plot layout\n\n\n\n\nCode\nusing GLMakie,Distributions,Pipe\n\nws=[1,0.8,0.5,0.3,0.2,0.1] #Δx 数组\nd=Normal()\nxs=-3:0.1:3 \nys=pdf.(d,xs)\n\n\"\"\"\n## define Rect\nrect = Rect(start-x,start-y,width,height)\n\"\"\"\nfunction rect(;x=0,w=0.5)\n return Rect(x,0,w,pdf.(d,x))\nend\n\n\nfunction plot_hist(ax,box)\n mesh!(ax, box, color=(:lightblue,0.4))\n wireframe!(ax, box; color = :black, transparency=true)\nend\n\nfunction plot_res()\n fig=Figure()\n for (idx,w) in enumerate(ws)\n local ax=Axis(fig[fldmod1(idx,3)...],title=L\"Δx=%$(w)\")\n lines!(ax,xs,ys)\n @pipe Vector(-3:w:3).|>rect(;x=_,w=w).|>plot_hist(ax,_)\n end\n fig\n #save(\"one-single-variable-euler-method-2.png\",fig)\nend\n\nplot_res()", "crumbs": [ "Math", - "2-Two Variables Function corss-section" + "4-single-variable-integral" ] }, { - "objectID": "math/1-double-integral.html", - "href": "math/1-double-integral.html", - "title": "1-double integral picture", + "objectID": "math/5-himmelblau-function.html", + "href": "math/5-himmelblau-function.html", + "title": "5-himmelblau-plot", "section": "", - "text": "二元函数积分示意\n\n\n\n利用小立方体来近似二元函数曲线下面积\n为了增强效果使用了 GLMakie ssao 方法", + "text": "配置 ssao\n\n\n\nCode\n using GLMakie\n GLMakie.activate!(ssao=true)\n GLMakie.closeall() \n fig = Figure(resolution = (800, 800))\n ssao = Makie.SSAO(radius = 5.0, blur = 3)\n ax = LScene(fig[1, 1], scenekw = (ssao=ssao,),show_axis=false)\n ax.scene.ssao.bias[] = 0.025\n\n\n0.025", "crumbs": [ "Math", - "1-double integral picture" + "5-himmelblau-plot" ] }, { - "objectID": "math/1-double-integral.html#load-package", - "href": "math/1-double-integral.html#load-package", - "title": "1-double integral picture", - "section": "1. load package", - "text": "1. load package\n\n配置 ssao\n\n\n\nCode\n using GLMakie\n GLMakie.activate!(ssao=true)\n GLMakie.closeall() \n fig = Figure(resolution = (800, 800))\n ssao = Makie.SSAO(radius = 5.0, blur = 3)\n ax = LScene(fig[1, 1], scenekw = (ssao=ssao,),show_axis=false)\n ax.scene.ssao.bias[] = 0.025\n\n\n0.025", + "objectID": "math/5-himmelblau-function.html#load-package", + "href": "math/5-himmelblau-function.html#load-package", + "title": "5-himmelblau-plot", + "section": "", + "text": "配置 ssao\n\n\n\nCode\n using GLMakie\n GLMakie.activate!(ssao=true)\n GLMakie.closeall() \n fig = Figure(resolution = (800, 800))\n ssao = Makie.SSAO(radius = 5.0, blur = 3)\n ax = LScene(fig[1, 1], scenekw = (ssao=ssao,),show_axis=false)\n ax.scene.ssao.bias[] = 0.025\n\n\n0.025", "crumbs": [ "Math", - "1-double integral picture" + "5-himmelblau-plot" ] }, { - "objectID": "math/1-double-integral.html#define-bio-variate-funciton", - "href": "math/1-double-integral.html#define-bio-variate-funciton", - "title": "1-double integral picture", + "objectID": "math/5-himmelblau-function.html#define-bio-variate-funciton", + "href": "math/5-himmelblau-function.html#define-bio-variate-funciton", + "title": "5-himmelblau-plot", "section": "2 define bio-variate funciton", - "text": "2 define bio-variate funciton\n\n2.1 配置函数\n\n\nmake_cubic: plot 小立方体函数\nmack_z : 根据传入函数的(x,y)坐标返回值, 即 z坐标值\n\n\n\n\nCode\n make_cubic(;x=0,y=0,h,w)=Rect3f(Vec3f(x,y,0), Vec3f(w,w,h))\n\n function make_z(func)\n return (x,y)->func(x,y)\n end\n\n\nmake_z (generic function with 1 method)\n\n\n\n\n2.2 二元函数\n\n不限于此\n\n\n\nCode\nf1(x,y)=ℯ^(−(x^2)−(y^2))\n f5(x,y)=x^2+y^2\n\n\nf5 (generic function with 1 method)", + "text": "2 define bio-variate funciton\n\n2.1 配置函数\n\n\nmake_cubic: plot 小立方体函数\nmack_z : 根据传入函数的(x,y)坐标返回值, 即 z坐标值\n\n\n\n\nCode\n make_cubic(;x=0,y=0,h,w)=Rect3f(Vec3f(x,y,0), Vec3f(w,w,h))\n\n function make_z(func)\n return (x,y)->func(x,y)\n end\n\n\nmake_z (generic function with 1 method)\n\n\n\n\n2.2 二元函数\n\n不限于此\n\n\n\nCode\n f(x,y)=cos(x) * sin(y)\n\n\nf (generic function with 1 method)", "crumbs": [ "Math", - "1-double integral picture" + "5-himmelblau-plot" ] }, { - "objectID": "math/1-double-integral.html#plot", - "href": "math/1-double-integral.html#plot", - "title": "1-double integral picture", + "objectID": "math/5-himmelblau-function.html#plot", + "href": "math/5-himmelblau-function.html#plot", + "title": "5-himmelblau-plot", "section": "3 plot", - "text": "3 plot\n\n\n可以改变 xs,ys step的值, 增加后, 立方体会变小\nbox_width=0.19 设置比xs,ys 的间隔小一点, 留出空隙\n\n\n\n\nCode\n xs=range(-2,2,20)\n ys=range(-2,2,20) \n box_width=0.19\n function plot_double_integ(funcz)\n local box = [make_cubic(;x=x,y=y,h=funcz(x,y),w=box_width) for x in xs for y in ys]\n local zs= [funcz(x,y) for x in xs , y in ys]\n for i in eachindex(box)\n mesh!(ax, box[i], ssao=true,color=:lightblue)\n wireframe!(ax, box[i]; color = :black, transparency=true)\n end\n\n #wireframe!(ax,xs,ys,zs,color=(:red,0.8),linewidth=1)\n fig\n end\n\n make_z(f1)|>plot_double_integ\n\n\n\n\n\n\n\n\n\n 二元函数\n f1(x,y)=ℯ^(−(x^2)−(y^2))\n f2(x,y)=y^2\n f3(x,y)=2+2x-y\n f4(x,y)=x+2y+3\n f5(x,y)=x^2+y^2\n f6(x,y)=-f5(x,y)+3\n f7(x,y)=-f5(x,y)\n f8(x,y)=ℯ^((x^2)+(y^2))\n f9(x,y)=sqrt(x^2+y^2)", + "text": "3 plot\n\n\n可以改变 xs,ys step的值, 增加后, 立方体会变小\nbox_width=0.19 设置比xs,ys 的间隔小一点, 留出空隙\n\n\n\n\nCode\n xs=range(0,15,50)\n ys=range(0,15,50) \n box_width=0.19\n function plot_double_integ(funcz)\n local box = [make_cubic(;x=x,y=y,h=funcz(x,y),w=box_width) for x in xs for y in ys]\n local zs= [funcz(x,y) for x in xs , y in ys]\n for i in eachindex(box)\n mesh!(ax, box[i], ssao=true,color=:lightblue)\n wireframe!(ax, box[i]; color = :black, transparency=true)\n end\n\n #wireframe!(ax,xs,ys,zs,color=(:red,0.8),linewidth=1)\n fig\n end\n\n make_z(f)|>plot_double_integ", "crumbs": [ "Math", - "1-double integral picture" + "5-himmelblau-plot" ] }, { - "objectID": "math/3-oval-spere.html", - "href": "math/3-oval-spere.html", - "title": "3-oval-sphere-params-equation", + "objectID": "machinelearning/1-classfication-comparison.html", + "href": "machinelearning/1-classfication-comparison.html", + "title": "classfication models comparison", "section": "", - "text": "球体参数方程\n球体可以看做是二元球面的连续积分,这里的离散球面图和二元函数的分解道理相同\n\n\n\nCode\n using GLMakie,LinearAlgebra\nlet\nuspan=0:0.01:1.82pi # 不要到 2pi, 留出空隙, 可以观察内部\nvspan=0:0.01:1pi\nf = Figure()\nax = Axis3(f[1, 1];azimuth=-0.1pi,elevation=-0.05pi)\ncol = repeat([1:50;50:-1:1],outer=2)\n\n\nfunction plot_sphere(r::Int)\n xs=[r*cos(u)*sin(v) for u in uspan,v in vspan]\n ys=[r*sin(u)*sin(v) for u in uspan,v in vspan]\n zs=[r*cos(v) for u in uspan,v in vspan]\n surface!(xs,ys,zs,color=color=col)\nend\n\n\nfor r in [2,5,8]\n plot_sphere(r)\nend\n\n\nf\nend", + "text": "import MLJ:predict,predict_mode\n using MLJ,GLMakie,DataFrames,Random\n Random.seed!(1222)\n\nTaskLocalRNG()", "crumbs": [ - "Math", - "3-oval-sphere-params-equation" + "Machine Learning", + "classfication models comparison" ] }, { - "objectID": "index.html", - "href": "index.html", - "title": "内容简介", + "objectID": "machinelearning/1-classfication-comparison.html#load-package", + "href": "machinelearning/1-classfication-comparison.html#load-package", + "title": "classfication models comparison", "section": "", - "text": "介绍\n\n\n\nGLMakie 方法\n\n\nCourse info & details here\ncommit chage gh-pages", + "text": "import MLJ:predict,predict_mode\n using MLJ,GLMakie,DataFrames,Random\n Random.seed!(1222)\n\nTaskLocalRNG()", "crumbs": [ - "Home" + "Machine Learning", + "classfication models comparison" ] }, { - "objectID": "statistics/15-bubble-plot.html", - "href": "statistics/15-bubble-plot.html", - "title": "15-bubble-plot", - "section": "", - "text": "简介\n\n\n\n\nref csv", + "objectID": "machinelearning/1-classfication-comparison.html#make-data", + "href": "machinelearning/1-classfication-comparison.html#make-data", + "title": "classfication models comparison", + "section": "2. make data", + "text": "2. make data\n\n function circle_data()\n X, y = make_circles(400; noise=0.1, factor=0.3)\n df = DataFrame(X)\n df.y = y\n return df\n end\n function moons_data()\n X, y = make_moons(400; noise=0.1)\n df = DataFrame(X)\n df.y = y\n return df\n end\n function blob_data()\n X, y = make_blobs(400, 2; centers=2, cluster_std=[1.0, 2.0])\n df = DataFrame(X)\n df.y = y\n return df\n end\n #cat=df1.y|>levels|>unique\n colors=[:green, :purple]\n\n2-element Vector{Symbol}:\n :green\n :purple", "crumbs": [ - "Statistics", - "15-bubble-plot" + "Machine Learning", + "classfication models comparison" ] }, { - "objectID": "statistics/15-bubble-plot.html#load-pacakge", - "href": "statistics/15-bubble-plot.html#load-pacakge", - "title": "15-bubble-plot", - "section": "1. load pacakge", - "text": "1. load pacakge\n\n\nCode\nusing GLMakie,CSV,DataFrames,GLMakie,LinearAlgebra,Pipe", - "crumbs": [ - "Statistics", - "15-bubble-plot" + "objectID": "machinelearning/1-classfication-comparison.html#define-function", + "href": "machinelearning/1-classfication-comparison.html#define-function", + "title": "classfication models comparison", + "section": "3. define function", + "text": "3. define function\n\nfunction plot_origin_data(df)\n fig=Figure()\n ax=Axis(fig[1,1])\n local cat=df.y|>levels|>unique\n \n local colors=[:green, :purple]\n for (i,c) in enumerate(cat)\n d=df[y.==c,:]\n scatter!(ax, d[:,1],d[:,2],color=(colors[i],0.6))\n #@show d\n end\n fig\nend\n\nnums=100\nfunction boundary_data(df,;n=nums)\n n1=n2=n\n xlow,xhigh=extrema(df[:,:x1])\n ylow,yhigh=extrema(df[:,:x2])\n tx = LinRange(xlow,xhigh,n1)\n ty = LinRange(ylow,yhigh,n2)\n x_test = mapreduce(collect, hcat, Iterators.product(tx, ty));\n x_test=MLJ.table(x_test')\n return tx,ty,x_test\nend\n\nfunction plot_desc_boudary(fig,ytest,i;df=df1,row=1)\n tx,ty,xs,ys, xtest=boundary_data(df)\n local ax=Axis(fig[row,i],title=\"$(names[i])\")\n\n contourf!(ax, tx,ty,ytest,levels=length(cat),colormap=:phase)\n\n for (i,c) in enumerate(cat)\n d=df[y.==c,:]\n scatter!(ax, d[:,1],d[:,2],color=(colors[i],0.6))\n end\n hidedecorations!(ax)\nend\n\nplot_desc_boudary (generic function with 1 method)", + "crumbs": [ + "Machine Learning", + "classfication models comparison" ] }, { - "objectID": "statistics/15-bubble-plot.html#load-data-dataframe", - "href": "statistics/15-bubble-plot.html#load-data-dataframe", - "title": "15-bubble-plot", - "section": "2. load data->dataframe", - "text": "2. load data->dataframe\n\n\nCode\nfeature=[\"Height\",\"Weight\",\"Bodyfat\"]\ndata=@pipe CSV.File(\"./data/BodyFat.csv\")|>DataFrame|>dropmissing|>select(_,feature)\nfirst(data,10)\n\n\n10×3 DataFrame\n\n\n\nRow\nHeight\nWeight\nBodyfat\n\n\n\nFloat64\nFloat64\nFloat64\n\n\n\n\n1\n73.5\n247.25\n32.3\n\n\n2\n71.5\n177.25\n22.5\n\n\n3\n69.0\n156.25\n22.0\n\n\n4\n67.75\n154.25\n12.3\n\n\n5\n70.0\n177.0\n20.5\n\n\n6\n72.0\n198.0\n22.6\n\n\n7\n71.5\n200.5\n28.7\n\n\n8\n70.25\n163.0\n21.3\n\n\n9\n71.5\n241.25\n29.9\n\n\n10\n71.0\n218.5\n21.3", + "objectID": "machinelearning/1-classfication-comparison.html#define-machine-learning-models", + "href": "machinelearning/1-classfication-comparison.html#define-machine-learning-models", + "title": "classfication models comparison", + "section": "4. define machine learning models", + "text": "4. define machine learning models\n\n using CatBoost.MLJCatBoostInterface\n SVC = @load SVC pkg=LIBSVM \n KNNClassifier = @load KNNClassifier pkg=NearestNeighborModels\n DecisionTreeClassifier = @load DecisionTreeClassifier pkg=DecisionTree\n RandomForestClassifier = @load RandomForestClassifier pkg=DecisionTree\n CatBoostClassifier = @load CatBoostClassifier pkg=CatBoost\n BayesianLDA = @load BayesianLDA pkg=MultivariateStats\n Booster = @load AdaBoostStumpClassifier pkg=DecisionTree\n \n models=[KNNClassifier,DecisionTreeClassifier,RandomForestClassifier,CatBoostClassifier,BayesianLDA,SVC]\n names=[\"KNN\",\"DecisionTree\",\"RandomForest\",\"CatBoost\",\"BayesianLDA\",\"SVC\"]\n function _fit(df::DataFrame,m)\n X,y=df[:,1:2],df[:,3]\n _,_,xtest=boundary_data(df;n=nums)\n local predict= m==MLJLIBSVMInterface.SVC ? MLJ.predict : MLJ.predict_mode \n model=m()\n mach = machine(model, X, y)|>fit!\n yhat=predict(mach, xtest)\n ytest=yhat|>Array|>d->reshape(d,nums,nums)\n return ytest\nend\n\n\n\nfunction plot_desc_boudary(fig,ytest,i;df=df1,row=1)\n tx,ty,_=boundary_data(df)\n local y=df.y\n local ax=Axis(fig[row,i],title=\"$(names[i])\")\n cat=y|>levels|>unique\n contourf!(ax, tx,ty,ytest,levels=length(cat),colormap=:redsblues)\n\n for (i,c) in enumerate(cat)\n d=df[y.==c,:]\n scatter!(ax, d[:,1],d[:,2],color=(colors[i],0.6))\n end\n hidedecorations!(ax)\n \n\nend\n\nfunction plot_comparsion(testdata,df;row=1)\n \n for (i,data) in enumerate(testdata)\n plot_desc_boudary(fig,data,i;df=df,row=row)\n end\n fig\nend\n\n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n\n\nimport MLJLIBSVMInterface ✔\nimport NearestNeighborModels ✔\nimport MLJDecisionTreeInterface ✔\nimport MLJDecisionTreeInterface ✔\nimport CatBoost ✔\nimport MLJMultivariateStatsInterface ✔\nimport MLJDecisionTreeInterface ✔\n\n\nplot_comparsion (generic function with 1 method)\n\n\n\nfig=Figure(resolution=(2100,1000))\nfunction plot_comparsion(testdata,df,row=1)\n \n for i in eachindex(testdata)\n plot_desc_boudary(fig,testdata[i],i;df=df,row=row)\n end\n fig\nend\n\n\n\ndf1=circle_data()\n\nytest1=[_fit(df1,m) for (i,m) in enumerate(models)]\n\ndf2=moons_data()\nytest2=[_fit(df2,m) for (i,m) in enumerate(models)]\n\ndf3=blob_data()\nytest3=[_fit(df3,m) for (i,m) in enumerate(models)]\n\ndfs=[df2,df1,df3]\nytests=[ytest2,ytest1,ytest3]\n\nfig=Figure(resolution=(2100,1000))\n\nfor (df, data,i) in zip(dfs,ytests,[1,2,3])\n plot_comparsion(data,df;row=i)\nend\n\nfig\n\n[ Info: Training machine(KNNClassifier(K = 5, …), …).\n[ Info: Training machine(DecisionTreeClassifier(max_depth = -1, …), …).\n[ Info: Training machine(RandomForestClassifier(max_depth = -1, …), …).\n[ Info: Training machine(CatBoostClassifier(iterations = 1000, …), …).\n[ Info: Training machine(BayesianLDA(method = gevd, …), …).\n[ Info: Training machine(SVC(kernel = RadialBasis, …), …).\n[ Info: Training machine(KNNClassifier(K = 5, …), …).\n[ Info: Training machine(DecisionTreeClassifier(max_depth = -1, …), …).\n[ Info: Training machine(RandomForestClassifier(max_depth = -1, …), …).\n[ Info: Training machine(CatBoostClassifier(iterations = 1000, …), …).\n[ Info: Training machine(BayesianLDA(method = gevd, …), …).\n[ Info: Training machine(SVC(kernel = RadialBasis, …), …).\n[ Info: Training machine(KNNClassifier(K = 5, …), …).\n[ Info: Training machine(DecisionTreeClassifier(max_depth = -1, …), …).\n[ Info: Training machine(RandomForestClassifier(max_depth = -1, …), …).\n[ Info: Training machine(CatBoostClassifier(iterations = 1000, …), …).\n[ Info: Training machine(BayesianLDA(method = gevd, …), …).\n[ Info: Training machine(SVC(kernel = RadialBasis, …), …).", "crumbs": [ - "Statistics", - "15-bubble-plot" + "Machine Learning", + "classfication models comparison" ] }, { - "objectID": "statistics/15-bubble-plot.html#plot-bubble-img", - "href": "statistics/15-bubble-plot.html#plot-bubble-img", - "title": "15-bubble-plot", - "section": "3. plot bubble img", - "text": "3. plot bubble img\n\n\nCode\n function plot_bubble()\n fig=Figure()\n ax=Axis(fig[1,1];title=\"Bubble size: BodyFat\",xlabel=\"$(feature[1])\",ylabel=\"$(feature[2])\")\n Box(fig[1,1];color = (:orange,0.1),strokewidth=0.5)\n scatter!(ax,data[!,1],data[!,2],markersize=data[!,3].*2,color=(:blue,0.2),strokewidth=3,strokecolor=:black)\n fig\n #save(\"Bubble-size-BodyFat.png\",fig)\n end\n\n plot_bubble()", + "objectID": "statistics/14-unicode-plot.html", + "href": "statistics/14-unicode-plot.html", + "title": "14-unicode-plot-method", + "section": "", + "text": "简介\n\n\n\n使用Readme ·UnicodePlots.jl 以字符形式作图\n\n\n\n\nCode\nusing Plots,UnicodePlots\ndata=[21, 17, 21, 18, 22, 31, 31, 34, 34, 35, 35, 36, 39, 36,35]\nplt=scatterplot(data)\nshow(plt)\n\n\n ┌────────────────────────────────────────┐ \n 40 │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠂⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡀⠀⠀⠀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡀⠀⡀⠀⠂⠀⠂⠀⠀⠀⠀⠀⠀⠀⠂⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠄⠀⠄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠄⠀⠀⠀⠄⠀⠀⠀⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠄⠀⠀⠀⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n 10 │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n └────────────────────────────────────────┘ \n ⠀0⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀20⠀ \n\n\n(18, 49)", "crumbs": [ "Statistics", - "15-bubble-plot" + "14-unicode-plot-method" ] }, { - "objectID": "statistics/1-normaldist.html", - "href": "statistics/1-normaldist.html", - "title": "1-normal-distributions", + "objectID": "statistics/7-mvnormal-sampling.html", + "href": "statistics/7-mvnormal-sampling.html", + "title": "7-mv-normal-sampling", "section": "", - "text": "gaussian distributions\n\n\n\n目的: 增强显示效果\n\n\n\n\nCode\n using GLMakie, Distributions, Random\n Random.seed!(124)\n\n colors = [\"#FF410D\", \"#6EE2FF\", \"#F7C530\", \"#95CC5E\", \"#D0DFE6\", \"#F79D1E\"]\n μσpairs = [[-3, 1], [-0.3, 1], [0, 1], [1.2, 1], [4, 1], [5, 1]] #方差相同, 均值变化\n μσpairs2 = [[0, 0.8], [0, 1], [0, 2], [0, 3], [0, 4], [0, 5]] # 均值相同, 方差变化\n\n ts = range(-10, 10, 200)\n fig = Figure(resolution=(1000, 600))\n ax1 = Axis(fig[1, 1:2], xlabel=\"μ change, σ=1\")\n ax2 = Axis(fig[1, 3], xlabel=\"μ=0,σ change\")\n\n for (p, p2, c) in zip(μσpairs, μσpairs2, colors)\n local pdf1(x) = pdf(Normal(p...), x)\n local pdf2(x) = pdf(Normal(p2...), x)\n lines!(ax1, ts, pdf1.(ts), label=\"$(p[1]), $(p[2])\", color=c, linewidth=3)\n band!(ax1, ts, fill(0, length(ts)), pdf1.(ts); color=(c, 0.1)) #半透明区域\n lines!(ax2, ts, pdf2.(ts), label=\"$(p2[1]), $(p2[2])\", color=c, linewidth=3)\n band!(ax2, ts, fill(0, length(ts)), pdf2.(ts); color=(c, 0.1)) #半透明区域\n end\n axislegend(ax1)\n axislegend(ax2)\n fig", + "text": "方法\n\n\n\n通过contour:levels参数控制曲线的范围\nlevels=1 则只绘制出一条等位线", "crumbs": [ "Statistics", - "1-normal-distributions" + "7-mv-normal-sampling" ] }, { - "objectID": "statistics/2-2d-descison-boundary.html", - "href": "statistics/2-2d-descison-boundary.html", - "title": "2-2dimension decision boundary", - "section": "", - "text": "Code\nusing GLMakie\nusing StatsFuns: logistic", + "objectID": "statistics/7-mvnormal-sampling.html#load-package", + "href": "statistics/7-mvnormal-sampling.html#load-package", + "title": "7-mv-normal-sampling", + "section": "1. load package", + "text": "1. load package\n\n\nCode\nusing Distributions,GLMakie,Random\nRandom.seed!(343434)\n\n\nTaskLocalRNG()", "crumbs": [ "Statistics", - "2-2dimension decision boundary" + "7-mv-normal-sampling" ] }, { - "objectID": "statistics/2-2d-descison-boundary.html#load-package", - "href": "statistics/2-2d-descison-boundary.html#load-package", - "title": "2-2dimension decision boundary", - "section": "", - "text": "Code\nusing GLMakie\nusing StatsFuns: logistic", + "objectID": "statistics/7-mvnormal-sampling.html#define-distributions-params", + "href": "statistics/7-mvnormal-sampling.html#define-distributions-params", + "title": "7-mv-normal-sampling", + "section": "2. define distributions params", + "text": "2. define distributions params\n\n\nCode\nμ=[3,2]\nΣ=[1 -1.5;-1.5 4]\nd=MvNormal(μ,Σ)\nxs=range(0,6,100)\nys=range(-4,7,100)\ndens_mvnormal = [pdf(d, [i, j]) for i in xs, j in ys]\nrand_sample=rand(d,100)\n\n\n2×100 Matrix{Float64}:\n 2.04893 3.14939 2.04006 2.96766 3.14777 … 2.9465 2.47969 3.17612\n 3.31172 2.04522 2.43717 1.83891 1.69139 2.44297 0.862216 0.327061", "crumbs": [ "Statistics", - "2-2dimension decision boundary" + "7-mv-normal-sampling" ] }, { - "objectID": "statistics/2-2d-descison-boundary.html#plots", - "href": "statistics/2-2d-descison-boundary.html#plots", - "title": "2-2dimension decision boundary", - "section": "3. plots", - "text": "3. plots\n\n\nCode\nfontsize_theme = Theme(fontsize=16)\nset_theme!(fontsize_theme)\n\nfig = Figure(resolution=(900,900))\n\nfunction surface_data(w)\n fun(x, y) = logistic(w' * [x, y])\n span = range(-10, 10, 100)\n zs = [fun(x, y) for x in span, y in span]\n return span, span, zs\nend\n\nws = [3, 0, -3]\nlet\n for (idx, x) in enumerate(ws)\n for (idy, y) in enumerate(ws)\n w = [x, y] == [0, 0] ? vec([0, 0.5]) : vec([x, y])\n xs, ys, zs = surface_data(w)\n ax = Axis3(fig[idx, idy], title=\"w=($(w[1]),$(w[2]))\")\n surface!(ax, xs, ys, zs)\n end\n end\nend\n\nfig\n\n\n\n\n\n\n\n\nFigure 1: 2d-decision-boundary", + "objectID": "statistics/7-mvnormal-sampling.html#plot-res", + "href": "statistics/7-mvnormal-sampling.html#plot-res", + "title": "7-mv-normal-sampling", + "section": "3. plot res", + "text": "3. plot res\n\n\nCode\nfunction plot_func(fig,idx,levels=1)\n ax=Axis(fig[1,idx], xlabel=L\"X\", ylabel=L\"Y\")\n Box(fig[1,idx];color = (:orange,0.1))\n scatter!(ax,rand_sample;marker=:circle,markersize=10,color=(:green,0.1),\n strokewidth=1,strokecolor=:black)\n contour!(ax,xs, ys,dens_mvnormal;levels=levels,linewidth=1)\nend\nfunction plot_mvnormal()\n fig=Figure(resolution=(800,400))\n [plot_func(fig,idx,levels) for (idx,levels) in enumerate([1,16])]\n fig\nend\nplot_mvnormal()", "crumbs": [ "Statistics", - "2-2dimension decision boundary" + "7-mv-normal-sampling" ] }, { - "objectID": "statistics/6-mutli-variable-distributions.html", - "href": "statistics/6-mutli-variable-distributions.html", - "title": "6-bio-normal-dist", + "objectID": "statistics/12-likelihood-coin-toss.html", + "href": "statistics/12-likelihood-coin-toss.html", + "title": "12-likelihood of coin toss", "section": "", - "text": "Code\nusing GLMakie\nusing Distributions\nusing Latexify\n\nGLMakie.activate!()\n\nn=100\nxs=ys=range(-4,4,n)\nμ=[0 , 0]\nΣ=[1 0 ; 0 1]\nd=MvNormal(μ,Σ)\nd2=Normal()\nzs=[pdf(d,[x,y]) for x in xs , y in ys]\n\n# 投影到 x-z 平面数据\nxz=[Point3f(4,y,pdf(d2,y)/2) for y in ys]\n\n# 投影到 y-z 平面数据\nyz=[Point3f(x,4,pdf(d2,x)/2) for x in ys]\n\n\nstr=latexify([0,0])\nfig = Figure(size=(600,600))\nax = Axis3(fig[1, 1],title=L\"\\mu \")\nsurface!(xs,ys,zs;space=:data)\nlines!(ax, xz;linewidth=3)\nlines!(ax, yz;linewidth=3)\n\nfig\n#save(\"./imgs/mutli-normal-dist.png\",fig)", + "text": "简介\n\n\n\nlikelihook解释 参见:understanding-bayes-a-look-at-the-likelihood\n\n\n\n问题:硬币的\\(p(head)=?\\)\n模型:二项式分布\n搜索所有\\(p(head)\\) 产生观测结果的概率, 选出最有可能的一个 \\(p(head)\\)\n\n似然率要解决的问题 对于投掷 10 次硬币, 获取 7 次正面的结果 pdf(Binomial(10,0.5),data=7)\npdf(Binomial(10,0.7),data=7)\npdf(Binomial(10,0.1),data=7)\n返回值都不为 0,意思是p(head)=[0.1,0.5,0.7]的硬币都有机会获得这个观测结果 在这种硬币P(head)参数未知的情况下, 我们挑选出最有可能的一个参数值,这就是最大似然率估计", "crumbs": [ "Statistics", - "6-bio-normal-dist" + "12-likelihood of coin toss" ] }, { - "objectID": "statistics/13-understand-bayes.html", - "href": "statistics/13-understand-bayes.html", - "title": "13-understanding-bayes", - "section": "", - "text": "简介\n\n\n\n下面我们考察一下一枚硬币真实参数为:p(head)=0.6的硬币在不同观察结果(obs)下的似然率估计\n估计两个点p(head)=[a=0.5,b=0.8]\n在这两个点的最大似然率的估计可以表示为赔率(odds), 注意这不是概率\n可以直观的在概率密度图上观察到两者的差异\n当 观测值逐渐增加以后, 两个点的条件概率会趋近于 0, 赔率会趋近于 1 \\[L(a,b)=\\frac{p(obs|a)}{p(obs|b)}\\]", + "objectID": "statistics/12-likelihood-coin-toss.html#load-package", + "href": "statistics/12-likelihood-coin-toss.html#load-package", + "title": "12-likelihood of coin toss", + "section": "1. load package", + "text": "1. load package\n\n\nCode\nusing Distributions,GLMakie,Random,Pipe\nRandom.seed!(123)\n\n\nTaskLocalRNG()", "crumbs": [ "Statistics", - "13-understanding-bayes" + "12-likelihood of coin toss" ] }, { - "objectID": "statistics/13-understand-bayes.html#load-package", - "href": "statistics/13-understand-bayes.html#load-package", - "title": "13-understanding-bayes", - "section": "1. load package", - "text": "1. load package\n\n using Distributions,GLMakie", + "objectID": "statistics/12-likelihood-coin-toss.html#基本概率模型二项式分布", + "href": "statistics/12-likelihood-coin-toss.html#基本概率模型二项式分布", + "title": "12-likelihood of coin toss", + "section": "2. 基本概率模型:二项式分布", + "text": "2. 基本概率模型:二项式分布\n\n\nCode\n@doc(Binomial)\n\n\nBinomial(n,p)\nA Binomial distribution characterizes the number of successes in a sequence of independent trials. It has two parameters: n, the number of trials, and p, the probability of success in an individual trial, with the distribution:\n\\[\nP(X = k) = {n \\choose k}p^k(1-p)^{n-k}, \\quad \\text{ for } k = 0,1,2, \\ldots, n.\n\\]\nBinomial() # Binomial distribution with n = 1 and p = 0.5\nBinomial(n) # Binomial distribution for n trials with success rate p = 0.5\nBinomial(n, p) # Binomial distribution for n trials with success rate p\n\nparams(d) # Get the parameters, i.e. (n, p)\nntrials(d) # Get the number of trials, i.e. n\nsuccprob(d) # Get the success rate, i.e. p\nfailprob(d) # Get the failure rate, i.e. 1 - p\nExternal links:\n\nBinomial distribution on Wikipedia", "crumbs": [ "Statistics", - "13-understanding-bayes" + "12-likelihood of coin toss" ] }, { - "objectID": "statistics/13-understand-bayes.html#define-likelihood-function", - "href": "statistics/13-understand-bayes.html#define-likelihood-function", - "title": "13-understanding-bayes", - "section": "2. define likelihood function", - "text": "2. define likelihood function\n\nxc=[0.5,0.8]\nfunction plot_likelihood(;p=0.6,n=10,s=6)\n probrange=range(0.0,1.0,50)\n fig=Figure(resolution=(500,300))\n ax=Axis(fig[1,1])\n fun(p)=Binomial(n,p)|>d->pdf(d,s)\n data=Float64[fun(p) for p in probrange]\n \n\n pdf1=fun(xc[1]);pdf2=fun(xc[2])\n lines!(ax,probrange,data,label=\"n=$(n),success=$(s)\")\n scatter!(ax,xc,[pdf1,pdf2];color=[:red,:green],markersize=20)\n lines!(ax,[xc[1],xc[2],xc[2]],[pdf1,pdf1,pdf2];linestyle=:dot)\n axislegend(halign =:left, valign =:top)\n fig\nend\n\nplot_likelihood (generic function with 1 method)", + "objectID": "statistics/12-likelihood-coin-toss.html#硬币试验的不同观测值概率模型定义", + "href": "statistics/12-likelihood-coin-toss.html#硬币试验的不同观测值概率模型定义", + "title": "12-likelihood of coin toss", + "section": "3. 硬币试验的不同观测值概率模型定义", + "text": "3. 硬币试验的不同观测值概率模型定义\n定义input_data 函数接受data观测值,返回一个新的函数, 参数是概率值, 返回值为概率密度\n\n\nCode\n@kwdef struct CoinToss\n total::Int=10\n success::Int=7\nend\nfunction input_data(params::CoinToss)\n total,success=params.total,params.success\n return (p::Real)-> @pipe Binomial(total,p)|>pdf(_,success)\nend\n\n\ninput_data (generic function with 1 method)", "crumbs": [ "Statistics", - "13-understanding-bayes" + "12-likelihood of coin toss" ] }, { - "objectID": "statistics/13-understand-bayes.html#plot-results", - "href": "statistics/13-understand-bayes.html#plot-results", - "title": "13-understanding-bayes", - "section": "3. plot results", - "text": "3. plot results\n\n3.1 10 次硬币试验,6 次正面\n两个点的高度相除就是基于实验观察的赔率\n\nplot_likelihood()\n\n\n\n\n\n\n\n\n\n\n3.2 100次试验 60 次正面\n\nplot_likelihood(;p=0.6,n=100,s=60)\n\n\n\n\n\n\n\n\n\n\n3.3 500次试验,300 次正面\n\nplot_likelihood(;p=0.6,n=500,s=300)\n\n\n\n\n\n\n\n\n\n在上述实验中模型参数是事先知道的:p(head)=0.6,但是对于很多问题, 参数未知. 在这种情况下, 如果给定两个参数, 优先选择赔率最大的参数作为估计", + "objectID": "statistics/12-likelihood-coin-toss.html#次试验不同正面观察结果下的概率密度图", + "href": "statistics/12-likelihood-coin-toss.html#次试验不同正面观察结果下的概率密度图", + "title": "12-likelihood of coin toss", + "section": "4. 10次试验,不同正面观察结果下的概率密度图", + "text": "4. 10次试验,不同正面观察结果下的概率密度图\n\n\nCode\nprobrange=range(0.0,1.0,50)\nsuccess_arr=0:10 #不同正面次数\nfig=Figure(resolution=(600,600))\nfor i in success_arr\n params=CoinToss(success=i)\n local row= div(i,3)\n local col=rem(i,3)\n local ax=Axis(fig[row+1,col])\n \n local likelihood=input_data(params)\n max_likelihood=likelihood.(probrange)|>maximum\n x=undef\n for i in probrange\n if likelihood(i)==max_likelihood\n x=round(i,digits=2)\n \n end\n end\n lines!(ax,probrange,likelihood.(probrange),label=\"head=$(i)\")\n vlines!(ax,[x],label=\"p=$(x)\")\n ax.title=\"head=$(i),p=$(x)\"\n \nend\nfig\n\n\n\n\n\n\n\n\n\n\n5. 最大似然率估计的正式方法\nDistributions.jl提供了fit_mle函数\n\n\nCode\n@doc(fit_mle)\n\n\nfit_mle(D, x)\nFit a distribution of type D to a given data set x.\n\nFor univariate distribution, x can be an array of arbitrary size.\nFor multivariate distribution, x should be a matrix, where each column is a sample.\n\nfit_mle(D, x, w)\nFit a distribution of type D to a weighted data set x, with weights given by w.\nHere, w should be an array with length n, where n is the number of samples contained in x.\nfit_mle(::Type{<:Beta}, x::AbstractArray{T})\nMaximum Likelihood Estimate of Beta Distribution via Newton’s Method\nfit_mle(::Type{<:Weibull}, x::AbstractArray{<:Real}; \nalpha0::Real = 1, maxiter::Int = 1000, tol::Real = 1e-16)\nCompute the maximum likelihood estimate of the Weibull distribution with Newton’s method.\n\n\n\n\nCode\nfor i in 1:10\n@info \"total=10,success=$i mle\"=> fit_mle(Binomial, 10,[i])\nend\n\n\n[ Info: \"total=10,success=1 mle\" => Binomial{Float64}(n=10, p=0.1)\n[ Info: \"total=10,success=2 mle\" => Binomial{Float64}(n=10, p=0.2)\n[ Info: \"total=10,success=3 mle\" => Binomial{Float64}(n=10, p=0.3)\n[ Info: \"total=10,success=4 mle\" => Binomial{Float64}(n=10, p=0.4)\n[ Info: \"total=10,success=5 mle\" => Binomial{Float64}(n=10, p=0.5)\n[ Info: \"total=10,success=6 mle\" => Binomial{Float64}(n=10, p=0.6)\n[ Info: \"total=10,success=7 mle\" => Binomial{Float64}(n=10, p=0.7)\n[ Info: \"total=10,success=8 mle\" => Binomial{Float64}(n=10, p=0.8)\n[ Info: \"total=10,success=9 mle\" => Binomial{Float64}(n=10, p=0.9)\n[ Info: \"total=10,success=10 mle\" => Binomial{Float64}(n=10, p=1.0)", "crumbs": [ "Statistics", - "13-understanding-bayes" + "12-likelihood of coin toss" ] }, { - "objectID": "statistics/5-2d-decision-boundary.html", - "href": "statistics/5-2d-decision-boundary.html", - "title": "5-2d-decision-boundary", + "objectID": "statistics/10-dirichlet-dist.html", + "href": "statistics/10-dirichlet-dist.html", + "title": "10-dirichlet-dist", "section": "", - "text": "Code\nimport MLJ:fit!,predict,predict_mode,predict_mean,machine\nusing MLJ,GLMakie,Random,DataFrames\n\niris = load_iris(); \niris =DataFrame(iris);\nnums=100\n\niris[!, :target] = [r.target == \"virginica\" ? 1.0 : 0.0 for r in eachrow(iris)]\niris=coerce(iris, :target=> Multiclass )\ngdf=groupby(iris, :target)\nX,y=iris[:,3:4],iris[:,:target]\n\ncats=levels(y)\n\nfunction boundary_data(df,;n=nums)\n n1=n2=n\n xlow,xhigh=extrema(df[:,1])\n ylow,yhigh=extrema(df[:,2])\n tx = LinRange(xlow,xhigh,n1)\n ty = LinRange(ylow,yhigh,n2)\n x_test = mapreduce(collect, hcat, Iterators.product(tx, ty));\n x_test=MLJ.table(x_test')\n return tx,ty,x_test\nend\ntx,ty,x_test=boundary_data(X)\n\n\nusing MLJ\nLogisticClassifier = @load LogisticClassifier pkg=MLJLinearModels\n#X, y = make_blobs(centers = 2)\nmach = fit!(machine(LogisticClassifier(), X, y))\npredict(mach, X)\nfitted_params(mach)\nprobs=predict(mach, x_test)|>Array #返回分类概率值\nprobs_res=broadcast(pdf, probs, 1.0).|>(d->round(d,digits=2))|>d->reshape(d,nums,nums) #返回概率为1.0(\"virginica\")的概率值\n\n\n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: Training machine(LogisticClassifier(lambda = 2.220446049250313e-16, …), …).\n┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, NamedTuple{(), Tuple{}}}\n│ optim_options: Optim.Options{Float64, Nothing}\n└ lbfgs_options: NamedTuple{(), Tuple{}} NamedTuple()\n\n\nimport MLJLinearModels ✔\n\n\n100×100 Matrix{Float64}:\n 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n ⋮ ⋮ ⋱ ⋮ \n 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.0 0.0 0.0 0.0 0.0 0.0 … 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.0 0.0 0.0 0.0 0.01 0.01 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.0 0.0 0.0 0.01 0.01 0.01 … 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.0 0.0 0.01 0.01 0.01 0.01 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.01 0.01 0.01 0.01 0.01 0.02 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.01 0.01 0.01 0.02 0.02 0.03 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.01 0.01 0.02 0.02 0.03 0.04 1.0 1.0 1.0 1.0 1.0 1.0 1.0", + "text": "简介\n\n\n\ndirichlet分布是Beta 分布的多元泛化\n参照 :Visualizing Dirichlet Distributions with Matplotlib", "crumbs": [ "Statistics", - "5-2d-decision-boundary" + "10-dirichlet-dist" ] }, { - "objectID": "statistics/5-2d-decision-boundary.html#plot-results", - "href": "statistics/5-2d-decision-boundary.html#plot-results", - "title": "5-2d-decision-boundary", - "section": "plot results", - "text": "plot results\n\n\nCode\n fig=Figure(resolution=(1600,800))\n\n function plot_res_contour()\n\n ax = Axis(fig[1, 1], xlabel=\"petal-length\", ylabel=\"petal-width\", title=\"2d2class-contour\")\n contour!(ax, tx, ty, probs_res; labels=true)\n colors = [:red, :blue]\n for i in 1:2\n scatter!(ax, gdf[i][:, 3], gdf[i][:, 4], color=(colors[i], 0.8), marker=:circle, markersize=10, strokewidth=1, strokecolor=:black, label=gdf[i][1, 5] == 1 ? \"virginica\" : \"non-virginica\")\n end\n axislegend(ax, position=:lt)\n #save(\"./imgs/iris-logreg-2d-2class-contourf.png\",fig)\n fig\n end\n\n function plot_res_contourf()\n\n ax = Axis(fig[1, 2], xlabel=\"petal-length\", ylabel=\"petal-width\", title=\"2d2class-contourf\")\n contourf!(ax, tx, ty, probs_res; levels=6, colormap=(:heat, 0.5))\n #contourf!(ax,tx,ty,yhat;levels=length(cats),colormap=(:heat,0.5))\n colors = [:red, :blue]\n for i in 1:2\n scatter!(ax, gdf[i][:, 3], gdf[i][:, 4], color=(colors[i], 0.8), marker=:circle, markersize=10, strokewidth=1, strokecolor=:black, label=gdf[i][1, 5] == 1.0 ? \"virginica\" : \"non-virginica\")\n end\n axislegend(ax, position=:lt)\n #save(\"./imgs/iris-logreg-2d-2class-contourf.png\",fig)\n fig\n end\n\n plot_res_contourf()\n plot_res_contour()", + "objectID": "statistics/10-dirichlet-dist.html#load-package", + "href": "statistics/10-dirichlet-dist.html#load-package", + "title": "10-dirichlet-dist", + "section": "1. load package", + "text": "1. load package\n\n\nCode\nusing Distributions, GLMakie, Random, Pipe\nRandom.seed!(34343)\n\n\nTaskLocalRNG()", "crumbs": [ "Statistics", - "5-2d-decision-boundary" + "10-dirichlet-dist" ] }, { - "objectID": "statistics/9-2d-decision-boundary.html", - "href": "statistics/9-2d-decision-boundary.html", - "title": "9-2d-descision-boundary", + "objectID": "statistics/10-dirichlet-dist.html#beta-分布", + "href": "statistics/10-dirichlet-dist.html#beta-分布", + "title": "10-dirichlet-dist", + "section": "2. Beta 分布", + "text": "2. Beta 分布\n\n2.1 定义\nBeta分布的定义:\n\n\nCode\n@doc(Beta)\n\n\nBeta(α, β)\nThe Beta distribution has probability density function\n\\[\nf(x; \\alpha, \\beta) = \\frac{1}{B(\\alpha, \\beta)}\nx^{\\alpha - 1} (1 - x)^{\\beta - 1}, \\quad x \\in [0, 1]\n\\]\nThe Beta distribution is related to the Gamma distribution via the property that if \\(X \\sim \\operatorname{Gamma}(\\alpha)\\) and \\(Y \\sim \\operatorname{Gamma}(\\beta)\\) independently, then \\(X / (X + Y) \\sim \\operatorname{Beta}(\\alpha, \\beta)\\).\nBeta() # equivalent to Beta(1, 1)\nBeta(α) # equivalent to Beta(α, α)\nBeta(α, β) # Beta distribution with shape parameters α and β\n\nparams(d) # Get the parameters, i.e. (α, β)\nExternal links\n\nBeta distribution on Wikipedia\n\n\n\nBeta分布有两个参数α,β, 当两个参数取不同值的时候, 分布的概率密度函数变现不同.\n\n\n2.2 不同参数的 pdf曲线\n\n\nCode\nxs=range(0,1,100)\nparams=[[.1,.1],[.5,.5],[1,1],[5,5],[10,10],[50,50]]\n#sas1 = @pipe params1.|>(pdf(Beta(_...),xs))\nfunction plot_res()\n fig = Figure(resolution=(900,900))\n axs = [Axis(fig[i, j]) for i in 1:2, j in 1:3]\n\n for (ax,vec) in zip(axs,params)\n ax.title=\"$vec\"\n lines!(ax,Beta(vec...),color=(:orange, 0.8))\n end\n fig\nend\nplot_res()", + "crumbs": [ + "Statistics", + "10-dirichlet-dist" + ] + }, + { + "objectID": "statistics/10-dirichlet-dist.html#dirichlet分布", + "href": "statistics/10-dirichlet-dist.html#dirichlet分布", + "title": "10-dirichlet-dist", + "section": "3 Dirichlet分布", + "text": "3 Dirichlet分布\n\n3.1 定义\n\n\nCode\n@doc(Dirichlet)\n\n\nDirichlet\nThe Dirichlet distribution is often used as the conjugate prior for Categorical or Multinomial distributions. The probability density function of a Dirichlet distribution with parameter \\(\\alpha = (\\alpha_1, \\ldots, \\alpha_k)\\) is:\n\\[\nf(x; \\alpha) = \\frac{1}{B(\\alpha)} \\prod_{i=1}^k x_i^{\\alpha_i - 1}, \\quad \\text{ with }\nB(\\alpha) = \\frac{\\prod_{i=1}^k \\Gamma(\\alpha_i)}{\\Gamma \\left( \\sum_{i=1}^k \\alpha_i \\right)},\n\\quad x_1 + \\cdots + x_k = 1\n\\]\n# Let alpha be a vector\nDirichlet(alpha) # Dirichlet distribution with parameter vector alpha\n\n# Let a be a positive scalar\nDirichlet(k, a) # Dirichlet distribution with parameter a * ones(k)\n\n\n\n\n3.2 Dirichlet sampling plot\n\n\nCode\nlet\n vecs = [[0.1,0.1,0.1],[0.2,0.2,0.2],[1, 1, 1], [2, 2, 2], [6, 6, 6], [10, 10, 10],[20,20,20],[50,50,50],[100,100,100]]\n sas = @pipe vecs |> Dirichlet.(_) |> rand.(_, 1000)\n\n xs = ys = zs = range(0, 1, 100)\n function plot_res()\n fig = Figure(resolution=(900,900))\n axs = [Axis3(fig[i, j];azimuth=0.2pi) for i in 1:3, j in 1:3]\n\n for (ax, sa,vec) in zip(axs, sas,vecs)\n ax.title=\"$vec\"\n lines!(ax, [1, 0, 0,1], [0, 1, 0,0], [0, 0, 1,0];color=(:lightgreen,0.5),linewidth=3)\n scatter!(ax, eachrow(sa)...; markersize=4, color=(:orange, 0.9))\n end\n\n fig\n end\n plot_res()\n\nend", + "crumbs": [ + "Statistics", + "10-dirichlet-dist" + ] + }, + { + "objectID": "statistics/16-rotate-plots.html", + "href": "statistics/16-rotate-plots.html", + "title": "16-rotate-plots", "section": "", - "text": "简介\n\n\n\n\nref: probml page84 figure 2.13\ndataset:iris\nplots:使用 GLMakie:contourf 方法", + "text": "参考\n\n\n\nMakie: how to make a vertical (rotated by 90deg) lines! plot\n\n\n\n\nCode\nusing GLMakie\n\nwith_theme(theme_light()) do \n fig = Figure()\n ax_main = Axis(fig[1,1:2])\n ax_side = Axis(fig[1,3], xlabel = \"y label\", ylabel = \"x label\") # well, just flip these ones\n Xs = Ys = -10:0.1:10\n contour!(ax_main, Xs, Ys, exp.((-Xs'.^2 .- Ys.^2)/25))\n\n obj = lines!(ax_side, Xs, exp.(-Xs.^2/25))\n rotate!(obj, -π/2) # rotates your plotted object\n ax_side.yreversed = true\n fig\nend", "crumbs": [ "Statistics", - "9-2d-descision-boundary" + "16-rotate-plots" ] }, { - "objectID": "statistics/9-2d-decision-boundary.html#load-package", - "href": "statistics/9-2d-decision-boundary.html#load-package", - "title": "9-2d-descision-boundary", - "section": "1. load package", - "text": "1. load package\n\n\nCode\n import MLJ:fit!,fitted_params\n using GLMakie,MLJ,CSV,DataFrames", + "objectID": "statistics/3-bio-norm-dist.html", + "href": "statistics/3-bio-norm-dist.html", + "title": "3-biovariate-normal-dist", + "section": "", + "text": "Code\n using GLMakie, Distributions\n using FileIO", "crumbs": [ "Statistics", - "9-2d-descision-boundary" + "3-biovariate-normal-dist" ] }, { - "objectID": "statistics/9-2d-decision-boundary.html#process-data", - "href": "statistics/9-2d-decision-boundary.html#process-data", - "title": "9-2d-descision-boundary", - "section": "2 process data", - "text": "2 process data\n\n2.1 import iris datset\n\n\nCode\niris = load_iris();\n\n#selectrows(iris, 1:3) |> pretty\n\niris = DataFrames.DataFrame(iris);\nfirst(iris,5)|>display\ny, X = unpack(iris, ==(:target); rng=123);\n\nX=select!(X,3:4)\n\nbyCat = iris.target\ncateg = unique(byCat)\ncolors = [:orange,:lightgreen,:purple];\n\n\n5×5 DataFrame\n\n\n\nRow\nsepal_length\nsepal_width\npetal_length\npetal_width\ntarget\n\n\n\nFloat64\nFloat64\nFloat64\nFloat64\nCat…\n\n\n\n\n1\n5.1\n3.5\n1.4\n0.2\nsetosa\n\n\n2\n4.9\n3.0\n1.4\n0.2\nsetosa\n\n\n3\n4.7\n3.2\n1.3\n0.2\nsetosa\n\n\n4\n4.6\n3.1\n1.5\n0.2\nsetosa\n\n\n5\n5.0\n3.6\n1.4\n0.2\nsetosa\n\n\n\n\n\n\n\n\n2.2 make desc boundary data\n\n生成决策边界实际是利用训练模型对区间内的每个点都做出预测,利用两个属性的最大值和最小值 生成 grid 数据,这是 test数据\n\n\n\nCode\n# grid data\n n1 = n2 = 20\n tx = LinRange(0, 8, 40)\n ty = LinRange(-1, 4, 40)\n X_test = mapreduce(collect, hcat, Iterators.product(tx, ty))\n X_test = MLJ.table(X_test')\n\n\nTables.MatrixTable{LinearAlgebra.Adjoint{Float64, Matrix{Float64}}} with 1600 rows, 2 columns, and schema:\n :x1 Float64\n :x2 Float64", + "objectID": "statistics/3-bio-norm-dist.html#load-package", + "href": "statistics/3-bio-norm-dist.html#load-package", + "title": "3-biovariate-normal-dist", + "section": "", + "text": "Code\n using GLMakie, Distributions\n using FileIO", "crumbs": [ "Statistics", - "9-2d-descision-boundary" + "3-biovariate-normal-dist" ] }, { - "objectID": "statistics/9-2d-decision-boundary.html#logisitcs-model", - "href": "statistics/9-2d-decision-boundary.html#logisitcs-model", - "title": "9-2d-descision-boundary", - "section": "3. Logisitcs model", - "text": "3. Logisitcs model\n\n3.1 training model\n\n\nCode\n LogisticClassifier = @load LogisticClassifier pkg=MLJLinearModels\n #X, y = make_blobs(centers = 2)\n mach = fit!(machine(LogisticClassifier(), X, y))\n predict(mach, X)\n fitted_params(mach)\n probs=predict(mach, X_test)|>Array #返回分类概率值\n probres=[broadcast(pdf, probs,cat) for cat in categ]\n\n\nimport MLJLinearModels ✔\n\n\n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: Training machine(LogisticClassifier(lambda = 2.220446049250313e-16, …), …).\n┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, NamedTuple{(), Tuple{}}}\n│ optim_options: Optim.Options{Float64, Nothing}\n└ lbfgs_options: NamedTuple{(), Tuple{}} NamedTuple()\n\n\n3-element Vector{Vector{Float64}}:\n [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 … 9.671834765600391e-132, 7.370874765820906e-135, 5.617320408186208e-138, 4.2809421636823794e-141, 3.2624925190464766e-144, 2.4863352574885955e-147, 1.8948282567827453e-150, 1.4440426374071983e-153, 1.1005003388488668e-156, 8.38687836794779e-160]\n [2.6707314921073497e-56, 1.0760270944086094e-53, 4.335270360659973e-51, 1.7466631832673993e-48, 7.037236485793986e-46, 2.835274587075858e-43, 1.142320852844153e-40, 4.6023652763324474e-38, 1.8542746623291115e-35, 7.470798854313656e-33 … 1.3627080472304016e-14, 4.184137656019423e-15, 1.2847218419308875e-15, 3.9446842977544977e-16, 1.2111986969540427e-16, 3.718934578207533e-17, 1.1418831965200673e-17, 3.5061042539858484e-18, 1.0765345419987361e-18, 3.305453963038536e-19]\n [1.6814079540479983e-80, 2.2062917118777524e-77, 2.8950280068446543e-74, 3.798766552624987e-71, 4.9846244275441556e-68, 6.540670593853871e-65, 8.582466430351137e-62, 1.1261648017760677e-58, 1.4777187549191659e-55, 1.9390170205959138e-52 … 0.9999999999999865, 0.9999999999999958, 0.9999999999999987, 0.9999999999999996, 0.9999999999999998, 1.0, 1.0, 1.0, 1.0, 1.0]\n\n\n\n\nCode\nfunction plot_res_contour()\n fig=Figure()\n ax = Axis(fig[1, 1], xlabel=\"petal-length\", ylabel=\"petal-width\", title=\"2d3class-contour\")\n \n \n for (idx,cat) in enumerate(categ)\n indc = findall(x -> x == cat, byCat)\n scatter!(ax,iris[:,3][indc],iris[:,4][indc];color=(colors[idx], 0.8), markersize=probres[idx].*10, strokewidth=1, strokecolor=:black, label=\"$cat\")\n \n end\n fig\nend\nplot_res_contour()", + "objectID": "statistics/3-bio-norm-dist.html#plot", + "href": "statistics/3-bio-norm-dist.html#plot", + "title": "3-biovariate-normal-dist", + "section": "2. plot", + "text": "2. plot\n\n\nCode\n μ = [0, 0]\n Σ₁ = [2 1.8; 1.8 2]\n Σ₂ = [1 0; 0 3]\n Σ₃ = [1 0; 0 1]\n simga_dict = Dict(\"full\" => Σ₁, \"diagonal\" => Σ₂, \"spherical\" => Σ₃)\n fig = Figure(resolution=(1200, 800))\n\n function load_img(src)\n load(\"./imgs/$(src).png\")\n end\n\n function binormal_plot(; μ=[0, 0], Σmatrix::Dict=simga_dict, up::Int=4, n::Int=100)\n xs = ys = range(-up, up, n)\n simga_keys = keys(Σmatrix)\n for (idx, val) in enumerate(simga_keys)\n local cormatrix = Σmatrix[val]\n local img=load_img(val)\n local d = MvNormal(μ, cormatrix)\n local zs = [pdf(d, [x, y]) for x in xs, y in ys]\n local ax1 = Axis3(fig[1, idx], title=\"$(val)\")\n local ax2= Axis(fig[2, idx],aspect = DataAspect(),height=60)\n local ax3 = Axis(fig[3, idx];)\n hidedecorations!(ax2)\n hidespines!(ax2)\n surface!(ax1, xs, ys, zs)\n image!(ax2, rotr90(img))\n contour!(ax3, xs, ys, zs, levels=10)\n end\n end\n\n binormal_plot()\n fig", "crumbs": [ "Statistics", - "9-2d-descision-boundary" + "3-biovariate-normal-dist" + ] + }, + { + "objectID": "statistics/4-pair-plots.html", + "href": "statistics/4-pair-plots.html", + "title": "4-pair-plots", + "section": "", + "text": "\"\"\"\n probml page 35 fig1.3\n findall(x -> x == c, byCat) 参考 beautiful makie 代码\n\"\"\"\n\n\nCode\nusing MLJ,DataFrames,GLMakie\n fontsize_theme = Theme(fontsize = 10)\n set_theme!(fontsize_theme)\n\n iris = load_iris(); \n iris = DataFrames.DataFrame(iris);\n #y, X = unpack(iris, ==(:target); rng=123);\n byCat = iris.target\n categ = unique(byCat)\n label=[\"sepal_length\",\"sepal_width\",\"petal_length\",\"petal_width\"]\n axs=[]\n colors1 = [:orange,:lightgreen,:purple]\n \n\n fig = Figure(resolution=(1400,1400))\n \n function plot_diag(i,j)\n \n ax = Axis(fig[i, i])\n push!(axs,ax)\n for (j, c) in enumerate(categ)\n indc = findall(x -> x == c, byCat)\n density!(ax, iris[:,i][indc]; color = (colors1[j], 0.5), label = \"$(c)\",\n strokewidth = 1.25, strokecolor = colors1[j])\n end\n end\n\n\n\"\"\"\n plot_cor(i,j)\n 生成非对角列的散点图\nTBW\n\"\"\"\nfunction plot_cor(i,j)\n ax = Axis(fig[i, j])\n #push!(axs,ax)\n for (k, c) in enumerate(categ)\n indc = findall(x -> x == c, byCat)\n #@show indc\n scatter!(ax, iris[:,i][indc],iris[:,j][indc];color=colors1[k])\n end\n end\n\n function plot_pair()\n [( i==j ? plot_diag(i,j) : plot_cor(i,j)) for i in 1:4,j in 1:4]\n end\n\n function add_xy_label()\n for i in 1:4\n axx=Axis(fig[4, i], xlabel =label[i],)\n axy=Axis(fig[i, 1], ylabel =label[i],)\n end\n end\n \n function add_legend()\n Legend(fig[2:3, 5], axs[1],\"Label\";width=100,height=200)\n end\n \n \n \n function main()\n plot_pair()\n add_xy_label()\n add_legend()\n fig\n #save(\"./imgs/iris-corner-plot2.png\",fig)\n end\n #save(\"./imgs/iris-corner-plot.png\",fig)\n\n\nmain (generic function with 1 method)\n\n\n\nPlotData\n\n\n\n\nCode\nwith_theme(fontsize_theme, fontsize = 25) do\n main()\nend\n\n\n\n\n\n\n\n\n\n\n\n\n\nCode\n first(iris,10)\n\n\n10×5 DataFrame\n\n\n\nRow\nsepal_length\nsepal_width\npetal_length\npetal_width\ntarget\n\n\n\nFloat64\nFloat64\nFloat64\nFloat64\nCat…\n\n\n\n\n1\n5.1\n3.5\n1.4\n0.2\nsetosa\n\n\n2\n4.9\n3.0\n1.4\n0.2\nsetosa\n\n\n3\n4.7\n3.2\n1.3\n0.2\nsetosa\n\n\n4\n4.6\n3.1\n1.5\n0.2\nsetosa\n\n\n5\n5.0\n3.6\n1.4\n0.2\nsetosa\n\n\n6\n5.4\n3.9\n1.7\n0.4\nsetosa\n\n\n7\n4.6\n3.4\n1.4\n0.3\nsetosa\n\n\n8\n5.0\n3.4\n1.5\n0.2\nsetosa\n\n\n9\n4.4\n2.9\n1.4\n0.2\nsetosa\n\n\n10\n4.9\n3.1\n1.5\n0.1\nsetosa", + "crumbs": [ + "Statistics", + "4-pair-plots" ] }, { @@ -559,432 +725,376 @@ ] }, { - "objectID": "statistics/4-pair-plots.html", - "href": "statistics/4-pair-plots.html", - "title": "4-pair-plots", + "objectID": "statistics/9-2d-decision-boundary.html", + "href": "statistics/9-2d-decision-boundary.html", + "title": "9-2d-descision-boundary", "section": "", - "text": "\"\"\"\n probml page 35 fig1.3\n findall(x -> x == c, byCat) 参考 beautiful makie 代码\n\"\"\"\n\n\nCode\nusing MLJ,DataFrames,GLMakie\n fontsize_theme = Theme(fontsize = 10)\n set_theme!(fontsize_theme)\n\n iris = load_iris(); \n iris = DataFrames.DataFrame(iris);\n #y, X = unpack(iris, ==(:target); rng=123);\n byCat = iris.target\n categ = unique(byCat)\n label=[\"sepal_length\",\"sepal_width\",\"petal_length\",\"petal_width\"]\n axs=[]\n colors1 = [:orange,:lightgreen,:purple]\n \n\n fig = Figure(resolution=(1400,1400))\n \n function plot_diag(i,j)\n \n ax = Axis(fig[i, i])\n push!(axs,ax)\n for (j, c) in enumerate(categ)\n indc = findall(x -> x == c, byCat)\n density!(ax, iris[:,i][indc]; color = (colors1[j], 0.5), label = \"$(c)\",\n strokewidth = 1.25, strokecolor = colors1[j])\n end\n end\n\n\n\"\"\"\n plot_cor(i,j)\n 生成非对角列的散点图\nTBW\n\"\"\"\nfunction plot_cor(i,j)\n ax = Axis(fig[i, j])\n #push!(axs,ax)\n for (k, c) in enumerate(categ)\n indc = findall(x -> x == c, byCat)\n #@show indc\n scatter!(ax, iris[:,i][indc],iris[:,j][indc];color=colors1[k])\n end\n end\n\n function plot_pair()\n [( i==j ? plot_diag(i,j) : plot_cor(i,j)) for i in 1:4,j in 1:4]\n end\n\n function add_xy_label()\n for i in 1:4\n axx=Axis(fig[4, i], xlabel =label[i],)\n axy=Axis(fig[i, 1], ylabel =label[i],)\n end\n end\n \n function add_legend()\n Legend(fig[2:3, 5], axs[1],\"Label\";width=100,height=200)\n end\n \n \n \n function main()\n plot_pair()\n add_xy_label()\n add_legend()\n fig\n #save(\"./imgs/iris-corner-plot2.png\",fig)\n end\n #save(\"./imgs/iris-corner-plot.png\",fig)\n\n\nmain (generic function with 1 method)\n\n\n\nPlotData\n\n\n\n\nCode\nwith_theme(fontsize_theme, fontsize = 25) do\n main()\nend\n\n\n\n\n\n\n\n\n\n\n\n\n\nCode\n first(iris,10)\n\n\n10×5 DataFrame\n\n\n\nRow\nsepal_length\nsepal_width\npetal_length\npetal_width\ntarget\n\n\n\nFloat64\nFloat64\nFloat64\nFloat64\nCat…\n\n\n\n\n1\n5.1\n3.5\n1.4\n0.2\nsetosa\n\n\n2\n4.9\n3.0\n1.4\n0.2\nsetosa\n\n\n3\n4.7\n3.2\n1.3\n0.2\nsetosa\n\n\n4\n4.6\n3.1\n1.5\n0.2\nsetosa\n\n\n5\n5.0\n3.6\n1.4\n0.2\nsetosa\n\n\n6\n5.4\n3.9\n1.7\n0.4\nsetosa\n\n\n7\n4.6\n3.4\n1.4\n0.3\nsetosa\n\n\n8\n5.0\n3.4\n1.5\n0.2\nsetosa\n\n\n9\n4.4\n2.9\n1.4\n0.2\nsetosa\n\n\n10\n4.9\n3.1\n1.5\n0.1\nsetosa", + "text": "简介\n\n\n\n\nref: probml page84 figure 2.13\ndataset:iris\nplots:使用 GLMakie:contourf 方法", "crumbs": [ "Statistics", - "4-pair-plots" + "9-2d-descision-boundary" ] }, { - "objectID": "statistics/3-bio-norm-dist.html", - "href": "statistics/3-bio-norm-dist.html", - "title": "3-biovariate-normal-dist", - "section": "", - "text": "Code\n using GLMakie, Distributions\n using FileIO", + "objectID": "statistics/9-2d-decision-boundary.html#load-package", + "href": "statistics/9-2d-decision-boundary.html#load-package", + "title": "9-2d-descision-boundary", + "section": "1. load package", + "text": "1. load package\n\n\nCode\n import MLJ:fit!,fitted_params\n using GLMakie,MLJ,CSV,DataFrames", "crumbs": [ "Statistics", - "3-biovariate-normal-dist" + "9-2d-descision-boundary" ] }, { - "objectID": "statistics/3-bio-norm-dist.html#load-package", - "href": "statistics/3-bio-norm-dist.html#load-package", - "title": "3-biovariate-normal-dist", - "section": "", - "text": "Code\n using GLMakie, Distributions\n using FileIO", + "objectID": "statistics/9-2d-decision-boundary.html#process-data", + "href": "statistics/9-2d-decision-boundary.html#process-data", + "title": "9-2d-descision-boundary", + "section": "2 process data", + "text": "2 process data\n\n2.1 import iris datset\n\n\nCode\niris = load_iris();\n\n#selectrows(iris, 1:3) |> pretty\n\niris = DataFrames.DataFrame(iris);\nfirst(iris,5)|>display\ny, X = unpack(iris, ==(:target); rng=123);\n\nX=select!(X,3:4)\n\nbyCat = iris.target\ncateg = unique(byCat)\ncolors = [:orange,:lightgreen,:purple];\n\n\n5×5 DataFrame\n\n\n\nRow\nsepal_length\nsepal_width\npetal_length\npetal_width\ntarget\n\n\n\nFloat64\nFloat64\nFloat64\nFloat64\nCat…\n\n\n\n\n1\n5.1\n3.5\n1.4\n0.2\nsetosa\n\n\n2\n4.9\n3.0\n1.4\n0.2\nsetosa\n\n\n3\n4.7\n3.2\n1.3\n0.2\nsetosa\n\n\n4\n4.6\n3.1\n1.5\n0.2\nsetosa\n\n\n5\n5.0\n3.6\n1.4\n0.2\nsetosa\n\n\n\n\n\n\n\n\n2.2 make desc boundary data\n\n生成决策边界实际是利用训练模型对区间内的每个点都做出预测,利用两个属性的最大值和最小值 生成 grid 数据,这是 test数据\n\n\n\nCode\n# grid data\n n1 = n2 = 20\n tx = LinRange(0, 8, 40)\n ty = LinRange(-1, 4, 40)\n X_test = mapreduce(collect, hcat, Iterators.product(tx, ty))\n X_test = MLJ.table(X_test')\n\n\nTables.MatrixTable{LinearAlgebra.Adjoint{Float64, Matrix{Float64}}} with 1600 rows, 2 columns, and schema:\n :x1 Float64\n :x2 Float64", "crumbs": [ "Statistics", - "3-biovariate-normal-dist" + "9-2d-descision-boundary" ] }, { - "objectID": "statistics/3-bio-norm-dist.html#plot", - "href": "statistics/3-bio-norm-dist.html#plot", - "title": "3-biovariate-normal-dist", - "section": "2. plot", - "text": "2. plot\n\n\nCode\n μ = [0, 0]\n Σ₁ = [2 1.8; 1.8 2]\n Σ₂ = [1 0; 0 3]\n Σ₃ = [1 0; 0 1]\n simga_dict = Dict(\"full\" => Σ₁, \"diagonal\" => Σ₂, \"spherical\" => Σ₃)\n fig = Figure(resolution=(1200, 800))\n\n function load_img(src)\n load(\"./imgs/$(src).png\")\n end\n\n function binormal_plot(; μ=[0, 0], Σmatrix::Dict=simga_dict, up::Int=4, n::Int=100)\n xs = ys = range(-up, up, n)\n simga_keys = keys(Σmatrix)\n for (idx, val) in enumerate(simga_keys)\n local cormatrix = Σmatrix[val]\n local img=load_img(val)\n local d = MvNormal(μ, cormatrix)\n local zs = [pdf(d, [x, y]) for x in xs, y in ys]\n local ax1 = Axis3(fig[1, idx], title=\"$(val)\")\n local ax2= Axis(fig[2, idx],aspect = DataAspect(),height=60)\n local ax3 = Axis(fig[3, idx];)\n hidedecorations!(ax2)\n hidespines!(ax2)\n surface!(ax1, xs, ys, zs)\n image!(ax2, rotr90(img))\n contour!(ax3, xs, ys, zs, levels=10)\n end\n end\n\n binormal_plot()\n fig", + "objectID": "statistics/9-2d-decision-boundary.html#logisitcs-model", + "href": "statistics/9-2d-decision-boundary.html#logisitcs-model", + "title": "9-2d-descision-boundary", + "section": "3. Logisitcs model", + "text": "3. Logisitcs model\n\n3.1 training model\n\n\nCode\n LogisticClassifier = @load LogisticClassifier pkg=MLJLinearModels\n #X, y = make_blobs(centers = 2)\n mach = fit!(machine(LogisticClassifier(), X, y))\n predict(mach, X)\n fitted_params(mach)\n probs=predict(mach, X_test)|>Array #返回分类概率值\n probres=[broadcast(pdf, probs,cat) for cat in categ]\n\n\nimport MLJLinearModels ✔\n\n\n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: Training machine(LogisticClassifier(lambda = 2.220446049250313e-16, …), …).\n┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, NamedTuple{(), Tuple{}}}\n│ optim_options: Optim.Options{Float64, Nothing}\n└ lbfgs_options: NamedTuple{(), Tuple{}} NamedTuple()\n\n\n3-element Vector{Vector{Float64}}:\n [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 … 9.671834765600391e-132, 7.370874765820906e-135, 5.617320408186208e-138, 4.2809421636823794e-141, 3.2624925190464766e-144, 2.4863352574885955e-147, 1.8948282567827453e-150, 1.4440426374071983e-153, 1.1005003388488668e-156, 8.38687836794779e-160]\n [2.6707314921073497e-56, 1.0760270944086094e-53, 4.335270360659973e-51, 1.7466631832673993e-48, 7.037236485793986e-46, 2.835274587075858e-43, 1.142320852844153e-40, 4.6023652763324474e-38, 1.8542746623291115e-35, 7.470798854313656e-33 … 1.3627080472304016e-14, 4.184137656019423e-15, 1.2847218419308875e-15, 3.9446842977544977e-16, 1.2111986969540427e-16, 3.718934578207533e-17, 1.1418831965200673e-17, 3.5061042539858484e-18, 1.0765345419987361e-18, 3.305453963038536e-19]\n [1.6814079540479983e-80, 2.2062917118777524e-77, 2.8950280068446543e-74, 3.798766552624987e-71, 4.9846244275441556e-68, 6.540670593853871e-65, 8.582466430351137e-62, 1.1261648017760677e-58, 1.4777187549191659e-55, 1.9390170205959138e-52 … 0.9999999999999865, 0.9999999999999958, 0.9999999999999987, 0.9999999999999996, 0.9999999999999998, 1.0, 1.0, 1.0, 1.0, 1.0]\n\n\n\n\nCode\nfunction plot_res_contour()\n fig=Figure()\n ax = Axis(fig[1, 1], xlabel=\"petal-length\", ylabel=\"petal-width\", title=\"2d3class-contour\")\n \n \n for (idx,cat) in enumerate(categ)\n indc = findall(x -> x == cat, byCat)\n scatter!(ax,iris[:,3][indc],iris[:,4][indc];color=(colors[idx], 0.8), markersize=probres[idx].*10, strokewidth=1, strokecolor=:black, label=\"$cat\")\n \n end\n fig\nend\nplot_res_contour()", "crumbs": [ "Statistics", - "3-biovariate-normal-dist" + "9-2d-descision-boundary" ] }, { - "objectID": "statistics/16-rotate-plots.html", - "href": "statistics/16-rotate-plots.html", - "title": "16-rotate-plots", + "objectID": "statistics/5-2d-decision-boundary.html", + "href": "statistics/5-2d-decision-boundary.html", + "title": "5-2d-decision-boundary", "section": "", - "text": "参考\n\n\n\nMakie: how to make a vertical (rotated by 90deg) lines! plot\n\n\n\n\nCode\nusing GLMakie\n\nwith_theme(theme_light()) do \n fig = Figure()\n ax_main = Axis(fig[1,1:2])\n ax_side = Axis(fig[1,3], xlabel = \"y label\", ylabel = \"x label\") # well, just flip these ones\n Xs = Ys = -10:0.1:10\n contour!(ax_main, Xs, Ys, exp.((-Xs'.^2 .- Ys.^2)/25))\n\n obj = lines!(ax_side, Xs, exp.(-Xs.^2/25))\n rotate!(obj, -π/2) # rotates your plotted object\n ax_side.yreversed = true\n fig\nend", + "text": "Code\nimport MLJ:fit!,predict,predict_mode,predict_mean,machine\nusing MLJ,GLMakie,Random,DataFrames\n\niris = load_iris(); \niris =DataFrame(iris);\nnums=100\n\niris[!, :target] = [r.target == \"virginica\" ? 1.0 : 0.0 for r in eachrow(iris)]\niris=coerce(iris, :target=> Multiclass )\ngdf=groupby(iris, :target)\nX,y=iris[:,3:4],iris[:,:target]\n\ncats=levels(y)\n\nfunction boundary_data(df,;n=nums)\n n1=n2=n\n xlow,xhigh=extrema(df[:,1])\n ylow,yhigh=extrema(df[:,2])\n tx = LinRange(xlow,xhigh,n1)\n ty = LinRange(ylow,yhigh,n2)\n x_test = mapreduce(collect, hcat, Iterators.product(tx, ty));\n x_test=MLJ.table(x_test')\n return tx,ty,x_test\nend\ntx,ty,x_test=boundary_data(X)\n\n\nusing MLJ\nLogisticClassifier = @load LogisticClassifier pkg=MLJLinearModels\n#X, y = make_blobs(centers = 2)\nmach = fit!(machine(LogisticClassifier(), X, y))\npredict(mach, X)\nfitted_params(mach)\nprobs=predict(mach, x_test)|>Array #返回分类概率值\nprobs_res=broadcast(pdf, probs, 1.0).|>(d->round(d,digits=2))|>d->reshape(d,nums,nums) #返回概率为1.0(\"virginica\")的概率值\n\n\n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: Training machine(LogisticClassifier(lambda = 2.220446049250313e-16, …), …).\n┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, NamedTuple{(), Tuple{}}}\n│ optim_options: Optim.Options{Float64, Nothing}\n└ lbfgs_options: NamedTuple{(), Tuple{}} NamedTuple()\n\n\nimport MLJLinearModels ✔\n\n\n100×100 Matrix{Float64}:\n 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n ⋮ ⋮ ⋱ ⋮ \n 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.0 0.0 0.0 0.0 0.0 0.0 … 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.0 0.0 0.0 0.0 0.01 0.01 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.0 0.0 0.0 0.01 0.01 0.01 … 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.0 0.0 0.01 0.01 0.01 0.01 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.01 0.01 0.01 0.01 0.01 0.02 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.01 0.01 0.01 0.02 0.02 0.03 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n 0.01 0.01 0.02 0.02 0.03 0.04 1.0 1.0 1.0 1.0 1.0 1.0 1.0", "crumbs": [ "Statistics", - "16-rotate-plots" + "5-2d-decision-boundary" ] }, { - "objectID": "statistics/10-dirichlet-dist.html", - "href": "statistics/10-dirichlet-dist.html", - "title": "10-dirichlet-dist", - "section": "", - "text": "简介\n\n\n\ndirichlet分布是Beta 分布的多元泛化\n参照 :Visualizing Dirichlet Distributions with Matplotlib", + "objectID": "statistics/5-2d-decision-boundary.html#plot-results", + "href": "statistics/5-2d-decision-boundary.html#plot-results", + "title": "5-2d-decision-boundary", + "section": "plot results", + "text": "plot results\n\n\nCode\n fig=Figure(resolution=(1600,800))\n\n function plot_res_contour()\n\n ax = Axis(fig[1, 1], xlabel=\"petal-length\", ylabel=\"petal-width\", title=\"2d2class-contour\")\n contour!(ax, tx, ty, probs_res; labels=true)\n colors = [:red, :blue]\n for i in 1:2\n scatter!(ax, gdf[i][:, 3], gdf[i][:, 4], color=(colors[i], 0.8), marker=:circle, markersize=10, strokewidth=1, strokecolor=:black, label=gdf[i][1, 5] == 1 ? \"virginica\" : \"non-virginica\")\n end\n axislegend(ax, position=:lt)\n #save(\"./imgs/iris-logreg-2d-2class-contourf.png\",fig)\n fig\n end\n\n function plot_res_contourf()\n\n ax = Axis(fig[1, 2], xlabel=\"petal-length\", ylabel=\"petal-width\", title=\"2d2class-contourf\")\n contourf!(ax, tx, ty, probs_res; levels=6, colormap=(:heat, 0.5))\n #contourf!(ax,tx,ty,yhat;levels=length(cats),colormap=(:heat,0.5))\n colors = [:red, :blue]\n for i in 1:2\n scatter!(ax, gdf[i][:, 3], gdf[i][:, 4], color=(colors[i], 0.8), marker=:circle, markersize=10, strokewidth=1, strokecolor=:black, label=gdf[i][1, 5] == 1.0 ? \"virginica\" : \"non-virginica\")\n end\n axislegend(ax, position=:lt)\n #save(\"./imgs/iris-logreg-2d-2class-contourf.png\",fig)\n fig\n end\n\n plot_res_contourf()\n plot_res_contour()", "crumbs": [ "Statistics", - "10-dirichlet-dist" + "5-2d-decision-boundary" ] }, { - "objectID": "statistics/10-dirichlet-dist.html#load-package", - "href": "statistics/10-dirichlet-dist.html#load-package", - "title": "10-dirichlet-dist", - "section": "1. load package", - "text": "1. load package\n\n\nCode\nusing Distributions, GLMakie, Random, Pipe\nRandom.seed!(34343)\n\n\nTaskLocalRNG()", + "objectID": "statistics/13-understand-bayes.html", + "href": "statistics/13-understand-bayes.html", + "title": "13-understanding-bayes", + "section": "", + "text": "简介\n\n\n\n下面我们考察一下一枚硬币真实参数为:p(head)=0.6的硬币在不同观察结果(obs)下的似然率估计\n估计两个点p(head)=[a=0.5,b=0.8]\n在这两个点的最大似然率的估计可以表示为赔率(odds), 注意这不是概率\n可以直观的在概率密度图上观察到两者的差异\n当 观测值逐渐增加以后, 两个点的条件概率会趋近于 0, 赔率会趋近于 1 \\[L(a,b)=\\frac{p(obs|a)}{p(obs|b)}\\]", "crumbs": [ "Statistics", - "10-dirichlet-dist" + "13-understanding-bayes" ] }, { - "objectID": "statistics/10-dirichlet-dist.html#beta-分布", - "href": "statistics/10-dirichlet-dist.html#beta-分布", - "title": "10-dirichlet-dist", - "section": "2. Beta 分布", - "text": "2. Beta 分布\n\n2.1 定义\nBeta分布的定义:\n\n\nCode\n@doc(Beta)\n\n\nBeta(α, β)\nThe Beta distribution has probability density function\n\\[\nf(x; \\alpha, \\beta) = \\frac{1}{B(\\alpha, \\beta)}\nx^{\\alpha - 1} (1 - x)^{\\beta - 1}, \\quad x \\in [0, 1]\n\\]\nThe Beta distribution is related to the Gamma distribution via the property that if \\(X \\sim \\operatorname{Gamma}(\\alpha)\\) and \\(Y \\sim \\operatorname{Gamma}(\\beta)\\) independently, then \\(X / (X + Y) \\sim \\operatorname{Beta}(\\alpha, \\beta)\\).\nBeta() # equivalent to Beta(1, 1)\nBeta(α) # equivalent to Beta(α, α)\nBeta(α, β) # Beta distribution with shape parameters α and β\n\nparams(d) # Get the parameters, i.e. (α, β)\nExternal links\n\nBeta distribution on Wikipedia\n\n\n\nBeta分布有两个参数α,β, 当两个参数取不同值的时候, 分布的概率密度函数变现不同.\n\n\n2.2 不同参数的 pdf曲线\n\n\nCode\nxs=range(0,1,100)\nparams=[[.1,.1],[.5,.5],[1,1],[5,5],[10,10],[50,50]]\n#sas1 = @pipe params1.|>(pdf(Beta(_...),xs))\nfunction plot_res()\n fig = Figure(resolution=(900,900))\n axs = [Axis(fig[i, j]) for i in 1:2, j in 1:3]\n\n for (ax,vec) in zip(axs,params)\n ax.title=\"$vec\"\n lines!(ax,Beta(vec...),color=(:orange, 0.8))\n end\n fig\nend\nplot_res()", + "objectID": "statistics/13-understand-bayes.html#load-package", + "href": "statistics/13-understand-bayes.html#load-package", + "title": "13-understanding-bayes", + "section": "1. load package", + "text": "1. load package\n\n using Distributions,GLMakie", "crumbs": [ "Statistics", - "10-dirichlet-dist" + "13-understanding-bayes" ] }, { - "objectID": "statistics/10-dirichlet-dist.html#dirichlet分布", - "href": "statistics/10-dirichlet-dist.html#dirichlet分布", - "title": "10-dirichlet-dist", - "section": "3 Dirichlet分布", - "text": "3 Dirichlet分布\n\n3.1 定义\n\n\nCode\n@doc(Dirichlet)\n\n\nDirichlet\nThe Dirichlet distribution is often used as the conjugate prior for Categorical or Multinomial distributions. The probability density function of a Dirichlet distribution with parameter \\(\\alpha = (\\alpha_1, \\ldots, \\alpha_k)\\) is:\n\\[\nf(x; \\alpha) = \\frac{1}{B(\\alpha)} \\prod_{i=1}^k x_i^{\\alpha_i - 1}, \\quad \\text{ with }\nB(\\alpha) = \\frac{\\prod_{i=1}^k \\Gamma(\\alpha_i)}{\\Gamma \\left( \\sum_{i=1}^k \\alpha_i \\right)},\n\\quad x_1 + \\cdots + x_k = 1\n\\]\n# Let alpha be a vector\nDirichlet(alpha) # Dirichlet distribution with parameter vector alpha\n\n# Let a be a positive scalar\nDirichlet(k, a) # Dirichlet distribution with parameter a * ones(k)\n\n\n\n\n3.2 Dirichlet sampling plot\n\n\nCode\nlet\n vecs = [[0.1,0.1,0.1],[0.2,0.2,0.2],[1, 1, 1], [2, 2, 2], [6, 6, 6], [10, 10, 10],[20,20,20],[50,50,50],[100,100,100]]\n sas = @pipe vecs |> Dirichlet.(_) |> rand.(_, 1000)\n\n xs = ys = zs = range(0, 1, 100)\n function plot_res()\n fig = Figure(resolution=(900,900))\n axs = [Axis3(fig[i, j];azimuth=0.2pi) for i in 1:3, j in 1:3]\n\n for (ax, sa,vec) in zip(axs, sas,vecs)\n ax.title=\"$vec\"\n lines!(ax, [1, 0, 0,1], [0, 1, 0,0], [0, 0, 1,0];color=(:lightgreen,0.5),linewidth=3)\n scatter!(ax, eachrow(sa)...; markersize=4, color=(:orange, 0.9))\n end\n\n fig\n end\n plot_res()\n\nend", + "objectID": "statistics/13-understand-bayes.html#define-likelihood-function", + "href": "statistics/13-understand-bayes.html#define-likelihood-function", + "title": "13-understanding-bayes", + "section": "2. define likelihood function", + "text": "2. define likelihood function\n\nxc=[0.5,0.8]\nfunction plot_likelihood(;p=0.6,n=10,s=6)\n probrange=range(0.0,1.0,50)\n fig=Figure(resolution=(500,300))\n ax=Axis(fig[1,1])\n fun(p)=Binomial(n,p)|>d->pdf(d,s)\n data=Float64[fun(p) for p in probrange]\n \n\n pdf1=fun(xc[1]);pdf2=fun(xc[2])\n lines!(ax,probrange,data,label=\"n=$(n),success=$(s)\")\n scatter!(ax,xc,[pdf1,pdf2];color=[:red,:green],markersize=20)\n lines!(ax,[xc[1],xc[2],xc[2]],[pdf1,pdf1,pdf2];linestyle=:dot)\n axislegend(halign =:left, valign =:top)\n fig\nend\n\nplot_likelihood (generic function with 1 method)", "crumbs": [ "Statistics", - "10-dirichlet-dist" + "13-understanding-bayes" ] }, { - "objectID": "statistics/12-likelihood-coin-toss.html", - "href": "statistics/12-likelihood-coin-toss.html", - "title": "12-likelihood of coin toss", - "section": "", - "text": "简介\n\n\n\nlikelihook解释 参见:understanding-bayes-a-look-at-the-likelihood\n\n\n\n问题:硬币的\\(p(head)=?\\)\n模型:二项式分布\n搜索所有\\(p(head)\\) 产生观测结果的概率, 选出最有可能的一个 \\(p(head)\\)\n\n似然率要解决的问题 对于投掷 10 次硬币, 获取 7 次正面的结果 pdf(Binomial(10,0.5),data=7)\npdf(Binomial(10,0.7),data=7)\npdf(Binomial(10,0.1),data=7)\n返回值都不为 0,意思是p(head)=[0.1,0.5,0.7]的硬币都有机会获得这个观测结果 在这种硬币P(head)参数未知的情况下, 我们挑选出最有可能的一个参数值,这就是最大似然率估计", + "objectID": "statistics/13-understand-bayes.html#plot-results", + "href": "statistics/13-understand-bayes.html#plot-results", + "title": "13-understanding-bayes", + "section": "3. plot results", + "text": "3. plot results\n\n3.1 10 次硬币试验,6 次正面\n两个点的高度相除就是基于实验观察的赔率\n\nplot_likelihood()\n\n\n\n\n\n\n\n\n\n\n3.2 100次试验 60 次正面\n\nplot_likelihood(;p=0.6,n=100,s=60)\n\n\n\n\n\n\n\n\n\n\n3.3 500次试验,300 次正面\n\nplot_likelihood(;p=0.6,n=500,s=300)\n\n\n\n\n\n\n\n\n\n在上述实验中模型参数是事先知道的:p(head)=0.6,但是对于很多问题, 参数未知. 在这种情况下, 如果给定两个参数, 优先选择赔率最大的参数作为估计", "crumbs": [ "Statistics", - "12-likelihood of coin toss" + "13-understanding-bayes" ] }, { - "objectID": "statistics/12-likelihood-coin-toss.html#load-package", - "href": "statistics/12-likelihood-coin-toss.html#load-package", - "title": "12-likelihood of coin toss", - "section": "1. load package", - "text": "1. load package\n\n\nCode\nusing Distributions,GLMakie,Random,Pipe\nRandom.seed!(123)\n\n\nTaskLocalRNG()", + "objectID": "statistics/6-mutli-variable-distributions.html", + "href": "statistics/6-mutli-variable-distributions.html", + "title": "6-bio-normal-dist", + "section": "", + "text": "Code\nusing GLMakie\nusing Distributions\nusing Latexify\n\nGLMakie.activate!()\n\nn=100\nxs=ys=range(-4,4,n)\nμ=[0 , 0]\nΣ=[1 0 ; 0 1]\nd=MvNormal(μ,Σ)\nd2=Normal()\nzs=[pdf(d,[x,y]) for x in xs , y in ys]\n\n# 投影到 x-z 平面数据\nxz=[Point3f(4,y,pdf(d2,y)/2) for y in ys]\n\n# 投影到 y-z 平面数据\nyz=[Point3f(x,4,pdf(d2,x)/2) for x in ys]\n\n\nstr=latexify([0,0])\nfig = Figure(size=(600,600))\nax = Axis3(fig[1, 1],title=L\"\\mu \")\nsurface!(xs,ys,zs;space=:data)\nlines!(ax, xz;linewidth=3)\nlines!(ax, yz;linewidth=3)\n\nfig\n#save(\"./imgs/mutli-normal-dist.png\",fig)", "crumbs": [ "Statistics", - "12-likelihood of coin toss" + "6-bio-normal-dist" ] }, { - "objectID": "statistics/12-likelihood-coin-toss.html#基本概率模型二项式分布", - "href": "statistics/12-likelihood-coin-toss.html#基本概率模型二项式分布", - "title": "12-likelihood of coin toss", - "section": "2. 基本概率模型:二项式分布", - "text": "2. 基本概率模型:二项式分布\n\n\nCode\n@doc(Binomial)\n\n\nBinomial(n,p)\nA Binomial distribution characterizes the number of successes in a sequence of independent trials. It has two parameters: n, the number of trials, and p, the probability of success in an individual trial, with the distribution:\n\\[\nP(X = k) = {n \\choose k}p^k(1-p)^{n-k}, \\quad \\text{ for } k = 0,1,2, \\ldots, n.\n\\]\nBinomial() # Binomial distribution with n = 1 and p = 0.5\nBinomial(n) # Binomial distribution for n trials with success rate p = 0.5\nBinomial(n, p) # Binomial distribution for n trials with success rate p\n\nparams(d) # Get the parameters, i.e. (n, p)\nntrials(d) # Get the number of trials, i.e. n\nsuccprob(d) # Get the success rate, i.e. p\nfailprob(d) # Get the failure rate, i.e. 1 - p\nExternal links:\n\nBinomial distribution on Wikipedia", + "objectID": "statistics/2-2d-descison-boundary.html", + "href": "statistics/2-2d-descison-boundary.html", + "title": "2-2dimension decision boundary", + "section": "", + "text": "Code\nusing GLMakie\nusing StatsFuns: logistic", "crumbs": [ "Statistics", - "12-likelihood of coin toss" + "2-2dimension decision boundary" ] }, { - "objectID": "statistics/12-likelihood-coin-toss.html#硬币试验的不同观测值概率模型定义", - "href": "statistics/12-likelihood-coin-toss.html#硬币试验的不同观测值概率模型定义", - "title": "12-likelihood of coin toss", - "section": "3. 硬币试验的不同观测值概率模型定义", - "text": "3. 硬币试验的不同观测值概率模型定义\n定义input_data 函数接受data观测值,返回一个新的函数, 参数是概率值, 返回值为概率密度\n\n\nCode\n@kwdef struct CoinToss\n total::Int=10\n success::Int=7\nend\nfunction input_data(params::CoinToss)\n total,success=params.total,params.success\n return (p::Real)-> @pipe Binomial(total,p)|>pdf(_,success)\nend\n\n\ninput_data (generic function with 1 method)", + "objectID": "statistics/2-2d-descison-boundary.html#load-package", + "href": "statistics/2-2d-descison-boundary.html#load-package", + "title": "2-2dimension decision boundary", + "section": "", + "text": "Code\nusing GLMakie\nusing StatsFuns: logistic", "crumbs": [ "Statistics", - "12-likelihood of coin toss" + "2-2dimension decision boundary" ] }, { - "objectID": "statistics/12-likelihood-coin-toss.html#次试验不同正面观察结果下的概率密度图", - "href": "statistics/12-likelihood-coin-toss.html#次试验不同正面观察结果下的概率密度图", - "title": "12-likelihood of coin toss", - "section": "4. 10次试验,不同正面观察结果下的概率密度图", - "text": "4. 10次试验,不同正面观察结果下的概率密度图\n\n\nCode\nprobrange=range(0.0,1.0,50)\nsuccess_arr=0:10 #不同正面次数\nfig=Figure(resolution=(600,600))\nfor i in success_arr\n params=CoinToss(success=i)\n local row= div(i,3)\n local col=rem(i,3)\n local ax=Axis(fig[row+1,col])\n \n local likelihood=input_data(params)\n max_likelihood=likelihood.(probrange)|>maximum\n x=undef\n for i in probrange\n if likelihood(i)==max_likelihood\n x=round(i,digits=2)\n \n end\n end\n lines!(ax,probrange,likelihood.(probrange),label=\"head=$(i)\")\n vlines!(ax,[x],label=\"p=$(x)\")\n ax.title=\"head=$(i),p=$(x)\"\n \nend\nfig\n\n\n\n\n\n\n\n\n\n\n5. 最大似然率估计的正式方法\nDistributions.jl提供了fit_mle函数\n\n\nCode\n@doc(fit_mle)\n\n\nfit_mle(D, x)\nFit a distribution of type D to a given data set x.\n\nFor univariate distribution, x can be an array of arbitrary size.\nFor multivariate distribution, x should be a matrix, where each column is a sample.\n\nfit_mle(D, x, w)\nFit a distribution of type D to a weighted data set x, with weights given by w.\nHere, w should be an array with length n, where n is the number of samples contained in x.\nfit_mle(::Type{<:Beta}, x::AbstractArray{T})\nMaximum Likelihood Estimate of Beta Distribution via Newton’s Method\nfit_mle(::Type{<:Weibull}, x::AbstractArray{<:Real}; \nalpha0::Real = 1, maxiter::Int = 1000, tol::Real = 1e-16)\nCompute the maximum likelihood estimate of the Weibull distribution with Newton’s method.\n\n\n\n\nCode\nfor i in 1:10\n@info \"total=10,success=$i mle\"=> fit_mle(Binomial, 10,[i])\nend\n\n\n[ Info: \"total=10,success=1 mle\" => Binomial{Float64}(n=10, p=0.1)\n[ Info: \"total=10,success=2 mle\" => Binomial{Float64}(n=10, p=0.2)\n[ Info: \"total=10,success=3 mle\" => Binomial{Float64}(n=10, p=0.3)\n[ Info: \"total=10,success=4 mle\" => Binomial{Float64}(n=10, p=0.4)\n[ Info: \"total=10,success=5 mle\" => Binomial{Float64}(n=10, p=0.5)\n[ Info: \"total=10,success=6 mle\" => Binomial{Float64}(n=10, p=0.6)\n[ Info: \"total=10,success=7 mle\" => Binomial{Float64}(n=10, p=0.7)\n[ Info: \"total=10,success=8 mle\" => Binomial{Float64}(n=10, p=0.8)\n[ Info: \"total=10,success=9 mle\" => Binomial{Float64}(n=10, p=0.9)\n[ Info: \"total=10,success=10 mle\" => Binomial{Float64}(n=10, p=1.0)", + "objectID": "statistics/2-2d-descison-boundary.html#plots", + "href": "statistics/2-2d-descison-boundary.html#plots", + "title": "2-2dimension decision boundary", + "section": "3. plots", + "text": "3. plots\n\n\nCode\nfontsize_theme = Theme(fontsize=16)\nset_theme!(fontsize_theme)\n\nfig = Figure(resolution=(900,900))\n\nfunction surface_data(w)\n fun(x, y) = logistic(w' * [x, y])\n span = range(-10, 10, 100)\n zs = [fun(x, y) for x in span, y in span]\n return span, span, zs\nend\n\nws = [3, 0, -3]\nlet\n for (idx, x) in enumerate(ws)\n for (idy, y) in enumerate(ws)\n w = [x, y] == [0, 0] ? vec([0, 0.5]) : vec([x, y])\n xs, ys, zs = surface_data(w)\n ax = Axis3(fig[idx, idy], title=\"w=($(w[1]),$(w[2]))\")\n surface!(ax, xs, ys, zs)\n end\n end\nend\n\nfig\n\n\n\n\n\n\n\n\nFigure 1: 2d-decision-boundary", "crumbs": [ "Statistics", - "12-likelihood of coin toss" + "2-2dimension decision boundary" ] }, { - "objectID": "statistics/7-mvnormal-sampling.html", - "href": "statistics/7-mvnormal-sampling.html", - "title": "7-mv-normal-sampling", + "objectID": "statistics/1-normaldist.html", + "href": "statistics/1-normaldist.html", + "title": "1-normal-distributions", "section": "", - "text": "方法\n\n\n\n通过contour:levels参数控制曲线的范围\nlevels=1 则只绘制出一条等位线", + "text": "gaussian distributions\n\n\n\n目的: 增强显示效果\n\n\n\n\nCode\n using GLMakie, Distributions, Random\n Random.seed!(124)\n\n colors = [\"#FF410D\", \"#6EE2FF\", \"#F7C530\", \"#95CC5E\", \"#D0DFE6\", \"#F79D1E\"]\n μσpairs = [[-3, 1], [-0.3, 1], [0, 1], [1.2, 1], [4, 1], [5, 1]] #方差相同, 均值变化\n μσpairs2 = [[0, 0.8], [0, 1], [0, 2], [0, 3], [0, 4], [0, 5]] # 均值相同, 方差变化\n\n ts = range(-10, 10, 200)\n fig = Figure(resolution=(1000, 600))\n ax1 = Axis(fig[1, 1:2], xlabel=\"μ change, σ=1\")\n ax2 = Axis(fig[1, 3], xlabel=\"μ=0,σ change\")\n\n for (p, p2, c) in zip(μσpairs, μσpairs2, colors)\n local pdf1(x) = pdf(Normal(p...), x)\n local pdf2(x) = pdf(Normal(p2...), x)\n lines!(ax1, ts, pdf1.(ts), label=\"$(p[1]), $(p[2])\", color=c, linewidth=3)\n band!(ax1, ts, fill(0, length(ts)), pdf1.(ts); color=(c, 0.1)) #半透明区域\n lines!(ax2, ts, pdf2.(ts), label=\"$(p2[1]), $(p2[2])\", color=c, linewidth=3)\n band!(ax2, ts, fill(0, length(ts)), pdf2.(ts); color=(c, 0.1)) #半透明区域\n end\n axislegend(ax1)\n axislegend(ax2)\n fig", "crumbs": [ "Statistics", - "7-mv-normal-sampling" + "1-normal-distributions" ] }, { - "objectID": "statistics/7-mvnormal-sampling.html#load-package", - "href": "statistics/7-mvnormal-sampling.html#load-package", - "title": "7-mv-normal-sampling", - "section": "1. load package", - "text": "1. load package\n\n\nCode\nusing Distributions,GLMakie,Random\nRandom.seed!(343434)\n\n\nTaskLocalRNG()", + "objectID": "statistics/15-bubble-plot.html", + "href": "statistics/15-bubble-plot.html", + "title": "15-bubble-plot", + "section": "", + "text": "简介\n\n\n\n\nref csv", "crumbs": [ "Statistics", - "7-mv-normal-sampling" + "15-bubble-plot" ] }, { - "objectID": "statistics/7-mvnormal-sampling.html#define-distributions-params", - "href": "statistics/7-mvnormal-sampling.html#define-distributions-params", - "title": "7-mv-normal-sampling", - "section": "2. define distributions params", - "text": "2. define distributions params\n\n\nCode\nμ=[3,2]\nΣ=[1 -1.5;-1.5 4]\nd=MvNormal(μ,Σ)\nxs=range(0,6,100)\nys=range(-4,7,100)\ndens_mvnormal = [pdf(d, [i, j]) for i in xs, j in ys]\nrand_sample=rand(d,100)\n\n\n2×100 Matrix{Float64}:\n 2.04893 3.14939 2.04006 2.96766 3.14777 … 2.9465 2.47969 3.17612\n 3.31172 2.04522 2.43717 1.83891 1.69139 2.44297 0.862216 0.327061", + "objectID": "statistics/15-bubble-plot.html#load-pacakge", + "href": "statistics/15-bubble-plot.html#load-pacakge", + "title": "15-bubble-plot", + "section": "1. load pacakge", + "text": "1. load pacakge\n\n\nCode\nusing GLMakie,CSV,DataFrames,GLMakie,LinearAlgebra,Pipe", "crumbs": [ "Statistics", - "7-mv-normal-sampling" + "15-bubble-plot" ] }, { - "objectID": "statistics/7-mvnormal-sampling.html#plot-res", - "href": "statistics/7-mvnormal-sampling.html#plot-res", - "title": "7-mv-normal-sampling", - "section": "3. plot res", - "text": "3. plot res\n\n\nCode\nfunction plot_func(fig,idx,levels=1)\n ax=Axis(fig[1,idx], xlabel=L\"X\", ylabel=L\"Y\")\n Box(fig[1,idx];color = (:orange,0.1))\n scatter!(ax,rand_sample;marker=:circle,markersize=10,color=(:green,0.1),\n strokewidth=1,strokecolor=:black)\n contour!(ax,xs, ys,dens_mvnormal;levels=levels,linewidth=1)\nend\nfunction plot_mvnormal()\n fig=Figure(resolution=(800,400))\n [plot_func(fig,idx,levels) for (idx,levels) in enumerate([1,16])]\n fig\nend\nplot_mvnormal()", + "objectID": "statistics/15-bubble-plot.html#load-data-dataframe", + "href": "statistics/15-bubble-plot.html#load-data-dataframe", + "title": "15-bubble-plot", + "section": "2. load data->dataframe", + "text": "2. load data->dataframe\n\n\nCode\nfeature=[\"Height\",\"Weight\",\"Bodyfat\"]\ndata=@pipe CSV.File(\"./data/BodyFat.csv\")|>DataFrame|>dropmissing|>select(_,feature)\nfirst(data,10)\n\n\n10×3 DataFrame\n\n\n\nRow\nHeight\nWeight\nBodyfat\n\n\n\nFloat64\nFloat64\nFloat64\n\n\n\n\n1\n73.5\n247.25\n32.3\n\n\n2\n71.5\n177.25\n22.5\n\n\n3\n69.0\n156.25\n22.0\n\n\n4\n67.75\n154.25\n12.3\n\n\n5\n70.0\n177.0\n20.5\n\n\n6\n72.0\n198.0\n22.6\n\n\n7\n71.5\n200.5\n28.7\n\n\n8\n70.25\n163.0\n21.3\n\n\n9\n71.5\n241.25\n29.9\n\n\n10\n71.0\n218.5\n21.3", "crumbs": [ "Statistics", - 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"14-unicode-plot-method" + "15-bubble-plot" ] }, { - "objectID": "machinelearning/1-classfication-comparison.html", - "href": "machinelearning/1-classfication-comparison.html", - "title": "classfication models comparison", + "objectID": "index.html", + "href": "index.html", + "title": "内容简介", "section": "", - "text": "import MLJ:predict,predict_mode\n using MLJ,GLMakie,DataFrames,Random\n Random.seed!(1222)\n\nTaskLocalRNG()", + "text": "介绍\n\n\n\nGLMakie 方法\n\n\nCourse info & details here\ncommit chage gh-pages", "crumbs": [ - "Machine Learning", - "classfication models comparison" + "Home" ] }, { - "objectID": "machinelearning/1-classfication-comparison.html#load-package", - "href": "machinelearning/1-classfication-comparison.html#load-package", - "title": "classfication models comparison", + "objectID": "math/3-oval-spere.html", + "href": "math/3-oval-spere.html", + "title": "3-oval-sphere-params-equation", "section": "", - "text": "import MLJ:predict,predict_mode\n using MLJ,GLMakie,DataFrames,Random\n Random.seed!(1222)\n\nTaskLocalRNG()", - "crumbs": [ - "Machine Learning", - "classfication models comparison" - ] - }, - { - "objectID": "machinelearning/1-classfication-comparison.html#make-data", - "href": "machinelearning/1-classfication-comparison.html#make-data", - "title": "classfication models comparison", - "section": "2. make data", - "text": "2. make data\n\n function circle_data()\n X, y = make_circles(400; noise=0.1, factor=0.3)\n df = DataFrame(X)\n df.y = y\n return df\n end\n function moons_data()\n X, y = make_moons(400; noise=0.1)\n df = DataFrame(X)\n df.y = y\n return df\n end\n function blob_data()\n X, y = make_blobs(400, 2; centers=2, cluster_std=[1.0, 2.0])\n df = DataFrame(X)\n df.y = y\n return df\n end\n #cat=df1.y|>levels|>unique\n colors=[:green, :purple]\n\n2-element Vector{Symbol}:\n :green\n :purple", - "crumbs": [ - "Machine Learning", - "classfication models comparison" - ] - }, - { - "objectID": "machinelearning/1-classfication-comparison.html#define-function", - "href": "machinelearning/1-classfication-comparison.html#define-function", - "title": "classfication models comparison", - "section": "3. define function", - "text": "3. define function\n\nfunction plot_origin_data(df)\n fig=Figure()\n ax=Axis(fig[1,1])\n local cat=df.y|>levels|>unique\n \n local colors=[:green, :purple]\n for (i,c) in enumerate(cat)\n d=df[y.==c,:]\n scatter!(ax, d[:,1],d[:,2],color=(colors[i],0.6))\n #@show d\n end\n fig\nend\n\nnums=100\nfunction boundary_data(df,;n=nums)\n n1=n2=n\n xlow,xhigh=extrema(df[:,:x1])\n ylow,yhigh=extrema(df[:,:x2])\n tx = LinRange(xlow,xhigh,n1)\n ty = LinRange(ylow,yhigh,n2)\n x_test = mapreduce(collect, hcat, Iterators.product(tx, ty));\n x_test=MLJ.table(x_test')\n return tx,ty,x_test\nend\n\nfunction plot_desc_boudary(fig,ytest,i;df=df1,row=1)\n tx,ty,xs,ys, xtest=boundary_data(df)\n local ax=Axis(fig[row,i],title=\"$(names[i])\")\n\n contourf!(ax, tx,ty,ytest,levels=length(cat),colormap=:phase)\n\n for (i,c) in enumerate(cat)\n d=df[y.==c,:]\n scatter!(ax, d[:,1],d[:,2],color=(colors[i],0.6))\n end\n hidedecorations!(ax)\nend\n\nplot_desc_boudary (generic function with 1 method)", - "crumbs": [ - "Machine Learning", - "classfication models comparison" - ] - }, - { - "objectID": "machinelearning/1-classfication-comparison.html#define-machine-learning-models", - "href": "machinelearning/1-classfication-comparison.html#define-machine-learning-models", - "title": "classfication models comparison", - "section": "4. define machine learning models", - "text": "4. define machine learning models\n\n using CatBoost.MLJCatBoostInterface\n SVC = @load SVC pkg=LIBSVM \n KNNClassifier = @load KNNClassifier pkg=NearestNeighborModels\n DecisionTreeClassifier = @load DecisionTreeClassifier pkg=DecisionTree\n RandomForestClassifier = @load RandomForestClassifier pkg=DecisionTree\n CatBoostClassifier = @load CatBoostClassifier pkg=CatBoost\n BayesianLDA = @load BayesianLDA pkg=MultivariateStats\n Booster = @load AdaBoostStumpClassifier pkg=DecisionTree\n \n models=[KNNClassifier,DecisionTreeClassifier,RandomForestClassifier,CatBoostClassifier,BayesianLDA,SVC]\n names=[\"KNN\",\"DecisionTree\",\"RandomForest\",\"CatBoost\",\"BayesianLDA\",\"SVC\"]\n function _fit(df::DataFrame,m)\n X,y=df[:,1:2],df[:,3]\n _,_,xtest=boundary_data(df;n=nums)\n local predict= m==MLJLIBSVMInterface.SVC ? MLJ.predict : MLJ.predict_mode \n model=m()\n mach = machine(model, X, y)|>fit!\n yhat=predict(mach, xtest)\n ytest=yhat|>Array|>d->reshape(d,nums,nums)\n return ytest\nend\n\n\n\nfunction plot_desc_boudary(fig,ytest,i;df=df1,row=1)\n tx,ty,_=boundary_data(df)\n local y=df.y\n local ax=Axis(fig[row,i],title=\"$(names[i])\")\n cat=y|>levels|>unique\n contourf!(ax, tx,ty,ytest,levels=length(cat),colormap=:redsblues)\n\n for (i,c) in enumerate(cat)\n d=df[y.==c,:]\n scatter!(ax, d[:,1],d[:,2],color=(colors[i],0.6))\n end\n hidedecorations!(ax)\n \n\nend\n\nfunction plot_comparsion(testdata,df;row=1)\n \n for (i,data) in enumerate(testdata)\n plot_desc_boudary(fig,data,i;df=df,row=row)\n end\n fig\nend\n\n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n\n\nimport MLJLIBSVMInterface ✔\nimport NearestNeighborModels ✔\nimport MLJDecisionTreeInterface ✔\nimport MLJDecisionTreeInterface ✔\nimport CatBoost ✔\nimport MLJMultivariateStatsInterface ✔\nimport MLJDecisionTreeInterface ✔\n\n\nplot_comparsion (generic function with 1 method)\n\n\n\nfig=Figure(resolution=(2100,1000))\nfunction plot_comparsion(testdata,df,row=1)\n \n for i in eachindex(testdata)\n plot_desc_boudary(fig,testdata[i],i;df=df,row=row)\n end\n fig\nend\n\n\n\ndf1=circle_data()\n\nytest1=[_fit(df1,m) for (i,m) in enumerate(models)]\n\ndf2=moons_data()\nytest2=[_fit(df2,m) for (i,m) in enumerate(models)]\n\ndf3=blob_data()\nytest3=[_fit(df3,m) for (i,m) in enumerate(models)]\n\ndfs=[df2,df1,df3]\nytests=[ytest2,ytest1,ytest3]\n\nfig=Figure(resolution=(2100,1000))\n\nfor (df, data,i) in zip(dfs,ytests,[1,2,3])\n plot_comparsion(data,df;row=i)\nend\n\nfig\n\n[ Info: Training machine(KNNClassifier(K = 5, …), …).\n[ Info: Training machine(DecisionTreeClassifier(max_depth = -1, …), …).\n[ Info: Training machine(RandomForestClassifier(max_depth = -1, …), …).\n[ Info: Training machine(CatBoostClassifier(iterations = 1000, …), …).\n[ Info: Training machine(BayesianLDA(method = gevd, …), …).\n[ Info: Training machine(SVC(kernel = RadialBasis, …), …).\n[ Info: Training machine(KNNClassifier(K = 5, …), …).\n[ Info: Training machine(DecisionTreeClassifier(max_depth = -1, …), …).\n[ Info: Training machine(RandomForestClassifier(max_depth = -1, …), …).\n[ Info: Training machine(CatBoostClassifier(iterations = 1000, …), …).\n[ Info: Training machine(BayesianLDA(method = gevd, …), …).\n[ Info: Training machine(SVC(kernel = RadialBasis, …), …).\n[ Info: Training machine(KNNClassifier(K = 5, …), …).\n[ Info: Training machine(DecisionTreeClassifier(max_depth = -1, …), …).\n[ Info: Training machine(RandomForestClassifier(max_depth = -1, …), …).\n[ Info: Training machine(CatBoostClassifier(iterations = 1000, …), …).\n[ Info: Training machine(BayesianLDA(method = gevd, …), …).\n[ Info: Training machine(SVC(kernel = RadialBasis, …), …).", + "text": "球体参数方程\n球体可以看做是二元球面的连续积分,这里的离散球面图和二元函数的分解道理相同\n\n\n\nCode\n using GLMakie,LinearAlgebra\nlet\nuspan=0:0.01:1.82pi # 不要到 2pi, 留出空隙, 可以观察内部\nvspan=0:0.01:1pi\nf = Figure()\nax = Axis3(f[1, 1];azimuth=-0.1pi,elevation=-0.05pi)\ncol = repeat([1:50;50:-1:1],outer=2)\n\n\nfunction plot_sphere(r::Int)\n xs=[r*cos(u)*sin(v) for u in uspan,v in vspan]\n ys=[r*sin(u)*sin(v) for u in uspan,v in vspan]\n zs=[r*cos(v) for u in uspan,v in vspan]\n surface!(xs,ys,zs,color=color=col)\nend\n\n\nfor r in [2,5,8]\n plot_sphere(r)\nend\n\n\nf\nend", "crumbs": [ - "Machine Learning", - "classfication models comparison" + "Math", + "3-oval-sphere-params-equation" ] }, { - "objectID": "math/5-himmelblau-function.html", - "href": "math/5-himmelblau-function.html", - "title": "5-himmelblau-plot", + "objectID": "math/1-double-integral.html", + "href": "math/1-double-integral.html", + "title": "1-double integral picture", "section": "", - "text": "配置 ssao\n\n\n\nCode\n using GLMakie\n GLMakie.activate!(ssao=true)\n GLMakie.closeall() \n fig = Figure(resolution = (800, 800))\n ssao = Makie.SSAO(radius = 5.0, blur = 3)\n ax = LScene(fig[1, 1], scenekw = (ssao=ssao,),show_axis=false)\n ax.scene.ssao.bias[] = 0.025\n\n\n0.025", + "text": "二元函数积分示意\n\n\n\n利用小立方体来近似二元函数曲线下面积\n为了增强效果使用了 GLMakie ssao 方法", "crumbs": [ "Math", - "5-himmelblau-plot" + "1-double integral picture" ] }, { - "objectID": "math/5-himmelblau-function.html#load-package", - "href": "math/5-himmelblau-function.html#load-package", - "title": "5-himmelblau-plot", - "section": "", - "text": "配置 ssao\n\n\n\nCode\n using GLMakie\n GLMakie.activate!(ssao=true)\n GLMakie.closeall() \n fig = Figure(resolution = (800, 800))\n ssao = Makie.SSAO(radius = 5.0, blur = 3)\n ax = LScene(fig[1, 1], scenekw = (ssao=ssao,),show_axis=false)\n ax.scene.ssao.bias[] = 0.025\n\n\n0.025", + "objectID": "math/1-double-integral.html#load-package", + "href": "math/1-double-integral.html#load-package", + "title": "1-double integral picture", + "section": "1. load package", + "text": "1. load package\n\n配置 ssao\n\n\n\nCode\n using GLMakie\n GLMakie.activate!(ssao=true)\n GLMakie.closeall() \n fig = Figure(resolution = (800, 800))\n ssao = Makie.SSAO(radius = 5.0, blur = 3)\n ax = LScene(fig[1, 1], scenekw = (ssao=ssao,),show_axis=false)\n ax.scene.ssao.bias[] = 0.025\n\n\n0.025", "crumbs": [ "Math", - "5-himmelblau-plot" + "1-double integral picture" ] }, { - "objectID": "math/5-himmelblau-function.html#define-bio-variate-funciton", - "href": "math/5-himmelblau-function.html#define-bio-variate-funciton", - "title": "5-himmelblau-plot", + "objectID": "math/1-double-integral.html#define-bio-variate-funciton", + "href": "math/1-double-integral.html#define-bio-variate-funciton", + "title": "1-double integral picture", "section": "2 define bio-variate funciton", - "text": "2 define bio-variate funciton\n\n2.1 配置函数\n\n\nmake_cubic: plot 小立方体函数\nmack_z : 根据传入函数的(x,y)坐标返回值, 即 z坐标值\n\n\n\n\nCode\n make_cubic(;x=0,y=0,h,w)=Rect3f(Vec3f(x,y,0), Vec3f(w,w,h))\n\n function make_z(func)\n return (x,y)->func(x,y)\n end\n\n\nmake_z (generic function with 1 method)\n\n\n\n\n2.2 二元函数\n\n不限于此\n\n\n\nCode\n f(x,y)=cos(x) * sin(y)\n\n\nf (generic function with 1 method)", + "text": "2 define bio-variate funciton\n\n2.1 配置函数\n\n\nmake_cubic: plot 小立方体函数\nmack_z : 根据传入函数的(x,y)坐标返回值, 即 z坐标值\n\n\n\n\nCode\n make_cubic(;x=0,y=0,h,w)=Rect3f(Vec3f(x,y,0), Vec3f(w,w,h))\n\n function make_z(func)\n return (x,y)->func(x,y)\n end\n\n\nmake_z (generic function with 1 method)\n\n\n\n\n2.2 二元函数\n\n不限于此\n\n\n\nCode\nf1(x,y)=ℯ^(−(x^2)−(y^2))\n f5(x,y)=x^2+y^2\n\n\nf5 (generic function with 1 method)", "crumbs": [ "Math", - "5-himmelblau-plot" + "1-double integral picture" ] }, { - "objectID": "math/5-himmelblau-function.html#plot", - "href": "math/5-himmelblau-function.html#plot", - "title": "5-himmelblau-plot", + "objectID": "math/1-double-integral.html#plot", + "href": "math/1-double-integral.html#plot", + "title": "1-double integral picture", "section": "3 plot", - "text": "3 plot\n\n\n可以改变 xs,ys step的值, 增加后, 立方体会变小\nbox_width=0.19 设置比xs,ys 的间隔小一点, 留出空隙\n\n\n\n\nCode\n xs=range(0,15,50)\n ys=range(0,15,50) \n box_width=0.19\n function plot_double_integ(funcz)\n local box = [make_cubic(;x=x,y=y,h=funcz(x,y),w=box_width) for x in xs for y in ys]\n local zs= [funcz(x,y) for x in xs , y in ys]\n for i in eachindex(box)\n mesh!(ax, box[i], ssao=true,color=:lightblue)\n wireframe!(ax, box[i]; color = :black, transparency=true)\n end\n\n #wireframe!(ax,xs,ys,zs,color=(:red,0.8),linewidth=1)\n fig\n end\n\n make_z(f)|>plot_double_integ", + "text": "3 plot\n\n\n可以改变 xs,ys step的值, 增加后, 立方体会变小\nbox_width=0.19 设置比xs,ys 的间隔小一点, 留出空隙\n\n\n\n\nCode\n xs=range(-2,2,20)\n ys=range(-2,2,20) \n box_width=0.19\n function plot_double_integ(funcz)\n local box = [make_cubic(;x=x,y=y,h=funcz(x,y),w=box_width) for x in xs for y in ys]\n local zs= [funcz(x,y) for x in xs , y in ys]\n for i in eachindex(box)\n mesh!(ax, box[i], ssao=true,color=:lightblue)\n wireframe!(ax, box[i]; color = :black, transparency=true)\n end\n\n #wireframe!(ax,xs,ys,zs,color=(:red,0.8),linewidth=1)\n fig\n end\n\n make_z(f1)|>plot_double_integ\n\n\n\n\n\n\n\n\n\n 二元函数\n f1(x,y)=ℯ^(−(x^2)−(y^2))\n f2(x,y)=y^2\n f3(x,y)=2+2x-y\n f4(x,y)=x+2y+3\n f5(x,y)=x^2+y^2\n f6(x,y)=-f5(x,y)+3\n f7(x,y)=-f5(x,y)\n f8(x,y)=ℯ^((x^2)+(y^2))\n f9(x,y)=sqrt(x^2+y^2)", "crumbs": [ "Math", - "5-himmelblau-plot" + "1-double integral picture" ] }, { - "objectID": "math/4-one-variable-integral.html", - "href": "math/4-one-variable-integral.html", - "title": "4-single-variable-integral", + "objectID": "math/2-function.html", + "href": "math/2-function.html", + "title": "2-Two Variables Function corss-section", "section": "", - "text": "一元函数积分示意\n\n\n\n欧拉方法, 改变Δx, 提高近似精度\n使用 rect = Rect(start-x,start-y,width,height)方法 绘制线性近似的矩形\n\nstart-x: 初始的点的 x 坐标\nstart-y 初始的点的 y 坐标 这里为 0 矩形初始点都在 x轴上\nwidth Δx\nheight 函数值\n\nfig[fldmod1(idx,3)...] fldmod1 方法来生成 plot layout\n\n\n\n\nCode\nusing GLMakie,Distributions,Pipe\n\nws=[1,0.8,0.5,0.3,0.2,0.1] #Δx 数组\nd=Normal()\nxs=-3:0.1:3 \nys=pdf.(d,xs)\n\n\"\"\"\n## define Rect\nrect = Rect(start-x,start-y,width,height)\n\"\"\"\nfunction rect(;x=0,w=0.5)\n return Rect(x,0,w,pdf.(d,x))\nend\n\n\nfunction plot_hist(ax,box)\n mesh!(ax, box, color=(:lightblue,0.4))\n wireframe!(ax, box; color = :black, transparency=true)\nend\n\nfunction plot_res()\n fig=Figure()\n for (idx,w) in enumerate(ws)\n local ax=Axis(fig[fldmod1(idx,3)...],title=L\"Δx=%$(w)\")\n lines!(ax,xs,ys)\n @pipe Vector(-3:w:3).|>rect(;x=_,w=w).|>plot_hist(ax,_)\n end\n fig\n #save(\"one-single-variable-euler-method-2.png\",fig)\nend\n\nplot_res()", + "text": "二元函数的分解理解\n\n\n\n二元函数可以从两个变量的方向分解开来研究\n固定一个变量, 改变另一个变量,得到一组一元函数\n在下列函数图里:\n\n第一列固定 y值(取一组离散值),改变 x值(连续变量)\n第二列x,y 都是连续值,二元函数自身\n第三列固定 x值(取一组离散值),改变 y值(连续变量)\n\n这里的方法和概率概念的边缘概率(marginal probability)一样, 意思是给定一个条件(固定一个值), 改变另一个变量的取值. 二元分布的概率密度函数就是二元函数,只不过曲线下积分等于 1\nCode\nusing GLMakie\nxs = ys = -3:0.1:3\nxs2 = ys2 = -2:1:2\nlen = length(xs)\nf1(x, y) = x^2 - y^2\nf2(x, y) = ℯ^(−(x^2) − (y^2))\nf3(x, y) = y^2\nf4(x, y) = 2 + 2x - y\nf5(x, y) = x + 2y + 3\nf6(x, y) = x^2 + y^2\nf7(x, y) = -x^2 - y^2\nf8(x, y) = x^2 \n\nfunc_arr = [f1,f2,f3,f4,f5,f6,f7,f8]\nlatexstr_arr=[L\"f(x, y) = x^2 - y^2\",L\"f(x,y)=ℯ^{−x^2 −y^2}\",L\"f(x,y)=y^2\",\nL\"f(x,y)=2 + 2x - y\",L\"f(x,y)=x + 2y + 3\",L\"f(x,y)=x^2 + y^2\",L\"f(x,y)=-(x^2+y^2)\",L\"f(x,y)=x^2\"]\n\n\"\"\"\n make_z(index::Int)\n 根据函数索引返回 julia 函数表达式和\n 函数 latex string\n\"\"\"\nfunction make_z(index::Int)\n return ( (x,y)->func_arr[index](x,y),latexstr_arr[index])\nend\n\n\nmake_z\nCode\nfunction plot_func(func_z,latexstr)\n f = Figure(resolution=(1800, 1000))\n ax1 = Axis3(f[1, 1], title=L\"fixed y\", azimuth=0.3pi)\n ax2 = Axis3(f[1, 2], title=L\"surface\", azimuth=0.3pi)\n ax3 = Axis3(f[1, 3], title=L\"fixed x\", azimuth=0.3pi)\n ax4 = Axis(f[2, 1])\n ax5 = Axis(f[2, 3])\n ax6 = Axis3(f[2, 2], azimuth=0.3pi)\n Label(f[0, :], latexstr, fontsize=40)\n zs = [func_z(x, y) for x in xs, y in ys]\n surface!(ax2, xs, ys, zs, color=:lightblue)\n for y in ys2\n yspan = fill(y, len)\n lines!(ax1, xs, yspan, func_z.(xs, yspan), color=:black, lw=3)\n lines!(ax4, xs, func_z.(xs, y), color=:black, lw=3)\n lines!(ax6, xs, yspan, func_z.(xs, yspan), color=:black, lw=3)\n end\n for x in xs2\n xspan = fill(x, len)\n lines!(ax3, xspan, ys, func_z.(xspan, ys), color=:red, lw=3)\n lines!(ax5, ys, func_z.(x, ys), color=:red, lw=3)\n lines!(ax6, xspan, ys, func_z.(xspan, ys), color=:red, lw=3)\n\n end\n\n surface!(ax6, xs, ys, zs, color=:lightblue)\n\n\n return f\nend\n\n\nplot_func (generic function with 1 method)\nCode\nlet \nmake_z(1)|>d->plot_func(d[1],d[2]) \nend", "crumbs": [ "Math", - "4-single-variable-integral" + "2-Two Variables Function corss-section" ] }, { - "objectID": "vis/irish-whiskey-sales.html", - "href": "vis/irish-whiskey-sales.html", - "title": "Irish Whiskey Sales", + "objectID": "math/2-function.html#section", + "href": "math/2-function.html#section", + "title": "2-Two Variables Function corss-section", "section": "", - "text": "info\n\n\n\n\nref:Saint Patricks Irish Whiskey Sales\nsource :/Users/lunarcheung/Downloads/The Twisted Nature of Irish Whiskey Sales.png\nCode\ninclude(\"utils.jl\")\n\n\n[ Info: loading success", + "text": "Figure 1: 1\n\n\n\n\n\n\n\n\n\n\nFigure 2: 3\n\n\n\n\n\n\n\n\n\n\nFigure 3: 4\n\n\n\n\n\n\n\n\n\n\nFigure 4: 5\n\n\n\n\n\n\n\n\n\n\nFigure 5: 6\n\n\n\n\n\n\n\n\n\n\nFigure 6: 7\n\n\n\n\n\n\n\n\n\n\nFigure 7: 8\n\n\n\n\n\n\n\n\n\n\nFigure 8: 9\n\n\n\n\n\n\n\n\n\n\nFigure 9: 10", "crumbs": [ - "Data Viz", - "Irish Whiskey Sales" + "Math", + "2-Two Variables Function corss-section" ] }, { - "objectID": "vis/irish-whiskey-sales.html#load-data-dataframe", - "href": "vis/irish-whiskey-sales.html#load-data-dataframe", - "title": "Irish Whiskey Sales", - "section": "2. load data->dataframe", - "text": "2. load data->dataframe\n\n\nCode\n df=@pipe CSV.File(\"./data/irish.csv\")|>DataFrame|>dropmissing\n first(df,5)\n\n\n5×5 DataFrame\n\n\n\nRow\ncategory\nquality\ncountry\nyear\ncases\n\n\n\nString15\nString15\nString\nInt64\nInt64\n\n\n\n\n1\nIrish Whiskey\nStandard\nUnited States\n1990\n243000\n\n\n2\nIrish Whiskey\nStandard\nUnited States\n1991\n214000\n\n\n3\nIrish Whiskey\nStandard\nUnited States\n1992\n219500\n\n\n4\nIrish Whiskey\nStandard\nUnited States\n1993\n225000\n\n\n5\nIrish Whiskey\nStandard\nUnited States\n1994\n239500", + "objectID": "vis/use-ggplot-map.html", + "href": "vis/use-ggplot-map.html", + "title": "use ggplot2 map", + "section": "", + "text": "Code\nusing RCall \n\nR\"\"\"\nlibrary(ggplot2)\nlibrary(maps)\n\n# Import the data with coordinates\nworld <- map_data(\"world\")\n\n# Plot the map. group = group connects the points in the correct order\nggplot(data = world, aes(x = long, y = lat, group = group)) + \n geom_polygon() \n\n# Equivalent to:\nggplot(world, aes(map_id = region)) +\n geom_map(data = world, map = world,\n aes(x = long, y = lat, map_id = region))\n\"\"\"\n\n\n┌ Warning: RCall.jl: Warning: package ‘maps’ was built under R version 4.2.3\n└ @ RCall ~/.julia/packages/RCall/gOwEW/src/io.jl:172\n┌ Warning: RCall.jl: Warning in geom_map(data = world, map = world, aes(x = long, y = lat, map_id = region)) :\n│ Ignoring unknown aesthetics: x and y\n└ @ RCall ~/.julia/packages/RCall/gOwEW/src/io.jl:172\n\n\n\n\n\n\n\n\n\nRObject{VecSxp}", "crumbs": [ "Data Viz", - "Irish Whiskey Sales" + "use ggplot2 map" ] }, { - "objectID": "vis/irish-whiskey-sales.html#data-preprocessing", - "href": "vis/irish-whiskey-sales.html#data-preprocessing", - "title": "Irish Whiskey Sales", - "section": "3. data preprocessing", - "text": "3. data preprocessing\n\n\nCode\n df1=@chain df begin\n @select(country,year, cases)\n #@filter(country in [\"Ireland\",\"Germany\",\"Russia\",\"France\"])\n @group_by(country,year)\n @summarize(country,year,sales=sum(cases)/1000)\n @ungroup\n #@mutate(cases=cases/1000)\n # @filter(country in [\"Ireland\",\"Germany\",\"Russia\"])\n # @group_by(country)\n # #@arrange(year)\n # @mutate(cases =cases.-cases[1])\n # @ungroup\n unique!(_)\n @group_by(country)\n @arrange(year)\n @mutate(sales =sales.-sales[1])\n @ungroup\n\n end\n all_country_data=@chain df1 begin @filter(country ∉ [\"Ireland\",\"Germany\",\"Russia\",\"France\"])\n end\n Ireland_data=@chain df1 begin @filter(country == \"Ireland\")\n end\n four_country_data=@chain df1 begin @filter(country ∈ [\"Ireland\",\"Germany\",\"Russia\",\"France\",\"Spain\"])\n end\n\n\n134×3 DataFrame109 rows omitted\n\n\n\nRow\ncountry\nyear\nsales\n\n\n\nString\nInt64\nFloat64\n\n\n\n\n1\nIreland\n1990\n0.0\n\n\n2\nIreland\n1991\n9.5\n\n\n3\nIreland\n1992\n-26.5\n\n\n4\nIreland\n1993\n-13.0\n\n\n5\nIreland\n1994\n-12.5\n\n\n6\nIreland\n1995\n-22.5\n\n\n7\nIreland\n1996\n-2.4\n\n\n8\nIreland\n1997\n7.8\n\n\n9\nIreland\n1998\n9.25\n\n\n10\nIreland\n1999\n49.15\n\n\n11\nIreland\n2000\n52.1\n\n\n12\nIreland\n2001\n53.35\n\n\n13\nIreland\n2002\n32.8\n\n\n⋮\n⋮\n⋮\n⋮\n\n\n123\nRussia\n2005\n39.75\n\n\n124\nRussia\n2006\n63.25\n\n\n125\nRussia\n2007\n102.75\n\n\n126\nRussia\n2008\n146.75\n\n\n127\nRussia\n2009\n132.0\n\n\n128\nRussia\n2010\n182.0\n\n\n129\nRussia\n2011\n307.25\n\n\n130\nRussia\n2012\n365.25\n\n\n131\nRussia\n2013\n387.25\n\n\n132\nRussia\n2014\n445.75\n\n\n133\nRussia\n2015\n290.5\n\n\n134\nRussia\n2016\n293.5\n\n\n\n\n\n\n\n\nCode\nran=-100:50:350\naxis = (width = 500, height =500,xticks=(ran,[\"$(i)k\" for i in ran]), xticklabelrotation = pi/8,\ntitlealign = :center,titlesize=24,\ntitle=\"The Twisted Nature of Irish Whiskey Sales\",\n subtitle = rich(\n \"Within Europe sales have increased 225% since 1990. While\" , rich(\"Ireland's\",color =:blue), \"sales have decreased\",rich(\"Russia, France \\n and Germany\", color =:green),\"sales to have than made up the difference.\")\n\n)\n\ndata_layer1=data(four_country_data)\ndata_layer2=data(Ireland_data)\n#data_layer3=data(all_country_data)\nmapping_layer1=mapping(:sales,:year,color=:country)\nmapping_layer2=mapping(:sales,:year)\nvisual_layer1 = visual(Lines,linewidth=3)\nvisual_layer2 = visual(Lines, linewidth=8,color=:blue)\n#visual_layer3 = visual(Lines, linewidth=1,color=(:gray,0.3))\nfour_lines=data_layer1* mapping_layer1 * visual_layer1\nireland_line=data_layer2* mapping_layer2 * visual_layer2\n#all_lines=data_layer3* mapping_layer2 * visual_layer3\ndraw(four_lines+ ireland_line ,axis=axis)\n\n\n┌ Warning: Keyword argument `bgcolor` is deprecated, use `backgroundcolor` instead.\n└ @ Makie ~/.julia/packages/Makie/RgxaV/src/makielayout/blocks/legend.jl:23", + "objectID": "vis/dumbbell-plot.html", + "href": "vis/dumbbell-plot.html", + "title": "Dumbbell Plot", + "section": "", + "text": "ref\n\n\n\nDumbbell plot in ggplot2\n\n\n\n\nCode\ninclude(\"utils.jl\")\n\n\n[ Info: loading success\n\n\n\n\nCode\nR\"\"\"\n# Seed\nset.seed(1)\n\n# Data\ncustomers <- sample(50:150, 10)\npotential_customers <- sample(150:500, 10)\ncompany <- LETTERS[1:10]\n\n# Data frame\ndf <- data.frame(company = company,\n x = customers,\n y = potential_customers)\n\"\"\"\ndf=@rget df\nrename!(df,:x=>:customers,:y=>:potential_customers)\nsort(df,:company)\n#df2=@pivot_longer(df, customers:potential_customers)\n\n\n10×3 DataFrame\n\n\n\nRow\ncompany\ncustomers\npotential_customers\n\n\n\nString\nInt64\nInt64\n\n\n\n\n1\nA\n117\n234\n\n\n2\nB\n88\n426\n\n\n3\nC\n50\n479\n\n\n4\nD\n83\n412\n\n\n5\nE\n136\n478\n\n\n6\nF\n92\n228\n\n\n7\nG\n63\n362\n\n\n8\nH\n131\n186\n\n\n9\nI\n108\n254\n\n\n10\nJ\n100\n366\n\n\n\n\n\n\n\n\n\nCode\n fig=Figure()\n ax=Axis(fig[1,1])\n# for (idx,row) in enumerate(eachrow(df))\n\n# hlines!(ax,[idx]; xmin =[row.customers], xmax = [row.potential_customers])\n\n# end\n# hlines!(ax, 1:10;xmin=[df.customers...], xmax =[df.potential_customers...], color = :blue)\n fig", "crumbs": [ "Data Viz", - "Irish Whiskey Sales" + "Dumbbell Plot" ] }, { - "objectID": "vis/annual-industrial-robots-installed.html", - "href": "vis/annual-industrial-robots-installed.html", - "title": "Annual industrial robots installed", + "objectID": "vis/annual-death-rate-in-diff-age.html", + "href": "vis/annual-death-rate-in-diff-age.html", + "title": "Annual death rates in people of different ages", "section": "", - "text": "info\n\n\n\n\nref:Annual industrial robots installed\nload pacakge\nCode\ninclude(\"utils.jl\")\n\n\n[ Info: loading success", + "text": "ref\n\n\n\n\nref :Annual death rates in people of different ages\nCode\ninclude(\"utils.jl\")\n\n\n[ Info: loading success", "crumbs": [ "Data Viz", - "Annual industrial robots installed" + "Annual death rates in people of different ages" ] }, { - "objectID": "vis/annual-industrial-robots-installed.html#load-data-dataframe", - "href": "vis/annual-industrial-robots-installed.html#load-data-dataframe", - "title": "Annual industrial robots installed", + "objectID": "vis/annual-death-rate-in-diff-age.html#load-data-dataframe", + "href": "vis/annual-death-rate-in-diff-age.html#load-data-dataframe", + "title": "Annual death rates in people of different ages", "section": "2. load data->dataframe", - "text": "2. load data->dataframe\n\n\nCode\ndf=@pipe CSV.File(\"./data/annual-industrial-robots-installed.csv\")|>DataFrame|>coalesce.(_, 0.0)|>rename!(_,\"Total number of industrial robots installed by contry, 2021\"=>:total)|>sort(_,:total)\n\nfirst(df,5)\n\n\n5×4 DataFrame\n\n\n\nRow\nEntity\nCode\nYear\ntotal\n\n\n\nString15\nString3\nInt64\nInt64\n\n\n\n\n1\nPoland\nPOL\n2021\n3300\n\n\n2\nSpain\nESP\n2021\n3400\n\n\n3\nSingapore\nSGP\n2021\n3500\n\n\n4\nThailand\nTHA\n2021\n3900\n\n\n5\nCanada\nCAN\n2021\n4300", - "crumbs": [ - "Data Viz", - "Annual industrial robots installed" - ] - }, - { - "objectID": "vis/annual-industrial-robots-installed.html#plot", - "href": "vis/annual-industrial-robots-installed.html#plot", - "title": "Annual industrial robots installed", - "section": "3. plot", - "text": "3. plot\n\n\nCode\n row,col=size(df)\n fig=Figure()\n ax=Axis(fig[1,1])\n ax.title=\"Annual industrial robots installed\"\n ax.subtitle=\"2021\"\n ax.yticks=(1:row,df.Entity)\n ax.xgridvisible=false\n ax.ygridvisible=false\n xs=df.Entity\n ys=round.(Int,df.total)\n barplot!(ax,1:row,ys;direction=:x,color=ys,bar_labels=:y,#flip_labels_at=20,\n #color_over_background=:dodgerblue3,\n #color_over_bar=:white,\n )\n \n fig", + "text": "2. load data->dataframe\n\n\nCode\ndf=@pipe CSV.File(\"./data/annual-death-rates-in-different-age-groups.csv\")|>DataFrame\ncats=names(df)\nrename!(df, \"Central death rate - Type: period - Sex: both - Age: 0\"=>:age0,\n \"Central death rate - Type: period - Sex: both - Age: 10\"=>:age10,\n \"Central death rate - Type: period - Sex: both - Age: 25\"=>:age25,\n \"Central death rate - Type: period - Sex: both - Age: 45\"=>:age45,\n \"Central death rate - Type: period - Sex: both - Age: 65\"=>:age65,\n \"Central death rate - Type: period - Sex: both - Age: 80\"=>:age80 \n)\nfirst(df,10)\n\n\n10×9 DataFrame\n\n\n\nRow\nEntity\nCode\nYear\nage0\nage10\nage25\nage45\nage65\nage80\n\n\n\nString\nString15?\nInt64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\n\n\n\n\n1\nAfghanistan\nAFG\n1950\n353.469\n7.10599\n14.9553\n24.7113\n72.9251\n219.856\n\n\n2\nAfghanistan\nAFG\n1951\n350.066\n7.04221\n14.8208\n24.475\n72.3925\n218.43\n\n\n3\nAfghanistan\nAFG\n1952\n342.826\n6.91651\n14.5584\n24.0431\n71.3978\n215.819\n\n\n4\nAfghanistan\nAFG\n1953\n335.489\n6.79443\n14.3049\n23.6353\n70.4698\n213.431\n\n\n5\nAfghanistan\nAFG\n1954\n328.739\n6.88345\n14.2338\n23.4879\n70.0566\n211.938\n\n\n6\nAfghanistan\nAFG\n1955\n320.919\n6.55437\n13.8029\n22.8619\n68.6513\n208.779\n\n\n7\nAfghanistan\nAFG\n1956\n313.825\n6.44994\n13.5662\n22.4995\n67.7879\n206.572\n\n\n8\nAfghanistan\nAFG\n1957\n306.734\n6.31585\n13.2953\n22.0842\n66.7916\n204.067\n\n\n9\nAfghanistan\nAFG\n1958\n299.564\n6.1871\n13.0174\n21.6528\n65.7387\n201.421\n\n\n10\nAfghanistan\nAFG\n1959\n292.941\n6.06023\n12.7585\n21.249\n64.7374\n198.98\n\n\n\n\n\n\n\n\nCode\nmake_title(\"Part 2: Plot\")\n\n\n \n Part 2: Plot\n \n\n\n\n\nCode\nfrance=@chain df begin \n @filter(Entity==\"France\")\n @select(3:9)\n @pivot_longer(_, 2:7, names_to = age_group, values_to =rate)\n #@group_by(age_group)\nend\n\ntwo_country=@chain df begin \n @select(1,3:9)\n @pivot_longer(_, 3:8, names_to = age_group, values_to =rate)\n @filter(Entity ∈ [\"France\",\"South Africa\"])\n @group_by(Entity)\n @arrange(Year)\n @ungroup\nend\n\n\n1668×4 DataFrame1643 rows omitted\n\n\n\nRow\nEntity\nYear\nage_group\nrate\n\n\n\nString\nInt64\nString\nFloat64\n\n\n\n\n1\nFrance\n1816\nage0\n205.34\n\n\n2\nFrance\n1816\nage10\n6.15\n\n\n3\nFrance\n1816\nage25\n10.25\n\n\n4\nFrance\n1816\nage45\n14.52\n\n\n5\nFrance\n1816\nage65\n46.12\n\n\n6\nFrance\n1816\nage80\n163.13\n\n\n7\nFrance\n1817\nage0\n200.27\n\n\n8\nFrance\n1817\nage10\n6.42\n\n\n9\nFrance\n1817\nage25\n9.64\n\n\n10\nFrance\n1817\nage45\n14.81\n\n\n11\nFrance\n1817\nage65\n48.33\n\n\n12\nFrance\n1817\nage80\n159.54\n\n\n13\nFrance\n1818\nage0\n201.52\n\n\n⋮\n⋮\n⋮\n⋮\n⋮\n\n\n1657\nSouth Africa\n2020\nage0\n21.4911\n\n\n1658\nSouth Africa\n2020\nage10\n0.40998\n\n\n1659\nSouth Africa\n2020\nage25\n3.14994\n\n\n1660\nSouth Africa\n2020\nage45\n9.43341\n\n\n1661\nSouth Africa\n2020\nage65\n35.0478\n\n\n1662\nSouth Africa\n2020\nage80\n86.7938\n\n\n1663\nSouth Africa\n2021\nage0\n27.3008\n\n\n1664\nSouth Africa\n2021\nage10\n0.46976\n\n\n1665\nSouth Africa\n2021\nage25\n3.63036\n\n\n1666\nSouth Africa\n2021\nage45\n10.8592\n\n\n1667\nSouth Africa\n2021\nage65\n41.6478\n\n\n1668\nSouth Africa\n2021\nage80\n109.58\n\n\n\n\n\n\n\n\n\nCode\n data_layer=data(two_country)\n map_layer=mapping(:Year,:rate,layout=:age_group,color=:Entity)\n visual_layer = visual(Lines,linewidth=2)\n \n plt=data_layer*map_layer*visual_layer\n draw(plt,facet = (;linkyaxes = :none),legend=(position=:bottom, titleposition=:left, framevisible=true, padding=2))\n\n\n┌ Warning: Keyword argument `bgcolor` is deprecated, use `backgroundcolor` instead.\n└ @ Makie ~/.julia/packages/Makie/RgxaV/src/makielayout/blocks/legend.jl:23", "crumbs": [ "Data Viz", - "Annual industrial robots installed" + "Annual death rates in people of different ages" ] }, { - "objectID": "vis/fishing-with-net-type.html", - "href": "vis/fishing-with-net-type.html", - "title": "Wild fish catch by gear type, World", + "objectID": "vis/annual-death-rate-by-sex-years.html", + "href": "vis/annual-death-rate-by-sex-years.html", + "title": "Annual death rates in people of different ages, by sex world", "section": "", - "text": "info\n\n\n\n\nref:Wild fish catch by gear type, World\nfishing gear type : Fish and Overfishing - Our World in Data\nload pacakge\nCode\ninclude(\"utils.jl\")\n\n\n[ Info: loading success", + "text": "ref\n\n\n\n\nref :Annual death rates in people of different ages, by sex\nCode\ninclude(\"utils.jl\")\n\n\n[ Info: loading success", "crumbs": [ "Data Viz", - "Wild fish catch by gear type, World" + "Annual death rates in people of different ages, by sex world" ] }, { - "objectID": "vis/fishing-with-net-type.html#load-data-dataframe", - "href": "vis/fishing-with-net-type.html#load-data-dataframe", - "title": "Wild fish catch by gear type, World", + "objectID": "vis/annual-death-rate-by-sex-years.html#load-data-dataframe", + "href": "vis/annual-death-rate-by-sex-years.html#load-data-dataframe", + "title": "Annual death rates in people of different ages, by sex world", "section": "2. load data->dataframe", - "text": "2. load data->dataframe\n\n\nCode\ndf=@pipe CSV.File(\"./data/fish-catch-gear-type.csv\")|>DataFrame|>DataFrame|>coalesce.(_, 0.0)|>rename!(_,\"pelagic trawl\"=>:pelagic_trawl)\n\n\n13730×11 DataFrame13705 rows omitted\n\n\n\nRow\nEntity\nCode\nYear\nunknown_gear\nother_gear\nlongline\ngillnet\nsmall_scale\npurse_seine\npelagic_trawl\nbottom_trawl\n\n\n\nString\nAny\nInt64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\n\n\n\n\n1\nAlbania\nALB\n1995\n1.21382\n135.643\n0.0\n0.0\n39.7734\n54.0707\n104.631\n1120.05\n\n\n2\nAlbania\nALB\n1996\n16.5429\n209.023\n0.0\n0.0\n50.4084\n84.432\n163.963\n1804.86\n\n\n3\nAlbania\nALB\n1997\n14.2151\n113.616\n0.0\n0.0\n13.3192\n38.8843\n75.6516\n810.648\n\n\n4\nAlbania\nALB\n1998\n14.2151\n219.064\n0.0\n0.0\n10.9106\n88.8256\n173.728\n1897.43\n\n\n5\nAlbania\nALB\n1999\n14.2151\n227.661\n0.0\n0.0\n11.3388\n92.3117\n180.907\n1972.6\n\n\n6\nAlbania\nALB\n2011\n634.994\n0.0\n0.0\n0.0\n21.7128\n143.804\n0.0\n3144.62\n\n\n7\nAlbania\nALB\n2012\n1499.1\n0.0\n0.0\n0.0\n45.1773\n339.495\n0.0\n7423.87\n\n\n8\nAlbania\nALB\n2013\n1561.1\n0.0\n0.0\n0.0\n47.0043\n353.535\n0.0\n7730.9\n\n\n9\nAlbania\nALB\n2014\n1869.72\n0.0\n0.0\n0.0\n71.146\n282.188\n0.0\n6170.72\n\n\n10\nAlbania\nALB\n2015\n1149.99\n0.0\n0.0\n0.0\n33.2955\n273.578\n0.0\n5982.43\n\n\n11\nAlbania\nALB\n2016\n1447.69\n0.0\n0.0\n0.0\n51.6242\n251.592\n0.0\n5501.66\n\n\n12\nAlbania\nALB\n2017\n1566.67\n0.0\n0.0\n0.0\n58.2399\n249.588\n0.0\n5457.85\n\n\n13\nAlbania\nALB\n2018\n899.878\n0.0\n0.0\n0.0\n17.0815\n299.842\n0.0\n6556.76\n\n\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n\n\n13719\nYemen\nYEM\n1999\n0.0\n10150.0\n0.0\n0.0\n1.73005e5\n0.0\n0.0\n89861.3\n\n\n13720\nYemen\nYEM\n2000\n0.0\n11239.5\n0.0\n0.0\n1.47774e5\n0.0\n0.0\n89681.5\n\n\n13721\nYemen\nYEM\n2001\n0.0\n10217.8\n0.0\n0.0\n1.85588e5\n0.0\n0.0\n1.02895e5\n\n\n13722\nYemen\nYEM\n2002\n0.0\n9503.93\n0.0\n0.0\n2.49449e5\n0.0\n0.0\n1.14498e5\n\n\n13723\nYemen\nYEM\n2003\n0.0\n26166.0\n0.0\n0.0\n3.29698e5\n0.0\n0.0\n1.03919e5\n\n\n13724\nYemen\nYEM\n2004\n0.0\n32574.7\n0.0\n0.0\n372192.0\n0.0\n0.0\n1.0894e5\n\n\n13725\nYemen\nYEM\n2005\n0.0\n30072.7\n0.0\n0.0\n351902.0\n0.0\n0.0\n95306.5\n\n\n13726\nYemen\nYEM\n2006\n0.0\n30707.2\n0.0\n0.0\n3.45725e5\n0.0\n0.0\n86286.5\n\n\n13727\nYemen\nYEM\n2007\n0.0\n27540.5\n0.0\n0.0\n2.61181e5\n0.0\n0.0\n74653.9\n\n\n13728\nYemen\nYEM\n2008\n0.0\n20552.1\n0.0\n0.0\n1.80339e5\n0.0\n0.0\n43331.4\n\n\n13729\nYemen\nYEM\n2009\n0.0\n24330.6\n0.0\n0.0\n2.29675e5\n0.0\n0.0\n57511.8\n\n\n13730\nYemen\nYEM\n2010\n0.0\n28806.0\n0.0\n0.0\n2.64376e5\n0.0\n0.0\n93367.3", - "crumbs": [ - "Data Viz", - "Wild fish catch by gear type, World" - ] - }, - { - "objectID": "vis/fishing-with-net-type.html#data-processing", - "href": "vis/fishing-with-net-type.html#data-processing", - "title": "Wild fish catch by gear type, World", - "section": "3. data processing", - "text": "3. data processing\n\n\nCode\ndf=@chain df begin\n @group_by(Year)\n @summarize(unknown_gear=sum(unknown_gear),\n other_gear=sum(other_gear),\n longline=sum(other_gear),\n gillnet=sum(gillnet),\n small_scale=sum(small_scale),\n purse_seine=sum(purse_seine),\n pelagic_trawl=sum(pelagic_trawl),\n bottom_trawl=sum(bottom_trawl)\n )\n @ungroup\n @pivot_longer(_, unknown_gear:bottom_trawl,names_to = gear_type, values_to =weight)\nend\n\n\n552×3 DataFrame527 rows omitted\n\n\n\nRow\nYear\ngear_type\nweight\n\n\n\nInt64\nString\nFloat64\n\n\n\n\n1\n1995\nunknown_gear\n1.06338e7\n\n\n2\n1996\nunknown_gear\n1.0397e7\n\n\n3\n1997\nunknown_gear\n1.1286e7\n\n\n4\n1998\nunknown_gear\n1.08899e7\n\n\n5\n1999\nunknown_gear\n1.15815e7\n\n\n6\n2011\nunknown_gear\n1.05883e7\n\n\n7\n2012\nunknown_gear\n1.07134e7\n\n\n8\n2013\nunknown_gear\n1.0431e7\n\n\n9\n2014\nunknown_gear\n1.19992e7\n\n\n10\n2015\nunknown_gear\n1.54834e7\n\n\n11\n2016\nunknown_gear\n1.20867e7\n\n\n12\n2017\nunknown_gear\n1.6716e7\n\n\n13\n2018\nunknown_gear\n1.57526e7\n\n\n⋮\n⋮\n⋮\n⋮\n\n\n541\n1994\nbottom_trawl\n5.43778e7\n\n\n542\n2000\nbottom_trawl\n5.18786e7\n\n\n543\n2001\nbottom_trawl\n5.23496e7\n\n\n544\n2002\nbottom_trawl\n5.20457e7\n\n\n545\n2003\nbottom_trawl\n5.00434e7\n\n\n546\n2004\nbottom_trawl\n5.05469e7\n\n\n547\n2005\nbottom_trawl\n4.82271e7\n\n\n548\n2006\nbottom_trawl\n4.84797e7\n\n\n549\n2007\nbottom_trawl\n4.7881e7\n\n\n550\n2008\nbottom_trawl\n4.76333e7\n\n\n551\n2009\nbottom_trawl\n4.80774e7\n\n\n552\n2010\nbottom_trawl\n4.89898e7", + "text": "2. load data->dataframe\n\n\nCode\ndf=@pipe CSV.File(\"./data/annual-death-rates-in-different-age-groups-by-sex.csv\")|>DataFrame\n\n\n19922×17 DataFrame19897 rows omitted\n\n\n\nRow\nEntity\nCode\nYear\nCentral death rate - Type: period - Sex: female - Age: 0\nCentral death rate - Type: period - Sex: male - Age: 0\nCentral death rate - Type: period - Sex: female - Age: 10\nCentral death rate - Type: period - Sex: male - Age: 10\nCentral death rate - Type: period - Sex: female - Age: 15\nCentral death rate - Type: period - Sex: male - Age: 15\nCentral death rate - Type: period - Sex: female - Age: 25\nCentral death rate - Type: period - Sex: male - Age: 25\nCentral death rate - Type: period - Sex: female - Age: 45\nCentral death rate - Type: period - Sex: male - Age: 45\nCentral death rate - Type: period - Sex: female - Age: 65\nCentral death rate - Type: period - Sex: male - Age: 65\nCentral death rate - Type: period - Sex: female - Age: 80\nCentral death rate - Type: period - Sex: male - Age: 80\n\n\n\nString\nString15?\nInt64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\n\n\n\n\n1\nAfghanistan\nAFG\n1950\n328.053\n378.814\n7.72991\n6.52991\n9.96758\n8.11575\n15.0426\n14.8812\n21.2739\n27.5066\n67.6107\n77.0253\n211.223\n226.547\n\n\n2\nAfghanistan\nAFG\n1951\n324.777\n374.956\n7.65714\n6.47173\n9.87427\n8.0444\n14.9049\n14.7495\n21.0961\n27.2708\n67.1116\n76.5053\n209.885\n225.159\n\n\n3\nAfghanistan\nAFG\n1952\n318.286\n366.965\n7.51281\n6.3596\n9.6892\n7.90725\n14.6317\n14.4965\n20.7428\n26.8212\n66.1187\n75.5487\n207.22\n222.703\n\n\n4\nAfghanistan\nAFG\n1953\n311.888\n358.685\n7.37038\n6.25279\n9.50655\n7.77702\n14.3619\n14.2566\n20.3934\n26.3985\n65.1349\n74.6933\n204.573\n220.633\n\n\n5\nAfghanistan\nAFG\n1954\n305.975\n351.087\n7.47033\n6.32612\n9.54872\n7.80432\n14.3075\n14.1708\n20.3094\n26.2079\n64.6866\n74.327\n202.837\n219.442\n\n\n6\nAfghanistan\nAFG\n1955\n298.864\n342.552\n7.08519\n6.0428\n9.14193\n7.52088\n13.824\n13.7848\n19.7027\n25.5661\n63.2202\n72.9975\n199.518\n216.517\n\n\n7\nAfghanistan\nAFG\n1956\n292.437\n334.788\n6.9579\n5.95098\n8.97388\n7.40485\n13.5696\n13.5633\n19.3775\n25.173\n62.3198\n72.1939\n197.145\n214.542\n\n\n8\nAfghanistan\nAFG\n1957\n285.96\n327.082\n6.80497\n5.82437\n8.78425\n7.25426\n13.2964\n13.2944\n19.0277\n24.7002\n61.3636\n71.1901\n194.668\n212.095\n\n\n9\nAfghanistan\nAFG\n1958\n279.403\n319.298\n6.66214\n5.69684\n8.60189\n7.09851\n13.0273\n13.0086\n18.6822\n24.1954\n60.4097\n70.1002\n192.166\n209.401\n\n\n10\nAfghanistan\nAFG\n1959\n273.273\n312.183\n6.52603\n5.58004\n8.42824\n6.9558\n12.7715\n12.7468\n18.3544\n23.7325\n59.5094\n69.0955\n189.867\n206.911\n\n\n11\nAfghanistan\nAFG\n1960\n267.463\n305.598\n6.40648\n5.48074\n8.27133\n6.83256\n12.5389\n12.5175\n18.0579\n23.3241\n58.6735\n68.2023\n187.792\n204.59\n\n\n12\nAfghanistan\nAFG\n1961\n261.854\n298.886\n6.26721\n5.3612\n8.09831\n6.68825\n12.2866\n12.256\n17.7335\n22.8633\n57.8106\n67.1957\n185.652\n202.185\n\n\n13\nAfghanistan\nAFG\n1962\n256.655\n292.773\n6.14919\n5.26366\n7.9478\n6.56954\n12.0652\n12.0384\n17.4491\n22.4812\n57.0294\n66.3898\n183.704\n200.26\n\n\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n\n\n19911\nZimbabwe\nZWE\n2010\n53.9416\n60.8736\n1.63101\n1.90256\n2.49942\n2.61754\n8.802\n8.97857\n20.8596\n27.2927\n45.8422\n52.5112\n86.3175\n114.257\n\n\n19912\nZimbabwe\nZWE\n2011\n50.7671\n57.5466\n1.5059\n1.7603\n2.24074\n2.40589\n6.98757\n7.8604\n16.663\n23.4714\n41.1328\n49.1328\n85.6723\n113.942\n\n\n19913\nZimbabwe\nZWE\n2012\n45.8417\n52.3628\n1.2744\n1.53946\n1.90764\n2.12629\n5.97021\n6.73963\n14.7873\n20.558\n37.6887\n46.8993\n85.0377\n113.63\n\n\n19914\nZimbabwe\nZWE\n2013\n42.4171\n48.8878\n1.14827\n1.36861\n1.71382\n1.9229\n5.13833\n5.92327\n13.1951\n18.2398\n35.8164\n45.3136\n84.4419\n113.357\n\n\n19915\nZimbabwe\nZWE\n2014\n40.2433\n46.6497\n1.06513\n1.26043\n1.58333\n1.78416\n4.57123\n5.31936\n12.052\n16.3566\n34.5043\n43.8205\n83.7835\n113.008\n\n\n19916\nZimbabwe\nZWE\n2015\n39.3862\n45.7603\n1.03008\n1.21829\n1.51606\n1.7263\n4.23604\n5.0488\n11.3024\n15.4633\n33.6349\n43.0445\n83.1682\n112.702\n\n\n19917\nZimbabwe\nZWE\n2016\n37.7949\n44.0875\n0.97629\n1.14752\n1.43611\n1.64832\n3.93881\n4.80345\n10.7236\n14.872\n32.8932\n42.7217\n82.5508\n112.388\n\n\n19918\nZimbabwe\nZWE\n2017\n36.8661\n43.1389\n0.95685\n1.12218\n1.38986\n1.61538\n3.73174\n4.70667\n10.2912\n14.6938\n32.3443\n42.7099\n81.9914\n112.102\n\n\n19919\nZimbabwe\nZWE\n2018\n35.4646\n41.6297\n0.89795\n1.04302\n1.32043\n1.53221\n3.51856\n4.42719\n9.86634\n13.892\n31.726\n42.13\n81.3326\n111.776\n\n\n19920\nZimbabwe\nZWE\n2019\n35.3383\n41.4167\n0.93255\n1.07613\n1.32845\n1.55651\n3.48048\n4.53653\n9.78645\n14.3989\n31.6513\n42.7631\n80.8855\n111.541\n\n\n19921\nZimbabwe\nZWE\n2020\n34.9539\n40.9834\n0.87637\n1.02782\n1.20826\n1.39351\n3.11532\n4.14614\n9.38663\n15.0135\n33.6533\n48.0212\n82.9557\n120.98\n\n\n19922\nZimbabwe\nZWE\n2021\n34.9083\n40.922\n0.86883\n1.02383\n1.30698\n1.58042\n3.38878\n4.70133\n10.5541\n17.1106\n38.6799\n56.6425\n100.369\n143.754\n\n\n\n\n\n\n\n\nCode\ncats=names(df)\nrename!(df, \"Central death rate - Type: period - Sex: female - Age: 0\"=>:female_age0, \n \"Central death rate - Type: period - Sex: male - Age: 0\"=>:male_age0, \n \"Central death rate - Type: period - Sex: female - Age: 10\"=>:female_age10, \n \"Central death rate - Type: period - Sex: male - Age: 10\"=>:male_age10, \n \"Central death rate - Type: period - Sex: female - Age: 15\"=>:female_age15, \n \"Central death rate - Type: period - Sex: male - Age: 15\"=>:male_age15, \n \"Central death rate - Type: period - Sex: female - Age: 25\"=>:female_age25, \n \"Central death rate - Type: period - Sex: male - Age: 25\"=>:male_age25, \n \"Central death rate - Type: period - Sex: female - Age: 45\"=>:female_age45, \n \"Central death rate - Type: period - Sex: male - Age: 45\"=>:male_age45,\n \"Central death rate - Type: period - Sex: female - Age: 65\"=>:female_age65, \n \"Central death rate - Type: period - Sex: male - Age: 65\"=>:male_age65, \n \"Central death rate - Type: period - Sex: female - Age: 80\"=>:female_age80, \n \"Central death rate - Type: period - Sex: male - Age: 80\" =>:male_age80 \n)\n\n\n19922×17 DataFrame19897 rows omitted\n\n\n\nRow\nEntity\nCode\nYear\nfemale_age0\nmale_age0\nfemale_age10\nmale_age10\nfemale_age15\nmale_age15\nfemale_age25\nmale_age25\nfemale_age45\nmale_age45\nfemale_age65\nmale_age65\nfemale_age80\nmale_age80\n\n\n\nString\nString15?\nInt64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\n\n\n\n\n1\nAfghanistan\nAFG\n1950\n328.053\n378.814\n7.72991\n6.52991\n9.96758\n8.11575\n15.0426\n14.8812\n21.2739\n27.5066\n67.6107\n77.0253\n211.223\n226.547\n\n\n2\nAfghanistan\nAFG\n1951\n324.777\n374.956\n7.65714\n6.47173\n9.87427\n8.0444\n14.9049\n14.7495\n21.0961\n27.2708\n67.1116\n76.5053\n209.885\n225.159\n\n\n3\nAfghanistan\nAFG\n1952\n318.286\n366.965\n7.51281\n6.3596\n9.6892\n7.90725\n14.6317\n14.4965\n20.7428\n26.8212\n66.1187\n75.5487\n207.22\n222.703\n\n\n4\nAfghanistan\nAFG\n1953\n311.888\n358.685\n7.37038\n6.25279\n9.50655\n7.77702\n14.3619\n14.2566\n20.3934\n26.3985\n65.1349\n74.6933\n204.573\n220.633\n\n\n5\nAfghanistan\nAFG\n1954\n305.975\n351.087\n7.47033\n6.32612\n9.54872\n7.80432\n14.3075\n14.1708\n20.3094\n26.2079\n64.6866\n74.327\n202.837\n219.442\n\n\n6\nAfghanistan\nAFG\n1955\n298.864\n342.552\n7.08519\n6.0428\n9.14193\n7.52088\n13.824\n13.7848\n19.7027\n25.5661\n63.2202\n72.9975\n199.518\n216.517\n\n\n7\nAfghanistan\nAFG\n1956\n292.437\n334.788\n6.9579\n5.95098\n8.97388\n7.40485\n13.5696\n13.5633\n19.3775\n25.173\n62.3198\n72.1939\n197.145\n214.542\n\n\n8\nAfghanistan\nAFG\n1957\n285.96\n327.082\n6.80497\n5.82437\n8.78425\n7.25426\n13.2964\n13.2944\n19.0277\n24.7002\n61.3636\n71.1901\n194.668\n212.095\n\n\n9\nAfghanistan\nAFG\n1958\n279.403\n319.298\n6.66214\n5.69684\n8.60189\n7.09851\n13.0273\n13.0086\n18.6822\n24.1954\n60.4097\n70.1002\n192.166\n209.401\n\n\n10\nAfghanistan\nAFG\n1959\n273.273\n312.183\n6.52603\n5.58004\n8.42824\n6.9558\n12.7715\n12.7468\n18.3544\n23.7325\n59.5094\n69.0955\n189.867\n206.911\n\n\n11\nAfghanistan\nAFG\n1960\n267.463\n305.598\n6.40648\n5.48074\n8.27133\n6.83256\n12.5389\n12.5175\n18.0579\n23.3241\n58.6735\n68.2023\n187.792\n204.59\n\n\n12\nAfghanistan\nAFG\n1961\n261.854\n298.886\n6.26721\n5.3612\n8.09831\n6.68825\n12.2866\n12.256\n17.7335\n22.8633\n57.8106\n67.1957\n185.652\n202.185\n\n\n13\nAfghanistan\nAFG\n1962\n256.655\n292.773\n6.14919\n5.26366\n7.9478\n6.56954\n12.0652\n12.0384\n17.4491\n22.4812\n57.0294\n66.3898\n183.704\n200.26\n\n\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n\n\n19911\nZimbabwe\nZWE\n2010\n53.9416\n60.8736\n1.63101\n1.90256\n2.49942\n2.61754\n8.802\n8.97857\n20.8596\n27.2927\n45.8422\n52.5112\n86.3175\n114.257\n\n\n19912\nZimbabwe\nZWE\n2011\n50.7671\n57.5466\n1.5059\n1.7603\n2.24074\n2.40589\n6.98757\n7.8604\n16.663\n23.4714\n41.1328\n49.1328\n85.6723\n113.942\n\n\n19913\nZimbabwe\nZWE\n2012\n45.8417\n52.3628\n1.2744\n1.53946\n1.90764\n2.12629\n5.97021\n6.73963\n14.7873\n20.558\n37.6887\n46.8993\n85.0377\n113.63\n\n\n19914\nZimbabwe\nZWE\n2013\n42.4171\n48.8878\n1.14827\n1.36861\n1.71382\n1.9229\n5.13833\n5.92327\n13.1951\n18.2398\n35.8164\n45.3136\n84.4419\n113.357\n\n\n19915\nZimbabwe\nZWE\n2014\n40.2433\n46.6497\n1.06513\n1.26043\n1.58333\n1.78416\n4.57123\n5.31936\n12.052\n16.3566\n34.5043\n43.8205\n83.7835\n113.008\n\n\n19916\nZimbabwe\nZWE\n2015\n39.3862\n45.7603\n1.03008\n1.21829\n1.51606\n1.7263\n4.23604\n5.0488\n11.3024\n15.4633\n33.6349\n43.0445\n83.1682\n112.702\n\n\n19917\nZimbabwe\nZWE\n2016\n37.7949\n44.0875\n0.97629\n1.14752\n1.43611\n1.64832\n3.93881\n4.80345\n10.7236\n14.872\n32.8932\n42.7217\n82.5508\n112.388\n\n\n19918\nZimbabwe\nZWE\n2017\n36.8661\n43.1389\n0.95685\n1.12218\n1.38986\n1.61538\n3.73174\n4.70667\n10.2912\n14.6938\n32.3443\n42.7099\n81.9914\n112.102\n\n\n19919\nZimbabwe\nZWE\n2018\n35.4646\n41.6297\n0.89795\n1.04302\n1.32043\n1.53221\n3.51856\n4.42719\n9.86634\n13.892\n31.726\n42.13\n81.3326\n111.776\n\n\n19920\nZimbabwe\nZWE\n2019\n35.3383\n41.4167\n0.93255\n1.07613\n1.32845\n1.55651\n3.48048\n4.53653\n9.78645\n14.3989\n31.6513\n42.7631\n80.8855\n111.541\n\n\n19921\nZimbabwe\nZWE\n2020\n34.9539\n40.9834\n0.87637\n1.02782\n1.20826\n1.39351\n3.11532\n4.14614\n9.38663\n15.0135\n33.6533\n48.0212\n82.9557\n120.98\n\n\n19922\nZimbabwe\nZWE\n2021\n34.9083\n40.922\n0.86883\n1.02383\n1.30698\n1.58042\n3.38878\n4.70133\n10.5541\n17.1106\n38.6799\n56.6425\n100.369\n143.754\n\n\n\n\n\n\n\n\nCode\nfemale_data=@chain df begin\n @filter(Year==2021)\n @select(1,3:17)\n @select(startswith(\"female\"))\n @summarize(female_age0=mean(female_age0),female_age10=mean(female_age10),female_age15=mean(female_age15),female_age25=mean(female_age25), female_age45=mean(female_age45),female_age65=mean(female_age65),female_age80=mean(female_age80))\nend\n\n\n1×7 DataFrame\n\n\n\nRow\nfemale_age0\nfemale_age10\nfemale_age15\nfemale_age25\nfemale_age45\nfemale_age65\nfemale_age80\n\n\n\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\n\n\n\n\n1\n16.8304\n0.426706\n0.564174\n1.06755\n3.32511\n18.0247\n77.9603\n\n\n\n\n\n\n\n\nCode\nmale_data=@chain df begin\n @filter(Year==2021)\n @select(1,3:17)\n @select(startswith(\"male\"))\n @summarize(male_age0=mean(male_age0),male_age10=mean(male_age10),male_age15=mean(male_age15),male_age25=mean(male_age25),male_age45=mean(male_age45),male_age65=mean(male_age65),male_age80=mean(male_age80))\nend\n\n\n1×7 DataFrame\n\n\n\nRow\nmale_age0\nmale_age10\nmale_age15\nmale_age25\nmale_age45\nmale_age65\nmale_age80\n\n\n\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\n\n\n\n\n1\n20.0176\n0.504313\n0.759778\n1.84672\n5.45103\n29.892\n109.894", "crumbs": [ "Data Viz", - "Wild fish catch by gear type, World" + "Annual death rates in people of different ages, by sex world" ] }, { - "objectID": "vis/fishing-with-net-type.html#plot", - "href": "vis/fishing-with-net-type.html#plot", - "title": "Wild fish catch by gear type, World", - "section": "4. plot", - "text": "4. plot\n\n\nCode\n@rput df\nfunction plot_arestack_r()\n R\"\"\"\n library(ggplot2)\n library(ggthemes)\n library(viridis)\n ggplot(df, aes(x=Year, y=weight, fill=gear_type)) + \n geom_area(alpha=0.6 , size=.5, colour=\"white\")+scale_fill_viridis(discrete = T)+theme_wsj()\n \"\"\"\nend\nplot_arestack_r()\n\n\n┌ Warning: RCall.jl: Warning: package ‘ggthemes’ was built under R version 4.2.3\n└ @ RCall ~/.julia/packages/RCall/gOwEW/src/io.jl:172\n┌ Warning: RCall.jl: Loading required package: viridisLite\n└ @ RCall ~/.julia/packages/RCall/gOwEW/src/io.jl:172\n┌ Warning: RCall.jl: Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.\n│ ℹ Please use `linewidth` instead.\n└ @ RCall ~/.julia/packages/RCall/gOwEW/src/io.jl:172\n\n\n\n\n\n\n\n\n\n\n\n(a) different gear fihsing amount\n\n\n\n\n\n\n\nRObject{VecSxp}\n\n\n(b)\n\n\n\n\n\nFigure 1", + "objectID": "vis/choropleth-maps-in-R.html", + "href": "vis/choropleth-maps-in-R.html", + "title": "Choropleth maps in R", + "section": "", + "text": "ref\n\n\n\n\nref :Choropleth maps in R\n\n\n\n\n\nCode\n # install.packages(\"sf\")\nlibrary(sf)\n\n\nWarning: package 'sf' was built under R version 4.2.3\n\n\nLinking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE\n\n\nCode\n# install.packages(\"dplyr\")\nlibrary(dplyr)\n\n\nWarning: package 'dplyr' was built under R version 4.2.3\n\n\n\nAttaching package: 'dplyr'\n\n\nThe following objects are masked from 'package:stats':\n\n filter, lag\n\n\nThe following objects are masked from 'package:base':\n\n intersect, setdiff, setequal, union\n\n\nCode\n# install.packages(\"giscoR\")\nlibrary(giscoR)\n\nyear_ref <- 2013\n\nnuts2_IT <- gisco_get_nuts(\n year = year_ref,\n resolution = 20, \n nuts_level = 2,\n country = \"Italy\") %>%\n select(NUTS_ID, NAME_LATN)\n\n# Transform the shape\nnuts2_IT_32632 <- st_transform(nuts2_IT, 32632)\n\n# Filter to select data from 2013\ndisp_income <- giscoR::tgs00026 %>%\n filter(time == year_ref) %>%\n select(-time)\n\nnuts2_IT_32632_data <- nuts2_IT_32632 %>%\n left_join(disp_income, by = c(\"NUTS_ID\" = \"geo\"))\n\nplot(nuts2_IT_32632_data[, \"values\"],\n breaks = \"jenks\",\n main = \"Choropleth map\")", "crumbs": [ "Data Viz", - "Wild fish catch by gear type, World" + "Choropleth maps in R" ] }, { @@ -1032,36 +1142,58 @@ ] }, { - "objectID": "vis/cost-of-space-launch.html", - "href": "vis/cost-of-space-launch.html", - "title": "Cost of space launches to low Earth orbit", + "objectID": "vis/american-view-on-ai.html", + "href": "vis/american-view-on-ai.html", + "title": "Views of Americans about robot vs. human intelligence, All adults”", "section": "", - "text": "info\n\n\n\nref: Cost of space launches to low Earth orbit", + "text": "info\n\n\n\n\nref:Views of Americans about robot vs. human intelligence, All adults\nload pacakge\nCode\ninclude(\"utils.jl\")\n\n\n[ Info: loading success", "crumbs": [ "Data Viz", - "Cost of space launches to low Earth orbit" + "Views of Americans about robot vs. human intelligence, All adults\"" ] }, { - "objectID": "vis/cost-of-space-launch.html#load-package", - "href": "vis/cost-of-space-launch.html#load-package", - "title": "Cost of space launches to low Earth orbit", - "section": "load package", - "text": "load package\n\n\nCode\ninclude(\"utils.jl\")\n\n\n[ Info: loading success\n\n\n\n\nCode\nmake_title(\"Part One : Data Processing\")\n\n\n \n Part One : Data Processing", + "objectID": "vis/american-view-on-ai.html#load-data-dataframe", + "href": "vis/american-view-on-ai.html#load-data-dataframe", + "title": "Views of Americans about robot vs. human intelligence, All adults”", + "section": "2. load data->dataframe", + "text": "2. load data->dataframe\n\n\nCode\n df=@pipe CSV.File(\"./data/views-of-americans-robot-vs-human-intelligence.csv\")|>DataFrame|>coalesce.(_, 0.0)\n\nfirst(df,5)\n\n\n5×8 DataFrame\n\n\n\nRow\nEntity\nCode\nDay\nNone of these\nDon't know\nMost robots have already developed higher levels of intelligence than humans\nRobots will never be able to develop higher levels of intelligence than humans\nRobots will be able to develop higher levels of intelligence than humans in the future\n\n\n\nString31\nFloat64\nDate\nInt64\nInt64\nInt64\nInt64\nInt64\n\n\n\n\n1\n18-29 years\n0.0\n2021-01-07\n6\n18\n14\n14\n47\n\n\n2\n18-29 years\n0.0\n2021-07-01\n8\n22\n19\n12\n39\n\n\n3\n18-29 years\n0.0\n2022-01-06\n3\n20\n11\n17\n49\n\n\n4\n18-29 years\n0.0\n2022-06-30\n9\n18\n20\n18\n35\n\n\n5\n18-29 years\n0.0\n2023-01-05\n9\n14\n16\n15\n46", "crumbs": [ "Data Viz", - "Cost of space launches to low Earth orbit" + "Views of Americans about robot vs. human intelligence, All adults\"" ] }, { - "objectID": "vis/cost-of-space-launch.html#load-datadataframe", - "href": "vis/cost-of-space-launch.html#load-datadataframe", - "title": "Cost of space launches to low Earth orbit", - "section": "1.1 load data=>dataframe", - "text": "1.1 load data=>dataframe\n\n\nCode\n df=@pipe CSV.File(\"./data/cost-space-launches-low-earth-orbit.csv\")|>DataFrame\ndf =@chain df begin\n @select(1,3,4,5)\n @arrange(Year)\n @filter(Entity ∉ [\"Delta E\",\"Scout\",\"Minotaur I\",\"Space Shuttle\"])\n end\nfirst(df,5)\n\n\n5×4 DataFrame\n\n\n\nRow\nEntity\nYear\ncost_per_kg\nlaunch_class\n\n\n\nString31\nInt64\nInt64\nString7\n\n\n\n\n1\nKosmos\n1962\n14900\nSmall\n\n\n2\nAtlas Centaur\n1963\n29500\nMedium\n\n\n3\nTitan II\n1964\n30600\nMedium\n\n\n4\nProton\n1965\n8200\nHeavy\n\n\n5\nTitan III+\n1965\n21000\nMedium\n\n\n\n\n\n\n\n\nCode\nmake_title(\"Part Two : Plots\")\n\n\n \n Part Two : Plots\n \n\n\n\n\nCode\n axis = (width = 600, height =1200)\n data_layer=data(df) \n mapping_layer1=mapping(:Year,:cost_per_kg,color=:launch_class)\n visual_layer1 = visual(Scatter;strokewidth=2,strokecolor=:black)\n mapping_layer2=mapping(:Year,:cost_per_kg, text=:Entity => verbatim)\n visual_layer2 = visual(Makie.Text;color=:black)\n plt=data_layer*mapping_layer1*visual_layer1\n text_pipe=data_layer*mapping_layer2*visual_layer2\ndraw(plt+text_pipe,axis=axis)\n\n\n┌ Warning: Keyword argument `bgcolor` is deprecated, use `backgroundcolor` instead.\n└ @ Makie ~/.julia/packages/Makie/RgxaV/src/makielayout/blocks/legend.jl:23", + "objectID": "vis/american-view-on-ai.html#data-processing", + "href": "vis/american-view-on-ai.html#data-processing", + "title": "Views of Americans about robot vs. human intelligence, All adults”", + "section": "data processing", + "text": "data processing\n\n\nCode\n cats=names(df)\n rename!(df,\"None of these\"=>:none,\"Don't know\"=>:dtkonw,\n \"Most robots have already developed higher levels of intelligence than humans\"=>:better,\n \"Robots will never be able to develop higher levels of intelligence than humans\"=>:nobetter,\n \"Robots will be able to develop higher levels of intelligence than humans in the future\"=>:future\n \n )\n\n df2=@chain df begin\n @group_by(Day)\n @summarize(none=sum(none),dtkonw=sum(dtkonw), better=sum(better),nobetter=sum(better),future=sum(future))\n \n end\n\n\n6×6 DataFrame\n\n\n\nRow\nDay\nnone\ndtkonw\nbetter\nnobetter\nfuture\n\n\n\nDate\nInt64\nInt64\nInt64\nInt64\nInt64\n\n\n\n\n1\n2021-01-07\n136\n482\n263\n263\n958\n\n\n2\n2021-07-01\n139\n560\n345\n345\n793\n\n\n3\n2022-01-06\n146\n538\n277\n277\n933\n\n\n4\n2022-06-30\n243\n441\n346\n346\n883\n\n\n5\n2023-01-05\n240\n494\n303\n303\n906\n\n\n6\n2023-06-29\n181\n478\n410\n410\n832\n\n\n\n\n\n\n\n\nCode\naxis = (width = 1000, height =500,xticklabelrotation = pi/8,title=\"Attitude about AI development\",yticks=(range(100,1000,7),[\"$i %\" for i in 5:5:35]))\ndata_layer = data(df2)\nmapping_layer=mapping(:Day,[:none,:dtkonw,:better,:nobetter,:future].=> \"value\", color=dims(1) => renamer(cats[4:8]) => \"opinion\")\nvisual_layer = visual(ScatterLines;stroke=1,strokecolor=(:red,0.8),markersize=10,linewidth=3)\ndraw(data_layer * mapping_layer * visual_layer,axis=axis,\n legend=(position=:right, titleposition=:top, framevisible=true, padding=5)\n)\n\n\n┌ Warning: Keyword argument `bgcolor` is deprecated, use `backgroundcolor` instead.\n└ @ Makie ~/.julia/packages/Makie/RgxaV/src/makielayout/blocks/legend.jl:23", "crumbs": [ "Data Viz", - "Cost of space launches to low Earth orbit" + "Views of Americans about robot vs. human intelligence, All adults\"" + ] + }, + { + "objectID": "vis/private-investment-on-ai-by-focus.html", + "href": "vis/private-investment-on-ai-by-focus.html", + "title": "Private Investment on AI by focus", + "section": "", + "text": "ref\n\n\n\n\nref :Annual private investment in artificial intelligence, by focus area\nCode\ninclude(\"utils.jl\")\n\n\n[ Info: loading success\nCode\nmake_title(\"Part 1 : data processing\")\n\n\n \n Part 1 : data processing", + "crumbs": [ + "Data Viz", + "Private Investment on AI by focus" + ] + }, + { + "objectID": "vis/private-investment-on-ai-by-focus.html#load-data-dataframe", + "href": "vis/private-investment-on-ai-by-focus.html#load-data-dataframe", + "title": "Private Investment on AI by focus", + "section": "2. load data->dataframe", + "text": "2. load data->dataframe\n\n\nCode\ndf=@pipe CSV.File(\"./data/private-investment-in-artificial-intelligence-by-focus-area.csv\")|>DataFrame\nrename!(df,\"European Union and United Kingdom\"=>:EU,\"United States\"=>:US)\nselect!(df,Not(\"Code\"))\n\n\n160×6 DataFrame135 rows omitted\n\n\n\nRow\nEntity\nYear\nWorld\nEU\nChina\nUS\n\n\n\nString\nInt64\nInt64\nInt64\nInt64\nInt64\n\n\n\n\n1\nAI ventures\n2017\n705626292\n34720055\n233168581\n404163765\n\n\n2\nAI ventures\n2018\n1539250299\n30321937\n591347599\n857474812\n\n\n3\nAI ventures\n2019\n1212544984\n15388467\n93042361\n962517568\n\n\n4\nAI ventures\n2020\n1545821073\n5170265\n91954563\n1358836062\n\n\n5\nAI ventures\n2021\n2379370717\n1013359242\n258977775\n923024967\n\n\n6\nAI ventures\n2022\n1242838005\n14814431\n451988999\n641729703\n\n\n7\nAgricultural technology\n2017\n309478503\n7554717\n10391318\n261846503\n\n\n8\nAgricultural technology\n2018\n280024054\n33194058\n0\n194254290\n\n\n9\nAgricultural technology\n2019\n502672339\n116117865\n18018197\n283437548\n\n\n10\nAgricultural technology\n2020\n686735808\n25459779\n7959031\n624487776\n\n\n11\nAgricultural technology\n2021\n1351178581\n469886955\n0\n838525337\n\n\n12\nAgricultural technology\n2022\n810137282\n76739673\n95402811\n509921645\n\n\n13\nAugmented or virtual reality\n2017\n313686338\n28642184\n156899836\n107616472\n\n\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n\n\n149\nTotal\n2017\n28434536002\n2687729049\n7314151975\n15548790772\n\n\n150\nTotal\n2018\n46508641939\n2955166939\n15069705393\n22716597215\n\n\n151\nTotal\n2019\n58389972850\n6202067697\n14892596238\n30795184157\n\n\n152\nTotal\n2020\n64505628706\n5599432467\n17094749150\n35531472847\n\n\n153\nTotal\n2021\n125356874235\n12500782453\n22848295305\n73395940859\n\n\n154\nTotal\n2022\n85055265067\n10217823819\n12412402667\n43848726664\n\n\n155\nVenture capital\n2017\n160103509\n9424792\n115426266\n15476426\n\n\n156\nVenture capital\n2018\n217600809\n0\n107910146\n109690663\n\n\n157\nVenture capital\n2019\n106903067\n26497349\n0\n28617137\n\n\n158\nVenture capital\n2020\n179347432\n0\n124590452\n4502008\n\n\n159\nVenture capital\n2021\n225784599\n0\n0\n60011724\n\n\n160\nVenture capital\n2022\n14814431\n14814431\n0\n0\n\n\n\n\n\n\n\n\nCode\ndf2=@chain df begin \n @pivot_longer(_, 3:6, names_to =area, values_to =investment)\n @mutate(investment=investment/10_000_000_000)\n @group_by(Entity)\n @arrange(Year)\n @ungroup\n @filter(Entity ∈ [\"Medical and healthcare\",\"Data management\",\"Cybersecurity\",\"Industrial automation\",\"Semiconductors\",\"Drones\"])\nend\n\n\n144×4 DataFrame119 rows omitted\n\n\n\nRow\nEntity\nYear\narea\ninvestment\n\n\n\nString\nInt64\nString\nFloat64\n\n\n\n\n1\nCybersecurity\n2017\nWorld\n0.247797\n\n\n2\nCybersecurity\n2017\nEU\n0.0211647\n\n\n3\nCybersecurity\n2017\nChina\n0.0799647\n\n\n4\nCybersecurity\n2017\nUS\n0.0895792\n\n\n5\nCybersecurity\n2018\nWorld\n0.407162\n\n\n6\nCybersecurity\n2018\nEU\n0.0172524\n\n\n7\nCybersecurity\n2018\nChina\n0.0583573\n\n\n8\nCybersecurity\n2018\nUS\n0.191833\n\n\n9\nCybersecurity\n2019\nWorld\n0.411088\n\n\n10\nCybersecurity\n2019\nEU\n0.0161193\n\n\n11\nCybersecurity\n2019\nChina\n0.196212\n\n\n12\nCybersecurity\n2019\nUS\n0.171356\n\n\n13\nCybersecurity\n2020\nWorld\n0.379812\n\n\n⋮\n⋮\n⋮\n⋮\n⋮\n\n\n133\nSemiconductors\n2020\nWorld\n0.092762\n\n\n134\nSemiconductors\n2020\nEU\n0.00523489\n\n\n135\nSemiconductors\n2020\nChina\n0.0207361\n\n\n136\nSemiconductors\n2020\nUS\n0.0576268\n\n\n137\nSemiconductors\n2021\nWorld\n0.0632604\n\n\n138\nSemiconductors\n2021\nEU\n0.00857049\n\n\n139\nSemiconductors\n2021\nChina\n0.020434\n\n\n140\nSemiconductors\n2021\nUS\n0.0156559\n\n\n141\nSemiconductors\n2022\nWorld\n0.153211\n\n\n142\nSemiconductors\n2022\nEU\n0.00114812\n\n\n143\nSemiconductors\n2022\nChina\n0.0946097\n\n\n144\nSemiconductors\n2022\nUS\n0.0536569\n\n\n\n\n\n\n\n\nCode\n make_title(\"Part 2 :Plot\")\n\n\n \n Part 2 :Plot\n \n\n\n\n\nCode\ndata_layer=data(df2)\n map_layer=mapping(:Year,:investment,layout=:Entity,color=:area)\n visual_layer = visual(Lines,linewidth=2)\n \n plt=data_layer*map_layer*visual_layer\n draw(plt,legend=(position=:bottom, titleposition=:left, framevisible=true, padding=2),facet = (;linkyaxes = :true))\n\n\n┌ Warning: Keyword argument `bgcolor` is deprecated, use `backgroundcolor` instead.\n└ @ Makie ~/.julia/packages/Makie/RgxaV/src/makielayout/blocks/legend.jl:23", + "crumbs": [ + "Data Viz", + "Private Investment on AI by focus" ] }, { @@ -1106,82 +1238,5 @@ "crumbs": [ "Materials" ] - }, - { - "objectID": "vis/annual-death-rate-in-diff-age.html", - "href": "vis/annual-death-rate-in-diff-age.html", - "title": "Annual death rates in people of different ages", - "section": "", - "text": "ref\n\n\n\n\nref :Annual death rates in people of different ages\nCode\ninclude(\"utils.jl\")\n\n\n[ Info: loading success", - "crumbs": [ - "Data Viz", - "Annual death rates in people of different ages" - ] - }, - { - "objectID": "vis/annual-death-rate-in-diff-age.html#load-data-dataframe", - "href": "vis/annual-death-rate-in-diff-age.html#load-data-dataframe", - "title": "Annual death rates in people of different ages", - "section": "2. load data->dataframe", - "text": "2. load data->dataframe\n\n\nCode\ndf=@pipe CSV.File(\"./data/annual-death-rates-in-different-age-groups.csv\")|>DataFrame\ncats=names(df)\nrename!(df, \"Central death rate - Type: period - Sex: both - Age: 0\"=>:age0,\n \"Central death rate - Type: period - Sex: both - Age: 10\"=>:age10,\n \"Central death rate - Type: period - Sex: both - Age: 25\"=>:age25,\n \"Central death rate - Type: period - Sex: both - Age: 45\"=>:age45,\n \"Central death rate - Type: period - Sex: both - Age: 65\"=>:age65,\n \"Central death rate - Type: period - Sex: both - Age: 80\"=>:age80 \n)\nfirst(df,10)\n\n\n10×9 DataFrame\n\n\n\nRow\nEntity\nCode\nYear\nage0\nage10\nage25\nage45\nage65\nage80\n\n\n\nString\nString15?\nInt64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\n\n\n\n\n1\nAfghanistan\nAFG\n1950\n353.469\n7.10599\n14.9553\n24.7113\n72.9251\n219.856\n\n\n2\nAfghanistan\nAFG\n1951\n350.066\n7.04221\n14.8208\n24.475\n72.3925\n218.43\n\n\n3\nAfghanistan\nAFG\n1952\n342.826\n6.91651\n14.5584\n24.0431\n71.3978\n215.819\n\n\n4\nAfghanistan\nAFG\n1953\n335.489\n6.79443\n14.3049\n23.6353\n70.4698\n213.431\n\n\n5\nAfghanistan\nAFG\n1954\n328.739\n6.88345\n14.2338\n23.4879\n70.0566\n211.938\n\n\n6\nAfghanistan\nAFG\n1955\n320.919\n6.55437\n13.8029\n22.8619\n68.6513\n208.779\n\n\n7\nAfghanistan\nAFG\n1956\n313.825\n6.44994\n13.5662\n22.4995\n67.7879\n206.572\n\n\n8\nAfghanistan\nAFG\n1957\n306.734\n6.31585\n13.2953\n22.0842\n66.7916\n204.067\n\n\n9\nAfghanistan\nAFG\n1958\n299.564\n6.1871\n13.0174\n21.6528\n65.7387\n201.421\n\n\n10\nAfghanistan\nAFG\n1959\n292.941\n6.06023\n12.7585\n21.249\n64.7374\n198.98\n\n\n\n\n\n\n\n\nCode\nmake_title(\"Part 2: Plot\")\n\n\n \n Part 2: Plot\n \n\n\n\n\nCode\nfrance=@chain df begin \n @filter(Entity==\"France\")\n @select(3:9)\n @pivot_longer(_, 2:7, names_to = age_group, values_to =rate)\n #@group_by(age_group)\nend\n\ntwo_country=@chain df begin \n @select(1,3:9)\n @pivot_longer(_, 3:8, names_to = age_group, values_to =rate)\n @filter(Entity ∈ [\"France\",\"South Africa\"])\n @group_by(Entity)\n @arrange(Year)\n @ungroup\nend\n\n\n1668×4 DataFrame1643 rows omitted\n\n\n\nRow\nEntity\nYear\nage_group\nrate\n\n\n\nString\nInt64\nString\nFloat64\n\n\n\n\n1\nFrance\n1816\nage0\n205.34\n\n\n2\nFrance\n1816\nage10\n6.15\n\n\n3\nFrance\n1816\nage25\n10.25\n\n\n4\nFrance\n1816\nage45\n14.52\n\n\n5\nFrance\n1816\nage65\n46.12\n\n\n6\nFrance\n1816\nage80\n163.13\n\n\n7\nFrance\n1817\nage0\n200.27\n\n\n8\nFrance\n1817\nage10\n6.42\n\n\n9\nFrance\n1817\nage25\n9.64\n\n\n10\nFrance\n1817\nage45\n14.81\n\n\n11\nFrance\n1817\nage65\n48.33\n\n\n12\nFrance\n1817\nage80\n159.54\n\n\n13\nFrance\n1818\nage0\n201.52\n\n\n⋮\n⋮\n⋮\n⋮\n⋮\n\n\n1657\nSouth Africa\n2020\nage0\n21.4911\n\n\n1658\nSouth Africa\n2020\nage10\n0.40998\n\n\n1659\nSouth Africa\n2020\nage25\n3.14994\n\n\n1660\nSouth Africa\n2020\nage45\n9.43341\n\n\n1661\nSouth Africa\n2020\nage65\n35.0478\n\n\n1662\nSouth Africa\n2020\nage80\n86.7938\n\n\n1663\nSouth Africa\n2021\nage0\n27.3008\n\n\n1664\nSouth Africa\n2021\nage10\n0.46976\n\n\n1665\nSouth Africa\n2021\nage25\n3.63036\n\n\n1666\nSouth Africa\n2021\nage45\n10.8592\n\n\n1667\nSouth Africa\n2021\nage65\n41.6478\n\n\n1668\nSouth Africa\n2021\nage80\n109.58\n\n\n\n\n\n\n\n\n\nCode\n data_layer=data(two_country)\n map_layer=mapping(:Year,:rate,layout=:age_group,color=:Entity)\n visual_layer = visual(Lines,linewidth=2)\n \n plt=data_layer*map_layer*visual_layer\n draw(plt,facet = (;linkyaxes = :none),legend=(position=:bottom, titleposition=:left, framevisible=true, padding=2))\n\n\n┌ Warning: Keyword argument `bgcolor` is deprecated, use `backgroundcolor` instead.\n└ @ Makie ~/.julia/packages/Makie/RgxaV/src/makielayout/blocks/legend.jl:23", - "crumbs": [ - "Data Viz", - "Annual death rates in people of different ages" - ] - }, - { - "objectID": "vis/annual-death-rate-by-sex-years.html", - "href": "vis/annual-death-rate-by-sex-years.html", - "title": "Annual death rates in people of different ages, by sex world", - "section": "", - "text": "ref\n\n\n\n\nref :Annual death rates in people of different ages, by sex\nCode\ninclude(\"utils.jl\")\n\n\n[ Info: loading success", - "crumbs": [ - "Data Viz", - "Annual death rates in people of different ages, by sex world" - ] - }, - { - "objectID": "vis/annual-death-rate-by-sex-years.html#load-data-dataframe", - "href": "vis/annual-death-rate-by-sex-years.html#load-data-dataframe", - "title": "Annual death rates in people of different ages, by sex world", - "section": "2. load data->dataframe", - "text": "2. load data->dataframe\n\n\nCode\ndf=@pipe CSV.File(\"./data/annual-death-rates-in-different-age-groups-by-sex.csv\")|>DataFrame\n\n\n19922×17 DataFrame19897 rows omitted\n\n\n\nRow\nEntity\nCode\nYear\nCentral death rate - Type: period - Sex: female - Age: 0\nCentral death rate - Type: period - Sex: male - Age: 0\nCentral death rate - Type: period - Sex: female - Age: 10\nCentral death rate - Type: period - Sex: male - Age: 10\nCentral death rate - Type: period - Sex: female - Age: 15\nCentral death rate - Type: period - Sex: male - Age: 15\nCentral death rate - Type: period - Sex: female - Age: 25\nCentral death rate - Type: period - Sex: male - Age: 25\nCentral death rate - Type: period - Sex: female - Age: 45\nCentral death rate - Type: period - Sex: male - Age: 45\nCentral death rate - Type: period - Sex: female - Age: 65\nCentral death rate - Type: period - Sex: male - Age: 65\nCentral death rate - Type: period - Sex: female - Age: 80\nCentral death rate - Type: period - Sex: male - Age: 80\n\n\n\nString\nString15?\nInt64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\n\n\n\n\n1\nAfghanistan\nAFG\n1950\n328.053\n378.814\n7.72991\n6.52991\n9.96758\n8.11575\n15.0426\n14.8812\n21.2739\n27.5066\n67.6107\n77.0253\n211.223\n226.547\n\n\n2\nAfghanistan\nAFG\n1951\n324.777\n374.956\n7.65714\n6.47173\n9.87427\n8.0444\n14.9049\n14.7495\n21.0961\n27.2708\n67.1116\n76.5053\n209.885\n225.159\n\n\n3\nAfghanistan\nAFG\n1952\n318.286\n366.965\n7.51281\n6.3596\n9.6892\n7.90725\n14.6317\n14.4965\n20.7428\n26.8212\n66.1187\n75.5487\n207.22\n222.703\n\n\n4\nAfghanistan\nAFG\n1953\n311.888\n358.685\n7.37038\n6.25279\n9.50655\n7.77702\n14.3619\n14.2566\n20.3934\n26.3985\n65.1349\n74.6933\n204.573\n220.633\n\n\n5\nAfghanistan\nAFG\n1954\n305.975\n351.087\n7.47033\n6.32612\n9.54872\n7.80432\n14.3075\n14.1708\n20.3094\n26.2079\n64.6866\n74.327\n202.837\n219.442\n\n\n6\nAfghanistan\nAFG\n1955\n298.864\n342.552\n7.08519\n6.0428\n9.14193\n7.52088\n13.824\n13.7848\n19.7027\n25.5661\n63.2202\n72.9975\n199.518\n216.517\n\n\n7\nAfghanistan\nAFG\n1956\n292.437\n334.788\n6.9579\n5.95098\n8.97388\n7.40485\n13.5696\n13.5633\n19.3775\n25.173\n62.3198\n72.1939\n197.145\n214.542\n\n\n8\nAfghanistan\nAFG\n1957\n285.96\n327.082\n6.80497\n5.82437\n8.78425\n7.25426\n13.2964\n13.2944\n19.0277\n24.7002\n61.3636\n71.1901\n194.668\n212.095\n\n\n9\nAfghanistan\nAFG\n1958\n279.403\n319.298\n6.66214\n5.69684\n8.60189\n7.09851\n13.0273\n13.0086\n18.6822\n24.1954\n60.4097\n70.1002\n192.166\n209.401\n\n\n10\nAfghanistan\nAFG\n1959\n273.273\n312.183\n6.52603\n5.58004\n8.42824\n6.9558\n12.7715\n12.7468\n18.3544\n23.7325\n59.5094\n69.0955\n189.867\n206.911\n\n\n11\nAfghanistan\nAFG\n1960\n267.463\n305.598\n6.40648\n5.48074\n8.27133\n6.83256\n12.5389\n12.5175\n18.0579\n23.3241\n58.6735\n68.2023\n187.792\n204.59\n\n\n12\nAfghanistan\nAFG\n1961\n261.854\n298.886\n6.26721\n5.3612\n8.09831\n6.68825\n12.2866\n12.256\n17.7335\n22.8633\n57.8106\n67.1957\n185.652\n202.185\n\n\n13\nAfghanistan\nAFG\n1962\n256.655\n292.773\n6.14919\n5.26366\n7.9478\n6.56954\n12.0652\n12.0384\n17.4491\n22.4812\n57.0294\n66.3898\n183.704\n200.26\n\n\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n\n\n19911\nZimbabwe\nZWE\n2010\n53.9416\n60.8736\n1.63101\n1.90256\n2.49942\n2.61754\n8.802\n8.97857\n20.8596\n27.2927\n45.8422\n52.5112\n86.3175\n114.257\n\n\n19912\nZimbabwe\nZWE\n2011\n50.7671\n57.5466\n1.5059\n1.7603\n2.24074\n2.40589\n6.98757\n7.8604\n16.663\n23.4714\n41.1328\n49.1328\n85.6723\n113.942\n\n\n19913\nZimbabwe\nZWE\n2012\n45.8417\n52.3628\n1.2744\n1.53946\n1.90764\n2.12629\n5.97021\n6.73963\n14.7873\n20.558\n37.6887\n46.8993\n85.0377\n113.63\n\n\n19914\nZimbabwe\nZWE\n2013\n42.4171\n48.8878\n1.14827\n1.36861\n1.71382\n1.9229\n5.13833\n5.92327\n13.1951\n18.2398\n35.8164\n45.3136\n84.4419\n113.357\n\n\n19915\nZimbabwe\nZWE\n2014\n40.2433\n46.6497\n1.06513\n1.26043\n1.58333\n1.78416\n4.57123\n5.31936\n12.052\n16.3566\n34.5043\n43.8205\n83.7835\n113.008\n\n\n19916\nZimbabwe\nZWE\n2015\n39.3862\n45.7603\n1.03008\n1.21829\n1.51606\n1.7263\n4.23604\n5.0488\n11.3024\n15.4633\n33.6349\n43.0445\n83.1682\n112.702\n\n\n19917\nZimbabwe\nZWE\n2016\n37.7949\n44.0875\n0.97629\n1.14752\n1.43611\n1.64832\n3.93881\n4.80345\n10.7236\n14.872\n32.8932\n42.7217\n82.5508\n112.388\n\n\n19918\nZimbabwe\nZWE\n2017\n36.8661\n43.1389\n0.95685\n1.12218\n1.38986\n1.61538\n3.73174\n4.70667\n10.2912\n14.6938\n32.3443\n42.7099\n81.9914\n112.102\n\n\n19919\nZimbabwe\nZWE\n2018\n35.4646\n41.6297\n0.89795\n1.04302\n1.32043\n1.53221\n3.51856\n4.42719\n9.86634\n13.892\n31.726\n42.13\n81.3326\n111.776\n\n\n19920\nZimbabwe\nZWE\n2019\n35.3383\n41.4167\n0.93255\n1.07613\n1.32845\n1.55651\n3.48048\n4.53653\n9.78645\n14.3989\n31.6513\n42.7631\n80.8855\n111.541\n\n\n19921\nZimbabwe\nZWE\n2020\n34.9539\n40.9834\n0.87637\n1.02782\n1.20826\n1.39351\n3.11532\n4.14614\n9.38663\n15.0135\n33.6533\n48.0212\n82.9557\n120.98\n\n\n19922\nZimbabwe\nZWE\n2021\n34.9083\n40.922\n0.86883\n1.02383\n1.30698\n1.58042\n3.38878\n4.70133\n10.5541\n17.1106\n38.6799\n56.6425\n100.369\n143.754\n\n\n\n\n\n\n\n\nCode\ncats=names(df)\nrename!(df, \"Central death rate - Type: period - Sex: female - Age: 0\"=>:female_age0, \n \"Central death rate - Type: period - Sex: male - Age: 0\"=>:male_age0, \n \"Central death rate - Type: period - Sex: female - Age: 10\"=>:female_age10, \n \"Central death rate - Type: period - Sex: male - Age: 10\"=>:male_age10, \n \"Central death rate - Type: period - Sex: female - Age: 15\"=>:female_age15, \n \"Central death rate - Type: period - Sex: male - Age: 15\"=>:male_age15, \n \"Central death rate - Type: period - Sex: female - Age: 25\"=>:female_age25, \n \"Central death rate - Type: period - Sex: male - Age: 25\"=>:male_age25, \n \"Central death rate - Type: period - Sex: female - Age: 45\"=>:female_age45, \n \"Central death rate - Type: period - Sex: male - Age: 45\"=>:male_age45,\n \"Central death rate - Type: period - Sex: female - Age: 65\"=>:female_age65, \n \"Central death rate - Type: period - Sex: male - Age: 65\"=>:male_age65, \n \"Central death rate - Type: period - Sex: female - Age: 80\"=>:female_age80, \n \"Central death rate - Type: period - Sex: male - Age: 80\" =>:male_age80 \n)\n\n\n19922×17 DataFrame19897 rows omitted\n\n\n\nRow\nEntity\nCode\nYear\nfemale_age0\nmale_age0\nfemale_age10\nmale_age10\nfemale_age15\nmale_age15\nfemale_age25\nmale_age25\nfemale_age45\nmale_age45\nfemale_age65\nmale_age65\nfemale_age80\nmale_age80\n\n\n\nString\nString15?\nInt64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\n\n\n\n\n1\nAfghanistan\nAFG\n1950\n328.053\n378.814\n7.72991\n6.52991\n9.96758\n8.11575\n15.0426\n14.8812\n21.2739\n27.5066\n67.6107\n77.0253\n211.223\n226.547\n\n\n2\nAfghanistan\nAFG\n1951\n324.777\n374.956\n7.65714\n6.47173\n9.87427\n8.0444\n14.9049\n14.7495\n21.0961\n27.2708\n67.1116\n76.5053\n209.885\n225.159\n\n\n3\nAfghanistan\nAFG\n1952\n318.286\n366.965\n7.51281\n6.3596\n9.6892\n7.90725\n14.6317\n14.4965\n20.7428\n26.8212\n66.1187\n75.5487\n207.22\n222.703\n\n\n4\nAfghanistan\nAFG\n1953\n311.888\n358.685\n7.37038\n6.25279\n9.50655\n7.77702\n14.3619\n14.2566\n20.3934\n26.3985\n65.1349\n74.6933\n204.573\n220.633\n\n\n5\nAfghanistan\nAFG\n1954\n305.975\n351.087\n7.47033\n6.32612\n9.54872\n7.80432\n14.3075\n14.1708\n20.3094\n26.2079\n64.6866\n74.327\n202.837\n219.442\n\n\n6\nAfghanistan\nAFG\n1955\n298.864\n342.552\n7.08519\n6.0428\n9.14193\n7.52088\n13.824\n13.7848\n19.7027\n25.5661\n63.2202\n72.9975\n199.518\n216.517\n\n\n7\nAfghanistan\nAFG\n1956\n292.437\n334.788\n6.9579\n5.95098\n8.97388\n7.40485\n13.5696\n13.5633\n19.3775\n25.173\n62.3198\n72.1939\n197.145\n214.542\n\n\n8\nAfghanistan\nAFG\n1957\n285.96\n327.082\n6.80497\n5.82437\n8.78425\n7.25426\n13.2964\n13.2944\n19.0277\n24.7002\n61.3636\n71.1901\n194.668\n212.095\n\n\n9\nAfghanistan\nAFG\n1958\n279.403\n319.298\n6.66214\n5.69684\n8.60189\n7.09851\n13.0273\n13.0086\n18.6822\n24.1954\n60.4097\n70.1002\n192.166\n209.401\n\n\n10\nAfghanistan\nAFG\n1959\n273.273\n312.183\n6.52603\n5.58004\n8.42824\n6.9558\n12.7715\n12.7468\n18.3544\n23.7325\n59.5094\n69.0955\n189.867\n206.911\n\n\n11\nAfghanistan\nAFG\n1960\n267.463\n305.598\n6.40648\n5.48074\n8.27133\n6.83256\n12.5389\n12.5175\n18.0579\n23.3241\n58.6735\n68.2023\n187.792\n204.59\n\n\n12\nAfghanistan\nAFG\n1961\n261.854\n298.886\n6.26721\n5.3612\n8.09831\n6.68825\n12.2866\n12.256\n17.7335\n22.8633\n57.8106\n67.1957\n185.652\n202.185\n\n\n13\nAfghanistan\nAFG\n1962\n256.655\n292.773\n6.14919\n5.26366\n7.9478\n6.56954\n12.0652\n12.0384\n17.4491\n22.4812\n57.0294\n66.3898\n183.704\n200.26\n\n\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n\n\n19911\nZimbabwe\nZWE\n2010\n53.9416\n60.8736\n1.63101\n1.90256\n2.49942\n2.61754\n8.802\n8.97857\n20.8596\n27.2927\n45.8422\n52.5112\n86.3175\n114.257\n\n\n19912\nZimbabwe\nZWE\n2011\n50.7671\n57.5466\n1.5059\n1.7603\n2.24074\n2.40589\n6.98757\n7.8604\n16.663\n23.4714\n41.1328\n49.1328\n85.6723\n113.942\n\n\n19913\nZimbabwe\nZWE\n2012\n45.8417\n52.3628\n1.2744\n1.53946\n1.90764\n2.12629\n5.97021\n6.73963\n14.7873\n20.558\n37.6887\n46.8993\n85.0377\n113.63\n\n\n19914\nZimbabwe\nZWE\n2013\n42.4171\n48.8878\n1.14827\n1.36861\n1.71382\n1.9229\n5.13833\n5.92327\n13.1951\n18.2398\n35.8164\n45.3136\n84.4419\n113.357\n\n\n19915\nZimbabwe\nZWE\n2014\n40.2433\n46.6497\n1.06513\n1.26043\n1.58333\n1.78416\n4.57123\n5.31936\n12.052\n16.3566\n34.5043\n43.8205\n83.7835\n113.008\n\n\n19916\nZimbabwe\nZWE\n2015\n39.3862\n45.7603\n1.03008\n1.21829\n1.51606\n1.7263\n4.23604\n5.0488\n11.3024\n15.4633\n33.6349\n43.0445\n83.1682\n112.702\n\n\n19917\nZimbabwe\nZWE\n2016\n37.7949\n44.0875\n0.97629\n1.14752\n1.43611\n1.64832\n3.93881\n4.80345\n10.7236\n14.872\n32.8932\n42.7217\n82.5508\n112.388\n\n\n19918\nZimbabwe\nZWE\n2017\n36.8661\n43.1389\n0.95685\n1.12218\n1.38986\n1.61538\n3.73174\n4.70667\n10.2912\n14.6938\n32.3443\n42.7099\n81.9914\n112.102\n\n\n19919\nZimbabwe\nZWE\n2018\n35.4646\n41.6297\n0.89795\n1.04302\n1.32043\n1.53221\n3.51856\n4.42719\n9.86634\n13.892\n31.726\n42.13\n81.3326\n111.776\n\n\n19920\nZimbabwe\nZWE\n2019\n35.3383\n41.4167\n0.93255\n1.07613\n1.32845\n1.55651\n3.48048\n4.53653\n9.78645\n14.3989\n31.6513\n42.7631\n80.8855\n111.541\n\n\n19921\nZimbabwe\nZWE\n2020\n34.9539\n40.9834\n0.87637\n1.02782\n1.20826\n1.39351\n3.11532\n4.14614\n9.38663\n15.0135\n33.6533\n48.0212\n82.9557\n120.98\n\n\n19922\nZimbabwe\nZWE\n2021\n34.9083\n40.922\n0.86883\n1.02383\n1.30698\n1.58042\n3.38878\n4.70133\n10.5541\n17.1106\n38.6799\n56.6425\n100.369\n143.754\n\n\n\n\n\n\n\n\nCode\nfemale_data=@chain df begin\n @filter(Year==2021)\n @select(1,3:17)\n @select(startswith(\"female\"))\n @summarize(female_age0=mean(female_age0),female_age10=mean(female_age10),female_age15=mean(female_age15),female_age25=mean(female_age25), female_age45=mean(female_age45),female_age65=mean(female_age65),female_age80=mean(female_age80))\nend\n\n\n1×7 DataFrame\n\n\n\nRow\nfemale_age0\nfemale_age10\nfemale_age15\nfemale_age25\nfemale_age45\nfemale_age65\nfemale_age80\n\n\n\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\n\n\n\n\n1\n16.8304\n0.426706\n0.564174\n1.06755\n3.32511\n18.0247\n77.9603\n\n\n\n\n\n\n\n\nCode\nmale_data=@chain df begin\n @filter(Year==2021)\n @select(1,3:17)\n @select(startswith(\"male\"))\n @summarize(male_age0=mean(male_age0),male_age10=mean(male_age10),male_age15=mean(male_age15),male_age25=mean(male_age25),male_age45=mean(male_age45),male_age65=mean(male_age65),male_age80=mean(male_age80))\nend\n\n\n1×7 DataFrame\n\n\n\nRow\nmale_age0\nmale_age10\nmale_age15\nmale_age25\nmale_age45\nmale_age65\nmale_age80\n\n\n\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\nFloat64\n\n\n\n\n1\n20.0176\n0.504313\n0.759778\n1.84672\n5.45103\n29.892\n109.894", - "crumbs": [ - "Data Viz", - "Annual death rates in people of different ages, by sex world" - ] - }, - { - "objectID": "vis/use-ggplot-map.html", - "href": "vis/use-ggplot-map.html", - "title": "use ggplot2 map", - "section": "", - "text": "Code\nusing RCall \n\nR\"\"\"\nlibrary(ggplot2)\nlibrary(maps)\n\n# Import the data with coordinates\nworld <- map_data(\"world\")\n\n# Plot the map. group = group connects the points in the correct order\nggplot(data = world, aes(x = long, y = lat, group = group)) + \n geom_polygon() \n\n# Equivalent to:\nggplot(world, aes(map_id = region)) +\n geom_map(data = world, map = world,\n aes(x = long, y = lat, map_id = region))\n\"\"\"\n\n\n┌ Warning: RCall.jl: Warning: package ‘maps’ was built under R version 4.2.3\n└ @ RCall ~/.julia/packages/RCall/gOwEW/src/io.jl:172\n┌ Warning: RCall.jl: Warning in geom_map(data = world, map = world, aes(x = long, y = lat, map_id = region)) :\n│ Ignoring unknown aesthetics: x and y\n└ @ RCall ~/.julia/packages/RCall/gOwEW/src/io.jl:172\n\n\n\n\n\n\n\n\n\nRObject{VecSxp}", - "crumbs": [ - "Data Viz", - "use ggplot2 map" - ] - }, - { - "objectID": "vis/private-investment-on-ai-by-focus.html", - "href": "vis/private-investment-on-ai-by-focus.html", - "title": "Private Investment on AI by focus", - "section": "", - "text": "ref\n\n\n\n\nref :Annual private investment in artificial intelligence, by focus area\nCode\ninclude(\"utils.jl\")\n\n\n[ Info: loading success\nCode\nmake_title(\"Part 1 : data processing\")\n\n\n \n Part 1 : data processing", - "crumbs": [ - "Data Viz", - "Private Investment on AI by focus" - ] - }, - { - "objectID": "vis/private-investment-on-ai-by-focus.html#load-data-dataframe", - "href": "vis/private-investment-on-ai-by-focus.html#load-data-dataframe", - "title": "Private Investment on AI by focus", - "section": "2. load data->dataframe", - "text": "2. load data->dataframe\n\n\nCode\ndf=@pipe CSV.File(\"./data/private-investment-in-artificial-intelligence-by-focus-area.csv\")|>DataFrame\nrename!(df,\"European Union and United Kingdom\"=>:EU,\"United States\"=>:US)\nselect!(df,Not(\"Code\"))\n\n\n160×6 DataFrame135 rows omitted\n\n\n\nRow\nEntity\nYear\nWorld\nEU\nChina\nUS\n\n\n\nString\nInt64\nInt64\nInt64\nInt64\nInt64\n\n\n\n\n1\nAI ventures\n2017\n705626292\n34720055\n233168581\n404163765\n\n\n2\nAI ventures\n2018\n1539250299\n30321937\n591347599\n857474812\n\n\n3\nAI ventures\n2019\n1212544984\n15388467\n93042361\n962517568\n\n\n4\nAI ventures\n2020\n1545821073\n5170265\n91954563\n1358836062\n\n\n5\nAI ventures\n2021\n2379370717\n1013359242\n258977775\n923024967\n\n\n6\nAI ventures\n2022\n1242838005\n14814431\n451988999\n641729703\n\n\n7\nAgricultural technology\n2017\n309478503\n7554717\n10391318\n261846503\n\n\n8\nAgricultural technology\n2018\n280024054\n33194058\n0\n194254290\n\n\n9\nAgricultural technology\n2019\n502672339\n116117865\n18018197\n283437548\n\n\n10\nAgricultural technology\n2020\n686735808\n25459779\n7959031\n624487776\n\n\n11\nAgricultural technology\n2021\n1351178581\n469886955\n0\n838525337\n\n\n12\nAgricultural technology\n2022\n810137282\n76739673\n95402811\n509921645\n\n\n13\nAugmented or virtual reality\n2017\n313686338\n28642184\n156899836\n107616472\n\n\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n⋮\n\n\n149\nTotal\n2017\n28434536002\n2687729049\n7314151975\n15548790772\n\n\n150\nTotal\n2018\n46508641939\n2955166939\n15069705393\n22716597215\n\n\n151\nTotal\n2019\n58389972850\n6202067697\n14892596238\n30795184157\n\n\n152\nTotal\n2020\n64505628706\n5599432467\n17094749150\n35531472847\n\n\n153\nTotal\n2021\n125356874235\n12500782453\n22848295305\n73395940859\n\n\n154\nTotal\n2022\n85055265067\n10217823819\n12412402667\n43848726664\n\n\n155\nVenture capital\n2017\n160103509\n9424792\n115426266\n15476426\n\n\n156\nVenture capital\n2018\n217600809\n0\n107910146\n109690663\n\n\n157\nVenture capital\n2019\n106903067\n26497349\n0\n28617137\n\n\n158\nVenture capital\n2020\n179347432\n0\n124590452\n4502008\n\n\n159\nVenture capital\n2021\n225784599\n0\n0\n60011724\n\n\n160\nVenture capital\n2022\n14814431\n14814431\n0\n0\n\n\n\n\n\n\n\n\nCode\ndf2=@chain df begin \n @pivot_longer(_, 3:6, names_to =area, values_to =investment)\n @mutate(investment=investment/10_000_000_000)\n @group_by(Entity)\n @arrange(Year)\n @ungroup\n @filter(Entity ∈ [\"Medical and healthcare\",\"Data management\",\"Cybersecurity\",\"Industrial automation\",\"Semiconductors\",\"Drones\"])\nend\n\n\n144×4 DataFrame119 rows omitted\n\n\n\nRow\nEntity\nYear\narea\ninvestment\n\n\n\nString\nInt64\nString\nFloat64\n\n\n\n\n1\nCybersecurity\n2017\nWorld\n0.247797\n\n\n2\nCybersecurity\n2017\nEU\n0.0211647\n\n\n3\nCybersecurity\n2017\nChina\n0.0799647\n\n\n4\nCybersecurity\n2017\nUS\n0.0895792\n\n\n5\nCybersecurity\n2018\nWorld\n0.407162\n\n\n6\nCybersecurity\n2018\nEU\n0.0172524\n\n\n7\nCybersecurity\n2018\nChina\n0.0583573\n\n\n8\nCybersecurity\n2018\nUS\n0.191833\n\n\n9\nCybersecurity\n2019\nWorld\n0.411088\n\n\n10\nCybersecurity\n2019\nEU\n0.0161193\n\n\n11\nCybersecurity\n2019\nChina\n0.196212\n\n\n12\nCybersecurity\n2019\nUS\n0.171356\n\n\n13\nCybersecurity\n2020\nWorld\n0.379812\n\n\n⋮\n⋮\n⋮\n⋮\n⋮\n\n\n133\nSemiconductors\n2020\nWorld\n0.092762\n\n\n134\nSemiconductors\n2020\nEU\n0.00523489\n\n\n135\nSemiconductors\n2020\nChina\n0.0207361\n\n\n136\nSemiconductors\n2020\nUS\n0.0576268\n\n\n137\nSemiconductors\n2021\nWorld\n0.0632604\n\n\n138\nSemiconductors\n2021\nEU\n0.00857049\n\n\n139\nSemiconductors\n2021\nChina\n0.020434\n\n\n140\nSemiconductors\n2021\nUS\n0.0156559\n\n\n141\nSemiconductors\n2022\nWorld\n0.153211\n\n\n142\nSemiconductors\n2022\nEU\n0.00114812\n\n\n143\nSemiconductors\n2022\nChina\n0.0946097\n\n\n144\nSemiconductors\n2022\nUS\n0.0536569\n\n\n\n\n\n\n\n\nCode\n make_title(\"Part 2 :Plot\")\n\n\n \n Part 2 :Plot\n \n\n\n\n\nCode\ndata_layer=data(df2)\n map_layer=mapping(:Year,:investment,layout=:Entity,color=:area)\n visual_layer = visual(Lines,linewidth=2)\n \n plt=data_layer*map_layer*visual_layer\n draw(plt,legend=(position=:bottom, titleposition=:left, framevisible=true, padding=2),facet = (;linkyaxes = :true))\n\n\n┌ Warning: Keyword argument `bgcolor` is deprecated, use `backgroundcolor` instead.\n└ @ Makie ~/.julia/packages/Makie/RgxaV/src/makielayout/blocks/legend.jl:23", - "crumbs": [ - "Data Viz", - "Private Investment on AI by focus" - ] } ] \ No newline at end of file diff --git a/docs/statistics/1-normaldist.html b/docs/statistics/1-normaldist.html index b86478b..23aea95 100644 --- a/docs/statistics/1-normaldist.html +++ b/docs/statistics/1-normaldist.html @@ -337,6 +337,18 @@ Views of Americans about robot vs. human intelligence, All adults”
    +
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  • diff --git a/docs/statistics/10-dirichlet-dist.html b/docs/statistics/10-dirichlet-dist.html index 4302c28..1a3a1d2 100644 --- a/docs/statistics/10-dirichlet-dist.html +++ b/docs/statistics/10-dirichlet-dist.html @@ -339,6 +339,18 @@ Views of Americans about robot vs. human intelligence, All adults”
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  • diff --git a/docs/statistics/12-likelihood-coin-toss.html b/docs/statistics/12-likelihood-coin-toss.html index 651341d..35d7c07 100644 --- a/docs/statistics/12-likelihood-coin-toss.html +++ b/docs/statistics/12-likelihood-coin-toss.html @@ -339,6 +339,18 @@ Views of Americans about robot vs. human intelligence, All adults”
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  • Cost of space launches to low Earth orbit
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  • diff --git a/docs/statistics/13-understand-bayes.html b/docs/statistics/13-understand-bayes.html index b018d32..20c454e 100644 --- a/docs/statistics/13-understand-bayes.html +++ b/docs/statistics/13-understand-bayes.html @@ -339,6 +339,18 @@ Views of Americans about robot vs. human intelligence, All adults”
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  • Cost of space launches to low Earth orbit
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  • diff --git a/docs/statistics/14-unicode-plot.html b/docs/statistics/14-unicode-plot.html index 67c9aa2..7bb173b 100644 --- a/docs/statistics/14-unicode-plot.html +++ b/docs/statistics/14-unicode-plot.html @@ -337,6 +337,18 @@ Views of Americans about robot vs. human intelligence, All adults”
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  • Cost of space launches to low Earth orbit
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  • diff --git a/docs/statistics/15-bubble-plot.html b/docs/statistics/15-bubble-plot.html index ecece20..0c0467f 100644 --- a/docs/statistics/15-bubble-plot.html +++ b/docs/statistics/15-bubble-plot.html @@ -340,6 +340,18 @@ Views of Americans about robot vs. human intelligence, All adults”
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  • Cost of space launches to low Earth orbit
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  • diff --git a/docs/statistics/16-rotate-plots.html b/docs/statistics/16-rotate-plots.html index a5d96f8..efd555c 100644 --- a/docs/statistics/16-rotate-plots.html +++ b/docs/statistics/16-rotate-plots.html @@ -337,6 +337,18 @@ Views of Americans about robot vs. human intelligence, All adults”
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  • Cost of space launches to low Earth orbit
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  • Cost of space launches to low Earth orbit
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  • Share of United States households using specific technologies
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  • @@ -349,12 +361,30 @@ Average temperature anomaly, Global
    +
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    +
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  • Cost of space launches to low Earth orbit
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    +
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  • Share of United States households using specific technologies
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  • Cost of space launches to low Earth orbit
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  • Share of United States households using specific technologies
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  • @@ -349,12 +361,30 @@ Average temperature anomaly, Global
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  • Cost of space launches to low Earth orbit
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  • Cost of space launches to low Earth orbit
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  • Cost of space launches to low Earth orbit
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  • Share of United States households using specific technologies
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  • Cost of space launches to low Earth orbit
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  • Share of United States households using specific technologies
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    +
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    +
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  • Cost of space launches to low Earth orbit
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    +
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  • Share of United States households using specific technologies
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  • Cost of space launches to low Earth orbit
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    +
    • +
  • +
    + + use ggplot2 map +
  • diff --git a/docs/vis/ai-performance-with-datasize.html b/docs/vis/ai-performance-with-datasize.html index b3dd116..7c520d2 100644 --- a/docs/vis/ai-performance-with-datasize.html +++ b/docs/vis/ai-performance-with-datasize.html @@ -337,6 +337,18 @@ Views of Americans about robot vs. human intelligence, All adults”
    +
    • +
  • +
    + + Annual death rates in people of different ages, by sex world +
    +
    • +
  • +
    + + Annual death rates in people of different ages +
  • @@ -349,12 +361,30 @@ Average temperature anomaly, Global
    +
    • +
  • +
    + + Child mortality rate, 1960 to 2021 +
    +
    • +
  • +
    + + Choropleth maps in R +
  • Cost of space launches to low Earth orbit
    +
    • +
  • +
    + + Dumbbell Plot +
  • @@ -385,12 +415,24 @@ Market share for logic chip production, by manufacturing stage, 2021
    +
    • +
  • +
    + + Private Investment on AI by focus +
  • Share of United States households using specific technologies
    +
    • +
  • +
    + + use ggplot2 map +
  • diff --git a/docs/vis/ai-supercomputer-ability.html b/docs/vis/ai-supercomputer-ability.html index a0e7c76..b1b1556 100644 --- a/docs/vis/ai-supercomputer-ability.html +++ b/docs/vis/ai-supercomputer-ability.html @@ -340,6 +340,18 @@ Views of Americans about robot vs. human intelligence, All adults”
    +
    • +
  • +
    + + Annual death rates in people of different ages, by sex world +
    +
    • +
  • +
    + + Annual death rates in people of different ages +
  • @@ -352,12 +364,30 @@ Average temperature anomaly, Global
    +
    • +
  • +
    + + Child mortality rate, 1960 to 2021 +
    +
    • +
  • +
    + + Choropleth maps in R +
  • Cost of space launches to low Earth orbit
    +
    • +
  • +
    + + Dumbbell Plot +
  • @@ -388,12 +418,24 @@ Market share for logic chip production, by manufacturing stage, 2021
    +
    • +
  • +
    + + Private Investment on AI by focus +
  • Share of United States households using specific technologies
    +
    • +
  • +
    + + use ggplot2 map +
  • diff --git a/docs/vis/american-view-on-ai.html b/docs/vis/american-view-on-ai.html index 6a694bf..9654d42 100644 --- a/docs/vis/american-view-on-ai.html +++ b/docs/vis/american-view-on-ai.html @@ -340,6 +340,18 @@ Views of Americans about robot vs. human intelligence, All adults”
    +
    • +
  • +
    + + Annual death rates in people of different ages, by sex world +
    +
    • +
  • +
    + + Annual death rates in people of different ages +
  • @@ -352,12 +364,30 @@ Average temperature anomaly, Global
    +
    • +
  • +
    + + Child mortality rate, 1960 to 2021 +
    +
    • +
  • +
    + + Choropleth maps in R +
  • Cost of space launches to low Earth orbit
    +
    • +
  • +
    + + Dumbbell Plot +
  • @@ -388,12 +418,24 @@ Market share for logic chip production, by manufacturing stage, 2021
    +
    • +
  • +
    + + Private Investment on AI by focus +
  • Share of United States households using specific technologies
    +
    • +
  • +
    + + use ggplot2 map +
  • diff --git a/docs/vis/annual-death-rate-by-sex-years.html b/docs/vis/annual-death-rate-by-sex-years.html index ecd420f..883cfd9 100644 --- a/docs/vis/annual-death-rate-by-sex-years.html +++ b/docs/vis/annual-death-rate-by-sex-years.html @@ -364,12 +364,30 @@ Average temperature anomaly, Global
    +
    • +
  • +
    + + Child mortality rate, 1960 to 2021 +
    +
    • +
  • +
    + + Choropleth maps in R +
  • Cost of space launches to low Earth orbit
    +
    • +
  • +
    + + Dumbbell Plot +
  • @@ -400,12 +418,24 @@ Market share for logic chip production, by manufacturing stage, 2021
    +
    • +
  • +
    + + Private Investment on AI by focus +
  • Share of United States households using specific technologies
    +
    • +
  • +
    + + use ggplot2 map +
  • diff --git a/docs/vis/annual-death-rate-in-diff-age.html b/docs/vis/annual-death-rate-in-diff-age.html index 0a776bd..48427ef 100644 --- a/docs/vis/annual-death-rate-in-diff-age.html +++ b/docs/vis/annual-death-rate-in-diff-age.html @@ -364,12 +364,30 @@ Average temperature anomaly, Global
    +
    • +
  • +
    + + Child mortality rate, 1960 to 2021 +
    +
    • +
  • +
    + + Choropleth maps in R +
  • Cost of space launches to low Earth orbit
    +
    • +
  • +
    + + Dumbbell Plot +
  • @@ -400,12 +418,24 @@ Market share for logic chip production, by manufacturing stage, 2021
    +
    • +
  • +
    + + Private Investment on AI by focus +
  • Share of United States households using specific technologies
    +
    • +
  • +
    + + use ggplot2 map +
  • diff --git a/docs/vis/annual-industrial-robots-installed.html b/docs/vis/annual-industrial-robots-installed.html index 718b339..210c8f1 100644 --- a/docs/vis/annual-industrial-robots-installed.html +++ b/docs/vis/annual-industrial-robots-installed.html @@ -340,6 +340,18 @@ Views of Americans about robot vs. human intelligence, All adults”
    +
    • +
  • +
    + + Annual death rates in people of different ages, by sex world +
    +
    • +
  • +
    + + Annual death rates in people of different ages +
  • @@ -352,12 +364,30 @@ Average temperature anomaly, Global
    +
    • +
  • +
    + + Child mortality rate, 1960 to 2021 +
    +
    • +
  • +
    + + Choropleth maps in R +
  • Cost of space launches to low Earth orbit
    +
    • +
  • +
    + + Dumbbell Plot +
  • @@ -388,12 +418,24 @@ Market share for logic chip production, by manufacturing stage, 2021
    +
    • +
  • +
    + + Private Investment on AI by focus +
  • Share of United States households using specific technologies
    +
    • +
  • +
    + + use ggplot2 map +
  • diff --git a/docs/vis/average-temperature-anomaly-world.html b/docs/vis/average-temperature-anomaly-world.html index 76c9c3a..595664b 100644 --- a/docs/vis/average-temperature-anomaly-world.html +++ b/docs/vis/average-temperature-anomaly-world.html @@ -337,6 +337,18 @@ Views of Americans about robot vs. human intelligence, All adults”
    +
    • +
  • +
    + + Annual death rates in people of different ages, by sex world +
    +
    • +
  • +
    + + Annual death rates in people of different ages +
  • @@ -349,12 +361,30 @@ Average temperature anomaly, Global
    +
    • +
  • +
    + + Child mortality rate, 1960 to 2021 +
    +
    • +
  • +
    + + Choropleth maps in R +
  • Cost of space launches to low Earth orbit
    +
    • +
  • +
    + + Dumbbell Plot +
  • @@ -385,12 +415,24 @@ Market share for logic chip production, by manufacturing stage, 2021
    +
    • +
  • +
    + + Private Investment on AI by focus +
  • Share of United States households using specific technologies
    +
    • +
  • +
    + + use ggplot2 map +
  • diff --git a/docs/vis/child-mortality-rate-1960to2021.html b/docs/vis/child-mortality-rate-1960to2021.html new file mode 100644 index 0000000..13c6aea --- /dev/null +++ b/docs/vis/child-mortality-rate-1960to2021.html @@ -0,0 +1,1366 @@ + + + + + + + + + + +GLMakie Gallery - Child mortality rate, 1960 to 2021 + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + +
    + + + +
    + +
    +
    +

    Child mortality rate, 1960 to 2021

    +
    + + + +
    + +
    +
    Author
    +
    +

    math4mad

    +
    +
    + + + +
    + + + +
    + + +
    +
    +
    + +
    +
    +ref +
    +
    +
    + +
    +
    +
    +
    +Code +
     include("utils.jl")
    +
    +
    +
    [ Info: loading success
    +
    +
    +
    +
    +Code +
    make_title("Part one : Data processing")
    +
    +
    + +
    +
    +
    +

    load csv

    +
    +
    +Code +
    df=@pipe CSV.File("./data/child-mortality-1960-vs-latest-slope.csv")|>DataFrame|>rename(_,"Observation value - Unit_of_measure: Deaths per 100 live births - Indicator: Under-five mortality rate - Sex: Both sexes - Wealth_quintile: All wealth quintiles"=>:rate)|>select(_,Not(:Code,:Continent,"Population (historical estimates)"))|>dropmissing
    +
    +
    +
    13472×3 DataFrame
    13447 rows omitted
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    RowEntityYearrate
    StringInt64Float64
    1Afghanistan195737.5905
    2Afghanistan195836.9628
    3Afghanistan195936.3437
    4Afghanistan196035.7301
    5Afghanistan196135.1658
    6Afghanistan196234.584
    7Afghanistan196334.0159
    8Afghanistan196433.4848
    9Afghanistan196532.9421
    10Afghanistan196632.3938
    11Afghanistan196731.8366
    12Afghanistan196831.2825
    13Afghanistan196930.7243
    ⋮⋮⋮⋮
    13461Zimbabwe20108.61062
    13462Zimbabwe20118.05877
    13463Zimbabwe20127.21463
    13464Zimbabwe20136.62226
    13465Zimbabwe20146.26086
    13466Zimbabwe20156.04718
    13467Zimbabwe20165.78704
    13468Zimbabwe20175.61751
    13469Zimbabwe20185.36877
    13470Zimbabwe20195.26659
    13471Zimbabwe20205.17696
    13472Zimbabwe20214.9522
    +
    +
    +
    +
    +
    +

    tidy data

    +
    +
    +Code +
      query_years=[1960,1980,2000,2020]
+  query_countries=["Abkhazia","Uganda","Kenya","Egypt","Spain","Japan","France"]
+  @eval(Main, input_years=query_years)
+  @eval(Main, input_countries =query_countries)
+  @eval(Main,input_points=length(query_years))
+  df=@chain df begin
+      @filter(Year ∈ !!input_years)
+      @group_by(Entity)
+      #filter(d->nrow(d)==4,_) 
+      @filter(length(Year)==!!input_points) 
+      @arrange(Year)
+      @ungroup
+      coerce(_,  :Year=>OrderedFactor)
+      @mutate(rate=round(rate,digits=1))
+      @filter(Entity ∈ !!input_countries)
+  end
    +
    +
    +
    24×3 DataFrame
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    RowEntityYearrate
    StringCat…Float64
    1Egypt196031.8
    2Egypt198016.8
    3Egypt20004.7
    4Egypt20202.0
    5France19602.8
    6France19801.2
    7France20000.5
    8France20200.4
    9Japan19604.0
    10Japan19801.0
    11Japan20000.5
    12Japan20200.2
    13Kenya196019.7
    14Kenya198011.1
    15Kenya20009.9
    16Kenya20203.9
    17Spain19605.5
    18Spain19801.5
    19Spain20000.5
    20Spain20200.3
    21Uganda196022.3
    22Uganda198021.9
    23Uganda200014.6
    24Uganda20204.4
    +
    +
    +
    +
    +
    +Code +
    make_title("Part 2:  Plot")
    +
    +
    + +
    +
    +
    +
    +Code +
      @rput df
+  R"""
+    # install.packages("CGPfunctions")
+     library(CGPfunctions)
+     newggslopegraph(dataframe = df,
+                Times = Year,
+                Measurement = rate,
+                Grouping = Entity,
+                Title = "Child mortality rate",
+                SubTitle = "1960-2020",
+                Caption = "By R CHARTS",
+                LineThickness = 0.5,
+                # DataLabelPadding =0.2,
+                # DataLabelLineSize = 0.5,
+                # DataLabelFillColor = "lightblue",
+                ThemeChoice = "wsj"
+                )
+  """
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    RObject{VecSxp}
    +
    +
    + + +
    + +
    + +
    + + + + + \ No newline at end of file diff --git a/docs/vis/child-mortality-rate-1960to2021_files/figure-html/cell-7-output-1.png b/docs/vis/child-mortality-rate-1960to2021_files/figure-html/cell-7-output-1.png new file mode 100644 index 0000000..18a0ef5 Binary files /dev/null and b/docs/vis/child-mortality-rate-1960to2021_files/figure-html/cell-7-output-1.png differ diff --git a/docs/vis/child-mortality-rate-1960to2021_files/figure-html/cell-7-output-2.png b/docs/vis/child-mortality-rate-1960to2021_files/figure-html/cell-7-output-2.png new file mode 100644 index 0000000..430c1a7 Binary files /dev/null and b/docs/vis/child-mortality-rate-1960to2021_files/figure-html/cell-7-output-2.png differ diff --git a/docs/vis/choropleth-maps-in-R.html b/docs/vis/choropleth-maps-in-R.html new file mode 100644 index 0000000..cb36dfa --- /dev/null +++ b/docs/vis/choropleth-maps-in-R.html @@ -0,0 +1,985 @@ + + + + + + + + + + +GLMakie Gallery - Choropleth maps in R + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + +
    + + + +
    + +
    +
    +

    Choropleth maps in R

    +
    + + + +
    + +
    +
    Author
    +
    +

    math4mad

    +
    +
    + + + +
    + + + +
    + + +
    +
    +
    + +
    +
    +ref +
    +
    +
    + +
    +
    +
    +
    +Code +
     # install.packages("sf")
+library(sf)
    +
    +
    +
    Warning: package 'sf' was built under R version 4.2.3
    +
    +
    +
    Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE
    +
    +
    +Code +
    # install.packages("dplyr")
+library(dplyr)
    +
    +
    +
    Warning: package 'dplyr' was built under R version 4.2.3
    +
    +
    +
    
+Attaching package: 'dplyr'
    +
    +
    +
    The following objects are masked from 'package:stats':
+
+    filter, lag
    +
    +
    +
    The following objects are masked from 'package:base':
+
+    intersect, setdiff, setequal, union
    +
    +
    +Code +
    # install.packages("giscoR")
+library(giscoR)
+
+year_ref <- 2013
+
+nuts2_IT <- gisco_get_nuts(
+  year = year_ref,
+  resolution = 20, 
+  nuts_level = 2,
+  country = "Italy") %>%
+  select(NUTS_ID, NAME_LATN)
+
+# Transform the shape
+nuts2_IT_32632 <- st_transform(nuts2_IT, 32632)
+
+# Filter to select data from 2013
+disp_income <- giscoR::tgs00026 %>%
+  filter(time == year_ref) %>%
+  select(-time)
+
+nuts2_IT_32632_data <- nuts2_IT_32632 %>%
+  left_join(disp_income, by = c("NUTS_ID" = "geo"))
+
+plot(nuts2_IT_32632_data[, "values"],
+     breaks = "jenks",
+     main = "Choropleth map")
    +
    +
    +
    +
    +

    +
    +
    +
    +
    + + + +
    + +
    + + + + + \ No newline at end of file diff --git a/docs/vis/choropleth-maps-in-R_files/figure-html/unnamed-chunk-1-1.png b/docs/vis/choropleth-maps-in-R_files/figure-html/unnamed-chunk-1-1.png new file mode 100644 index 0000000..37b2c95 Binary files /dev/null and b/docs/vis/choropleth-maps-in-R_files/figure-html/unnamed-chunk-1-1.png differ diff --git a/docs/vis/cost-of-space-launch.html b/docs/vis/cost-of-space-launch.html index 38cb883..b6c1b91 100644 --- a/docs/vis/cost-of-space-launch.html +++ b/docs/vis/cost-of-space-launch.html @@ -340,6 +340,18 @@ Views of Americans about robot vs. human intelligence, All adults”
    +
    • +
  • +
    + + Annual death rates in people of different ages, by sex world +
    +
    • +
  • +
    + + Annual death rates in people of different ages +
  • @@ -352,12 +364,30 @@ Average temperature anomaly, Global
    +
    • +
  • +
    + + Child mortality rate, 1960 to 2021 +
    +
    • +
  • +
    + + Choropleth maps in R +
  • Cost of space launches to low Earth orbit
    +
    • +
  • +
    + + Dumbbell Plot +
  • @@ -388,12 +418,24 @@ Market share for logic chip production, by manufacturing stage, 2021
    +
    • +
  • +
    + + Private Investment on AI by focus +
  • Share of United States households using specific technologies
    +
    • +
  • +
    + + use ggplot2 map +
  • diff --git a/docs/vis/dumbbell-plot.html b/docs/vis/dumbbell-plot.html new file mode 100644 index 0000000..37b8e96 --- /dev/null +++ b/docs/vis/dumbbell-plot.html @@ -0,0 +1,1059 @@ + + + + + + + + + + +GLMakie Gallery - Dumbbell Plot + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + +
    + + + +
    + +
    +
    +

    Dumbbell Plot

    +
    + + + +
    + +
    +
    Author
    +
    +

    math4mad

    +
    +
    + + + +
    + + + +
    + + +
    +
    +
    + +
    +
    +ref +
    +
    +
    +

    Dumbbell plot in ggplot2

    +
    +
    +
    +
    +Code +
    include("utils.jl")
    +
    +
    +
    [ Info: loading success
    +
    +
    +
    +
    +Code +
    R"""
+# Seed
+set.seed(1)
+
+# Data
+customers <- sample(50:150, 10)
+potential_customers <- sample(150:500, 10)
+company <- LETTERS[1:10]
+
+# Data frame
+df <- data.frame(company = company,
+                 x = customers,
+                 y = potential_customers)
+"""
+df=@rget df
+rename!(df,:x=>:customers,:y=>:potential_customers)
+sort(df,:company)
+#df2=@pivot_longer(df, customers:potential_customers)
    +
    +
    +
    10×3 DataFrame
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Rowcompanycustomerspotential_customers
    StringInt64Int64
    1A117234
    2B88426
    3C50479
    4D83412
    5E136478
    6F92228
    7G63362
    8H131186
    9I108254
    10J100366
    +
    +
    +
    + +
    +
    +Code +
       fig=Figure()
+   ax=Axis(fig[1,1])
+#    for (idx,row) in enumerate(eachrow(df))
+
+#         hlines!(ax,[idx]; xmin =[row.customers], xmax = [row.potential_customers])
+
+#    end
+#    hlines!(ax, 1:10;xmin=[df.customers...], xmax =[df.potential_customers...], color = :blue)
+   fig
    +
    +
    +
    +
    +

    +
    +
    +
    +
    + + + +
    + +
    + + + + + \ No newline at end of file diff --git a/docs/vis/dumbbell-plot_files/figure-html/cell-4-output-1.svg b/docs/vis/dumbbell-plot_files/figure-html/cell-4-output-1.svg new file mode 100644 index 0000000..b6d3161 --- /dev/null +++ b/docs/vis/dumbbell-plot_files/figure-html/cell-4-output-1.svg @@ -0,0 +1,90 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/vis/dumbbell-plot_files/figure-html/cell-4-output-2.svg b/docs/vis/dumbbell-plot_files/figure-html/cell-4-output-2.svg new file mode 100644 index 0000000..9f77cd3 --- /dev/null +++ b/docs/vis/dumbbell-plot_files/figure-html/cell-4-output-2.svg @@ -0,0 +1,336 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/vis/employment-in-ai.html b/docs/vis/employment-in-ai.html index 72db5f0..efe2fc2 100644 --- a/docs/vis/employment-in-ai.html +++ b/docs/vis/employment-in-ai.html @@ -340,6 +340,18 @@ Views of Americans about robot vs. human intelligence, All adults”
    +
    • +
  • +
    + + Annual death rates in people of different ages, by sex world +
    +
    • +
  • +
    + + Annual death rates in people of different ages +
  • @@ -352,12 +364,30 @@ Average temperature anomaly, Global
    +
    • +
  • +
    + + Child mortality rate, 1960 to 2021 +
    +
    • +
  • +
    + + Choropleth maps in R +
  • Cost of space launches to low Earth orbit
    +
    • +
  • +
    + + Dumbbell Plot +
  • @@ -388,12 +418,24 @@ Market share for logic chip production, by manufacturing stage, 2021
    +
    • +
  • +
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  • Share of United States households using specific technologies
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  • diff --git a/docs/vis/fishing-with-net-type.html b/docs/vis/fishing-with-net-type.html index ed0830f..2b443da 100644 --- a/docs/vis/fishing-with-net-type.html +++ b/docs/vis/fishing-with-net-type.html @@ -340,6 +340,18 @@ Views of Americans about robot vs. human intelligence, All adults”
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  • diff --git a/docs/vis/internet-user-view.html b/docs/vis/internet-user-view.html index 3588ac9..f1be5d9 100644 --- a/docs/vis/internet-user-view.html +++ b/docs/vis/internet-user-view.html @@ -340,6 +340,18 @@ Views of Americans about robot vs. human intelligence, All adults”
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  • Share of United States households using specific technologies
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  • diff --git a/docs/vis/irish-whiskey-sales.html b/docs/vis/irish-whiskey-sales.html index ad2995e..5df00ec 100644 --- a/docs/vis/irish-whiskey-sales.html +++ b/docs/vis/irish-whiskey-sales.html @@ -340,6 +340,18 @@ Views of Americans about robot vs. human intelligence, All adults”
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  • Cost of space launches to low Earth orbit
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  • diff --git a/docs/vis/market-for-chip-production.html b/docs/vis/market-for-chip-production.html index 478ebb6..4943378 100644 --- a/docs/vis/market-for-chip-production.html +++ b/docs/vis/market-for-chip-production.html @@ -340,6 +340,18 @@ Views of Americans about robot vs. human intelligence, All adults”
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    Private Investment on AI by focus

    -
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    Code 
    include("utils.jl")
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    Private Investment on AI by focus

    [ Info: loading success
    -
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    make_title("Part 1 : data processing")
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    Private Investment on AI by focus

    2. load data->dataframe

    -
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    df=@pipe CSV.File("./data/private-investment-in-artificial-intelligence-by-focus-area.csv")|>DataFrame
 rename!(df,"European Union and United Kingdom"=>:EU,"United States"=>:US)
 select!(df,Not("Code"))
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    135 rows omitted
    @@ -788,7 +806,7 @@

    2. load data->dataf -
    +
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    df2=@chain df begin 
@@ -800,7 +818,7 @@ 
    2. load data->dataf
    @filter(Entity ∈ ["Medical and healthcare","Data management","Cybersecurity","Industrial automation","Semiconductors","Drones"])
 end
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    119 rows omitted

    @@ -1007,18 +1025,18 @@

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     make_title("Part 2 :Plot")
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    data_layer=data(df2)
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    2. load data->dataf
 
    ┌ Warning: Keyword argument `bgcolor` is deprecated, use `backgroundcolor` instead.
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    diff --git a/docs/vis/private-investment-on-ai-by-focus_files/figure-html/cell-6-output-2.svg b/docs/vis/private-investment-on-ai-by-focus_files/figure-html/cell-6-output-2.svg new file mode 100644 index 0000000..fa230cd --- /dev/null +++ b/docs/vis/private-investment-on-ai-by-focus_files/figure-html/cell-6-output-2.svg @@ -0,0 +1,1225 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/vis/usa-household-use-technology.html b/docs/vis/usa-household-use-technology.html index 9fb1f3f..800930c 100644 --- a/docs/vis/usa-household-use-technology.html +++ b/docs/vis/usa-household-use-technology.html @@ -337,6 +337,18 @@ Views of Americans about robot vs. human intelligence, All adults”
    + +
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    +
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    + + Private Investment on AI by focus +
  • Share of United States households using specific technologies
    +
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    + + use ggplot2 map +
  • diff --git a/docs/vis/use-ggplot-map.html b/docs/vis/use-ggplot-map.html index 753e965..8c0c2dc 100644 --- a/docs/vis/use-ggplot-map.html +++ b/docs/vis/use-ggplot-map.html @@ -361,12 +361,30 @@ Average temperature anomaly, Global
    +
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    +
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    +
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    +
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    +
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  • @@ -388,12 +418,24 @@ Market share for logic chip production, by manufacturing stage, 2021
    +
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  • Share of United States households using specific technologies
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  • diff --git a/vis/child-mortality-rate-1960to2021.qmd b/vis/child-mortality-rate-1960to2021.qmd new file mode 100644 index 0000000..65be8be --- /dev/null +++ b/vis/child-mortality-rate-1960to2021.qmd @@ -0,0 +1,70 @@ +--- +title: "Child mortality rate, 1960 to 2021" +author: math4mad +code-fold: true +--- + +:::{.callout-note title="ref"} + - ref : [Child mortality rate, 1960 to 2021](https://ourworldindata.org/owid-grapher) + - package :[The newggslopegraph function from the CGPfunctions package](https://r-charts.com/evolution/newggslopegraph/) +::: +```{julia} + include("utils.jl") +``` + +```{julia} +make_title("Part one : Data processing") +``` + +## load csv +```{julia} +df=@pipe CSV.File("./data/child-mortality-1960-vs-latest-slope.csv")|>DataFrame|>rename(_,"Observation value - Unit_of_measure: Deaths per 100 live births - Indicator: Under-five mortality rate - Sex: Both sexes - Wealth_quintile: All wealth quintiles"=>:rate)|>select(_,Not(:Code,:Continent,"Population (historical estimates)"))|>dropmissing +``` + +## tidy data +```{julia} + query_years=[1960,1980,2000,2020] + query_countries=["Abkhazia","Uganda","Kenya","Egypt","Spain","Japan","France"] + @eval(Main, input_years=query_years) + @eval(Main, input_countries =query_countries) + @eval(Main,input_points=length(query_years)) + df=@chain df begin + @filter(Year ∈ !!input_years) + @group_by(Entity) + #filter(d->nrow(d)==4,_) + @filter(length(Year)==!!input_points) + @arrange(Year) + @ungroup + coerce(_, :Year=>OrderedFactor) + @mutate(rate=round(rate,digits=1)) + @filter(Entity ∈ !!input_countries) + end +``` + +```{julia} +make_title("Part 2: Plot") +``` + +```{julia} + @rput df + R""" + # install.packages("CGPfunctions") + library(CGPfunctions) + newggslopegraph(dataframe = df, + Times = Year, + Measurement = rate, + Grouping = Entity, + Title = "Child mortality rate", + SubTitle = "1960-2020", + Caption = "By R CHARTS", + LineThickness = 0.5, + # DataLabelPadding =0.2, + # DataLabelLineSize = 0.5, + # DataLabelFillColor = "lightblue", + ThemeChoice = "wsj" + ) + """ +``` + + + diff --git a/vis/choropleth-maps-in-R.qmd b/vis/choropleth-maps-in-R.qmd new file mode 100644 index 0000000..232001c --- /dev/null +++ b/vis/choropleth-maps-in-R.qmd @@ -0,0 +1,45 @@ +--- +title: "Choropleth maps in R" +author: math4mad +code-fold: true +--- + +:::{.callout-note title="ref"} +- ref :[Choropleth maps in R](https://r-charts.com/spatial/choropleth-map/) +::: + + +```{r} + # install.packages("sf") +library(sf) +# install.packages("dplyr") +library(dplyr) +# install.packages("giscoR") +library(giscoR) + +year_ref <- 2013 + +nuts2_IT <- gisco_get_nuts( + year = year_ref, + resolution = 20, + nuts_level = 2, + country = "Italy") %>% + select(NUTS_ID, NAME_LATN) + +# Transform the shape +nuts2_IT_32632 <- st_transform(nuts2_IT, 32632) + +# Filter to select data from 2013 +disp_income <- giscoR::tgs00026 %>% + filter(time == year_ref) %>% + select(-time) + +nuts2_IT_32632_data <- nuts2_IT_32632 %>% + left_join(disp_income, by = c("NUTS_ID" = "geo")) + +plot(nuts2_IT_32632_data[, "values"], + breaks = "jenks", + main = "Choropleth map") +``` + + diff --git a/vis/data/child-mortality-1960-vs-latest-slope.csv b/vis/data/child-mortality-1960-vs-latest-slope.csv new file mode 100644 index 0000000..6be0dd5 --- /dev/null +++ b/vis/data/child-mortality-1960-vs-latest-slope.csv @@ -0,0 +1,19603 @@ +Entity,Code,Year,Observation value - Unit_of_measure: Deaths per 100 live births - Indicator: Under-five mortality rate - Sex: Both sexes - Wealth_quintile: All wealth quintiles,Population (historical estimates),Continent +Abkhazia,OWID_ABK,2015,,,Asia +Afghanistan,AFG,1950,,7480464, +Afghanistan,AFG,1951,,7571542, +Afghanistan,AFG,1952,,7667534, +Afghanistan,AFG,1953,,7764549, +Afghanistan,AFG,1954,,7864289, +Afghanistan,AFG,1955,,7971933, +Afghanistan,AFG,1956,,8087730, +Afghanistan,AFG,1957,37.590538,8210207, +Afghanistan,AFG,1958,36.96284,8333827, +Afghanistan,AFG,1959,36.343746,8468220, +Afghanistan,AFG,1960,35.73006,8622473, +Afghanistan,AFG,1961,35.165825,8790140, +Afghanistan,AFG,1962,34.58399,8969055, +Afghanistan,AFG,1963,34.015938,9157463, +Afghanistan,AFG,1964,33.48478,9355510, +Afghanistan,AFG,1965,32.942093,9565154, +Afghanistan,AFG,1966,32.393776,9783153, +Afghanistan,AFG,1967,31.836605,10010037, +Afghanistan,AFG,1968,31.282513,10247782, +Afghanistan,AFG,1969,30.724308,10494491, +Afghanistan,AFG,1970,30.185291,10752973, +Afghanistan,AFG,1971,29.640371,11015853, +Afghanistan,AFG,1972,29.073618,11286753, +Afghanistan,AFG,1973,28.522757,11575308, +Afghanistan,AFG,1974,27.958233,11869881, +Afghanistan,AFG,1975,27.400227,12157390, +Afghanistan,AFG,1976,26.825668,12425276, +Afghanistan,AFG,1977,26.238773,12687308, +Afghanistan,AFG,1978,25.639389,12938864, +Afghanistan,AFG,1979,25.027561,12986378, +Afghanistan,AFG,1980,24.384136,12486640, +Afghanistan,AFG,1981,23.73085,11155196, +Afghanistan,AFG,1982,23.08045,10088290, +Afghanistan,AFG,1983,22.42028,9951447, +Afghanistan,AFG,1984,21.770617,10243689, +Afghanistan,AFG,1985,21.109753,10512220, +Afghanistan,AFG,1986,20.456223,10448447, +Afghanistan,AFG,1987,19.79429,10322767, +Afghanistan,AFG,1988,19.139517,10383459, +Afghanistan,AFG,1989,18.486126,10673172, +Afghanistan,AFG,1990,17.845098,10694804, +Afghanistan,AFG,1991,17.222792,10745168, +Afghanistan,AFG,1992,16.634613,12057436, +Afghanistan,AFG,1993,16.07751,14003764, +Afghanistan,AFG,1994,15.550792,15455560, +Afghanistan,AFG,1995,15.058935,16418911, +Afghanistan,AFG,1996,14.5990095,17106600, +Afghanistan,AFG,1997,14.157362,17788818, +Afghanistan,AFG,1998,13.734812,18493134, +Afghanistan,AFG,1999,13.330147,19262854, +Afghanistan,AFG,2000,12.933889,19542986, +Afghanistan,AFG,2001,12.5305195,19688634, +Afghanistan,AFG,2002,12.118846,21000258, +Afghanistan,AFG,2003,11.702546,22645136, +Afghanistan,AFG,2004,11.279189,23553554, +Afghanistan,AFG,2005,10.855847,24411196, 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Saba,BES,1979,,11388, +Bonaire Sint Eustatius and Saba,BES,1980,,11568, +Bonaire Sint Eustatius and Saba,BES,1981,,11717, +Bonaire Sint Eustatius and Saba,BES,1982,,11822, +Bonaire Sint Eustatius and Saba,BES,1983,,11901, +Bonaire Sint Eustatius and Saba,BES,1984,,11996, +Bonaire Sint Eustatius and Saba,BES,1985,,12092, +Bonaire Sint Eustatius and Saba,BES,1986,,12180, +Bonaire Sint Eustatius and Saba,BES,1987,,12284, +Bonaire Sint Eustatius and Saba,BES,1988,,12389, +Bonaire Sint Eustatius and Saba,BES,1989,,12507, +Bonaire Sint Eustatius and Saba,BES,1990,,12630, +Bonaire Sint Eustatius and Saba,BES,1991,,12751, +Bonaire Sint Eustatius and Saba,BES,1992,,12866, +Bonaire Sint Eustatius and Saba,BES,1993,,12979, +Bonaire Sint Eustatius and Saba,BES,1994,,13097, +Bonaire Sint Eustatius and Saba,BES,1995,,13228, +Bonaire Sint Eustatius and Saba,BES,1996,,13371, +Bonaire Sint Eustatius and Saba,BES,1997,,13518, +Bonaire Sint Eustatius and Saba,BES,1998,,13669, +Bonaire Sint Eustatius and Saba,BES,1999,,13825, +Bonaire Sint Eustatius and Saba,BES,2000,,13984, +Bonaire Sint Eustatius and Saba,BES,2001,,14264, +Bonaire Sint Eustatius and Saba,BES,2002,,14782, +Bonaire Sint Eustatius and Saba,BES,2003,,15414, +Bonaire Sint Eustatius and Saba,BES,2004,,16076, +Bonaire Sint Eustatius and Saba,BES,2005,,16748, +Bonaire Sint Eustatius and Saba,BES,2006,,17428, +Bonaire Sint Eustatius and Saba,BES,2007,,18123, +Bonaire Sint Eustatius and Saba,BES,2008,,18824, +Bonaire Sint Eustatius and Saba,BES,2009,,19535, +Bonaire Sint Eustatius and Saba,BES,2010,,20245, +Bonaire Sint Eustatius and Saba,BES,2011,,20897, +Bonaire Sint Eustatius and Saba,BES,2012,,21469, +Bonaire Sint Eustatius and Saba,BES,2013,,22026, +Bonaire Sint Eustatius and Saba,BES,2014,,22599, +Bonaire Sint Eustatius and Saba,BES,2015,,23193,North America +Bonaire Sint Eustatius and Saba,BES,2016,,23793, +Bonaire Sint Eustatius and Saba,BES,2017,,24392, +Bonaire Sint Eustatius and Saba,BES,2018,,24995, +Bonaire Sint Eustatius and Saba,BES,2019,,25597, +Bonaire Sint Eustatius and Saba,BES,2020,,26178, +Bonaire Sint Eustatius and Saba,BES,2021,,26730, +Bosnia and Herzegovina,BIH,1950,,2692914, +Bosnia and Herzegovina,BIH,1951,,2738125, +Bosnia and Herzegovina,BIH,1952,,2787386, +Bosnia and Herzegovina,BIH,1953,,2841474, +Bosnia and Herzegovina,BIH,1954,,2899036, +Bosnia and Herzegovina,BIH,1955,,2958434, +Bosnia and Herzegovina,BIH,1956,,3018308, +Bosnia and Herzegovina,BIH,1957,,3078144, +Bosnia and Herzegovina,BIH,1958,,3138475, +Bosnia and Herzegovina,BIH,1959,,3200054, +Bosnia and Herzegovina,BIH,1960,,3262540, +Bosnia and Herzegovina,BIH,1961,,3325338, +Bosnia and Herzegovina,BIH,1962,,3387521, +Bosnia and Herzegovina,BIH,1963,,3448535, +Bosnia and Herzegovina,BIH,1964,,3507961, +Bosnia and Herzegovina,BIH,1965,,3565526, +Bosnia and Herzegovina,BIH,1966,,3620850, +Bosnia and Herzegovina,BIH,1967,,3673376, +Bosnia and Herzegovina,BIH,1968,,3723069, +Bosnia and Herzegovina,BIH,1969,,3770379, 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Herzegovina,BIH,1991,1.6833258,4502389, +Bosnia and Herzegovina,BIH,1992,3.5490768,4275737, +Bosnia and Herzegovina,BIH,1993,3.5032284,3942985, +Bosnia and Herzegovina,BIH,1994,2.6124759,3762334, +Bosnia and Herzegovina,BIH,1995,1.3970135,3750528, +Bosnia and Herzegovina,BIH,1996,1.2217196,3907749, +Bosnia and Herzegovina,BIH,1997,1.15339,4047756, +Bosnia and Herzegovina,BIH,1998,1.0897787,4115062, +Bosnia and Herzegovina,BIH,1999,1.0336453,4153006, +Bosnia and Herzegovina,BIH,2000,0.98880273,4179355, +Bosnia and Herzegovina,BIH,2001,0.956209,4194941, +Bosnia and Herzegovina,BIH,2002,0.9335127,4198410, +Bosnia and Herzegovina,BIH,2003,0.918136,4183758, +Bosnia and Herzegovina,BIH,2004,0.9045436,4142858, +Bosnia and Herzegovina,BIH,2005,0.8866998,4094297, +Bosnia and Herzegovina,BIH,2006,0.8605097,4058086, +Bosnia and Herzegovina,BIH,2007,0.8278178,4007877, +Bosnia and Herzegovina,BIH,2008,0.79116076,3943390, +Bosnia and Herzegovina,BIH,2009,0.75351137,3877755, +Bosnia and Herzegovina,BIH,2010,0.7192075,3811093, +Bosnia and Herzegovina,BIH,2011,0.68992037,3743143, +Bosnia and Herzegovina,BIH,2012,0.66648334,3674376, +Bosnia and Herzegovina,BIH,2013,0.6489398,3617564, +Bosnia and Herzegovina,BIH,2014,0.63582045,3571070, +Bosnia and Herzegovina,BIH,2015,0.6262254,3524328,Europe +Bosnia and Herzegovina,BIH,2016,0.6187813,3480985, +Bosnia and Herzegovina,BIH,2017,0.61087525,3440025, +Bosnia and Herzegovina,BIH,2018,0.60053366,3400132, +Bosnia and Herzegovina,BIH,2019,0.5882487,3360718, +Bosnia and Herzegovina,BIH,2020,0.57497275,3318410, +Bosnia and Herzegovina,BIH,2021,0.56006,3270948, +Botswana,BWA,1950,,413489, +Botswana,BWA,1951,,422896, +Botswana,BWA,1952,,432601, +Botswana,BWA,1953,,442494, +Botswana,BWA,1954,20.256195,452270, +Botswana,BWA,1955,19.628292,461892, +Botswana,BWA,1956,19.022247,471467, +Botswana,BWA,1957,18.377552,481230, +Botswana,BWA,1958,17.784332,491312, +Botswana,BWA,1959,17.205471,501837, +Botswana,BWA,1960,16.63889,512874, 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(SDG),,1993,7.2630997,, +Central Asia (SDG),,1994,7.299182,, +Central Asia (SDG),,1995,7.2601976,, +Central Asia (SDG),,1996,7.142784,, +Central Asia (SDG),,1997,6.959349,, +Central Asia (SDG),,1998,6.715432,, +Central Asia (SDG),,1999,6.409672,, +Central Asia (SDG),,2000,6.063299,, +Central Asia (SDG),,2001,5.695621,, +Central Asia (SDG),,2002,5.3181276,, +Central Asia (SDG),,2003,4.942005,, +Central Asia (SDG),,2004,4.586858,, +Central Asia (SDG),,2005,4.2604327,, +Central Asia (SDG),,2006,3.9573045,, +Central Asia (SDG),,2007,3.6763794,, +Central Asia (SDG),,2008,3.417083,, +Central Asia (SDG),,2009,3.1928062,, +Central Asia (SDG),,2010,2.9940283,, +Central Asia (SDG),,2011,2.8144424,, +Central Asia (SDG),,2012,2.64919,, +Central Asia (SDG),,2013,2.5065148,, +Central Asia (SDG),,2014,2.3748307,, +Central Asia (SDG),,2015,2.2667477,, +Central Asia (SDG),,2016,2.1705654,, +Central Asia (SDG),,2017,2.090827,, +Central Asia (SDG),,2018,2.0074422,, +Central Asia (SDG),,2019,1.9384434,, +Central Asia (SDG),,2020,1.8626133,, +Central Asia (SDG),,2021,1.8136765,, +Central Asia and Southern Asia (SDG),,1990,12.47905,, +Central Asia and Southern Asia (SDG),,1991,12.117302,, +Central Asia and Southern Asia (SDG),,1992,11.781256,, +Central Asia and Southern Asia (SDG),,1993,11.466978,, +Central Asia and Southern Asia (SDG),,1994,11.169882,, +Central Asia and Southern Asia (SDG),,1995,10.852142,, +Central Asia and Southern Asia (SDG),,1996,10.523257,, +Central Asia and Southern Asia (SDG),,1997,10.180365,, +Central Asia and Southern Asia (SDG),,1998,9.825952,, +Central Asia and Southern Asia (SDG),,1999,9.463904,, +Central Asia and Southern Asia (SDG),,2000,9.102287,, +Central Asia and Southern Asia (SDG),,2001,8.747347,, +Central Asia and Southern Asia (SDG),,2002,8.403934,, +Central Asia and Southern Asia (SDG),,2003,8.076025,, +Central Asia and Southern Asia (SDG),,2004,7.769204,, +Central Asia and Southern Asia (SDG),,2005,7.4437766,, +Central Asia and Southern Asia (SDG),,2006,7.135267,, +Central Asia and Southern Asia (SDG),,2007,6.8353844,, +Central Asia and Southern Asia (SDG),,2008,6.5365076,, +Central Asia and Southern Asia (SDG),,2009,6.2579484,, +Central Asia and Southern Asia (SDG),,2010,5.9735155,, +Central Asia and Southern Asia (SDG),,2011,5.7018294,, +Central Asia and Southern Asia (SDG),,2012,5.434678,, +Central Asia and Southern Asia (SDG),,2013,5.172741,, +Central Asia and Southern Asia (SDG),,2014,4.917758,, +Central Asia and Southern Asia (SDG),,2015,4.6751213,, +Central Asia and Southern Asia (SDG),,2016,4.4478283,, +Central Asia and Southern Asia (SDG),,2017,4.2398076,, +Central Asia and Southern Asia (SDG),,2018,4.043168,, +Central Asia and Southern Asia (SDG),,2019,3.866607,, +Central Asia and Southern Asia (SDG),,2020,3.6988266,, +Central Asia and Southern Asia (SDG),,2021,3.5386841,, +Chad,TCD,1950,,2512693, +Chad,TCD,1951,,2560170, +Chad,TCD,1952,,2607682, +Chad,TCD,1953,,2656088, +Chad,TCD,1954,,2705625, 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Congo,COD,1992,18.186804,38332300, +Democratic Republic of Congo,COD,1993,17.976936,39580848, +Democratic Republic of Congo,COD,1994,17.782406,41511988, +Democratic Republic of Congo,COD,1995,17.585154,43285788, +Democratic Republic of Congo,COD,1996,17.359776,44118716, +Democratic Republic of Congo,COD,1997,17.097008,44822200, +Democratic Republic of Congo,COD,1998,16.77988,45895532, +Democratic Republic of Congo,COD,1999,16.41683,47227236, +Democratic Republic of Congo,COD,2000,16.008343,48616328, +Democratic Republic of Congo,COD,2001,15.581366,50106660, +Democratic Republic of Congo,COD,2002,15.120807,51662080, +Democratic Republic of Congo,COD,2003,14.652819,53205640, +Democratic Republic of Congo,COD,2004,14.175245,54815608, +Democratic Republic of Congo,COD,2005,13.688332,56550244, +Democratic Republic of Congo,COD,2006,13.223819,58381636, +Democratic Republic of Congo,COD,2007,12.754469,60289424, +Democratic Republic of Congo,COD,2008,12.302153,62249724, +Democratic Republic of Congo,COD,2009,11.876535,64270236, +Democratic Republic of Congo,COD,2010,11.4600315,66391260, +Democratic Republic of Congo,COD,2011,11.067396,68654272, +Democratic Republic of Congo,COD,2012,10.681911,70997872, +Democratic Republic of Congo,COD,2013,10.328633,73460032, +Democratic Republic of Congo,COD,2014,9.973719,76035592, +Democratic Republic of Congo,COD,2015,9.64925,78656904,Africa +Democratic Republic of Congo,COD,2016,9.342161,81430976, +Democratic Republic of Congo,COD,2017,9.026071,84283272, +Democratic Republic of Congo,COD,2018,8.724299,87087352, +Democratic Republic of Congo,COD,2019,8.4404125,89906896, +Democratic Republic of Congo,COD,2020,8.167984,92853168, +Democratic Republic of Congo,COD,2021,7.8960543,95894120, +Denmark,DNK,1950,3.4036365,4267702, +Denmark,DNK,1951,3.3389637,4299583, +Denmark,DNK,1952,3.2632604,4330760, +Denmark,DNK,1953,3.1640384,4362493, +Denmark,DNK,1954,3.0481231,4394327, +Denmark,DNK,1955,2.9255986,4426513, +Denmark,DNK,1956,2.8069022,4459563, +Denmark,DNK,1957,2.7036877,4491048, +Denmark,DNK,1958,2.6222286,4521333, +Denmark,DNK,1959,2.557877,4551440, +Denmark,DNK,1960,2.5011268,4581730, +Denmark,DNK,1961,2.4426079,4613437, +Denmark,DNK,1962,2.3779688,4645997, +Denmark,DNK,1963,2.303028,4680814, +Denmark,DNK,1964,2.2146182,4718021, +Denmark,DNK,1965,2.1135068,4756336, +Denmark,DNK,1966,2.003444,4796410, +Denmark,DNK,1967,1.899342,4834734, +Denmark,DNK,1968,1.8131062,4867744, +Denmark,DNK,1969,1.7381344,4896156, +Denmark,DNK,1970,1.661323,4922968, +Denmark,DNK,1971,1.5755275,4951490, +Denmark,DNK,1972,1.485081,4981082, +Denmark,DNK,1973,1.3943394,5008581, +Denmark,DNK,1974,1.3080683,5033574, +Denmark,DNK,1975,1.230071,5058203, +Denmark,DNK,1976,1.1639247,5078773, +Denmark,DNK,1977,1.1080402,5094605, +Denmark,DNK,1978,1.0625576,5108931, +Denmark,DNK,1979,1.0263197,5119417, +Denmark,DNK,1980,0.99750316,5125397, +Denmark,DNK,1981,0.9752307,5126983, +Denmark,DNK,1982,0.961383,5126402, +Denmark,DNK,1983,0.95675147,5124341, +Denmark,DNK,1984,0.9588879,5121317, +Denmark,DNK,1985,0.9652098,5119453, +Denmark,DNK,1986,0.9713521,5119368, +Denmark,DNK,1987,0.9713399,5121515, +Denmark,DNK,1988,0.9596388,5126107, +Denmark,DNK,1989,0.93341064,5133937, +Denmark,DNK,1990,0.8929861,5144626, +Denmark,DNK,1991,0.8419968,5158325, +Denmark,DNK,1992,0.7875792,5175466, +Denmark,DNK,1993,0.7363081,5193776, +Denmark,DNK,1994,0.6914167,5213801, +Denmark,DNK,1995,0.65468913,5235205, +Denmark,DNK,1996,0.62590104,5256002, +Denmark,DNK,1997,0.60418165,5277385, +Denmark,DNK,1998,0.5877807,5298991, +Denmark,DNK,1999,0.57482755,5319856, +Denmark,DNK,2000,0.56335646,5340662, +Denmark,DNK,2001,0.55159336,5360753, +Denmark,DNK,2002,0.538025,5378700, +Denmark,DNK,2003,0.52155,5396355, +Denmark,DNK,2004,0.5032989,5415583, +Denmark,DNK,2005,0.4838546,5436314, +Denmark,DNK,2006,0.4648409,5457863, +Denmark,DNK,2007,0.44730657,5479722, +Denmark,DNK,2008,0.43207484,5502752, +Denmark,DNK,2009,0.42017835,5526455, +Denmark,DNK,2010,0.41240233,5550850, +Denmark,DNK,2011,0.4087697,5576016, +Denmark,DNK,2012,0.40829092,5600955, +Denmark,DNK,2013,0.4088769,5625393, +Denmark,DNK,2014,0.4092958,5650654, +Denmark,DNK,2015,0.40770864,5677795,Europe +Denmark,DNK,2016,0.40338016,5706856, +Denmark,DNK,2017,0.39673975,5737286, +Denmark,DNK,2018,0.3877173,5766689, +Denmark,DNK,2019,0.37793633,5795879, +Denmark,DNK,2020,0.36786562,5825638, +Denmark,DNK,2021,0.3584227,5854246, +Djibouti,DJI,1950,,62351, +Djibouti,DJI,1951,,64334, +Djibouti,DJI,1952,,66380, +Djibouti,DJI,1953,,68509, +Djibouti,DJI,1954,,70731, +Djibouti,DJI,1955,,73010, +Djibouti,DJI,1956,,75391, +Djibouti,DJI,1957,,77865, +Djibouti,DJI,1958,,80411, +Djibouti,DJI,1959,,83041, +Djibouti,DJI,1960,,85956, +Djibouti,DJI,1961,,89284, +Djibouti,DJI,1962,,93138, +Djibouti,DJI,1963,,97682, +Djibouti,DJI,1964,,102956, +Djibouti,DJI,1965,,108788, +Djibouti,DJI,1966,,114885, +Djibouti,DJI,1967,,121177, +Djibouti,DJI,1968,,127684, +Djibouti,DJI,1969,,135404, +Djibouti,DJI,1970,,144393, +Djibouti,DJI,1971,,154236, +Djibouti,DJI,1972,,165247, +Djibouti,DJI,1973,,177334, +Djibouti,DJI,1974,,191402, +Djibouti,DJI,1975,,208591, +Djibouti,DJI,1976,17.268902,229316, +Djibouti,DJI,1977,16.786781,253092, +Djibouti,DJI,1978,16.30711,278438, +Djibouti,DJI,1979,15.848035,302869, +Djibouti,DJI,1980,15.4264965,324130, +Djibouti,DJI,1981,14.989311,341325, +Djibouti,DJI,1982,14.576085,355119, +Djibouti,DJI,1983,14.14963,367734, +Djibouti,DJI,1984,13.746651,382169, +Djibouti,DJI,1985,13.356732,400717, +Djibouti,DJI,1986,12.978686,424292, +Djibouti,DJI,1987,12.635967,452286, +Djibouti,DJI,1988,12.320571,482344, +Djibouti,DJI,1989,12.036086,521114, +Djibouti,DJI,1990,11.781853,577183, +Djibouti,DJI,1991,11.560705,628528, +Djibouti,DJI,1992,11.3704815,623208, +Djibouti,DJI,1993,11.210508,611239, +Djibouti,DJI,1994,11.068733,623482, +Djibouti,DJI,1995,10.933002,629628, +Djibouti,DJI,1996,10.791851,644659, +Djibouti,DJI,1997,10.634999,667793, +Djibouti,DJI,1998,10.466353,694931, +Djibouti,DJI,1999,10.26816,719381, +Djibouti,DJI,2000,10.057133,742046, +Djibouti,DJI,2001,9.835054,765496, +Djibouti,DJI,2002,9.594259,789140, +Djibouti,DJI,2003,9.341862,806412, +Djibouti,DJI,2004,9.099429,818380, +Djibouti,DJI,2005,8.850935,830863, +Djibouti,DJI,2006,8.608207,846957, +Djibouti,DJI,2007,8.367368,865202, +Djibouti,DJI,2008,8.124907,882893, +Djibouti,DJI,2009,7.8874497,901106, +Djibouti,DJI,2010,7.650786,919206, +Djibouti,DJI,2011,7.4313507,936819, +Djibouti,DJI,2012,7.2163124,954311, +Djibouti,DJI,2013,6.98844,971761, +Djibouti,DJI,2014,6.7844453,989091, +Djibouti,DJI,2015,6.56322,1006266,Africa +Djibouti,DJI,2016,6.378919,1023269, +Djibouti,DJI,2017,6.1922026,1040242, +Djibouti,DJI,2018,5.994133,1057204, +Djibouti,DJI,2019,5.7923765,1073999, +Djibouti,DJI,2020,5.5950885,1090162, +Djibouti,DJI,2021,5.414123,1105562, +Dominica,DMA,1950,,50208, +Dominica,DMA,1951,,50384, +Dominica,DMA,1952,,50567, +Dominica,DMA,1953,,50834, +Dominica,DMA,1954,,51234, +Dominica,DMA,1955,,51918, +Dominica,DMA,1956,,52974, +Dominica,DMA,1957,,54337, +Dominica,DMA,1958,,55992, +Dominica,DMA,1959,,57839, +Dominica,DMA,1960,13.8352785,59396, +Dominica,DMA,1961,12.586949,60416, +Dominica,DMA,1962,11.453586,61244, +Dominica,DMA,1963,10.422074,62051, +Dominica,DMA,1964,9.490677,62863, +Dominica,DMA,1965,8.641612,63756, +Dominica,DMA,1966,7.873021,64740, +Dominica,DMA,1967,7.174769,65781, +Dominica,DMA,1968,6.5414033,66883, +Dominica,DMA,1969,5.969152,68025, +Dominica,DMA,1970,5.4513083,68913, +Dominica,DMA,1971,4.9838276,69446, +Dominica,DMA,1972,4.5625963,69890, +Dominica,DMA,1973,4.1825957,70352, +Dominica,DMA,1974,3.841756,70840, +Dominica,DMA,1975,3.5361874,71319, +Dominica,DMA,1976,3.263343,71766, +Dominica,DMA,1977,3.0204568,72161, +Dominica,DMA,1978,2.804781,72498, +Dominica,DMA,1979,3.1393065,72788, +Dominica,DMA,1980,2.4486084,73000, +Dominica,DMA,1981,2.3034625,72949, +Dominica,DMA,1982,2.1770492,72650, +Dominica,DMA,1983,2.066722,72290, +Dominica,DMA,1984,1.9714931,71957, +Dominica,DMA,1985,1.8895485,71613, +Dominica,DMA,1986,1.8195903,71233, +Dominica,DMA,1987,1.7602856,70824, +Dominica,DMA,1988,1.7110367,70408, +Dominica,DMA,1989,1.6701982,69996, +Dominica,DMA,1990,1.6378381,69501, +Dominica,DMA,1991,1.6135213,69170, +Dominica,DMA,1992,1.5960896,69135, +Dominica,DMA,1993,1.5852114,69196, +Dominica,DMA,1994,1.5821837,69219, +Dominica,DMA,1995,1.584921,69224, +Dominica,DMA,1996,1.5937021,69182, +Dominica,DMA,1997,1.6089771,69096, +Dominica,DMA,1998,1.6304318,68957, +Dominica,DMA,1999,1.6580365,68713, +Dominica,DMA,2000,1.6928841,68365, +Dominica,DMA,2001,1.7342045,68171, +Dominica,DMA,2002,1.7823946,68281, +Dominica,DMA,2003,1.8380473,68457, +Dominica,DMA,2004,1.9018707,68591, +Dominica,DMA,2005,1.973451,68686, +Dominica,DMA,2006,2.0532207,68761, +Dominica,DMA,2007,2.1420207,68789, +Dominica,DMA,2008,2.2390456,68794, +Dominica,DMA,2009,2.3444753,68805, +Dominica,DMA,2010,2.459938,68775, +Dominica,DMA,2011,2.5840416,68758, +Dominica,DMA,2012,2.7159247,68908, +Dominica,DMA,2013,2.8512645,68839, +Dominica,DMA,2014,2.9881482,69388, +Dominica,DMA,2015,3.1221223,70027,North America +Dominica,DMA,2016,3.2474556,70098, +Dominica,DMA,2017,3.3597667,70422, +Dominica,DMA,2018,3.4509387,70840, +Dominica,DMA,2019,3.5175667,71446, +Dominica,DMA,2020,3.5621521,72011, +Dominica,DMA,2021,3.5894768,72435, +Dominican Republic,DOM,1950,14.474894,2381743, +Dominican Republic,DOM,1951,14.527441,2455308, +Dominican Republic,DOM,1952,14.591718,2532760, +Dominican Republic,DOM,1953,14.65851,2614570, +Dominican Republic,DOM,1954,14.703603,2700720, +Dominican Republic,DOM,1955,14.764601,2791109, +Dominican Republic,DOM,1956,14.799936,2885529, +Dominican Republic,DOM,1957,14.822142,2983696, +Dominican Republic,DOM,1958,14.822992,3085353, +Dominican Republic,DOM,1959,14.79304,3190239, +Dominican Republic,DOM,1960,14.72298,3298024, +Dominican Republic,DOM,1961,14.610118,3408420, +Dominican Republic,DOM,1962,14.453405,3521168, +Dominican Republic,DOM,1963,14.26447,3635808, +Dominican Republic,DOM,1964,14.030431,3752316, +Dominican Republic,DOM,1965,13.775116,3868717, +Dominican Republic,DOM,1966,13.500673,3987097, +Dominican Republic,DOM,1967,13.222307,4108923, +Dominican Republic,DOM,1968,12.941324,4230885, +Dominican Republic,DOM,1969,12.654939,4352958, +Dominican Republic,DOM,1970,12.347478,4475880, +Dominican Republic,DOM,1971,12.014807,4599848, +Dominican Republic,DOM,1972,11.651738,4724807, +Dominican Republic,DOM,1973,11.26507,4851190, +Dominican Republic,DOM,1974,10.869482,4978588, +Dominican Republic,DOM,1975,10.468717,5106460, +Dominican Republic,DOM,1976,10.083754,5235324, +Dominican Republic,DOM,1977,9.711849,5365077, +Dominican Republic,DOM,1978,9.355804,5495451, +Dominican Republic,DOM,1979,9.015906,5625872, +Dominican Republic,DOM,1980,8.688076,5755808, +Dominican Republic,DOM,1981,8.373589,5885248, +Dominican Republic,DOM,1982,8.070448,6014975, +Dominican Republic,DOM,1983,7.775284,6147216, +Dominican Republic,DOM,1984,7.4955482,6282107, +Dominican Republic,DOM,1985,7.2202797,6417803, +Dominican Republic,DOM,1986,6.9564233,6554326, +Dominican Republic,DOM,1987,6.698912,6692727, +Dominican Republic,DOM,1988,6.4468994,6834210, +Dominican Republic,DOM,1989,6.201362,6980041, +Dominican Republic,DOM,1990,5.9577174,7129004, +Dominican Republic,DOM,1991,5.7168593,7278410, +Dominican Republic,DOM,1992,5.4783697,7427347, +Dominican Republic,DOM,1993,5.241133,7576083, +Dominican Republic,DOM,1994,5.0150223,7723940, +Dominican Republic,DOM,1995,4.799319,7869754, +Dominican Republic,DOM,1996,4.6038637,8012315, +Dominican Republic,DOM,1997,4.423116,8150078, +Dominican Republic,DOM,1998,4.2645226,8282125, +Dominican Republic,DOM,1999,4.1237006,8411380, +Dominican Republic,DOM,2000,4.0045233,8540790, +Dominican Republic,DOM,2001,3.905864,8669038, +Dominican Republic,DOM,2002,3.8235435,8795104, +Dominican Republic,DOM,2003,3.7514446,8919857, +Dominican Republic,DOM,2004,3.6948762,9043135, +Dominican Republic,DOM,2005,3.6485438,9164770, +Dominican Republic,DOM,2006,3.6087017,9284169, +Dominican Republic,DOM,2007,3.5774333,9402209, +Dominican Republic,DOM,2008,3.5529525,9522954, +Dominican Republic,DOM,2009,3.5356827,9648061, +Dominican Republic,DOM,2010,3.5235286,9775756, +Dominican Republic,DOM,2011,3.5150738,9903743, +Dominican Republic,DOM,2012,3.51183,10030885, +Dominican Republic,DOM,2013,3.508826,10157054, +Dominican Republic,DOM,2014,3.5097427,10282116, +Dominican Republic,DOM,2015,3.5089016,10405832,North America +Dominican Republic,DOM,2016,3.5092754,10527589, +Dominican Republic,DOM,2017,3.5050359,10647247, +Dominican Republic,DOM,2018,3.4759316,10765532, +Dominican Republic,DOM,2019,3.4340744,10881886, +Dominican Republic,DOM,2020,3.3736863,10999668, +Dominican Republic,DOM,2021,3.3002884,11117873, +East Asia and Pacific (UNICEF),,1990,5.657014,, +East Asia and Pacific (UNICEF),,1991,5.5394096,, +East Asia and Pacific (UNICEF),,1992,5.418272,, +East Asia and Pacific (UNICEF),,1993,5.266303,, +East Asia and Pacific (UNICEF),,1994,5.0939317,, +East Asia and Pacific (UNICEF),,1995,4.918006,, +East Asia and Pacific (UNICEF),,1996,4.737563,, +East Asia and Pacific (UNICEF),,1997,4.549483,, +East Asia and Pacific (UNICEF),,1998,4.353054,, +East Asia and Pacific (UNICEF),,1999,4.151893,, +East Asia and Pacific (UNICEF),,2000,3.9320042,, +East Asia and Pacific (UNICEF),,2001,3.7171302,, +East Asia and Pacific (UNICEF),,2002,3.5070062,, +East Asia and Pacific (UNICEF),,2003,3.2944462,, +East Asia and Pacific (UNICEF),,2004,3.1821342,, +East Asia and Pacific (UNICEF),,2005,2.8998218,, +East Asia and Pacific (UNICEF),,2006,2.7274747,, +East Asia and Pacific (UNICEF),,2007,2.5696692,, +East Asia and Pacific (UNICEF),,2008,2.516754,, +East Asia and Pacific (UNICEF),,2009,2.2875288,, +East Asia and Pacific (UNICEF),,2010,2.1695118,, +East Asia and Pacific (UNICEF),,2011,2.0634623,, +East Asia and Pacific (UNICEF),,2012,1.9407358,, +East Asia and Pacific (UNICEF),,2013,1.8550556,, +East Asia and Pacific (UNICEF),,2014,1.7626617,, +East Asia and Pacific (UNICEF),,2015,1.698349,, +East Asia and Pacific (UNICEF),,2016,1.6116197,, +East Asia and Pacific (UNICEF),,2017,1.5358782,, +East Asia and Pacific (UNICEF),,2018,1.5224284,, +East Asia and Pacific (UNICEF),,2019,1.4868572,, +East Asia and Pacific (UNICEF),,2020,1.4845896,, +East Asia and Pacific (UNICEF),,2021,1.4693941,, +East Germany,OWID_GDR,1950,,18390000, +East Germany,OWID_GDR,1973,,16890000, +East Germany,OWID_GDR,1990,,16110000, +East Germany,OWID_GDR,2015,,,Europe +East Timor,TLS,1950,,414793, +East Timor,TLS,1951,,418342, +East Timor,TLS,1952,,422544, +East Timor,TLS,1953,,427168, +East Timor,TLS,1954,,432133, +East Timor,TLS,1955,,437441, +East Timor,TLS,1956,,443044, +East Timor,TLS,1957,,448964, +East Timor,TLS,1958,,455182, +East Timor,TLS,1959,,461708, +East Timor,TLS,1960,,468582, +East Timor,TLS,1961,,475805, +East Timor,TLS,1962,,483330, +East Timor,TLS,1963,,491121, +East Timor,TLS,1964,,499141, +East Timor,TLS,1965,,507479, +East Timor,TLS,1966,,516199, +East Timor,TLS,1967,,525279, +East Timor,TLS,1968,,534653, +East Timor,TLS,1969,,544247, +East Timor,TLS,1970,,554030, +East Timor,TLS,1971,,563963, +East Timor,TLS,1972,,574023, +East Timor,TLS,1973,,584038, +East Timor,TLS,1974,,592282, +East Timor,TLS,1975,,600132, +East Timor,TLS,1976,,609653, +East Timor,TLS,1977,,619312, +East Timor,TLS,1978,,627678, +East Timor,TLS,1979,,634557, +East Timor,TLS,1980,,642230, +East Timor,TLS,1981,,649257, +East Timor,TLS,1982,,655121, +East Timor,TLS,1983,,662421, +East Timor,TLS,1984,,671382, +East Timor,TLS,1985,22.102188,682119, +East Timor,TLS,1986,21.104717,694257, +East Timor,TLS,1987,20.147549,707764, +East Timor,TLS,1988,19.225666,722694, +East Timor,TLS,1989,18.362476,739744, +East Timor,TLS,1990,17.552658,758120, +East Timor,TLS,1991,16.790882,772663, +East Timor,TLS,1992,16.070232,783708, +East Timor,TLS,1993,15.373553,795184, +East Timor,TLS,1994,14.691389,807119, +East Timor,TLS,1995,14.039517,819234, +East Timor,TLS,1996,13.413643,831272, +East Timor,TLS,1997,12.812536,843347, +East Timor,TLS,1998,12.233586,855421, +East Timor,TLS,1999,11.672656,866652, +East Timor,TLS,2000,11.134386,878367, +East Timor,TLS,2001,10.607313,893003, +East Timor,TLS,2002,10.114142,909648, +East Timor,TLS,2003,9.636839,926725, +East Timor,TLS,2004,9.179839,945993, +East Timor,TLS,2005,8.758262,969318, +East Timor,TLS,2006,8.36702,994573, +East Timor,TLS,2007,8.00505,1019369, +East Timor,TLS,2008,7.6810913,1043085, +East Timor,TLS,2009,7.3985734,1065543, +East Timor,TLS,2010,7.1429687,1088492, +East Timor,TLS,2011,6.9219513,1112985, +East Timor,TLS,2012,6.71576,1137683, +East Timor,TLS,2013,6.5310707,1161558, +East Timor,TLS,2014,6.3473024,1184842, +East Timor,TLS,2015,6.156693,1205822,Asia +East Timor,TLS,2016,5.965932,1224568, +East Timor,TLS,2017,5.7691813,1243238, +East Timor,TLS,2018,5.5810046,1261850, +East Timor,TLS,2019,5.3917055,1280442, +East Timor,TLS,2020,5.2161202,1299998, +East Timor,TLS,2021,5.0548973,1320944, +Eastern Asia (SDG),,1990,5.0833864,, +Eastern Asia (SDG),,1991,4.98462,, +Eastern Asia (SDG),,1992,4.874401,, +Eastern Asia (SDG),,1993,4.7531166,, +Eastern Asia (SDG),,1994,4.6014366,, +Eastern Asia (SDG),,1995,4.4440045,, +Eastern Asia (SDG),,1996,4.270916,, +Eastern Asia (SDG),,1997,4.0822377,, +Eastern Asia (SDG),,1998,3.878659,, +Eastern Asia (SDG),,1999,3.664228,, +Eastern Asia (SDG),,2000,3.436725,, +Eastern Asia (SDG),,2001,3.1885993,, +Eastern Asia (SDG),,2002,2.9409833,, +Eastern Asia (SDG),,2003,2.6967702,, +Eastern Asia (SDG),,2004,2.4670722,, +Eastern Asia (SDG),,2005,2.2592673,, +Eastern Asia (SDG),,2006,2.070852,, +Eastern Asia (SDG),,2007,1.9033182,, +Eastern Asia (SDG),,2008,1.7577035,, +Eastern Asia (SDG),,2009,1.6278055,, +Eastern Asia (SDG),,2010,1.5053811,, +Eastern Asia (SDG),,2011,1.3947406,, +Eastern Asia (SDG),,2012,1.2954471,, +Eastern Asia (SDG),,2013,1.2010722,, +Eastern Asia (SDG),,2014,1.1161113,, +Eastern Asia (SDG),,2015,1.0357147,, +Eastern Asia (SDG),,2016,0.96431386,, +Eastern Asia (SDG),,2017,0.90034354,, +Eastern Asia (SDG),,2018,0.8397647,, +Eastern Asia (SDG),,2019,0.7836524,, +Eastern Asia (SDG),,2020,0.73147213,, +Eastern Asia (SDG),,2021,0.6815856,, +Eastern Asia and South-Eastern Asia (SDG),,1990,5.685617,, +Eastern Asia and South-Eastern Asia (SDG),,1991,5.5705056,, +Eastern Asia and South-Eastern Asia (SDG),,1992,5.451111,, +Eastern Asia and South-Eastern Asia (SDG),,1993,5.2986345,, +Eastern Asia and South-Eastern Asia (SDG),,1994,5.124706,, +Eastern Asia and South-Eastern Asia (SDG),,1995,4.9470196,, +Eastern Asia and South-Eastern Asia (SDG),,1996,4.7646017,, +Eastern Asia and South-Eastern Asia (SDG),,1997,4.574147,, +Eastern Asia and South-Eastern Asia (SDG),,1998,4.3749256,, +Eastern Asia and South-Eastern Asia (SDG),,1999,4.170968,, +Eastern Asia and South-Eastern Asia (SDG),,2000,3.9473414,, +Eastern Asia and South-Eastern Asia (SDG),,2001,3.7290502,, +Eastern Asia and South-Eastern Asia (SDG),,2002,3.5155327,, +Eastern Asia and South-Eastern Asia (SDG),,2003,3.2995982,, +Eastern Asia and South-Eastern Asia (SDG),,2004,3.1859884,, +Eastern Asia and South-Eastern Asia (SDG),,2005,2.8994079,, +Eastern Asia and South-Eastern Asia (SDG),,2006,2.7249076,, +Eastern Asia and South-Eastern Asia (SDG),,2007,2.5660362,, +Eastern Asia and South-Eastern Asia (SDG),,2008,2.5136664,, +Eastern Asia and South-Eastern Asia (SDG),,2009,2.280583,, +Eastern Asia and South-Eastern Asia (SDG),,2010,2.1611972,, +Eastern Asia and South-Eastern Asia (SDG),,2011,2.053747,, +Eastern Asia and South-Eastern Asia (SDG),,2012,1.9300264,, +Eastern Asia and South-Eastern Asia (SDG),,2013,1.8433056,, +Eastern Asia and South-Eastern Asia (SDG),,2014,1.7496946,, +Eastern Asia and South-Eastern Asia (SDG),,2015,1.6848328,, +Eastern Asia and South-Eastern Asia (SDG),,2016,1.5975208,, +Eastern Asia and South-Eastern Asia (SDG),,2017,1.5213964,, +Eastern Asia and South-Eastern Asia (SDG),,2018,1.507653,, +Eastern Asia and South-Eastern Asia (SDG),,2019,1.4713941,, +Eastern Asia and South-Eastern Asia (SDG),,2020,1.4685695,, +Eastern Asia and South-Eastern Asia (SDG),,2021,1.4534079,, +Eastern Europe and Central Asia (UNICEF),,1990,4.678743,, +Eastern Europe and Central Asia (UNICEF),,1991,4.661307,, +Eastern Europe and Central Asia (UNICEF),,1992,4.6736703,, +Eastern Europe and Central Asia (UNICEF),,1993,4.669858,, +Eastern Europe and Central Asia (UNICEF),,1994,4.6002965,, +Eastern Europe and Central Asia (UNICEF),,1995,4.506764,, +Eastern Europe and Central Asia (UNICEF),,1996,4.364282,, +Eastern Europe and Central Asia (UNICEF),,1997,4.193323,, +Eastern Europe and Central Asia (UNICEF),,1998,4.0002685,, +Eastern Europe and Central Asia (UNICEF),,1999,3.790168,, +Eastern Europe and Central Asia (UNICEF),,2000,3.5491378,, +Eastern Europe and Central Asia (UNICEF),,2001,3.3129566,, +Eastern Europe and Central Asia (UNICEF),,2002,3.0749059,, +Eastern Europe and Central Asia (UNICEF),,2003,2.8481166,, +Eastern Europe and Central Asia (UNICEF),,2004,2.6551583,, +Eastern Europe and Central Asia (UNICEF),,2005,2.4841962,, +Eastern Europe and Central Asia (UNICEF),,2006,2.3208363,, +Eastern Europe and Central Asia (UNICEF),,2007,2.163333,, +Eastern Europe and Central Asia (UNICEF),,2008,2.0185275,, +Eastern Europe and Central Asia (UNICEF),,2009,1.8957822,, +Eastern Europe and Central Asia (UNICEF),,2010,1.792701,, +Eastern Europe and Central Asia (UNICEF),,2011,1.6986357,, +Eastern Europe and Central Asia (UNICEF),,2012,1.6045971,, +Eastern Europe and Central Asia (UNICEF),,2013,1.5258439,, +Eastern Europe and Central Asia (UNICEF),,2014,1.447327,, +Eastern Europe and Central Asia (UNICEF),,2015,1.3703834,, +Eastern Europe and Central Asia (UNICEF),,2016,1.2982558,, +Eastern Europe and Central Asia (UNICEF),,2017,1.2453674,, +Eastern Europe and Central Asia (UNICEF),,2018,1.2017018,, +Eastern Europe and Central Asia (UNICEF),,2019,1.165751,, +Eastern Europe and Central Asia (UNICEF),,2020,1.1239173,, +Eastern Europe and Central Asia (UNICEF),,2021,1.0822335,, +Eastern and Southern Africa (UNICEF),,1990,16.285511,, +Eastern and Southern Africa (UNICEF),,1991,16.138134,, +Eastern and Southern Africa (UNICEF),,1992,15.984167,, +Eastern and Southern Africa (UNICEF),,1993,15.865443,, +Eastern and Southern Africa (UNICEF),,1994,16.008224,, +Eastern and Southern Africa (UNICEF),,1995,15.369138,, +Eastern and Southern Africa (UNICEF),,1996,15.0755415,, +Eastern and Southern Africa (UNICEF),,1997,14.783266,, +Eastern and Southern Africa (UNICEF),,1998,14.421201,, +Eastern and Southern Africa (UNICEF),,1999,13.969648,, +Eastern and Southern Africa (UNICEF),,2000,13.465116,, +Eastern and Southern Africa (UNICEF),,2001,12.910614,, +Eastern and Southern Africa (UNICEF),,2002,12.327147,, +Eastern and Southern Africa (UNICEF),,2003,11.743265,, +Eastern and Southern Africa (UNICEF),,2004,11.170023,, +Eastern and Southern Africa (UNICEF),,2005,10.625154,, +Eastern and Southern Africa (UNICEF),,2006,10.113228,, +Eastern and Southern Africa (UNICEF),,2007,9.594748,, +Eastern and Southern Africa (UNICEF),,2008,9.079521,, +Eastern and Southern Africa (UNICEF),,2009,8.582826,, +Eastern and Southern Africa (UNICEF),,2010,8.145726,, +Eastern and Southern Africa (UNICEF),,2011,7.7421823,, +Eastern and Southern Africa (UNICEF),,2012,7.3649435,, +Eastern and Southern Africa (UNICEF),,2013,7.0263596,, +Eastern and Southern Africa (UNICEF),,2014,6.7355127,, +Eastern and Southern Africa (UNICEF),,2015,6.4797797,, +Eastern and Southern Africa (UNICEF),,2016,6.2309194,, +Eastern and Southern Africa (UNICEF),,2017,6.0161796,, +Eastern and Southern Africa (UNICEF),,2018,5.808238,, +Eastern and Southern Africa (UNICEF),,2019,5.6158085,, +Eastern and Southern Africa (UNICEF),,2020,5.4478407,, +Eastern and Southern Africa (UNICEF),,2021,5.2817855,, +Ecuador,ECU,1950,,3518730, +Ecuador,ECU,1951,,3613182, +Ecuador,ECU,1952,,3710592, +Ecuador,ECU,1953,,3811223, +Ecuador,ECU,1954,,3915138, +Ecuador,ECU,1955,20.387873,4022386, +Ecuador,ECU,1956,19.76753,4133246, +Ecuador,ECU,1957,19.164024,4248164, +Ecuador,ECU,1958,18.555956,4367095, +Ecuador,ECU,1959,17.99132,4490254, +Ecuador,ECU,1960,17.468403,4618160, +Ecuador,ECU,1961,16.94786,4751510, +Ecuador,ECU,1962,16.466846,4890435, +Ecuador,ECU,1963,16.025145,5034337, +Ecuador,ECU,1964,15.61445,5183287, +Ecuador,ECU,1965,15.240024,5337356, +Ecuador,ECU,1966,14.881823,5495765, +Ecuador,ECU,1967,14.575025,5658454, +Ecuador,ECU,1968,14.277193,5825663, +Ecuador,ECU,1969,13.993967,5997045, +Ecuador,ECU,1970,13.69856,6172219, +Ecuador,ECU,1971,13.354276,6350954, +Ecuador,ECU,1972,12.9581585,6533633, +Ecuador,ECU,1973,12.510378,6720451, +Ecuador,ECU,1974,12.006369,6911130, +Ecuador,ECU,1975,11.475569,7105791, +Ecuador,ECU,1976,10.945078,7304352, +Ecuador,ECU,1977,10.465418,7506669, +Ecuador,ECU,1978,10.010942,7712722, +Ecuador,ECU,1979,9.563677,7922496, +Ecuador,ECU,1980,9.126227,8135850, +Ecuador,ECU,1981,8.685856,8352607, +Ecuador,ECU,1982,8.23407,8572618, +Ecuador,ECU,1983,7.788447,8795879, +Ecuador,ECU,1984,7.357839,9022978, +Ecuador,ECU,1985,6.949781,9254308, +Ecuador,ECU,1986,6.5833955,9490096, +Ecuador,ECU,1987,6.2545495,9729380, +Ecuador,ECU,1988,5.952761,9969790, +Ecuador,ECU,1989,5.673817,10210190, +Ecuador,ECU,1990,5.406391,10449845, +Ecuador,ECU,1991,5.141081,10686277, +Ecuador,ECU,1992,4.879456,10914222, +Ecuador,ECU,1993,4.612423,11132833, +Ecuador,ECU,1994,4.345665,11347655, +Ecuador,ECU,1995,4.0774455,11561683, +Ecuador,ECU,1996,3.8158073,11775223, +Ecuador,ECU,1997,3.5691562,11987840, +Ecuador,ECU,1998,3.3445504,12199692, +Ecuador,ECU,1999,3.1426208,12412056, +Ecuador,ECU,2000,2.9625802,12626511, +Ecuador,ECU,2001,2.806173,12845523, +Ecuador,ECU,2002,2.6696362,13070617, +Ecuador,ECU,2003,2.5515141,13301188, +Ecuador,ECU,2004,2.44675,13534598, +Ecuador,ECU,2005,2.351581,13770015, +Ecuador,ECU,2006,2.2598934,14009064, +Ecuador,ECU,2007,2.1683643,14251838, +Ecuador,ECU,2008,2.0768387,14496800, +Ecuador,ECU,2009,1.986557,14742768, +Ecuador,ECU,2010,1.8979139,14989581, +Ecuador,ECU,2011,1.8131933,15237734, +Ecuador,ECU,2012,1.7334816,15483884, +Ecuador,ECU,2013,1.6602113,15722988, +Ecuador,ECU,2014,1.5952384,15957995, +Ecuador,ECU,2015,1.5365337,16195904,South America +Ecuador,ECU,2016,1.4837742,16439588, +Ecuador,ECU,2017,1.4351072,16696947, +Ecuador,ECU,2018,1.388452,17015672, +Ecuador,ECU,2019,1.3411065,17343742, +Ecuador,ECU,2020,1.292855,17588596, +Ecuador,ECU,2021,1.2460622,17797736, +Egypt,EGY,1950,,21150060, +Egypt,EGY,1951,,21606364, +Egypt,EGY,1952,,22108170, +Egypt,EGY,1953,,22659266, +Egypt,EGY,1954,,23241220, +Egypt,EGY,1955,36.467896,23841704, +Egypt,EGY,1956,35.53984,24453502, +Egypt,EGY,1957,34.630238,25078382, +Egypt,EGY,1958,33.70455,25700976, +Egypt,EGY,1959,32.764297,26345770, +Egypt,EGY,1960,31.779888,27034500, +Egypt,EGY,1961,30.776043,27747872, +Egypt,EGY,1962,29.765615,28485028, +Egypt,EGY,1963,28.76242,29245942, +Egypt,EGY,1964,27.860455,30026652, +Egypt,EGY,1965,27.048283,30818472, +Egypt,EGY,1966,26.352106,31613136, +Egypt,EGY,1967,25.797958,32408416, +Egypt,EGY,1968,25.347172,33204632, +Egypt,EGY,1969,24.969196,33995960, +Egypt,EGY,1970,24.62892,34781992, +Egypt,EGY,1971,24.260403,35555972, +Egypt,EGY,1972,23.813396,36330772, +Egypt,EGY,1973,23.251114,37120780, +Egypt,EGY,1974,22.529226,37930376, +Egypt,EGY,1975,21.67217,38775584, +Egypt,EGY,1976,20.731972,39649056, +Egypt,EGY,1977,19.75131,40577356, +Egypt,EGY,1978,18.760418,41576640, +Egypt,EGY,1979,17.785637,42632456, +Egypt,EGY,1980,16.827856,43748552, +Egypt,EGY,1981,15.867132,44899572, +Egypt,EGY,1982,14.888873,46088652, +Egypt,EGY,1983,13.89151,47353672, +Egypt,EGY,1984,12.878443,48676444, +Egypt,EGY,1985,11.892592,50035844, +Egypt,EGY,1986,10.996984,51424316, +Egypt,EGY,1987,10.223175,52841316, +Egypt,EGY,1988,9.573048,54298448, +Egypt,EGY,1989,9.030246,55765848, +Egypt,EGY,1990,8.568529,57214632, +Egypt,EGY,1991,8.146222,58611032, +Egypt,EGY,1992,7.735627,59989148, +Egypt,EGY,1993,7.3191786,61382204, +Egypt,EGY,1994,6.892095,62775844, +Egypt,EGY,1995,6.463936,64166908, +Egypt,EGY,1996,6.04934,65565196, +Egypt,EGY,1997,5.659663,66993732, +Egypt,EGY,1998,5.2967277,68446008, +Egypt,EGY,1999,4.9677286,69907888, +Egypt,EGY,2000,4.670007,71371368, +Egypt,EGY,2001,4.401723,72854256, +Egypt,EGY,2002,4.16202,74393760, +Egypt,EGY,2003,3.948634,75963328, +Egypt,EGY,2004,3.7592304,77522424, +Egypt,EGY,2005,3.586391,79075312, +Egypt,EGY,2006,3.4268386,80629664, +Egypt,EGY,2007,3.2809768,82218760, +Egypt,EGY,2008,3.1410937,83844784, +Egypt,EGY,2009,3.007844,85501064, +Egypt,EGY,2010,2.8787465,87252416, +Egypt,EGY,2011,2.7575502,89200056, +Egypt,EGY,2012,2.6436455,91240384, +Egypt,EGY,2013,2.5338464,93377896, +Egypt,EGY,2014,2.4318435,95592328, +Egypt,EGY,2015,2.3422954,97723792,Africa +Egypt,EGY,2016,2.2580986,99784032, +Egypt,EGY,2017,2.1783555,101789384, +Egypt,EGY,2018,2.101623,103740768, +Egypt,EGY,2019,2.029165,105618672, +Egypt,EGY,2020,1.9584423,107465128, +Egypt,EGY,2021,1.8957832,109262184, +El Salvador,SLV,1950,,2182419, +El Salvador,SLV,1951,,2232154, +El Salvador,SLV,1952,,2283112, +El Salvador,SLV,1953,,2335947, +El Salvador,SLV,1954,,2390860, +El Salvador,SLV,1955,20.623158,2448639, +El Salvador,SLV,1956,20.178,2509194, +El Salvador,SLV,1957,19.72519,2572512, +El Salvador,SLV,1958,19.28564,2637605, +El Salvador,SLV,1959,18.864025,2705559, +El Salvador,SLV,1960,18.481989,2779332, +El Salvador,SLV,1961,18.091587,2858899, +El Salvador,SLV,1962,17.713259,2937826, +El Salvador,SLV,1963,17.346565,3014882, +El Salvador,SLV,1964,17.000385,3095173, +El Salvador,SLV,1965,16.660122,3179515, +El Salvador,SLV,1966,16.31478,3266444, +El Salvador,SLV,1967,15.991275,3354614, +El Salvador,SLV,1968,15.667435,3443257, +El Salvador,SLV,1969,15.338938,3530960, +El Salvador,SLV,1970,15.003459,3619097, +El Salvador,SLV,1971,14.672785,3708015, +El Salvador,SLV,1972,14.321111,3798110, +El Salvador,SLV,1973,13.935251,3890643, +El Salvador,SLV,1974,13.530138,3984052, +El Salvador,SLV,1975,13.093509,4076754, +El Salvador,SLV,1976,12.632872,4166894, +El Salvador,SLV,1977,12.139095,4256703, +El Salvador,SLV,1978,11.638603,4346535, +El Salvador,SLV,1979,11.111143,4433043, +El Salvador,SLV,1980,12.495874,4509000, +El Salvador,SLV,1981,10.045502,4549183, +El Salvador,SLV,1982,9.513395,4599905, +El Salvador,SLV,1983,8.99139,4689685, +El Salvador,SLV,1984,8.48131,4782222, +El Salvador,SLV,1985,7.990157,4876757, +El Salvador,SLV,1986,7.5286956,4970790, +El Salvador,SLV,1987,7.100505,5067205, +El Salvador,SLV,1988,6.7060966,5170444, +El Salvador,SLV,1989,6.3378363,5270833, +El 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countries,,1953,,692819154, +High-income countries,,1954,,701492294, +High-income countries,,1955,,710382120, +High-income countries,,1956,,719265066, +High-income countries,,1957,,728148307, +High-income countries,,1958,,737635313, +High-income countries,,1959,,747296445, +High-income countries,,1960,,756613763, +High-income countries,,1961,,766062145, +High-income countries,,1962,,775529446, +High-income countries,,1963,,784955820, +High-income countries,,1964,,794527190, +High-income countries,,1965,,803939083, +High-income countries,,1966,,812785800, +High-income countries,,1967,,821405210, +High-income countries,,1968,,829928109, +High-income countries,,1969,,838295343, +High-income countries,,1970,,847035032, +High-income countries,,1971,,856236764, +High-income countries,,1972,,865281941, +High-income countries,,1973,,873811230, +High-income countries,,1974,,882068516, +High-income countries,,1975,,889965424, +High-income countries,,1976,,897381759, +High-income countries,,1977,,904794975, +High-income countries,,1978,,912288970, +High-income countries,,1979,,919797067, +High-income countries,,1980,,927538869, +High-income countries,,1981,,935113193, +High-income countries,,1982,,942356988, +High-income countries,,1983,,949463548, +High-income countries,,1984,,956450952, +High-income countries,,1985,,963419473, +High-income countries,,1986,,970437010, +High-income countries,,1987,,977626715, +High-income countries,,1988,,985025837, +High-income countries,,1989,,992663898, +High-income countries,,1990,1.2862871,1000128696, +High-income countries,,1991,1.2244338,1007966262, +High-income countries,,1992,1.1649778,1016302312, +High-income countries,,1993,1.109451,1024111317, +High-income countries,,1994,1.0559347,1031535629, +High-income countries,,1995,1.0055332,1038643992, +High-income countries,,1996,0.9579881,1045645066, +High-income countries,,1997,0.917613,1052805303, +High-income countries,,1998,0.8813839,1059873149, +High-income countries,,1999,0.8488876,1066734633, +High-income countries,,2000,0.8185562,1073716442, +High-income countries,,2001,0.79405123,1080682831, +High-income countries,,2002,0.77112484,1087599947, +High-income countries,,2003,0.75008196,1094702298, +High-income countries,,2004,0.7288869,1101994336, +High-income countries,,2005,0.7094277,1109756320, +High-income countries,,2006,0.6891947,1118325687, +High-income countries,,2007,0.6697351,1127897627, +High-income countries,,2008,0.6496897,1137726149, +High-income countries,,2009,0.63145214,1146851619, +High-income countries,,2010,0.61195517,1154843353, +High-income countries,,2011,0.5962984,1162275154, +High-income countries,,2012,0.58069485,1169663185, +High-income countries,,2013,0.5680919,1176689524, +High-income countries,,2014,0.55693316,1183328941, +High-income countries,,2015,0.54583603,1189869736, +High-income countries,,2016,0.5355078,1196437875, +High-income countries,,2017,0.52716005,1202872536, +High-income countries,,2018,0.5178943,1209044728, +High-income countries,,2019,0.5093073,1214846354, +High-income countries,,2020,0.49869642,1218093003, +High-income countries,,2021,0.48749867,1219584303, +Honduras,HND,1950,,1557940, +Honduras,HND,1951,,1602234, +Honduras,HND,1952,,1648145, +Honduras,HND,1953,,1695869, +Honduras,HND,1954,,1745351, +Honduras,HND,1955,,1796536, +Honduras,HND,1956,23.909748,1849402, +Honduras,HND,1957,23.071909,1904021, +Honduras,HND,1958,22.304508,1960498, +Honduras,HND,1959,21.566599,2018746, +Honduras,HND,1960,20.855219,2078624, +Honduras,HND,1961,20.154915,2139932, +Honduras,HND,1962,19.48731,2202865, +Honduras,HND,1963,18.840448,2267717, +Honduras,HND,1964,18.195614,2334493, +Honduras,HND,1965,17.57122,2403468, +Honduras,HND,1966,16.959091,2474533, +Honduras,HND,1967,16.361816,2547504, +Honduras,HND,1968,15.759682,2622991, +Honduras,HND,1969,15.17544,2701384, +Honduras,HND,1970,14.601903,2782756, +Honduras,HND,1971,14.0403385,2867507, 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+Landlocked developing countries (SDG),,1990,16.70614,, +Landlocked developing countries (SDG),,1991,16.482199,, +Landlocked developing countries (SDG),,1992,16.276794,, +Landlocked developing countries (SDG),,1993,16.124348,, +Landlocked developing countries (SDG),,1994,16.23344,, +Landlocked developing countries (SDG),,1995,15.505255,, +Landlocked developing countries (SDG),,1996,15.171275,, +Landlocked developing countries (SDG),,1997,14.860991,, +Landlocked developing countries (SDG),,1998,14.49839,, +Landlocked developing countries (SDG),,1999,14.073442,, +Landlocked developing countries (SDG),,2000,13.583941,, +Landlocked developing countries (SDG),,2001,13.035704,, +Landlocked developing countries (SDG),,2002,12.454832,, +Landlocked developing countries (SDG),,2003,11.862803,, +Landlocked developing countries (SDG),,2004,11.268709,, +Landlocked developing countries (SDG),,2005,10.702154,, +Landlocked developing countries (SDG),,2006,10.175096,, +Landlocked developing countries (SDG),,2007,9.672138,, +Landlocked developing countries (SDG),,2008,9.185896,, +Landlocked developing countries (SDG),,2009,8.74196,, +Landlocked developing countries (SDG),,2010,8.331434,, +Landlocked developing countries (SDG),,2011,7.9622393,, +Landlocked developing countries (SDG),,2012,7.6045847,, +Landlocked developing countries (SDG),,2013,7.2748327,, +Landlocked developing countries (SDG),,2014,6.9858193,, +Landlocked developing countries (SDG),,2015,6.7301064,, +Landlocked developing countries (SDG),,2016,6.4864945,, +Landlocked developing countries (SDG),,2017,6.285732,, +Landlocked developing countries (SDG),,2018,6.0847187,, +Landlocked developing countries (SDG),,2019,5.8949757,, +Landlocked developing countries (SDG),,2020,5.7207327,, +Landlocked developing countries (SDG),,2021,5.5703945,, +Laos,LAO,1950,,1684682, +Laos,LAO,1951,,1725439, +Laos,LAO,1952,,1766299, +Laos,LAO,1953,,1807912, +Laos,LAO,1954,,1850258, +Laos,LAO,1955,,1893389, +Laos,LAO,1956,,1937520, 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Caribbean (UNICEF),,2007,2.3915303,, +Latin America and Caribbean (UNICEF),,2008,2.2972622,, +Latin America and Caribbean (UNICEF),,2009,2.2118287,, +Latin America and Caribbean (UNICEF),,2010,2.2655556,, +Latin America and Caribbean (UNICEF),,2011,2.0613623,, +Latin America and Caribbean (UNICEF),,2012,1.9966425,, +Latin America and Caribbean (UNICEF),,2013,1.9364951,, +Latin America and Caribbean (UNICEF),,2014,1.8826202,, +Latin America and Caribbean (UNICEF),,2015,1.8379772,, +Latin America and Caribbean (UNICEF),,2016,1.8526167,, +Latin America and Caribbean (UNICEF),,2017,1.7776353,, +Latin America and Caribbean (UNICEF),,2018,1.7306778,, +Latin America and Caribbean (UNICEF),,2019,1.6846918,, +Latin America and Caribbean (UNICEF),,2020,1.6375904,, +Latin America and Caribbean (UNICEF),,2021,1.5917643,, +Latin America and the Caribbean (SDG),,1990,5.4905534,, +Latin America and the Caribbean (SDG),,1991,5.2532954,, +Latin America and the Caribbean (SDG),,1992,5.013655,, +Latin America and the Caribbean (SDG),,1993,4.777466,, +Latin America and the Caribbean (SDG),,1994,4.5413966,, +Latin America and the Caribbean (SDG),,1995,4.3139186,, +Latin America and the Caribbean (SDG),,1996,4.094578,, +Latin America and the Caribbean (SDG),,1997,3.8822918,, +Latin America and the Caribbean (SDG),,1998,3.7349725,, +Latin America and the Caribbean (SDG),,1999,3.4921913,, +Latin America and the Caribbean (SDG),,2000,3.319039,, +Latin America and the Caribbean (SDG),,2001,3.1557012,, +Latin America and the Caribbean (SDG),,2002,3.0007803,, +Latin America and the Caribbean (SDG),,2003,2.8599448,, +Latin America and the Caribbean (SDG),,2004,2.7291343,, +Latin America and the Caribbean (SDG),,2005,2.6062639,, +Latin America and the Caribbean (SDG),,2006,2.4944756,, +Latin America and the Caribbean (SDG),,2007,2.3915303,, +Latin America and the Caribbean (SDG),,2008,2.2972622,, +Latin America and the Caribbean (SDG),,2009,2.2118287,, +Latin America and the Caribbean (SDG),,2010,2.2655556,, +Latin America and the Caribbean (SDG),,2011,2.0613623,, +Latin America and the Caribbean (SDG),,2012,1.9966425,, +Latin America and the Caribbean (SDG),,2013,1.9364951,, +Latin America and the Caribbean (SDG),,2014,1.8826202,, +Latin America and the Caribbean (SDG),,2015,1.8379772,, +Latin America and the Caribbean (SDG),,2016,1.8526167,, +Latin America and the Caribbean (SDG),,2017,1.7776353,, +Latin America and the Caribbean (SDG),,2018,1.7306778,, +Latin America and the Caribbean (SDG),,2019,1.6846918,, +Latin America and the Caribbean (SDG),,2020,1.6375904,, +Latin America and the Caribbean (SDG),,2021,1.5917643,, +Latin America and the Caribbean (UN),,1950,,168335840, +Latin America and the Caribbean (UN),,1951,,172783136, +Latin America and the Caribbean (UN),,1952,,177354128, +Latin America and the Caribbean (UN),,1953,,182056288, +Latin America and the Caribbean (UN),,1954,,186910160, +Latin America and the Caribbean (UN),,1955,,191936400, +Latin America and the Caribbean (UN),,1956,,197136640, +Latin America and the Caribbean (UN),,1957,,202519696, +Latin America and the Caribbean (UN),,1958,,208049440, +Latin America and the Caribbean (UN),,1959,,213778336, +Latin America and the Caribbean (UN),,1960,,219754160, +Latin America and the Caribbean (UN),,1961,,225927488, +Latin America and the Caribbean (UN),,1962,,232266368, +Latin America and the Caribbean (UN),,1963,,238750448, +Latin America and the Caribbean (UN),,1964,,245366832, +Latin America and the Caribbean (UN),,1965,,252093552, +Latin America and the Caribbean (UN),,1966,,258880176, +Latin America and the Caribbean (UN),,1967,,265713488, +Latin America and the Caribbean (UN),,1968,,272595808, +Latin America and the Caribbean (UN),,1969,,279527936, +Latin America and the Caribbean (UN),,1970,,286525536, +Latin America and the Caribbean (UN),,1971,,293609376, +Latin America and the Caribbean (UN),,1972,,300799296, +Latin America and the Caribbean (UN),,1973,,308110208, 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(UN),,1991,,450839616, +Latin America and the Caribbean (UN),,1992,,459026880, +Latin America and the Caribbean (UN),,1993,,467183200, +Latin America and the Caribbean (UN),,1994,,475330400, +Latin America and the Caribbean (UN),,1995,,483431616, +Latin America and the Caribbean (UN),,1996,,491423968, +Latin America and the Caribbean (UN),,1997,,499347552, +Latin America and the Caribbean (UN),,1998,,507199392, +Latin America and the Caribbean (UN),,1999,,514937056, +Latin America and the Caribbean (UN),,2000,,522512128, +Latin America and the Caribbean (UN),,2001,,529912672, +Latin America and the Caribbean (UN),,2002,,537159488, +Latin America and the Caribbean (UN),,2003,,544202048, +Latin America and the Caribbean (UN),,2004,,551122112, +Latin America and the Caribbean (UN),,2005,,557964032, +Latin America and the Caribbean (UN),,2006,,564664768, +Latin America and the Caribbean (UN),,2007,,571246592, +Latin America and the Caribbean (UN),,2008,,577692608, +Latin America and the Caribbean (UN),,2009,,584072320, +Latin America and the Caribbean (UN),,2010,,590547008, +Latin America and the Caribbean (UN),,2011,,597179840, +Latin America and the Caribbean (UN),,2012,,603818816, +Latin America and the Caribbean (UN),,2013,,610323392, +Latin America and the Caribbean (UN),,2014,,616734080, +Latin America and the Caribbean (UN),,2015,,623076480, +Latin America and the Caribbean (UN),,2016,,629340992, +Latin America and the Caribbean (UN),,2017,,635448192, +Latin America and the Caribbean (UN),,2018,,641303744, +Latin America and the Caribbean (UN),,2019,,646846784, +Latin America and the Caribbean (UN),,2020,,651836096, +Latin America and the Caribbean (UN),,2021,,656098112, +Latvia,LVA,1950,,1922524, +Latvia,LVA,1951,,1944999, +Latvia,LVA,1952,,1967170, +Latvia,LVA,1953,,1988469, +Latvia,LVA,1954,,2010067, +Latvia,LVA,1955,,2032506, +Latvia,LVA,1956,,2054645, +Latvia,LVA,1957,,2077358, +Latvia,LVA,1958,,2101587, +Latvia,LVA,1959,,2127087, 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(SDG),,2007,9.968896,, +Least developed countries (SDG),,2008,9.653351,, +Least developed countries (SDG),,2009,9.163666,, +Least developed countries (SDG),,2010,8.859962,, +Least developed countries (SDG),,2011,8.486139,, +Least developed countries (SDG),,2012,8.175298,, +Least developed countries (SDG),,2013,7.872901,, +Least developed countries (SDG),,2014,7.6034727,, +Least developed countries (SDG),,2015,7.3632035,, +Least developed countries (SDG),,2016,7.1109314,, +Least developed countries (SDG),,2017,6.8933783,, +Least developed countries (SDG),,2018,6.689721,, +Least developed countries (SDG),,2019,6.487957,, +Least developed countries (SDG),,2020,6.2954245,, +Least developed countries (SDG),,2021,6.124121,, +Lebanon,LBN,1950,,1349596, +Lebanon,LBN,1951,,1388220, +Lebanon,LBN,1952,,1428098, +Lebanon,LBN,1953,,1469433, +Lebanon,LBN,1954,,1512267, +Lebanon,LBN,1955,,1556332, +Lebanon,LBN,1956,8.634417,1601858, +Lebanon,LBN,1957,8.341284,1649154, 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+Liechtenstein,LIE,2006,,34914, +Liechtenstein,LIE,2007,,35180, +Liechtenstein,LIE,2008,,35425, +Liechtenstein,LIE,2009,,35699, +Liechtenstein,LIE,2010,,35948, +Liechtenstein,LIE,2011,,36213, +Liechtenstein,LIE,2012,,36526, +Liechtenstein,LIE,2013,,36836, +Liechtenstein,LIE,2014,,37120, +Liechtenstein,LIE,2015,,37378,Europe +Liechtenstein,LIE,2016,,37630, +Liechtenstein,LIE,2017,,37916, +Liechtenstein,LIE,2018,,38205, +Liechtenstein,LIE,2019,,38500, +Liechtenstein,LIE,2020,,38783, +Liechtenstein,LIE,2021,,39058, +Lithuania,LTU,1950,,2564806, +Lithuania,LTU,1951,,2589209, +Lithuania,LTU,1952,,2615048, +Lithuania,LTU,1953,,2641914, +Lithuania,LTU,1954,,2669865, +Lithuania,LTU,1955,,2699055, +Lithuania,LTU,1956,,2729754, +Lithuania,LTU,1957,,2762990, +Lithuania,LTU,1958,,2799009, +Lithuania,LTU,1959,,2836511, +Lithuania,LTU,1960,,2875686, +Lithuania,LTU,1961,,2915636, +Lithuania,LTU,1962,,2952463, +Lithuania,LTU,1963,,2986535, +Lithuania,LTU,1964,,3020459, +Lithuania,LTU,1965,,3052981, 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+Low-income countries,,1969,,173777968, +Low-income countries,,1970,,178354049, +Low-income countries,,1971,,183027534, +Low-income countries,,1972,,187707196, +Low-income countries,,1973,,192522076, +Low-income countries,,1974,,197579060, +Low-income countries,,1975,,202766588, +Low-income countries,,1976,,208058587, +Low-income countries,,1977,,213322704, +Low-income countries,,1978,,219077893, +Low-income countries,,1979,,225116202, +Low-income countries,,1980,,230070428, +Low-income countries,,1981,,234481298, +Low-income countries,,1982,,239979172, +Low-income countries,,1983,,246279320, +Low-income countries,,1984,,252850490, +Low-income countries,,1985,,259600786, +Low-income countries,,1986,,266329993, +Low-income countries,,1987,,273290467, +Low-income countries,,1988,,280325878, +Low-income countries,,1989,,287707539, +Low-income countries,,1990,18.535782,295113902, +Low-income countries,,1991,18.231812,302719388, +Low-income countries,,1992,17.937098,311829848, +Low-income countries,,1993,17.661835,321719678, +Low-income countries,,1994,17.598658,331110172, +Low-income countries,,1995,16.897942,340649422, +Low-income countries,,1996,16.488945,350714341, +Low-income countries,,1997,16.084986,360598492, +Low-income countries,,1998,15.6386,370450935, +Low-income countries,,1999,15.143468,380754981, +Low-income countries,,2000,14.601288,390930582, +Low-income countries,,2001,14.022684,401370112, +Low-income countries,,2002,13.433449,413349829, +Low-income countries,,2003,12.851176,425985207, +Low-income countries,,2004,12.269895,438315500, +Low-income countries,,2005,11.724727,451026810, +Low-income countries,,2006,11.219399,464643750, +Low-income countries,,2007,10.723508,478557950, +Low-income countries,,2008,10.255262,492420651, +Low-income countries,,2009,9.844627,506630550, +Low-income countries,,2010,9.480949,521168124, +Low-income countries,,2011,9.146673,536034115, +Low-income countries,,2012,8.874623,550791385, +Low-income countries,,2013,8.592492,564891338, +Low-income countries,,2014,8.33703,579035766, +Low-income countries,,2015,8.084887,593799102, +Low-income countries,,2016,7.820256,609614594, +Low-income countries,,2017,7.5669975,626187698, +Low-income countries,,2018,7.3556795,643420791, +Low-income countries,,2019,7.1352477,661633048, +Low-income countries,,2020,6.9232216,680258922, +Low-income countries,,2021,6.7406783,698782037, +Lower-middle-income countries,,1950,,789659061, +Lower-middle-income countries,,1951,,806659258, +Lower-middle-income countries,,1952,,824322739, +Lower-middle-income countries,,1953,,842609008, +Lower-middle-income countries,,1954,,861652153, +Lower-middle-income countries,,1955,,881619584, +Lower-middle-income countries,,1956,,902303202, +Lower-middle-income countries,,1957,,923719371, +Lower-middle-income countries,,1958,,945638200, +Lower-middle-income countries,,1959,,968358237, +Lower-middle-income countries,,1960,,992041992, +Lower-middle-income countries,,1961,,1016193166, +Lower-middle-income countries,,1962,,1040961789, +Lower-middle-income countries,,1963,,1066456125, +Lower-middle-income countries,,1964,,1092568923, +Lower-middle-income countries,,1965,,1118650842, +Lower-middle-income countries,,1966,,1144744120, +Lower-middle-income countries,,1967,,1171564606, +Lower-middle-income countries,,1968,,1199280293, +Lower-middle-income countries,,1969,,1227808434, +Lower-middle-income countries,,1970,,1256736434, +Lower-middle-income countries,,1971,,1285457753, +Lower-middle-income countries,,1972,,1315146462, +Lower-middle-income countries,,1973,,1346554674, +Lower-middle-income countries,,1974,,1378703081, +Lower-middle-income countries,,1975,,1411532827, +Lower-middle-income countries,,1976,,1444952534, +Lower-middle-income countries,,1977,,1479373685, +Lower-middle-income countries,,1978,,1514934199, +Lower-middle-income countries,,1979,,1551698522, +Lower-middle-income countries,,1980,,1590635339, +Lower-middle-income countries,,1981,,1631782844, +Lower-middle-income countries,,1982,,1673454338, +Lower-middle-income countries,,1983,,1715002716, +Lower-middle-income countries,,1984,,1757033374, +Lower-middle-income countries,,1985,,1799806499, +Lower-middle-income countries,,1986,,1843201184, +Lower-middle-income countries,,1987,,1886925482, +Lower-middle-income countries,,1988,,1930937086, +Lower-middle-income countries,,1989,,1975269229, +Lower-middle-income countries,,1990,11.994043,2020653165, +Lower-middle-income countries,,1991,11.712127,2066065851, +Lower-middle-income countries,,1992,11.451601,2110223537, +Lower-middle-income countries,,1993,11.210153,2153745205, +Lower-middle-income countries,,1994,10.98443,2197553286, +Lower-middle-income countries,,1995,10.747474,2241781730, +Lower-middle-income countries,,1996,10.493343,2285832700, +Lower-middle-income countries,,1997,10.211586,2330207743, +Lower-middle-income countries,,1998,9.918276,2375067059, +Lower-middle-income countries,,1999,9.598774,2419995017, +Lower-middle-income countries,,2000,9.280966,2465592691, +Lower-middle-income countries,,2001,8.95323,2511799308, +Lower-middle-income countries,,2002,8.627813,2556960325, +Lower-middle-income countries,,2003,8.311066,2601596128, +Lower-middle-income countries,,2004,8.051492,2646723113, +Lower-middle-income countries,,2005,7.7046685,2691406988, +Lower-middle-income countries,,2006,7.4158845,2735471783, +Lower-middle-income countries,,2007,7.1323547,2779588288, +Lower-middle-income countries,,2008,6.900637,2823712552, +Lower-middle-income countries,,2009,6.60363,2868312458, +Lower-middle-income countries,,2010,6.373421,2914138506, +Lower-middle-income countries,,2011,6.1196623,2960590210, +Lower-middle-income countries,,2012,5.887856,3006968165, +Lower-middle-income countries,,2013,5.6685896,3053128864, +Lower-middle-income countries,,2014,5.4669447,3099123675, +Lower-middle-income countries,,2015,5.282653,3144964232, +Lower-middle-income countries,,2016,5.1134605,3190920278, +Lower-middle-income countries,,2017,4.9536304,3236754793, +Lower-middle-income countries,,2018,4.7926335,3281847839, +Lower-middle-income countries,,2019,4.6482167,3326051409, +Lower-middle-income countries,,2020,4.5076694,3369707355, +Lower-middle-income countries,,2021,4.3651476,3411037255, +Luxembourg,LUX,1950,6.227043,296589, +Luxembourg,LUX,1951,5.9628115,298220, +Luxembourg,LUX,1952,5.7034087,300064, +Luxembourg,LUX,1953,5.4453745,301764, +Luxembourg,LUX,1954,5.186075,303514, +Luxembourg,LUX,1955,4.929719,305521, +Luxembourg,LUX,1956,4.680117,307264, +Luxembourg,LUX,1957,4.438073,308849, +Luxembourg,LUX,1958,4.206378,310587, +Luxembourg,LUX,1959,3.9839592,312509, +Luxembourg,LUX,1960,3.769156,314170, +Luxembourg,LUX,1961,3.566333,316593, +Luxembourg,LUX,1962,3.376307,319858, +Luxembourg,LUX,1963,3.1987426,323015, +Luxembourg,LUX,1964,3.0343783,326372, +Luxembourg,LUX,1965,2.8827596,329793, +Luxembourg,LUX,1966,2.7399373,333135, 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+Niger,NER,1970,33.08526,4669707, +Niger,NER,1971,33.531937,4797069, +Niger,NER,1972,33.907978,4926325, +Niger,NER,1973,34.21237,5059388, +Niger,NER,1974,34.31592,5196585, +Niger,NER,1975,34.216393,5339497, +Niger,NER,1976,33.9414,5488666, +Niger,NER,1977,33.489784,5644706, +Niger,NER,1978,32.975704,5810358, +Niger,NER,1979,32.564064,5987157, +Niger,NER,1980,32.30023,6173179, +Niger,NER,1981,32.25198,6366270, +Niger,NER,1982,32.454647,6564456, +Niger,NER,1983,32.832966,6766616, +Niger,NER,1984,33.283672,6973850, +Niger,NER,1985,33.687874,7187622, +Niger,NER,1986,33.986538,7408370, +Niger,NER,1987,34.091507,7636591, +Niger,NER,1988,33.967083,7872257, +Niger,NER,1989,33.64924,8116080, +Niger,NER,1990,33.162117,8370650, +Niger,NER,1991,32.501938,8634640, +Niger,NER,1992,31.66364,8907642, +Niger,NER,1993,30.64021,9193081, +Niger,NER,1994,29.438747,9493322, +Niger,NER,1995,28.127686,9813920, +Niger,NER,1996,26.860249,10149939, +Niger,NER,1997,25.736517,10494203, +Niger,NER,1998,24.719961,10854919, +Niger,NER,1999,23.786411,11231469, +Niger,NER,2000,22.850826,11622673, +Niger,NER,2001,21.825388,12031431, +Niger,NER,2002,20.686996,12456520, +Niger,NER,2003,19.502602,12900793, +Niger,NER,2004,18.31322,13366889, +Niger,NER,2005,17.183369,13855225, +Niger,NER,2006,16.162998,14365171, +Niger,NER,2007,15.275571,14897877, +Niger,NER,2008,14.506467,15455181, +Niger,NER,2009,13.857821,16037917, +Niger,NER,2010,13.328829,16647545, +Niger,NER,2011,12.913847,17283118, +Niger,NER,2012,12.597533,17954410, +Niger,NER,2013,12.34389,18653204, +Niger,NER,2014,12.162797,19372012, +Niger,NER,2015,12.016442,20128132,Africa +Niger,NER,2016,11.914344,20921748, +Niger,NER,2017,11.836512,21737924, +Niger,NER,2018,11.766191,22577060, +Niger,NER,2019,11.702052,23443400, +Niger,NER,2020,11.631723,24333644, +Niger,NER,2021,11.515188,25252722, +Nigeria,NGA,1950,,37189376, +Nigeria,NGA,1951,,37903172, +Nigeria,NGA,1952,,38624424, +Nigeria,NGA,1953,,39347724, +Nigeria,NGA,1954,,40082480, +Nigeria,NGA,1955,,40839224, +Nigeria,NGA,1956,,41616580, +Nigeria,NGA,1957,,42413364, +Nigeria,NGA,1958,,43214756, +Nigeria,NGA,1959,,44044232, +Nigeria,NGA,1960,,44928336, +Nigeria,NGA,1961,,45855508, +Nigeria,NGA,1962,,46821856, +Nigeria,NGA,1963,,47822572, +Nigeria,NGA,1964,32.519665,48856336, +Nigeria,NGA,1965,31.834644,49925800, +Nigeria,NGA,1966,31.146511,51020304, +Nigeria,NGA,1967,30.48179,52106684, +Nigeria,NGA,1968,29.796503,53210124, +Nigeria,NGA,1969,29.102808,54360756, +Nigeria,NGA,1970,28.367441,55569268, +Nigeria,NGA,1971,27.603916,56837616, +Nigeria,NGA,1972,26.769947,58173832, +Nigeria,NGA,1973,25.93804,59605448, +Nigeria,NGA,1974,25.109308,61157940, +Nigeria,NGA,1975,24.295431,62851316, +Nigeria,NGA,1976,23.539665,64658312, +Nigeria,NGA,1977,22.84126,66589656, +Nigeria,NGA,1978,22.222874,68633344, +Nigeria,NGA,1979,21.690662,70750312, +Nigeria,NGA,1980,21.256535,72951440, +Nigeria,NGA,1981,20.941107,75175392, +Nigeria,NGA,1982,20.739826,77388072, +Nigeria,NGA,1983,20.65575,79351592, +Nigeria,NGA,1984,20.657,81337552, +Nigeria,NGA,1985,20.720413,83585256, +Nigeria,NGA,1986,20.808899,85804192, +Nigeria,NGA,1987,20.896166,88044192, +Nigeria,NGA,1988,20.94149,90351464, +Nigeria,NGA,1989,20.955812,92744064, +Nigeria,NGA,1990,20.933117,95214256, +Nigeria,NGA,1991,20.878344,97685360, +Nigeria,NGA,1992,20.807688,100182048, +Nigeria,NGA,1993,20.71595,102775472, +Nigeria,NGA,1994,20.570477,105456120, +Nigeria,NGA,1995,20.374355,108187616, +Nigeria,NGA,1996,20.099087,110956184, +Nigeria,NGA,1997,19.724453,113791184, +Nigeria,NGA,1998,19.278797,116690528, +Nigeria,NGA,1999,18.771532,119695568, +Nigeria,NGA,2000,18.243053,122851984, +Nigeria,NGA,2001,17.696613,126152680, +Nigeria,NGA,2002,17.142738,129583032, +Nigeria,NGA,2003,16.581188,133119808, +Nigeria,NGA,2004,16.03806,136756848, +Nigeria,NGA,2005,15.510857,140490720, +Nigeria,NGA,2006,15.0238085,144329760, +Nigeria,NGA,2007,14.578901,148294032, +Nigeria,NGA,2008,14.190722,152382512, +Nigeria,NGA,2009,13.850962,156595744, +Nigeria,NGA,2010,13.571821,160952864, +Nigeria,NGA,2011,13.341574,165463744, +Nigeria,NGA,2012,13.135157,170075936, +Nigeria,NGA,2013,12.962277,174726128, +Nigeria,NGA,2014,12.802221,179379024, +Nigeria,NGA,2015,12.641256,183995776,Africa +Nigeria,NGA,2016,12.471412,188666928, +Nigeria,NGA,2017,12.239011,193495904, +Nigeria,NGA,2018,11.9566555,198387616, +Nigeria,NGA,2019,11.680171,203304496, +Nigeria,NGA,2020,11.381948,208327408, +Nigeria,NGA,2021,11.081927,213401328, +Niue,NIU,1950,13.288869,4509, +Niue,NIU,1951,13.027336,4581, +Niue,NIU,1952,12.723591,4643, +Niue,NIU,1953,12.41095,4660, +Niue,NIU,1954,12.092558,4669, +Niue,NIU,1955,11.761922,4695, +Niue,NIU,1956,11.405727,4727, +Niue,NIU,1957,11.03205,4776, +Niue,NIU,1958,10.65697,4825, +Niue,NIU,1959,10.252732,4868, +Niue,NIU,1960,9.849869,4897, +Niue,NIU,1961,9.433067,4935, +Niue,NIU,1962,9.012778,5010, +Niue,NIU,1963,8.595836,5109, +Niue,NIU,1964,8.166267,5222, +Niue,NIU,1965,7.7549033,5336, +Niue,NIU,1966,7.3418617,5433, +Niue,NIU,1967,6.933066,5445, +Niue,NIU,1968,6.5282173,5381, +Niue,NIU,1969,6.1281195,5299, +Niue,NIU,1970,5.736554,5206, +Niue,NIU,1971,5.357118,5101, +Niue,NIU,1972,4.9899855,4945, +Niue,NIU,1973,4.6289473,4742, +Niue,NIU,1974,4.288781,4518, +Niue,NIU,1975,3.9625957,4293, +Niue,NIU,1976,3.650341,4074, +Niue,NIU,1977,3.370591,3935, +Niue,NIU,1978,3.1301463,3853, +Niue,NIU,1979,2.9275568,3757, +Niue,NIU,1980,2.760682,3659, +Niue,NIU,1981,2.6340165,3553, +Niue,NIU,1982,2.5371902,3419, +Niue,NIU,1983,2.4703298,3246, +Niue,NIU,1984,2.4304125,3059, +Niue,NIU,1985,2.4122665,2870, +Niue,NIU,1986,2.4139895,2704, +Niue,NIU,1987,2.4259706,2627, +Niue,NIU,1988,2.4447927,2607, +Niue,NIU,1989,2.4711683,2587, +Niue,NIU,1990,2.4999614,2560, +Niue,NIU,1991,2.537128,2520, +Niue,NIU,1992,2.5836992,2483, +Niue,NIU,1993,2.639091,2439, +Niue,NIU,1994,2.7038019,2396, +Niue,NIU,1995,2.784401,2362, +Niue,NIU,1996,2.8714087,2323, +Niue,NIU,1997,2.9588923,2282, +Niue,NIU,1998,3.0514674,2230, +Niue,NIU,1999,3.139096,2167, +Niue,NIU,2000,3.2306504,2096, +Niue,NIU,2001,3.3239903,2038, +Niue,NIU,2002,3.415144,1990, +Niue,NIU,2003,3.4914823,1952, +Niue,NIU,2004,3.5472748,1914, +Niue,NIU,2005,3.588494,1881, +Niue,NIU,2006,3.5807896,1857, +Niue,NIU,2007,3.5553613,1846, +Niue,NIU,2008,3.5061214,1847, +Niue,NIU,2009,3.4372406,1841, +Niue,NIU,2010,3.3683467,1838, +Niue,NIU,2011,3.2825532,1833, +Niue,NIU,2012,3.1875093,1833, +Niue,NIU,2013,3.0901337,1846, +Niue,NIU,2014,2.9991052,1858, +Niue,NIU,2015,2.901957,1871,Oceania +Niue,NIU,2016,2.8228111,1883, +Niue,NIU,2017,2.7405598,1891, +Niue,NIU,2018,2.6591682,1907, +Niue,NIU,2019,2.5758643,1943, +Niue,NIU,2020,2.4964185,1963, +Niue,NIU,2021,2.4131455,1957, +Norfolk Island,NFK,2015,,,Oceania +North America,,1950,,217078493, +North America,,1951,,220921951, +North America,,1952,,225040618, +North America,,1953,,229381691, +North America,,1954,,234024926, +North America,,1955,,238891598, +North America,,1956,,243833832, +North America,,1957,,248844412, +North America,,1958,,254195090, +North America,,1959,,259556906, +North America,,1960,,264916376, +North America,,1961,,269992478, +North America,,1962,,275281702, +North America,,1963,,280538351, +North America,,1964,,285767926, +North America,,1965,,290870105, +North America,,1966,,295724864, +North America,,1967,,300431421, +North America,,1968,,305159077, +North America,,1969,,310053134, +North America,,1970,,315691695, +North America,,1971,,321049543, +North America,,1972,,326320039, +North America,,1973,,331321157, +North America,,1974,,336273223, +North America,,1975,,341300296, +North America,,1976,,346309935, +North America,,1977,,351506528, +North America,,1978,,357016142, +North America,,1979,,362654436, +North America,,1980,,368574942, +North America,,1981,,373589053, +North America,,1982,,378615781, +North America,,1983,,383734903, +North America,,1984,,388847666, +North America,,1985,,393949236, +North America,,1986,,399045023, +North America,,1987,,404153729, +North America,,1988,,409453275, +North America,,1989,,415061982, +North America,,1990,1.0948544,421575397, +North America,,1991,1.0607493,427934509, +North America,,1992,1.0248927,434703257, +North America,,1993,0.989076,441450384, +North America,,1994,0.95511305,448069901, +North America,,1995,0.9240473,454557639, +North America,,1996,0.8971143,460925637, +North America,,1997,0.87484026,467334865, +North America,,1998,0.8563946,473719801, +North America,,1999,0.8408658,479976626, +North America,,2000,0.8283968,486364446, +North America,,2001,0.81710255,492319208, +North America,,2002,0.80766964,498067243, +North America,,2003,0.7993182,503641588, +North America,,2004,0.7911564,509273976, +North America,,2005,0.78229225,514942529, +North America,,2006,0.7723876,520586895, +North America,,2007,0.7605973,526315123, +North America,,2008,0.7475508,532006442, +North America,,2009,0.7337916,537567761, +North America,,2010,0.72008383,543052879, +North America,,2011,0.7070733,548684151, +North America,,2012,0.69521075,554435782, +North America,,2013,0.6848133,560056144, +North America,,2014,0.67558044,565486685, +North America,,2015,0.66722155,570726015, +North America,,2016,0.6589674,576008328, +North America,,2017,0.6503378,581258143, +North America,,2018,0.6416012,585864285, +North America,,2019,0.632825,590497015, +North America,,2020,0.62337565,594263186, +North America,,2021,0.61332315,597123384, +North Korea,PRK,1950,,11103386, +North Korea,PRK,1951,,10557969, +North Korea,PRK,1952,,10228064, +North Korea,PRK,1953,,10147606, +North Korea,PRK,1954,,10206243, +North Korea,PRK,1955,,10360274, +North Korea,PRK,1956,,10552186, +North Korea,PRK,1957,,10802110, +North Korea,PRK,1958,,11106861, +North Korea,PRK,1959,,11391383, +North Korea,PRK,1960,,11655668, +North Korea,PRK,1961,,11916512, +North Korea,PRK,1962,,12180506, +North Korea,PRK,1963,,12475236, +North Korea,PRK,1964,,12787529, +North Korea,PRK,1965,,13105948, +North Korea,PRK,1966,,13444052, +North Korea,PRK,1967,,13821411, +North Korea,PRK,1968,,14216305, +North Korea,PRK,1969,,14605482, +North Korea,PRK,1970,,14996881, +North Korea,PRK,1971,,15381625, +North Korea,PRK,1972,,15768368, +North Korea,PRK,1973,,16128127, +North Korea,PRK,1974,,16437012, +North Korea,PRK,1975,,16723254, +North Korea,PRK,1976,,16987170, +North Korea,PRK,1977,,17231392, +North Korea,PRK,1978,,17472096, +North Korea,PRK,1979,,17719176, +North Korea,PRK,1980,,17973654, +North Korea,PRK,1981,,18235208, +North Korea,PRK,1982,,18502090, +North Korea,PRK,1983,,18771366, +North Korea,PRK,1984,,19045158, +North Korea,PRK,1985,3.5099998,19325494, +North Korea,PRK,1986,3.47,19613038, +North Korea,PRK,1987,3.52,19903900, +North Korea,PRK,1988,3.6799998,20197470, +North Korea,PRK,1989,3.95,20494968, +North Korea,PRK,1990,4.34,20799522, +North Korea,PRK,1991,4.8599997,21115534, +North Korea,PRK,1992,5.48,21439992, +North Korea,PRK,1993,6.13,21773020, +North Korea,PRK,1994,6.7699995,22105230, +North Korea,PRK,1995,7.28,22385538, +North Korea,PRK,1996,7.5699997,22614348, +North Korea,PRK,1997,7.5699997,22827378, +North Korea,PRK,1998,7.29,23023642, +North Korea,PRK,1999,6.7400002,23204504, +North Korea,PRK,2000,6,23367064, +North Korea,PRK,2001,5.21,23512524, +North Korea,PRK,2002,4.4700003,23638414, +North Korea,PRK,2003,3.89,23781706, +North Korea,PRK,2004,3.5,23948936, +North Korea,PRK,2005,3.3,24100980, +North Korea,PRK,2006,3.2400002,24235764, +North Korea,PRK,2007,3.22,24356508, +North Korea,PRK,2008,3.1799998,24469050, +North Korea,PRK,2009,3.1,24581508, +North Korea,PRK,2010,2.95,24686436, +North Korea,PRK,2011,2.77,24783790, +North Korea,PRK,2012,2.5700002,24887770, +North Korea,PRK,2013,2.4,25001824, +North Korea,PRK,2014,2.24,25126140, +North Korea,PRK,2015,2.1100001,25258012,Asia +North Korea,PRK,2016,2,25389614, +North Korea,PRK,2017,1.9,25516318, +North Korea,PRK,2018,1.82,25638152, +North Korea,PRK,2019,1.7299999,25755442, +North Korea,PRK,2020,1.65,25867474, +North Korea,PRK,2021,1.54,25971910, +North Macedonia,MKD,1950,,1251437, +North Macedonia,MKD,1951,,1282535, +North Macedonia,MKD,1952,,1311489, +North Macedonia,MKD,1953,,1339586, +North Macedonia,MKD,1954,,1361963, +North Macedonia,MKD,1955,,1379146, +North Macedonia,MKD,1956,,1395504, +North Macedonia,MKD,1957,,1411822, +North Macedonia,MKD,1958,,1428609, +North Macedonia,MKD,1959,,1445691, +North Macedonia,MKD,1960,,1462373, +North Macedonia,MKD,1961,,1481115, +North Macedonia,MKD,1962,,1497344, +North Macedonia,MKD,1963,,1511858, +North Macedonia,MKD,1964,,1529508, +North Macedonia,MKD,1965,,1547983, +North Macedonia,MKD,1966,,1567712, +North Macedonia,MKD,1967,,1588644, +North Macedonia,MKD,1968,,1610542, +North Macedonia,MKD,1969,,1633508, +North Macedonia,MKD,1970,,1656789, +North Macedonia,MKD,1971,,1679018, +North Macedonia,MKD,1972,,1701503, +North Macedonia,MKD,1973,,1725235, +North Macedonia,MKD,1974,,1749334, +North Macedonia,MKD,1975,,1774013, +North Macedonia,MKD,1976,,1799338, +North Macedonia,MKD,1977,,1825303, +North Macedonia,MKD,1978,,1851984, +North Macedonia,MKD,1979,,1879359, +North Macedonia,MKD,1980,,1907035, +North Macedonia,MKD,1981,5.7985907,1935204, +North Macedonia,MKD,1982,5.5362988,1958278, +North Macedonia,MKD,1983,5.246922,1974443, +North Macedonia,MKD,1984,5.060255,1988692, +North Macedonia,MKD,1985,4.99543,2001507, +North Macedonia,MKD,1986,4.916203,2012925, +North Macedonia,MKD,1987,4.692882,2023098, +North Macedonia,MKD,1988,4.35407,2031937, +North Macedonia,MKD,1989,3.998735,2039429, +North Macedonia,MKD,1990,3.6899312,2044181, +North Macedonia,MKD,1991,3.4553227,2036691, +North Macedonia,MKD,1992,3.3033538,2018024, +North Macedonia,MKD,1993,3.145066,1996896, +North Macedonia,MKD,1994,2.8531604,1982458, +North Macedonia,MKD,1995,2.44827,1983971, +North Macedonia,MKD,1996,2.1059241,1994230, +North Macedonia,MKD,1997,1.9225447,2004240, +North Macedonia,MKD,1998,1.8343853,2012844, +North Macedonia,MKD,1999,1.7360685,2023781, +North Macedonia,MKD,2000,1.5950638,2037940, +North Macedonia,MKD,2001,1.4769386,2053391, +North Macedonia,MKD,2002,1.4327215,2064739, +North Macedonia,MKD,2003,1.4361846,2071150, +North Macedonia,MKD,2004,1.4257194,2075879, +North Macedonia,MKD,2005,1.3723241,2078667, +North Macedonia,MKD,2006,1.2981876,2081081, +North Macedonia,MKD,2007,1.2282795,2083494, +North Macedonia,MKD,2008,1.1656939,2086228, +North Macedonia,MKD,2009,1.0995312,2089793, +North Macedonia,MKD,2010,1.035335,2093828, +North Macedonia,MKD,2011,1.0000293,2097110, +North Macedonia,MKD,2012,1.0135704,2099486, +North Macedonia,MKD,2013,1.063844,2102220, +North Macedonia,MKD,2014,1.1163523,2105293, +North Macedonia,MKD,2015,1.1350324,2107965,Europe +North Macedonia,MKD,2016,1.0877999,2110194, +North Macedonia,MKD,2017,0.9621669,2111982, +North Macedonia,MKD,2018,0.8080589,2113497, +North Macedonia,MKD,2019,0.67927945,2114175, +North Macedonia,MKD,2020,0.5930749,2111078, +North Macedonia,MKD,2021,0.53106034,2103329, +Northern Africa (SDG),,1990,8.567749,, +Northern Africa (SDG),,1991,8.286727,, +Northern Africa (SDG),,1992,8.004869,, +Northern Africa (SDG),,1993,7.7223253,, +Northern Africa (SDG),,1994,7.444505,, +Northern Africa (SDG),,1995,7.175023,, +Northern Africa (SDG),,1996,6.914056,, +Northern Africa (SDG),,1997,6.6477213,, +Northern Africa (SDG),,1998,6.380161,, +Northern Africa (SDG),,1999,6.1223207,, +Northern Africa (SDG),,2000,5.8739443,, +Northern Africa (SDG),,2001,5.6273866,, +Northern Africa (SDG),,2002,5.3904634,, +Northern Africa (SDG),,2003,5.1566877,, +Northern Africa (SDG),,2004,4.9308305,, +Northern Africa (SDG),,2005,4.718395,, +Northern Africa (SDG),,2006,4.5327673,, +Northern Africa (SDG),,2007,4.369873,, +Northern Africa (SDG),,2008,4.2129335,, +Northern Africa (SDG),,2009,4.0628366,, +Northern Africa (SDG),,2010,3.907627,, +Northern Africa (SDG),,2011,3.764645,, +Northern Africa (SDG),,2012,3.612992,, +Northern Africa (SDG),,2013,3.4849076,, +Northern Africa (SDG),,2014,3.3736863,, +Northern Africa (SDG),,2015,3.2812111,, +Northern Africa (SDG),,2016,3.2110028,, +Northern Africa (SDG),,2017,3.1390655,, +Northern Africa (SDG),,2018,3.0636137,, +Northern Africa (SDG),,2019,2.9935384,, +Northern Africa (SDG),,2020,2.917213,, +Northern Africa (SDG),,2021,2.842005,, +Northern America (SDG),,1990,1.0948544,, +Northern America (SDG),,1991,1.0607493,, +Northern America (SDG),,1992,1.0248927,, +Northern America (SDG),,1993,0.989076,, +Northern America (SDG),,1994,0.95511305,, +Northern America (SDG),,1995,0.9240473,, +Northern America (SDG),,1996,0.8971143,, +Northern America (SDG),,1997,0.87484026,, +Northern America (SDG),,1998,0.8563946,, +Northern America (SDG),,1999,0.8408658,, +Northern America (SDG),,2000,0.8283968,, +Northern America (SDG),,2001,0.81710255,, +Northern America (SDG),,2002,0.80766964,, +Northern America (SDG),,2003,0.7993182,, +Northern America (SDG),,2004,0.7911564,, +Northern America (SDG),,2005,0.78229225,, +Northern America (SDG),,2006,0.7723876,, +Northern America (SDG),,2007,0.7605973,, +Northern America (SDG),,2008,0.7475508,, +Northern America (SDG),,2009,0.7337916,, +Northern America (SDG),,2010,0.72008383,, +Northern America (SDG),,2011,0.7070733,, +Northern America (SDG),,2012,0.69521075,, +Northern America (SDG),,2013,0.6848133,, +Northern America (SDG),,2014,0.67558044,, +Northern America (SDG),,2015,0.66722155,, +Northern America (SDG),,2016,0.6589674,, +Northern America (SDG),,2017,0.6503378,, +Northern America (SDG),,2018,0.6416012,, +Northern America (SDG),,2019,0.632825,, +Northern America (SDG),,2020,0.62337565,, +Northern America (SDG),,2021,0.61332315,, +Northern America (UN),,1950,,162089344, +Northern America (UN),,1951,,164749840, +Northern America (UN),,1952,,167494176, +Northern America (UN),,1953,,170420528, +Northern America (UN),,1954,,173598032, +Northern America (UN),,1955,,176940528, +Northern America (UN),,1956,,180302048, +Northern America (UN),,1957,,183672448, +Northern America (UN),,1958,,187335600, +Northern America (UN),,1959,,190926576, +Northern America (UN),,1960,,194177008, +Northern America (UN),,1961,,197442208, +Northern America (UN),,1962,,200623680, +Northern America (UN),,1963,,203706752, +Northern America (UN),,1964,,206691984, +Northern America (UN),,1965,,209482864, +Northern America (UN),,1966,,211979520, +Northern America (UN),,1967,,214286320, +Northern America (UN),,1968,,216573648, +Northern America (UN),,1969,,218993968, +Northern America (UN),,1970,,221865904, +Northern America (UN),,1971,,224901248, +Northern America (UN),,1972,,227566320, +Northern America (UN),,1973,,229923520, +Northern America (UN),,1974,,232198032, +Northern America (UN),,1975,,234519136, +Northern America (UN),,1976,,236821072, +Northern America (UN),,1977,,239261920, +Northern America (UN),,1978,,241953376, +Northern America (UN),,1979,,244791536, +Northern America (UN),,1980,,247761312, +Northern America (UN),,1981,,250580512, +Northern America (UN),,1982,,253214704, +Northern America (UN),,1983,,255863568, +Northern America (UN),,1984,,258482368, +Northern America (UN),,1985,,261105168, +Northern America (UN),,1986,,263744848, +Northern America (UN),,1987,,266406960, +Northern America (UN),,1988,,269220736, +Northern America (UN),,1989,,272317216, +Northern America (UN),,1990,,275860320, +Northern America (UN),,1991,,279695840, +Northern America (UN),,1992,,283643360, +Northern America (UN),,1993,,287568768, +Northern America (UN),,1994,,291376000, +Northern America (UN),,1995,,295071648, +Northern America (UN),,1996,,298700416, +Northern America (UN),,1997,,302400864, +Northern America (UN),,1998,,306102912, +Northern America (UN),,1999,,309704736, +Northern America (UN),,2000,,313205696, +Northern America (UN),,2001,,316597664, +Northern America (UN),,2002,,319797600, +Northern America (UN),,2003,,322854656, +Northern America (UN),,2004,,325985312, +Northern America (UN),,2005,,329184544, +Northern America (UN),,2006,,332410944, +Northern America (UN),,2007,,335731776, +Northern America (UN),,2008,,339039328, +Northern America (UN),,2009,,342231808, +Northern America (UN),,2010,,345272096, +Northern America (UN),,2011,,348326016, +Northern America (UN),,2012,,351468992, +Northern America (UN),,2013,,354564448, +Northern America (UN),,2014,,357563872, +Northern America (UN),,2015,,360464928, +Northern America (UN),,2016,,363448672, +Northern America (UN),,2017,,366470720, +Northern America (UN),,2018,,369300736, +Northern America (UN),,2019,,371967904, +Northern America (UN),,2020,,373956672, +Northern America (UN),,2021,,375278944, +Northern America and Europe (SDG),,1990,1.4199555,, +Northern America and Europe (SDG),,1991,1.3662304,, +Northern America and Europe (SDG),,1992,1.3212297,, +Northern America and Europe (SDG),,1993,1.2741363,, +Northern America and Europe (SDG),,1994,1.2301387,, +Northern America and Europe (SDG),,1995,1.1812885,, +Northern America and Europe (SDG),,1996,1.1348652,, +Northern America and Europe (SDG),,1997,1.0910214,, +Northern America and Europe (SDG),,1998,1.0499583,, +Northern America and Europe (SDG),,1999,1.0045536,, +Northern America and Europe (SDG),,2000,0.9670364,, +Northern America and Europe (SDG),,2001,0.9374,, +Northern America and Europe (SDG),,2002,0.9102479,, +Northern America and Europe (SDG),,2003,0.8806778,, +Northern America and Europe (SDG),,2004,0.8496872,, +Northern America and Europe (SDG),,2005,0.81772584,, +Northern America and Europe (SDG),,2006,0.78649396,, +Northern America and Europe (SDG),,2007,0.7592959,, +Northern America and Europe (SDG),,2008,0.7350024,, +Northern America and Europe (SDG),,2009,0.71344954,, +Northern America and Europe (SDG),,2010,0.6917237,, +Northern America and Europe (SDG),,2011,0.6739454,, +Northern America and Europe (SDG),,2012,0.65924454,, +Northern America and Europe (SDG),,2013,0.64379,, +Northern America and Europe (SDG),,2014,0.6257347,, +Northern America and Europe (SDG),,2015,0.6063105,, +Northern America and Europe (SDG),,2016,0.58567375,, +Northern America and Europe (SDG),,2017,0.56546414,, +Northern America and Europe (SDG),,2018,0.5478926,, +Northern America and Europe (SDG),,2019,0.5321437,, +Northern America and Europe (SDG),,2020,0.51778233,, +Northern America and Europe (SDG),,2021,0.50517446,, +Northern Cyprus,OWID_CYN,2015,,,Asia +Northern Mariana Islands,MNP,1950,,7083, +Northern Mariana Islands,MNP,1951,,7151, +Northern Mariana Islands,MNP,1952,,7235, +Northern Mariana Islands,MNP,1953,,7354, +Northern Mariana Islands,MNP,1954,,7492, +Northern Mariana Islands,MNP,1955,,7659, +Northern Mariana Islands,MNP,1956,,7837, +Northern Mariana Islands,MNP,1957,,8027, +Northern Mariana Islands,MNP,1958,,8255, +Northern Mariana Islands,MNP,1959,,8482, +Northern Mariana Islands,MNP,1960,,8721, +Northern Mariana Islands,MNP,1961,,8984, +Northern Mariana Islands,MNP,1962,,9274, +Northern Mariana Islands,MNP,1963,,9584, +Northern Mariana Islands,MNP,1964,,9910, +Northern Mariana Islands,MNP,1965,,10248, +Northern Mariana Islands,MNP,1966,,10597, +Northern Mariana Islands,MNP,1967,,10740, +Northern Mariana Islands,MNP,1968,,10462, +Northern Mariana Islands,MNP,1969,,9973, +Northern Mariana Islands,MNP,1970,,10166, +Northern Mariana Islands,MNP,1971,,11302, +Northern Mariana Islands,MNP,1972,,12754, +Northern Mariana Islands,MNP,1973,,14112, +Northern Mariana Islands,MNP,1974,,14924, +Northern Mariana Islands,MNP,1975,,15291, +Northern Mariana Islands,MNP,1976,,15618, +Northern Mariana Islands,MNP,1977,,15928, +Northern Mariana Islands,MNP,1978,,16212, +Northern Mariana Islands,MNP,1979,,16515, +Northern Mariana Islands,MNP,1980,,17637, +Northern Mariana Islands,MNP,1981,,19925, +Northern Mariana Islands,MNP,1982,,22668, +Northern Mariana Islands,MNP,1983,,25613, +Northern Mariana Islands,MNP,1984,,28760, +Northern Mariana Islands,MNP,1985,,32062, +Northern Mariana Islands,MNP,1986,,35482, +Northern Mariana Islands,MNP,1987,,38947, +Northern Mariana Islands,MNP,1988,,42402, +Northern Mariana Islands,MNP,1989,,45879, +Northern Mariana Islands,MNP,1990,,48021, +Northern Mariana Islands,MNP,1991,,48314, +Northern Mariana Islands,MNP,1992,,48146, +Northern Mariana Islands,MNP,1993,,47965, +Northern Mariana Islands,MNP,1994,,47808, +Northern Mariana Islands,MNP,1995,,48736, +Northern Mariana Islands,MNP,1996,,53387, +Northern Mariana Islands,MNP,1997,,60838, +Northern Mariana Islands,MNP,1998,,68516, +Northern Mariana Islands,MNP,1999,,76256, +Northern Mariana Islands,MNP,2000,,80353, +Northern Mariana Islands,MNP,2001,,79495, +Northern Mariana Islands,MNP,2002,,77177, +Northern Mariana Islands,MNP,2003,,74646, +Northern Mariana Islands,MNP,2004,,71917, +Northern Mariana Islands,MNP,2005,,69045, +Northern Mariana Islands,MNP,2006,,66078, +Northern Mariana Islands,MNP,2007,,63068, +Northern Mariana Islands,MNP,2008,,60047, +Northern Mariana Islands,MNP,2009,,57073, +Northern Mariana Islands,MNP,2010,,54109, +Northern Mariana Islands,MNP,2011,,52542, +Northern Mariana Islands,MNP,2012,,52383, +Northern Mariana Islands,MNP,2013,,52157, +Northern Mariana Islands,MNP,2014,,51878, +Northern Mariana Islands,MNP,2015,,51538,Oceania +Northern Mariana Islands,MNP,2016,,51151, +Northern Mariana Islands,MNP,2017,,50752, +Northern Mariana Islands,MNP,2018,,50328, +Northern Mariana Islands,MNP,2019,,49880, +Northern Mariana Islands,MNP,2020,,49611, +Northern Mariana Islands,MNP,2021,,49503, +Norway,NOR,1950,3.2816665,3281428, +Norway,NOR,1951,3.0834067,3296349, +Norway,NOR,1952,2.9105,3328323, +Norway,NOR,1953,2.7702098,3361502, +Norway,NOR,1954,2.6608536,3394899, +Norway,NOR,1955,2.5780025,3429096, +Norway,NOR,1956,2.514306,3461610, +Norway,NOR,1957,2.456161,3492505, +Norway,NOR,1958,2.3922439,3523483, +Norway,NOR,1959,2.3253212,3553264, +Norway,NOR,1960,2.2620273,3581521, +Norway,NOR,1961,2.2060478,3609997, +Norway,NOR,1962,2.154177,3639139, +Norway,NOR,1963,2.1006072,3666801, +Norway,NOR,1964,2.0413127,3694674, +Norway,NOR,1965,1.9741825,3723510, +Norway,NOR,1966,1.8991143,3753077, +Norway,NOR,1967,1.8236077,3784346, +Norway,NOR,1968,1.7529835,3816299, +Norway,NOR,1969,1.6875836,3847527, +Norway,NOR,1970,1.6242292,3875550, +Norway,NOR,1971,1.5600175,3902912, +Norway,NOR,1972,1.4950615,3933047, +Norway,NOR,1973,1.4300706,3960705, +Norway,NOR,1974,1.3621196,3985381, +Norway,NOR,1975,1.2917196,4007471, +Norway,NOR,1976,1.2200912,4026317, +Norway,NOR,1977,1.151336,4043365, +Norway,NOR,1978,1.090391,4058804, +Norway,NOR,1979,1.0414321,4072652, +Norway,NOR,1980,1.0081415,4085778, +Norway,NOR,1981,0.99198914,4099860, +Norway,NOR,1982,0.98951495,4114936, +Norway,NOR,1983,0.9975274,4128576, +Norway,NOR,1984,1.0109696,4140232, +Norway,NOR,1985,1.0221192,4152640, +Norway,NOR,1986,1.024843,4167465, +Norway,NOR,1987,1.0125674,4187034, +Norway,NOR,1988,0.9805393,4209655, +Norway,NOR,1989,0.9302724,4227072, +Norway,NOR,1990,0.866223,4241637, +Norway,NOR,1991,0.79470116,4261886, +Norway,NOR,1992,0.72471076,4286560, +Norway,NOR,1993,0.6623606,4312138, +Norway,NOR,1994,0.6110238,4336745, +Norway,NOR,1995,0.5719735,4359327, +Norway,NOR,1996,0.54456335,4381511, +Norway,NOR,1997,0.52506447,4405334, +Norway,NOR,1998,0.5106524,4431672, +Norway,NOR,1999,0.49849185,4462148, +Norway,NOR,2000,0.48587623,4491204, +Norway,NOR,2001,0.47134486,4514019, +Norway,NOR,2002,0.4556976,4538435, +Norway,NOR,2003,0.43884286,4565167, +Norway,NOR,2004,0.42179528,4592284, +Norway,NOR,2005,0.4051415,4623691, +Norway,NOR,2006,0.38920537,4661081, +Norway,NOR,2007,0.37344915,4709582, +Norway,NOR,2008,0.3575741,4768654, +Norway,NOR,2009,0.3418413,4829195, +Norway,NOR,2010,0.3271386,4889747, +Norway,NOR,2011,0.3135053,4953565, +Norway,NOR,2012,0.30135444,5019058, +Norway,NOR,2013,0.2906307,5080667, +Norway,NOR,2014,0.28084356,5137926, +Norway,NOR,2015,0.27160627,5190357,Europe +Norway,NOR,2016,0.26255828,5236591, +Norway,NOR,2017,0.25352216,5277396, +Norway,NOR,2018,0.24450009,5312321, +Norway,NOR,2019,0.23508914,5348285, +Norway,NOR,2020,0.22590879,5379836, +Norway,NOR,2021,0.21707293,5403021, +Oceania,,1950,,12578030, +Oceania,,1951,,12910351, +Oceania,,1952,,13224304, +Oceania,,1953,,13522000, +Oceania,,1954,,13819174, +Oceania,,1955,,14140071, +Oceania,,1956,,14479634, +Oceania,,1957,,14820933, +Oceania,,1958,,15162130, +Oceania,,1959,,15506698, +Oceania,,1960,,15859743, +Oceania,,1961,,16213869, +Oceania,,1962,,16557077, +Oceania,,1963,,16899883, +Oceania,,1964,,17248725, +Oceania,,1965,,17609458, +Oceania,,1966,,17976052, +Oceania,,1967,,18320665, +Oceania,,1968,,18657991, +Oceania,,1969,,19020565, +Oceania,,1970,,19480682, +Oceania,,1971,,20023056, +Oceania,,1972,,20481057, +Oceania,,1973,,20852770, +Oceania,,1974,,21213318, +Oceania,,1975,,21537776, +Oceania,,1976,,21820768, +Oceania,,1977,,22089877, +Oceania,,1978,,22356009, +Oceania,,1979,,22623402, +Oceania,,1980,,22920632, +Oceania,,1981,,23268830, +Oceania,,1982,,23646422, +Oceania,,1983,,24011447, +Oceania,,1984,,24357734, +Oceania,,1985,,24704912, +Oceania,,1986,,25069550, +Oceania,,1987,,25459682, +Oceania,,1988,,25868614, +Oceania,,1989,,26286142, +Oceania,,1990,3.3195324,26744211, +Oceania,,1991,3.2509315,27214477, +Oceania,,1992,3.1835275,27637470, +Oceania,,1993,3.148876,28038745, +Oceania,,1994,3.12829,28455275, +Oceania,,1995,3.1093173,28905477, +Oceania,,1996,3.1000006,29379071, +Oceania,,1997,3.0971775,29846703, +Oceania,,1998,3.0992668,30299542, +Oceania,,1999,3.0890172,30753529, +Oceania,,2000,3.072054,31223133, +Oceania,,2001,3.0688367,31714944, +Oceania,,2002,3.0539458,32230343, +Oceania,,2003,3.0265238,32756845, +Oceania,,2004,2.9830518,33278789, +Oceania,,2005,2.921883,33813365, +Oceania,,2006,2.8570433,34395875, +Oceania,,2007,2.7495666,35044174, +Oceania,,2008,2.6668606,35745111, +Oceania,,2009,2.6259556,36448615, +Oceania,,2010,2.5698647,37103109, +Oceania,,2011,2.5274222,37730469, +Oceania,,2012,2.4642599,38383766, +Oceania,,2013,2.4190333,39057783, +Oceania,,2014,2.3894658,39729340, +Oceania,,2015,2.323681,40403644, +Oceania,,2016,2.2636309,41101240, +Oceania,,2017,2.201865,41817267, +Oceania,,2018,2.1409545,42540030, +Oceania,,2019,2.1083927,43278474, +Oceania,,2020,2.082356,43933757, +Oceania,,2021,2.0269809,44492065, +Oceania (UN),,1950,,12577612, +Oceania (UN),,1951,,12909941, +Oceania (UN),,1952,,13223877, +Oceania (UN),,1953,,13521601, +Oceania (UN),,1954,,13818801, +Oceania (UN),,1955,,14139693, +Oceania (UN),,1956,,14479261, +Oceania (UN),,1957,,14820529, +Oceania (UN),,1958,,15161741, +Oceania (UN),,1959,,15506332, +Oceania (UN),,1960,,15859370, +Oceania (UN),,1961,,16213462, +Oceania (UN),,1962,,16556685, +Oceania (UN),,1963,,16899484, +Oceania (UN),,1964,,17248330, +Oceania (UN),,1965,,17609084, +Oceania (UN),,1966,,17975666, +Oceania (UN),,1967,,18320280, +Oceania (UN),,1968,,18657606, +Oceania (UN),,1969,,19020176, +Oceania (UN),,1970,,19480276, +Oceania (UN),,1971,,20022644, +Oceania (UN),,1972,,20480652, +Oceania (UN),,1973,,20852372, +Oceania (UN),,1974,,21212944, +Oceania (UN),,1975,,21537422, +Oceania (UN),,1976,,21820378, +Oceania (UN),,1977,,22089510, +Oceania (UN),,1978,,22355636, +Oceania (UN),,1979,,22623048, +Oceania (UN),,1980,,22920234, +Oceania (UN),,1981,,23268486, +Oceania (UN),,1982,,23646050, +Oceania (UN),,1983,,24011052, +Oceania (UN),,1984,,24357388, +Oceania (UN),,1985,,24704544, +Oceania (UN),,1986,,25069192, +Oceania (UN),,1987,,25459316, +Oceania (UN),,1988,,25868238, +Oceania (UN),,1989,,26285792, +Oceania (UN),,1990,,26743822, +Oceania (UN),,1991,,27214102, +Oceania (UN),,1992,,27637108, +Oceania (UN),,1993,,28038404, +Oceania (UN),,1994,,28454926, +Oceania (UN),,1995,,28905124, +Oceania (UN),,1996,,29378682, +Oceania (UN),,1997,,29846364, +Oceania (UN),,1998,,30299182, +Oceania (UN),,1999,,30753172, +Oceania (UN),,2000,,31222778, +Oceania (UN),,2001,,31714600, +Oceania (UN),,2002,,32230004, +Oceania (UN),,2003,,32756470, +Oceania (UN),,2004,,33278444, +Oceania (UN),,2005,,33813024, +Oceania (UN),,2006,,34395532, +Oceania (UN),,2007,,35043844, +Oceania (UN),,2008,,35744784, +Oceania (UN),,2009,,36448272, +Oceania (UN),,2010,,37102764, +Oceania (UN),,2011,,37730132, +Oceania (UN),,2012,,38383408, +Oceania (UN),,2013,,39057444, +Oceania (UN),,2014,,39728976, +Oceania (UN),,2015,,40403288, +Oceania (UN),,2016,,41100876, +Oceania (UN),,2017,,41816932, +Oceania (UN),,2018,,42539676, +Oceania (UN),,2019,,43278152, +Oceania (UN),,2020,,43933424, +Oceania (UN),,2021,,44491724, +Oceania excluding Australia and New Zealand,,1990,7.0579786,, +Oceania excluding Australia and New Zealand,,1991,6.9237375,, +Oceania excluding Australia and New Zealand,,1992,6.805524,, +Oceania excluding Australia and New Zealand,,1993,6.69839,, +Oceania excluding Australia and New Zealand,,1994,6.5931764,, +Oceania excluding Australia and New Zealand,,1995,6.495208,, +Oceania excluding Australia and New Zealand,,1996,6.403292,, +Oceania excluding Australia and New Zealand,,1997,6.3240504,, +Oceania excluding Australia and New Zealand,,1998,6.244199,, +Oceania excluding Australia and New Zealand,,1999,6.1624746,, +Oceania excluding Australia and New Zealand,,2000,6.080406,, +Oceania excluding Australia and New Zealand,,2001,5.9978366,, +Oceania excluding Australia and New Zealand,,2002,5.920376,, +Oceania excluding Australia and New Zealand,,2003,5.831666,, +Oceania excluding Australia and New Zealand,,2004,5.7431574,, +Oceania excluding Australia and New Zealand,,2005,5.651623,, +Oceania excluding Australia and New Zealand,,2006,5.5526514,, +Oceania excluding Australia and New Zealand,,2007,5.4505568,, +Oceania excluding Australia and New Zealand,,2008,5.343185,, +Oceania excluding Australia and New Zealand,,2009,5.244946,, +Oceania excluding Australia and New Zealand,,2010,5.121289,, +Oceania excluding Australia and New Zealand,,2011,5.0111184,, +Oceania excluding Australia and New Zealand,,2012,4.8972297,, +Oceania excluding Australia and New Zealand,,2013,4.785652,, +Oceania excluding Australia and New Zealand,,2014,4.6730514,, +Oceania excluding Australia and New Zealand,,2015,4.5618687,, +Oceania excluding Australia and New Zealand,,2016,4.454082,, +Oceania excluding Australia and New Zealand,,2017,4.340049,, +Oceania excluding Australia and New Zealand,,2018,4.2263894,, +Oceania excluding Australia and New Zealand,,2019,4.1136737,, +Oceania excluding Australia and New Zealand,,2020,4.002925,, +Oceania excluding Australia and New Zealand,,2021,3.8863266,, +Oman,OMN,1950,,457321, +Oman,OMN,1951,,463731, +Oman,OMN,1952,,470407, +Oman,OMN,1953,,477409, +Oman,OMN,1954,,484747, +Oman,OMN,1955,,492428, +Oman,OMN,1956,,500453, +Oman,OMN,1957,,508861, +Oman,OMN,1958,,517715, +Oman,OMN,1959,38.74472,527035, 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+Oman,OMN,1990,3.9013321,1804528, +Oman,OMN,1991,3.5335097,1895193, +Oman,OMN,1992,3.2023044,1985234, +Oman,OMN,1993,2.9041305,2071874, +Oman,OMN,1994,2.6428933,2133678, +Oman,OMN,1995,2.414375,2172681, +Oman,OMN,1996,2.2164314,2209965, +Oman,OMN,1997,2.0421193,2245591, +Oman,OMN,1998,1.888882,2279727, +Oman,OMN,1999,1.7528931,2312608, +Oman,OMN,2000,1.6344944,2344261, +Oman,OMN,2001,1.5320671,2374655, +Oman,OMN,2002,1.4452101,2403663, +Oman,OMN,2003,1.373063,2431603, +Oman,OMN,2004,1.317204,2468860, +Oman,OMN,2005,1.2733552,2515202, +Oman,OMN,2006,1.2408817,2560652, +Oman,OMN,2007,1.2163827,2605706, +Oman,OMN,2008,1.1987641,2651032, +Oman,OMN,2009,1.1851557,2697543, +Oman,OMN,2010,1.1730216,2881915, +Oman,OMN,2011,1.1604674,3206883, +Oman,OMN,2012,1.1479509,3535585, +Oman,OMN,2013,1.136132,3816688, +Oman,OMN,2014,1.1254126,4009272, +Oman,OMN,2015,1.1158928,4191784,Asia +Oman,OMN,2016,1.1076041,4398080, +Oman,OMN,2017,1.0969183,4541853, +Oman,OMN,2018,1.0806437,4601160, +Oman,OMN,2019,1.0598848,4602769, +Oman,OMN,2020,1.037148,4543406, +Oman,OMN,2021,1.013952,4520474, +Pakistan,PAK,1950,37.688896,37696264, +Pakistan,PAK,1951,36.115993,38215788, +Pakistan,PAK,1952,34.596153,38816780, +Pakistan,PAK,1953,33.185627,39488232, +Pakistan,PAK,1954,31.849857,40224096, +Pakistan,PAK,1955,30.567072,41023128, +Pakistan,PAK,1956,29.352291,41884992, +Pakistan,PAK,1957,28.210337,42808516, +Pakistan,PAK,1958,27.148342,43794992, +Pakistan,PAK,1959,26.142878,44843640, +Pakistan,PAK,1960,25.187794,45954228, +Pakistan,PAK,1961,24.290443,47060912, +Pakistan,PAK,1962,23.447287,48161844, +Pakistan,PAK,1963,22.660215,49325052, +Pakistan,PAK,1964,21.94231,50552600, +Pakistan,PAK,1965,21.292685,51841628, +Pakistan,PAK,1966,20.728914,53199420, +Pakistan,PAK,1967,20.233118,54629796, +Pakistan,PAK,1968,19.805954,56124740, +Pakistan,PAK,1969,19.408684,57676804, +Pakistan,PAK,1970,19.047432,59290872, +Pakistan,PAK,1971,18.722528,60878784, +Pakistan,PAK,1972,18.409925,62509568, +Pakistan,PAK,1973,18.125566,64285632, +Pakistan,PAK,1974,17.851929,66149172, +Pakistan,PAK,1975,17.604063,68127000, +Pakistan,PAK,1976,17.36973,70230928, +Pakistan,PAK,1977,17.138021,72451112, +Pakistan,PAK,1978,16.925062,74789328, +Pakistan,PAK,1979,16.709454,77407344, +Pakistan,PAK,1980,16.495733,80624056, +Pakistan,PAK,1981,16.278435,84270208, +Pakistan,PAK,1982,16.049372,87828200, +Pakistan,PAK,1983,15.807858,91080376, +Pakistan,PAK,1984,15.561732,94003880, +Pakistan,PAK,1985,15.306323,97121552, +Pakistan,PAK,1986,15.039671,100618520, +Pakistan,PAK,1987,14.774162,104251096, +Pakistan,PAK,1988,14.509607,107967840, +Pakistan,PAK,1989,14.24986,111670392, +Pakistan,PAK,1990,13.983246,115414072, +Pakistan,PAK,1991,13.705113,119203576, +Pakistan,PAK,1992,13.410013,122375176, +Pakistan,PAK,1993,13.107356,125546624, +Pakistan,PAK,1994,12.790445,129245136, +Pakistan,PAK,1995,12.45866,133117480, +Pakistan,PAK,1996,12.117929,137234816, +Pakistan,PAK,1997,11.775614,141330272, +Pakistan,PAK,1998,11.437784,145476112, +Pakistan,PAK,1999,11.111262,149694464, +Pakistan,PAK,2000,10.807917,154369920, +Pakistan,PAK,2001,10.528307,159217728, +Pakistan,PAK,2002,10.275327,163262800, +Pakistan,PAK,2003,10.051704,166876672, +Pakistan,PAK,2004,9.845633,170648624, +Pakistan,PAK,2005,9.651194,174372096, +Pakistan,PAK,2006,9.464972,178069984, +Pakistan,PAK,2007,9.285987,181924528, +Pakistan,PAK,2008,9.104574,185931952, +Pakistan,PAK,2009,8.921268,190123232, +Pakistan,PAK,2010,8.72238,194454496, +Pakistan,PAK,2011,8.514959,198602736, +Pakistan,PAK,2012,8.295439,202205856, +Pakistan,PAK,2013,8.071448,205337552, +Pakistan,PAK,2014,7.8434515,208251632, +Pakistan,PAK,2015,7.617705,210969296,Asia +Pakistan,PAK,2016,7.3949294,213524848, +Pakistan,PAK,2017,7.177063,216379648, +Pakistan,PAK,2018,6.964348,219731488, +Pakistan,PAK,2019,6.7495565,223293280, +Pakistan,PAK,2020,6.547601,227196736, +Pakistan,PAK,2021,6.333255,231402112, +Palau,PLW,1950,,7451, +Palau,PLW,1951,,7554, 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+Palau,PLW,1986,3.7867267,14010, +Palau,PLW,1987,3.714437,14335, +Palau,PLW,1988,3.6470191,14653, +Palau,PLW,1989,3.5901818,14980, +Palau,PLW,1990,3.5326564,15318, +Palau,PLW,1991,3.4704108,15665, +Palau,PLW,1992,3.4087539,16030, +Palau,PLW,1993,3.3418896,16399, +Palau,PLW,1994,3.2681508,16797, +Palau,PLW,1995,3.1930244,17233, +Palau,PLW,1996,3.1154249,17759, +Palau,PLW,1997,3.0412395,18323, +Palau,PLW,1998,2.9725,18869, +Palau,PLW,1999,2.9046926,19402, +Palau,PLW,2000,2.8472152,19745, +Palau,PLW,2001,2.7910123,19852, +Palau,PLW,2002,2.7408414,19875, +Palau,PLW,2003,2.6897852,19904, +Palau,PLW,2004,2.644318,19927, +Palau,PLW,2005,2.5937197,19853, +Palau,PLW,2006,2.541738,19645, +Palau,PLW,2007,2.4925585,19383, +Palau,PLW,2008,2.4329224,19121, +Palau,PLW,2009,2.3668687,18848, +Palau,PLW,2010,2.3082275,18563, +Palau,PLW,2011,2.2432237,18260, +Palau,PLW,2012,2.1794288,17974, +Palau,PLW,2013,2.1120834,17829, +Palau,PLW,2014,2.0430703,17821, +Palau,PLW,2015,1.9743068,17822,Oceania 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+Palestine,PSE,1976,10.121138,1300918, +Palestine,PSE,1977,9.520916,1337324, +Palestine,PSE,1978,8.965907,1374130, +Palestine,PSE,1979,8.440788,1412636, +Palestine,PSE,1980,7.949843,1453631, +Palestine,PSE,1981,7.4842353,1497550, +Palestine,PSE,1982,7.045025,1546097, +Palestine,PSE,1983,6.637873,1604103, +Palestine,PSE,1984,6.254784,1674190, +Palestine,PSE,1985,5.9024286,1731093, +Palestine,PSE,1986,5.572262,1775272, +Palestine,PSE,1987,5.26346,1862775, +Palestine,PSE,1988,4.9811435,1963258, +Palestine,PSE,1989,4.72566,2041388, +Palestine,PSE,1990,4.494345,2124623, +Palestine,PSE,1991,4.2877707,2214277, +Palestine,PSE,1992,4.098732,2310004, +Palestine,PSE,1993,3.9288356,2410651, +Palestine,PSE,1994,3.7711225,2515166, +Palestine,PSE,1995,3.6267624,2623149, +Palestine,PSE,1996,3.4893718,2734003, +Palestine,PSE,1997,3.3617694,2847222, +Palestine,PSE,1998,3.245092,2950926, +Palestine,PSE,1999,3.1358304,3044490, +Palestine,PSE,2000,3.0355725,3139950, +Palestine,PSE,2001,2.9431682,3230085, 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Barthelemy,BLM,2004,,7872, +Saint Barthelemy,BLM,2005,,8066, +Saint Barthelemy,BLM,2006,,8259, +Saint Barthelemy,BLM,2007,,8455, +Saint Barthelemy,BLM,2008,,8649, +Saint Barthelemy,BLM,2009,,8839, +Saint Barthelemy,BLM,2010,,9011, +Saint Barthelemy,BLM,2011,,9135, +Saint Barthelemy,BLM,2012,,9244, +Saint Barthelemy,BLM,2013,,9365, +Saint Barthelemy,BLM,2014,,9499, +Saint Barthelemy,BLM,2015,,9668,North America +Saint Barthelemy,BLM,2016,,9865, +Saint Barthelemy,BLM,2017,,10068, +Saint Barthelemy,BLM,2018,,10281, +Saint Barthelemy,BLM,2019,,10504, +Saint Barthelemy,BLM,2020,,10706, +Saint Barthelemy,BLM,2021,,10888, +Saint Helena,SHN,1950,,5249, +Saint Helena,SHN,1951,,5299, +Saint Helena,SHN,1952,,5325, +Saint Helena,SHN,1953,,5341, +Saint Helena,SHN,1954,,5347, +Saint Helena,SHN,1955,,5333, +Saint Helena,SHN,1956,,5322, +Saint Helena,SHN,1957,,5329, +Saint Helena,SHN,1958,,5352, +Saint Helena,SHN,1959,,5369, +Saint Helena,SHN,1960,,5390, +Saint Helena,SHN,1961,,5411, +Saint Helena,SHN,1962,,5439, +Saint Helena,SHN,1963,,5461, +Saint Helena,SHN,1964,,5480, +Saint Helena,SHN,1965,,5488, +Saint Helena,SHN,1966,,5512, +Saint Helena,SHN,1967,,5559, +Saint Helena,SHN,1968,,5632, +Saint Helena,SHN,1969,,5703, +Saint Helena,SHN,1970,,5780, +Saint Helena,SHN,1971,,5872, +Saint Helena,SHN,1972,,5947, +Saint Helena,SHN,1973,,6026, +Saint Helena,SHN,1974,,6086, +Saint Helena,SHN,1975,,6149, +Saint Helena,SHN,1976,,6188, +Saint Helena,SHN,1977,,6245, +Saint Helena,SHN,1978,,6315, +Saint Helena,SHN,1979,,6365, +Saint Helena,SHN,1980,,6421, +Saint Helena,SHN,1981,,6475, +Saint Helena,SHN,1982,,6540, +Saint Helena,SHN,1983,,6596, +Saint Helena,SHN,1984,,6637, +Saint Helena,SHN,1985,,6676, +Saint Helena,SHN,1986,,6705, +Saint Helena,SHN,1987,,6717, +Saint Helena,SHN,1988,,6700, +Saint Helena,SHN,1989,,6670, +Saint Helena,SHN,1990,,6633, +Saint Helena,SHN,1991,,6591, +Saint Helena,SHN,1992,,6541, +Saint Helena,SHN,1993,,6482, +Saint Helena,SHN,1994,,6429, +Saint Helena,SHN,1995,,6367, +Saint Helena,SHN,1996,,6312, +Saint Helena,SHN,1997,,6251, +Saint Helena,SHN,1998,,6183, +Saint Helena,SHN,1999,,6115, +Saint Helena,SHN,2000,,6048, +Saint Helena,SHN,2001,,5971, +Saint Helena,SHN,2002,,5875, +Saint Helena,SHN,2003,,5771, +Saint Helena,SHN,2004,,5664, +Saint Helena,SHN,2005,,5557, +Saint Helena,SHN,2006,,5448, +Saint Helena,SHN,2007,,5338, +Saint Helena,SHN,2008,,5282, +Saint Helena,SHN,2009,,5306, +Saint Helena,SHN,2010,,5338, +Saint Helena,SHN,2011,,5377, +Saint Helena,SHN,2012,,5410, +Saint Helena,SHN,2013,,5446, +Saint Helena,SHN,2014,,5480, +Saint Helena,SHN,2015,,5520,Africa +Saint Helena,SHN,2016,,5533, +Saint Helena,SHN,2017,,5521, +Saint Helena,SHN,2018,,5494, +Saint Helena,SHN,2019,,5470, +Saint Helena,SHN,2020,,5448, +Saint Helena,SHN,2021,,5428, +Saint Kitts and Nevis,KNA,1950,,47275, +Saint Kitts and Nevis,KNA,1951,,48384, +Saint Kitts and Nevis,KNA,1952,,49497, +Saint Kitts and Nevis,KNA,1953,,50592, +Saint Kitts and Nevis,KNA,1954,,51527, +Saint Kitts and Nevis,KNA,1955,,52363, +Saint Kitts and Nevis,KNA,1956,,53298, +Saint Kitts and Nevis,KNA,1957,,54275, +Saint Kitts and Nevis,KNA,1958,,55276, +Saint Kitts and Nevis,KNA,1959,,56251, +Saint Kitts and Nevis,KNA,1960,,56679, +Saint Kitts and Nevis,KNA,1961,,56262, +Saint Kitts and Nevis,KNA,1962,,55424, +Saint Kitts and Nevis,KNA,1963,,54409, +Saint Kitts and Nevis,KNA,1964,,53277, +Saint Kitts and Nevis,KNA,1965,,52036, +Saint Kitts and Nevis,KNA,1966,,50707, +Saint Kitts and Nevis,KNA,1967,,49289, +Saint Kitts and Nevis,KNA,1968,,47792, +Saint Kitts and Nevis,KNA,1969,,46157, +Saint Kitts and Nevis,KNA,1970,,44991, +Saint Kitts and Nevis,KNA,1971,7.152546,44605, +Saint Kitts and Nevis,KNA,1972,6.860659,44437, +Saint Kitts and Nevis,KNA,1973,6.5865297,44263, +Saint Kitts and Nevis,KNA,1974,6.3400335,44078, +Saint Kitts and Nevis,KNA,1975,6.111346,43908, +Saint Kitts and Nevis,KNA,1976,5.898178,43764, +Saint Kitts and Nevis,KNA,1977,5.6965213,43625, +Saint Kitts and Nevis,KNA,1978,5.491708,43486, +Saint Kitts and Nevis,KNA,1979,5.270905,43298, +Saint Kitts and Nevis,KNA,1980,5.028355,43117, +Saint Kitts and Nevis,KNA,1981,4.768387,42971, +Saint Kitts and Nevis,KNA,1982,4.4979677,42816, +Saint Kitts and Nevis,KNA,1983,4.229535,42627, +Saint Kitts and Nevis,KNA,1984,3.976577,42402, +Saint Kitts and Nevis,KNA,1985,3.7479362,42170, +Saint Kitts and Nevis,KNA,1986,3.5460086,41910, +Saint Kitts and Nevis,KNA,1987,3.378823,41643, +Saint Kitts and Nevis,KNA,1988,3.2416122,41341, +Saint Kitts and Nevis,KNA,1989,3.1307192,41015, +Saint Kitts and Nevis,KNA,1990,3.0423634,40663, +Saint Kitts and Nevis,KNA,1991,2.9746575,40562, +Saint Kitts and Nevis,KNA,1992,2.9216106,40924, +Saint Kitts and Nevis,KNA,1993,2.8785312,41470, +Saint Kitts and Nevis,KNA,1994,2.837204,42029, +Saint Kitts and Nevis,KNA,1995,2.7891116,42576, +Saint Kitts and Nevis,KNA,1996,2.7335496,43120, +Saint Kitts and Nevis,KNA,1997,2.6656113,43681, +Saint Kitts and Nevis,KNA,1998,2.5810025,44256, +Saint Kitts and Nevis,KNA,1999,2.485621,44852, +Saint Kitts and Nevis,KNA,2000,2.3806243,45492, +Saint Kitts and Nevis,KNA,2001,2.2710316,46013, +Saint Kitts and Nevis,KNA,2002,2.1636076,46288, +Saint Kitts and Nevis,KNA,2003,2.0650918,46456, +Saint Kitts and Nevis,KNA,2004,1.9798746,46602, +Saint Kitts and Nevis,KNA,2005,1.9089237,46752, +Saint Kitts and Nevis,KNA,2006,1.852589,46900, +Saint Kitts and Nevis,KNA,2007,1.8120499,47038, +Saint Kitts and Nevis,KNA,2008,1.7830206,47180, +Saint Kitts and Nevis,KNA,2009,1.7632322,47310, +Saint Kitts and Nevis,KNA,2010,1.750684,47429, +Saint Kitts and Nevis,KNA,2011,1.7420374,47605, +Saint Kitts and Nevis,KNA,2012,1.7379367,47754, +Saint Kitts and Nevis,KNA,2013,1.7305918,47793, +Saint Kitts and Nevis,KNA,2014,1.7185633,47816, +Saint Kitts and Nevis,KNA,2015,1.6943474,47814,North America +Saint Kitts and Nevis,KNA,2016,1.6614214,47814, +Saint Kitts and Nevis,KNA,2017,1.6257118,47811, +Saint Kitts and Nevis,KNA,2018,1.5858723,47787, +Saint Kitts and Nevis,KNA,2019,1.5439911,47739, +Saint Kitts and Nevis,KNA,2020,1.5022923,47666, +Saint Kitts and Nevis,KNA,2021,1.4560239,47631, +Saint Lucia,LCA,1950,20.223314,86716, +Saint Lucia,LCA,1951,19.935055,87330, +Saint Lucia,LCA,1952,19.646357,87546, +Saint Lucia,LCA,1953,19.357647,87787, +Saint Lucia,LCA,1954,19.06577,88101, +Saint Lucia,LCA,1955,18.766666,88481, +Saint Lucia,LCA,1956,18.44761,88934, +Saint Lucia,LCA,1957,18.078114,89449, +Saint Lucia,LCA,1958,17.630527,90049, +Saint Lucia,LCA,1959,17.080477,90695, +Saint Lucia,LCA,1960,16.408112,91629, +Saint Lucia,LCA,1961,15.606198,92783, +Saint Lucia,LCA,1962,14.70141,93843, +Saint Lucia,LCA,1963,13.736302,94905, +Saint Lucia,LCA,1964,12.739623,95965, +Saint Lucia,LCA,1965,11.742877,97042, +Saint Lucia,LCA,1966,10.76985,98142, +Saint Lucia,LCA,1967,9.838206,99268, +Saint Lucia,LCA,1968,8.959291,100427, +Saint Lucia,LCA,1969,8.146028,101623, +Saint Lucia,LCA,1970,7.401357,103108, +Saint Lucia,LCA,1971,6.7304406,104798, +Saint Lucia,LCA,1972,6.1272783,106474, +Saint Lucia,LCA,1973,5.590013,108136, +Saint Lucia,LCA,1974,5.1146245,109757, +Saint Lucia,LCA,1975,4.6944475,111511, +Saint Lucia,LCA,1976,4.327094,113445, +Saint Lucia,LCA,1977,4.00623,115398, +Saint Lucia,LCA,1978,3.7260616,117414, +Saint Lucia,LCA,1979,3.481893,119480, +Saint Lucia,LCA,1980,3.2719924,121651, +Saint Lucia,LCA,1981,3.0911915,123929, +Saint Lucia,LCA,1982,2.93441,126208, +Saint Lucia,LCA,1983,2.7973626,128455, +Saint Lucia,LCA,1984,2.6772065,130640, +Saint Lucia,LCA,1985,2.5708864,132770, +Saint Lucia,LCA,1986,2.4766116,134820, +Saint Lucia,LCA,1987,2.3917985,136792, +Saint Lucia,LCA,1988,2.3159604,138676, +Saint Lucia,LCA,1989,2.2472737,140471, +Saint Lucia,LCA,1990,2.185397,142315, +Saint Lucia,LCA,1991,2.1291397,144287, +Saint Lucia,LCA,1992,2.0784898,146433, +Saint Lucia,LCA,1993,2.0330908,148610, +Saint Lucia,LCA,1994,1.9927889,150574, +Saint Lucia,LCA,1995,1.9563004,152368, +Saint Lucia,LCA,1996,1.9239162,153982, +Saint Lucia,LCA,1997,1.8958832,155472, +Saint Lucia,LCA,1998,1.8723568,156872, +Saint Lucia,LCA,1999,1.8541692,158272, +Saint Lucia,LCA,2000,1.8407595,159513, +Saint Lucia,LCA,2001,1.832806,160611, +Saint Lucia,LCA,2002,1.8297333,161814, +Saint Lucia,LCA,2003,1.8319209,163060, +Saint Lucia,LCA,2004,1.8386965,164252, +Saint Lucia,LCA,2005,1.8495756,165394, +Saint Lucia,LCA,2006,1.8649557,166476, +Saint Lucia,LCA,2007,1.8851411,167524, +Saint Lucia,LCA,2008,1.9096355,168584, +Saint Lucia,LCA,2009,1.9384353,169694, +Saint Lucia,LCA,2010,1.9710563,170950, +Saint Lucia,LCA,2011,2.0083144,172157, +Saint Lucia,LCA,2012,2.0494723,173139, +Saint Lucia,LCA,2013,2.0950685,173992, +Saint Lucia,LCA,2014,2.1426303,174814, +Saint Lucia,LCA,2015,2.1945915,175638,North America +Saint Lucia,LCA,2016,2.2496586,176429, +Saint Lucia,LCA,2017,2.3043513,177175, +Saint Lucia,LCA,2018,2.3572178,177894, +Saint Lucia,LCA,2019,2.4059632,178594, +Saint Lucia,LCA,2020,2.4479427,179245, +Saint Lucia,LCA,2021,2.4820638,179663, +Saint Martin (French part),MAF,1950,,2999, +Saint Martin (French part),MAF,1951,,3113, +Saint Martin (French part),MAF,1952,,3221, +Saint Martin (French part),MAF,1953,,3330, +Saint Martin (French part),MAF,1954,,3446, +Saint Martin (French part),MAF,1955,,3560, +Saint Martin (French part),MAF,1956,,3669, +Saint Martin (French part),MAF,1957,,3782, +Saint Martin (French part),MAF,1958,,3900, +Saint Martin (French part),MAF,1959,,4024, +Saint Martin (French part),MAF,1960,,4152, +Saint Martin (French part),MAF,1961,,4280, +Saint Martin (French part),MAF,1962,,4411, +Saint Martin (French part),MAF,1963,,4547, +Saint Martin (French part),MAF,1964,,4688, +Saint Martin (French part),MAF,1965,,4858, +Saint Martin (French part),MAF,1966,,5066, +Saint Martin (French part),MAF,1967,,5318, +Saint Martin (French part),MAF,1968,,5519, +Saint Martin (French part),MAF,1969,,5669, +Saint Martin (French part),MAF,1970,,5823, +Saint Martin (French part),MAF,1971,,5981, +Saint Martin (French part),MAF,1972,,6136, +Saint Martin (French part),MAF,1973,,6300, +Saint Martin (French part),MAF,1974,,6470, +Saint Martin (French part),MAF,1975,,6672, +Saint Martin (French part),MAF,1976,,6919, +Saint Martin (French part),MAF,1977,,7146, +Saint Martin (French part),MAF,1978,,7378, +Saint Martin (French part),MAF,1979,,7587, +Saint Martin (French part),MAF,1980,,7797, +Saint Martin (French part),MAF,1981,,7982, +Saint Martin (French part),MAF,1982,,8978, +Saint Martin (French part),MAF,1983,,11012, +Saint Martin (French part),MAF,1984,,13319, +Saint Martin (French part),MAF,1985,,15736, +Saint Martin (French part),MAF,1986,,18252, +Saint Martin (French part),MAF,1987,,20872, +Saint Martin (French part),MAF,1988,,23576, +Saint Martin (French part),MAF,1989,,26325, +Saint Martin (French part),MAF,1990,,28150, +Saint Martin (French part),MAF,1991,,28758, +Saint Martin (French part),MAF,1992,,29058, +Saint Martin (French part),MAF,1993,,29271, +Saint Martin (French part),MAF,1994,,29401, +Saint Martin (French part),MAF,1995,,29456, +Saint Martin (French part),MAF,1996,,29430, +Saint Martin (French part),MAF,1997,,29322, +Saint Martin (French part),MAF,1998,,29123, +Saint Martin (French part),MAF,1999,,29120, +Saint Martin (French part),MAF,2000,,29633, +Saint Martin (French part),MAF,2001,,30408, +Saint Martin (French part),MAF,2002,,31185, +Saint Martin (French part),MAF,2003,,31948, +Saint Martin (French part),MAF,2004,,32722, +Saint Martin (French part),MAF,2005,,33477, +Saint Martin (French part),MAF,2006,,34203, +Saint Martin (French part),MAF,2007,,34909, +Saint Martin (French part),MAF,2008,,35566, +Saint Martin (French part),MAF,2009,,36156, +Saint Martin (French part),MAF,2010,,36478, +Saint Martin (French part),MAF,2011,,36370, +Saint Martin (French part),MAF,2012,,36052, +Saint Martin (French part),MAF,2013,,35664, +Saint Martin (French part),MAF,2014,,35280, +Saint Martin (French part),MAF,2015,,35042,North America +Saint Martin (French part),MAF,2016,,34833, +Saint Martin (French part),MAF,2017,,34519, +Saint Martin (French part),MAF,2018,,33879, +Saint Martin (French part),MAF,2019,,33150, +Saint Martin (French part),MAF,2020,,32577, +Saint Martin (French part),MAF,2021,,31964, +Saint Pierre and Miquelon,SPM,1950,,4497, +Saint Pierre and Miquelon,SPM,1951,,4534, +Saint Pierre and Miquelon,SPM,1952,,4590, +Saint Pierre and Miquelon,SPM,1953,,4647, +Saint Pierre and Miquelon,SPM,1954,,4703, +Saint Pierre and Miquelon,SPM,1955,,4750, +Saint Pierre and Miquelon,SPM,1956,,4776, +Saint Pierre and Miquelon,SPM,1957,,4790, +Saint Pierre and Miquelon,SPM,1958,,4851, +Saint Pierre and Miquelon,SPM,1959,,4910, +Saint Pierre and Miquelon,SPM,1960,,4945, +Saint Pierre and Miquelon,SPM,1961,,4944, +Saint Pierre and Miquelon,SPM,1962,,4951, +Saint Pierre and Miquelon,SPM,1963,,5013, +Saint Pierre and Miquelon,SPM,1964,,5093, +Saint Pierre and Miquelon,SPM,1965,,5171, +Saint Pierre and Miquelon,SPM,1966,,5256, +Saint Pierre and Miquelon,SPM,1967,,5332, +Saint Pierre and Miquelon,SPM,1968,,5405, +Saint Pierre and Miquelon,SPM,1969,,5483, +Saint Pierre and Miquelon,SPM,1970,,5562, +Saint Pierre and Miquelon,SPM,1971,,5654, +Saint Pierre and Miquelon,SPM,1972,,5750, +Saint Pierre and Miquelon,SPM,1973,,5846, +Saint Pierre and Miquelon,SPM,1974,,5931, +Saint Pierre and Miquelon,SPM,1975,,5970, +Saint Pierre and Miquelon,SPM,1976,,6007, +Saint Pierre and Miquelon,SPM,1977,,6040, +Saint Pierre and Miquelon,SPM,1978,,6080, +Saint Pierre and Miquelon,SPM,1979,,6108, +Saint Pierre and Miquelon,SPM,1980,,6126, +Saint Pierre and Miquelon,SPM,1981,,6114, +Saint Pierre and Miquelon,SPM,1982,,6104, +Saint Pierre and Miquelon,SPM,1983,,6150, +Saint Pierre and Miquelon,SPM,1984,,6205, +Saint Pierre and Miquelon,SPM,1985,,6253, +Saint Pierre and Miquelon,SPM,1986,,6289, +Saint Pierre and Miquelon,SPM,1987,,6322, +Saint Pierre and Miquelon,SPM,1988,,6344, +Saint Pierre and Miquelon,SPM,1989,,6343, +Saint Pierre and Miquelon,SPM,1990,,6345, +Saint Pierre and Miquelon,SPM,1991,,6374, +Saint Pierre and Miquelon,SPM,1992,,6396, +Saint Pierre and Miquelon,SPM,1993,,6423, +Saint Pierre and Miquelon,SPM,1994,,6430, +Saint Pierre and Miquelon,SPM,1995,,6439, +Saint Pierre and Miquelon,SPM,1996,,6436, +Saint Pierre and Miquelon,SPM,1997,,6415, +Saint Pierre and Miquelon,SPM,1998,,6365, +Saint Pierre and Miquelon,SPM,1999,,6319, +Saint Pierre and Miquelon,SPM,2000,,6300, +Saint Pierre and Miquelon,SPM,2001,,6283, +Saint Pierre and Miquelon,SPM,2002,,6264, +Saint Pierre and Miquelon,SPM,2003,,6228, +Saint Pierre and Miquelon,SPM,2004,,6186, +Saint Pierre and Miquelon,SPM,2005,,6135, +Saint Pierre and Miquelon,SPM,2006,,6105, +Saint Pierre and Miquelon,SPM,2007,,6090, +Saint Pierre and Miquelon,SPM,2008,,6085, +Saint Pierre and Miquelon,SPM,2009,,6077, +Saint Pierre and Miquelon,SPM,2010,,6075, +Saint Pierre and Miquelon,SPM,2011,,6066, +Saint Pierre and Miquelon,SPM,2012,,6048, +Saint Pierre and Miquelon,SPM,2013,,6027, +Saint Pierre and Miquelon,SPM,2014,,6013, +Saint Pierre and Miquelon,SPM,2015,,6001,North America +Saint Pierre and Miquelon,SPM,2016,,5990, +Saint Pierre and Miquelon,SPM,2017,,5980, +Saint Pierre and Miquelon,SPM,2018,,5969, +Saint Pierre and Miquelon,SPM,2019,,5952, +Saint Pierre and Miquelon,SPM,2020,,5930, +Saint Pierre and Miquelon,SPM,2021,,5905, +Saint Vincent and the Grenadines,VCT,1950,,61563, +Saint Vincent and the Grenadines,VCT,1951,18.238712,63423, +Saint Vincent and the Grenadines,VCT,1952,18.339289,65383, +Saint Vincent and the Grenadines,VCT,1953,18.439676,67441, +Saint Vincent and the Grenadines,VCT,1954,18.525093,69609, +Saint Vincent and the Grenadines,VCT,1955,18.603281,71837, +Saint Vincent and the Grenadines,VCT,1956,18.659313,74181, +Saint Vincent and the Grenadines,VCT,1957,18.671461,76671, +Saint Vincent and the Grenadines,VCT,1958,18.575562,79273, +Saint Vincent and the Grenadines,VCT,1959,18.218334,82001, +Saint Vincent and the Grenadines,VCT,1960,17.466742,84157, +Saint Vincent and the Grenadines,VCT,1961,16.357662,85683, +Saint Vincent and the Grenadines,VCT,1962,14.995915,87202, +Saint Vincent and the Grenadines,VCT,1963,13.526154,88662, +Saint Vincent and the Grenadines,VCT,1964,12.09678,90067, +Saint Vincent and the Grenadines,VCT,1965,10.820249,91515, +Saint Vincent and the Grenadines,VCT,1966,9.740383,92976, +Saint Vincent and the Grenadines,VCT,1967,8.86799,94402, +Saint Vincent and the Grenadines,VCT,1968,8.201313,95775, +Saint Vincent and the Grenadines,VCT,1969,7.742573,97147, +Saint Vincent and the Grenadines,VCT,1970,7.4742365,98478, +Saint Vincent and the Grenadines,VCT,1971,7.369882,99718, +Saint Vincent and the Grenadines,VCT,1972,7.3928323,100867, +Saint Vincent and the Grenadines,VCT,1973,7.484828,101941, +Saint Vincent and the Grenadines,VCT,1974,7.5470953,102966, +Saint Vincent and the Grenadines,VCT,1975,7.481208,103897, +Saint Vincent and the Grenadines,VCT,1976,7.249231,104727, +Saint Vincent and the Grenadines,VCT,1977,6.863117,105516, +Saint Vincent and the Grenadines,VCT,1978,6.363427,106252, +Saint Vincent and the Grenadines,VCT,1979,5.8187423,106896, +Saint Vincent and the Grenadines,VCT,1980,5.285489,107499, +Saint Vincent and the Grenadines,VCT,1981,4.789673,108209, +Saint Vincent and the Grenadines,VCT,1982,4.328028,108989, +Saint Vincent and the Grenadines,VCT,1983,3.9092987,109700, +Saint Vincent and the Grenadines,VCT,1984,3.5404682,110344, +Saint Vincent and the Grenadines,VCT,1985,3.22761,110925, +Saint Vincent and the Grenadines,VCT,1986,2.9702988,111406, +Saint Vincent and the Grenadines,VCT,1987,2.765347,111789, +Saint Vincent and the Grenadines,VCT,1988,2.6045473,112098, +Saint Vincent and the Grenadines,VCT,1989,2.479116,112334, +Saint Vincent and the Grenadines,VCT,1990,2.3854215,112507, +Saint Vincent and the Grenadines,VCT,1991,2.3164406,112802, +Saint Vincent and the Grenadines,VCT,1992,2.2707086,113235, +Saint Vincent and the Grenadines,VCT,1993,2.2438066,113639, +Saint Vincent and the Grenadines,VCT,1994,2.2327316,113965, +Saint Vincent and the Grenadines,VCT,1995,2.232937,114187, +Saint Vincent and the Grenadines,VCT,1996,2.23669,114301, +Saint Vincent and the Grenadines,VCT,1997,2.2420764,114290, +Saint Vincent and the Grenadines,VCT,1998,2.2437863,114177, +Saint Vincent and the Grenadines,VCT,1999,2.2405565,114009, +Saint Vincent and the Grenadines,VCT,2000,2.2304587,113832, +Saint Vincent and the Grenadines,VCT,2001,2.2148662,113653, +Saint Vincent and the Grenadines,VCT,2002,2.1983542,113466, +Saint Vincent and the Grenadines,VCT,2003,2.1839237,113119, +Saint Vincent and the Grenadines,VCT,2004,2.1679668,112621, +Saint Vincent and the Grenadines,VCT,2005,2.153256,112056, +Saint Vincent and the Grenadines,VCT,2006,2.1356628,111443, +Saint Vincent and the Grenadines,VCT,2007,2.1117306,110836, +Saint Vincent and the Grenadines,VCT,2008,2.0794706,110333, +Saint Vincent and the Grenadines,VCT,2009,2.0366213,109852, +Saint Vincent and the Grenadines,VCT,2010,1.9845903,109324, +Saint Vincent and the Grenadines,VCT,2011,1.9254801,108722, +Saint Vincent and the Grenadines,VCT,2012,1.8637577,108105, +Saint Vincent and the Grenadines,VCT,2013,1.8019311,107469, +Saint Vincent and the Grenadines,VCT,2014,1.7406843,106934, +Saint Vincent and the Grenadines,VCT,2015,1.6819481,106493,North America +Saint Vincent and the Grenadines,VCT,2016,1.6245788,105971, +Saint Vincent and the Grenadines,VCT,2017,1.5681231,105568, +Saint Vincent and the Grenadines,VCT,2018,1.5170228,105297, +Saint Vincent and the Grenadines,VCT,2019,1.4637023,104940, +Saint Vincent and the Grenadines,VCT,2020,1.4164269,104644, +Saint Vincent and the Grenadines,VCT,2021,1.3710263,104340, +Samoa,WSM,1950,15.532956,87970, +Samoa,WSM,1951,15.092799,89926, +Samoa,WSM,1952,14.640109,91964, +Samoa,WSM,1953,14.154162,93991, +Samoa,WSM,1954,13.6503935,96017, +Samoa,WSM,1955,13.137329,98054, +Samoa,WSM,1956,12.626714,100314, +Samoa,WSM,1957,12.120596,103105, +Samoa,WSM,1958,11.629999,106365, +Samoa,WSM,1959,11.161348,109822, +Samoa,WSM,1960,10.71271,113351, +Samoa,WSM,1961,10.29257,116840, +Samoa,WSM,1962,9.9042225,120183, +Samoa,WSM,1963,9.558332,123435, +Samoa,WSM,1964,11.130438,126601, +Samoa,WSM,1965,8.915678,129803, +Samoa,WSM,1966,8.593176,132991, +Samoa,WSM,1967,8.2609,135765, +Samoa,WSM,1968,7.9160547,138172, +Samoa,WSM,1969,7.566449,140473, +Samoa,WSM,1970,7.217332,142785, +Samoa,WSM,1971,6.8771105,145076, +Samoa,WSM,1972,6.553849,147348, +Samoa,WSM,1973,6.238619,149605, +Samoa,WSM,1974,5.9410763,152250, +Samoa,WSM,1975,5.6522584,155271, +Samoa,WSM,1976,5.3736677,158155, +Samoa,WSM,1977,5.112815,160371, +Samoa,WSM,1978,4.8572817,162001, +Samoa,WSM,1979,4.6235313,163497, +Samoa,WSM,1980,4.4053802,164921, +Samoa,WSM,1981,4.205236,166201, +Samoa,WSM,1982,4.024078,166899, +Samoa,WSM,1983,3.8585048,166956, +Samoa,WSM,1984,3.7036762,166790, +Samoa,WSM,1985,3.55848,166533, +Samoa,WSM,1986,3.423024,166369, +Samoa,WSM,1987,3.2978325,166786, +Samoa,WSM,1988,3.183847,167464, +Samoa,WSM,1989,3.0774229,167904, +Samoa,WSM,1990,2.9831536,168202, +Samoa,WSM,1991,2.8924928,168713, +Samoa,WSM,1992,2.8035426,169817, +Samoa,WSM,1993,2.712501,171375, +Samoa,WSM,1994,2.6206036,173120, +Samoa,WSM,1995,2.5321283,174917, +Samoa,WSM,1996,2.4481614,176732, +Samoa,WSM,1997,2.365716,178555, +Samoa,WSM,1998,2.2831397,180392, +Samoa,WSM,1999,2.2021563,182224, +Samoa,WSM,2000,2.1264758,184022, +Samoa,WSM,2001,2.0599923,185542, +Samoa,WSM,2002,2.0072286,186645, +Samoa,WSM,2003,1.9679435,187455, +Samoa,WSM,2004,1.9420967,188083, +Samoa,WSM,2005,1.9288254,188634, +Samoa,WSM,2006,1.925873,189384, +Samoa,WSM,2007,1.9299606,190490, +Samoa,WSM,2008,1.9363453,191791, +Samoa,WSM,2009,2.5687032,193189, +Samoa,WSM,2010,1.9373589,194686, +Samoa,WSM,2011,1.9288727,196364, +Samoa,WSM,2012,1.9133574,198131, +Samoa,WSM,2013,1.8960491,199952, +Samoa,WSM,2014,1.8779004,201769, +Samoa,WSM,2015,1.8593903,203586,Oceania +Samoa,WSM,2016,1.840359,205559, +Samoa,WSM,2017,1.8198189,207642, +Samoa,WSM,2018,1.7887743,209715, +Samoa,WSM,2019,1.7526175,211915, +Samoa,WSM,2020,1.7133623,214934, +Samoa,WSM,2021,1.671849,218781, +San Marino,SMR,1950,,12987, +San Marino,SMR,1951,,13188, +San Marino,SMR,1952,,13367, +San Marino,SMR,1953,,13560, +San Marino,SMR,1954,,13774, +San Marino,SMR,1955,,13990, +San Marino,SMR,1956,,14259, +San Marino,SMR,1957,,14587, +San Marino,SMR,1958,,14920, +San Marino,SMR,1959,,15249, +San Marino,SMR,1960,,15579, +San Marino,SMR,1961,,15916, +San Marino,SMR,1962,,16266, +San Marino,SMR,1963,,16603, +San Marino,SMR,1964,,16955, +San Marino,SMR,1965,,17298, +San Marino,SMR,1966,,17616, +San Marino,SMR,1967,,17931, +San Marino,SMR,1968,,18320, +San Marino,SMR,1969,,18424, +San Marino,SMR,1970,,18197, +San Marino,SMR,1971,,18239, +San Marino,SMR,1972,,18602, +San Marino,SMR,1973,,19035, +San Marino,SMR,1974,,19464, +San Marino,SMR,1975,,19830, +San Marino,SMR,1976,,20185, +San Marino,SMR,1977,,20505, +San Marino,SMR,1978,,20816, +San Marino,SMR,1979,,21111, +San Marino,SMR,1980,,21373, +San Marino,SMR,1981,,21619, +San Marino,SMR,1982,,21831, +San Marino,SMR,1983,,21999, +San Marino,SMR,1984,,22119, +San Marino,SMR,1985,2.2531595,22265, +San Marino,SMR,1986,2.0422568,22412, +San Marino,SMR,1987,1.8573911,22462, +San Marino,SMR,1988,1.68625,22569, +San Marino,SMR,1989,1.5267951,22829, +San Marino,SMR,1990,1.388983,23156, +San Marino,SMR,1991,1.2610247,23483, +San Marino,SMR,1992,1.1453054,23827, +San Marino,SMR,1993,1.0410597,24210, +San Marino,SMR,1994,0.949333,24562, +San Marino,SMR,1995,0.8655836,24906, +San Marino,SMR,1996,0.7919774,25258, +San Marino,SMR,1997,0.7240315,25618, +San Marino,SMR,1998,0.6625769,26020, +San Marino,SMR,1999,0.6094709,26427, +San Marino,SMR,2000,0.5600693,26845, +San Marino,SMR,2001,0.5166201,27358, +San Marino,SMR,2002,0.47838077,27994, +San Marino,SMR,2003,0.44425035,28627, +San Marino,SMR,2004,0.41416377,29123, +San Marino,SMR,2005,0.38734588,29530, +San Marino,SMR,2006,0.36236936,29983, +San Marino,SMR,2007,0.34040573,30395, +San Marino,SMR,2008,0.32035846,30729, +San Marino,SMR,2009,0.30146882,31080, +San Marino,SMR,2010,0.28548282,31631, +San Marino,SMR,2011,0.2709089,32518, +San Marino,SMR,2012,0.25709847,33156, +San Marino,SMR,2013,0.24468796,33307, +San Marino,SMR,2014,0.23266403,33415, +San Marino,SMR,2015,0.22167544,33593,Europe +San Marino,SMR,2016,0.21142527,33860, +San Marino,SMR,2017,0.201353,34081, +San Marino,SMR,2018,0.19297864,34179, +San Marino,SMR,2019,0.18548442,34203, +San Marino,SMR,2020,0.17836553,34038, +San Marino,SMR,2021,0.17172202,33772, +Sao Tome and Principe,STP,1950,,60993, +Sao Tome and Principe,STP,1951,,62065, +Sao Tome and Principe,STP,1952,,63046, +Sao Tome and Principe,STP,1953,,63882, +Sao Tome and Principe,STP,1954,,64635, +Sao Tome and Principe,STP,1955,,65312, +Sao Tome and Principe,STP,1956,,65933, +Sao Tome and Principe,STP,1957,,66476, +Sao Tome and Principe,STP,1958,,67009, +Sao Tome and Principe,STP,1959,,67555, +Sao Tome and Principe,STP,1960,,68061, +Sao Tome and Principe,STP,1961,,68723, +Sao Tome and Principe,STP,1962,,69533, +Sao Tome and Principe,STP,1963,,70290, +Sao Tome and Principe,STP,1964,,71009, +Sao Tome and Principe,STP,1965,9.56789,71745, +Sao Tome and Principe,STP,1966,9.26906,72584, +Sao Tome and Principe,STP,1967,9.008815,73585, +Sao Tome and Principe,STP,1968,8.79243,74756, +Sao Tome and Principe,STP,1969,8.66278,76075, +Sao Tome and Principe,STP,1970,8.600922,77601, +Sao Tome and Principe,STP,1971,8.59652,79246, +Sao Tome and Principe,STP,1972,8.640628,80850, +Sao Tome and Principe,STP,1973,8.719306,82488, +Sao Tome and Principe,STP,1974,8.781836,84205, +Sao Tome and Principe,STP,1975,8.840193,86055, +Sao Tome 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(SDG),,1991,7.51958,, +Small island developing States (SDG),,1992,7.367734,, +Small island developing States (SDG),,1993,7.2021613,, +Small island developing States (SDG),,1994,7.0405054,, +Small island developing States (SDG),,1995,6.8504286,, +Small island developing States (SDG),,1996,6.712355,, +Small island developing States (SDG),,1997,6.5059624,, +Small island developing States (SDG),,1998,6.351154,, +Small island developing States (SDG),,1999,6.1976614,, +Small island developing States (SDG),,2000,6.0310655,, +Small island developing States (SDG),,2001,5.9214096,, +Small island developing States (SDG),,2002,5.7711434,, +Small island developing States (SDG),,2003,5.648223,, +Small island developing States (SDG),,2004,5.5522447,, +Small island developing States (SDG),,2005,5.4534183,, +Small island developing States (SDG),,2006,5.359594,, +Small island developing States (SDG),,2007,5.241004,, +Small island developing States (SDG),,2008,5.0840063,, +Small island developing States (SDG),,2009,4.93816,, +Small island developing States (SDG),,2010,5.967369,, +Small island developing States (SDG),,2011,4.7059402,, +Small island developing States (SDG),,2012,4.6023135,, +Small island developing States (SDG),,2013,4.513047,, +Small island developing States (SDG),,2014,4.415863,, +Small island developing States (SDG),,2015,4.3128996,, +Small island developing States (SDG),,2016,4.2545485,, +Small island developing States (SDG),,2017,4.177396,, +Small island developing States (SDG),,2018,4.0722933,, +Small island developing States (SDG),,2019,3.9865143,, +Small island developing States (SDG),,2020,3.887589,, +Small island developing States (SDG),,2021,3.7866483,, +Solomon Islands,SLB,1950,,105227, +Solomon Islands,SLB,1951,,107135, +Solomon Islands,SLB,1952,,109328, +Solomon Islands,SLB,1953,,111773, +Solomon Islands,SLB,1954,,114461, +Solomon Islands,SLB,1955,21.494463,117322, +Solomon Islands,SLB,1956,20.523342,120300, +Solomon Islands,SLB,1957,19.62152,123385, 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+Solomon Islands,SLB,1981,5.026264,241840, +Solomon Islands,SLB,1982,4.8322616,250262, +Solomon Islands,SLB,1983,4.6646843,258962, +Solomon Islands,SLB,1984,4.511447,267920, +Solomon Islands,SLB,1985,4.369234,277169, +Solomon Islands,SLB,1986,4.2411013,286534, +Solomon Islands,SLB,1987,4.1247,295851, +Solomon Islands,SLB,1988,4.018266,305141, +Solomon Islands,SLB,1989,3.9177604,314572, +Solomon Islands,SLB,1990,3.8216362,324184, +Solomon Islands,SLB,1991,3.7225862,333960, +Solomon Islands,SLB,1992,3.620254,343929, +Solomon Islands,SLB,1993,3.521541,354097, +Solomon Islands,SLB,1994,3.4300911,364510, +Solomon Islands,SLB,1995,3.3521423,375198, +Solomon Islands,SLB,1996,3.283708,386084, +Solomon Islands,SLB,1997,3.2232761,397059, +Solomon Islands,SLB,1998,3.1691375,408151, +Solomon Islands,SLB,1999,3.1203644,419242, +Solomon Islands,SLB,2000,3.0765362,429986, +Solomon Islands,SLB,2001,3.040243,440403, +Solomon Islands,SLB,2002,3.008842,450773, +Solomon Islands,SLB,2003,2.9825966,461226, 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Africa,ZAF,1971,,23031448, +South Africa,ZAF,1972,,23698512, +South Africa,ZAF,1973,,24382514, +South Africa,ZAF,1974,12.855034,25077016, +South Africa,ZAF,1975,12.2155905,25777966, +South Africa,ZAF,1976,11.61637,26480298, +South Africa,ZAF,1977,11.033549,27199844, +South Africa,ZAF,1978,10.48049,27943454, +South Africa,ZAF,1979,9.94717,28697016, +South Africa,ZAF,1980,9.4672365,29463546, +South Africa,ZAF,1981,9.011619,30232564, +South Africa,ZAF,1982,8.584645,31022418, +South Africa,ZAF,1983,8.185794,31865176, +South Africa,ZAF,1984,7.818433,32768206, +South Africa,ZAF,1985,7.4793596,33752964, +South Africa,ZAF,1986,7.1645975,34877840, +South Africa,ZAF,1987,6.8776946,36119328, +South Africa,ZAF,1988,6.6162934,37393848, +South Africa,ZAF,1989,6.399516,38668684, +South Africa,ZAF,1990,6.2217646,39877568, +South Africa,ZAF,1991,6.0888968,40910964, +South Africa,ZAF,1992,6.010109,41760756, +South Africa,ZAF,1993,5.992739,42525444, +South Africa,ZAF,1994,6.0357313,43267980, +South Africa,ZAF,1995,6.125186,43986088, +South Africa,ZAF,1996,6.260134,44661608, +South Africa,ZAF,1997,6.4466543,45285052, +South Africa,ZAF,1998,6.658084,45852172, +South Africa,ZAF,1999,6.8605866,46364680, +South Africa,ZAF,2000,7.1005263,46813272, +South Africa,ZAF,2001,7.3302536,47229712, +South Africa,ZAF,2002,7.515958,47661516, +South Africa,ZAF,2003,7.649241,48104044, +South Africa,ZAF,2004,7.7766113,48556064, +South Africa,ZAF,2005,7.9239144,49017156, +South Africa,ZAF,2006,7.8945236,49491764, +South Africa,ZAF,2007,7.461393,49996096, +South Africa,ZAF,2008,6.8265634,50565816, +South Africa,ZAF,2009,5.990456,51170780, +South Africa,ZAF,2010,5.1587687,51784928, +South Africa,ZAF,2011,4.5475416,52443320, +South Africa,ZAF,2012,4.153277,53145036, +South Africa,ZAF,2013,3.9577127,53873608, +South Africa,ZAF,2014,3.825845,54729556, +South Africa,ZAF,2015,3.7343516,55876504,Africa +South Africa,ZAF,2016,3.6559296,56422272, +South Africa,ZAF,2017,3.582232,56641208, +South Africa,ZAF,2018,3.5085475,57339632, +South Africa,ZAF,2019,3.4338288,58087060, +South Africa,ZAF,2020,3.3742332,58801928, +South Africa,ZAF,2021,3.2847328,59392256, +South America,,1950,,113520774, +South America,,1951,,116611722, +South America,,1952,,119808344, +South America,,1953,,123095752, +South America,,1954,,126483936, +South America,,1955,,129986019, +South America,,1956,,133605548, +South America,,1957,,137348367, +South America,,1958,,141190603, +South America,,1959,,145148672, +South America,,1960,,149224306, +South America,,1961,,153377880, +South America,,1962,,157609028, +South America,,1963,,161919488, +South America,,1964,,166291510, +South America,,1965,,170706949, +South America,,1966,,175135459, +South America,,1967,,179569020, +South America,,1968,,184011029, +South America,,1969,,188469386, +South America,,1970,,192947239, +South America,,1971,,197461711, +South America,,1972,,202046231, +South America,,1973,,206713196, +South America,,1974,,211454623, +South America,,1975,,216251828, +South America,,1976,,221111805, +South America,,1977,,226098323, +South America,,1978,,231226845, +South America,,1979,,236454207, +South America,,1980,,241789097, +South America,,1981,,247206236, +South America,,1982,,252673448, +South America,,1983,,258200327, +South America,,1984,,263760622, +South America,,1985,,269320599, +South America,,1986,,274880221, +South America,,1987,,280447634, +South America,,1988,,286026582, +South America,,1989,,291601460, +South America,,1990,,297146501, +South America,,1991,,302601570, +South America,,1992,,307967492, +South America,,1993,,313302144, +South America,,1994,,318637078, +South America,,1995,,323946224, +South America,,1996,,329199341, +South America,,1997,,334414072, +South America,,1998,,339583049, +South America,,1999,,344665738, +South America,,2000,,349634344, +South America,,2001,,354475220, +South America,,2002,,359177090, +South America,,2003,,363705424, +South America,,2004,,368126871, +South America,,2005,,372502549, +South America,,2006,,376788418, +South America,,2007,,380966010, +South America,,2008,,385031415, +South America,,2009,,389045327, +South America,,2010,,393078326, +South America,,2011,,397135158, +South America,,2012,,401166879, +South America,,2013,,405147963, +South America,,2014,,409128901, +South America,,2015,,413134461, +South America,,2016,,417101806, +South America,,2017,,420982650, +South America,,2018,,424740741, +South America,,2019,,428318218, +South America,,2020,,431530105, +South America,,2021,,434254167, +South Asia (UNICEF),,1990,13.023132,, +South Asia (UNICEF),,1991,12.636515,, +South Asia (UNICEF),,1992,12.264003,, +South Asia (UNICEF),,1993,11.905939,, +South Asia (UNICEF),,1994,11.5559025,, +South Asia (UNICEF),,1995,11.1925745,, +South Asia (UNICEF),,1996,10.829089,, +South Asia (UNICEF),,1997,10.458085,, +South Asia (UNICEF),,1998,10.085112,, +South Asia (UNICEF),,1999,9.711891,, +South Asia (UNICEF),,2000,9.342236,, +South Asia (UNICEF),,2001,8.981767,, +South Asia (UNICEF),,2002,8.635915,, +South Asia (UNICEF),,2003,8.307051,, +South Asia (UNICEF),,2004,8.003712,, +South Asia (UNICEF),,2005,7.682813,, +South Asia (UNICEF),,2006,7.381008,, +South Asia (UNICEF),,2007,7.0882654,, +South Asia (UNICEF),,2008,6.7954245,, +South Asia (UNICEF),,2009,6.51947,, +South Asia (UNICEF),,2010,6.2331934,, +South Asia (UNICEF),,2011,5.95971,, +South Asia (UNICEF),,2012,5.6921096,, +South Asia (UNICEF),,2013,5.4285064,, +South Asia (UNICEF),,2014,5.173881,, +South Asia (UNICEF),,2015,4.922854,, +South Asia (UNICEF),,2016,4.68288,, +South Asia (UNICEF),,2017,4.463466,, +South Asia (UNICEF),,2018,4.252613,, +South Asia (UNICEF),,2019,4.058672,, +South Asia (UNICEF),,2020,3.8800697,, +South Asia (UNICEF),,2021,3.7059104,, +South Georgia and the South Sandwich Islands,SGS,2015,,,South America +South Korea,KOR,1950,36.331314,20104016, +South Korea,KOR,1951,30.7624,20116896, +South Korea,KOR,1952,26.167492,20393376, +South Korea,KOR,1953,22.418026,20862402, +South Korea,KOR,1954,19.37907,21432886, +South Korea,KOR,1955,16.998251,22075012, +South Korea,KOR,1956,15.15659,22763458, +South Korea,KOR,1957,13.736295,23494054, +South Korea,KOR,1958,12.664853,24256850, +South Korea,KOR,1959,11.842546,25036094, +South Korea,KOR,1960,11.190193,25817530, +South Korea,KOR,1961,10.651789,26552230, +South Korea,KOR,1962,10.175204,27234476, +South Korea,KOR,1963,9.694917,27905876, +South Korea,KOR,1964,9.204897,28555858, +South Korea,KOR,1965,8.694628,29179406, +South Korea,KOR,1966,8.169943,29838228, +South Korea,KOR,1967,7.6356254,30540604, +South Korea,KOR,1968,7.1076217,31229640, +South Korea,KOR,1969,6.598971,31912022, +South Korea,KOR,1970,6.1294513,32601146, +South Korea,KOR,1971,5.7140403,33288372, +South Korea,KOR,1972,5.357882,33976860, +South Korea,KOR,1973,5.0567465,34659716, +South Korea,KOR,1974,4.807109,35319340, +South Korea,KOR,1975,4.593423,35930700, +South Korea,KOR,1976,4.4050736,36456328, +South 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Sudan,SSD,2001,17.266361,6394433, +South Sudan,SSD,2002,16.401733,6686105, +South Sudan,SSD,2003,15.553868,6992367, +South Sudan,SSD,2004,14.752571,7317127, +South Sudan,SSD,2005,14.008756,7662659, +South Sudan,SSD,2006,13.3239565,8029516, +South Sudan,SSD,2007,12.67406,8417830, +South Sudan,SSD,2008,12.041878,8823896, +South Sudan,SSD,2009,11.4688,9229234, +South Sudan,SSD,2010,10.914204,9714425, +South Sudan,SSD,2011,10.44998,10243057, +South Sudan,SSD,2012,10.101551,10701605, +South Sudan,SSD,2013,9.921022,11106037, +South Sudan,SSD,2014,9.871318,11213286, +South Sudan,SSD,2015,9.868964,11194300,Africa +South Sudan,SSD,2016,9.868964,11066116, +South Sudan,SSD,2017,9.868964,10658230, +South Sudan,SSD,2018,9.868964,10395332, +South Sudan,SSD,2019,9.868964,10447677, +South Sudan,SSD,2020,9.868964,10606224, +South Sudan,SSD,2021,9.868964,10748278, +South-Eastern Asia (SDG),,1990,7.2041383,, +South-Eastern Asia (SDG),,1991,6.904359,, +South-Eastern Asia (SDG),,1992,6.61247,, 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(SDG),,2015,2.7792668,, +South-Eastern Asia (SDG),,2016,2.701473,, +South-Eastern Asia (SDG),,2017,2.6226835,, +South-Eastern Asia (SDG),,2018,2.5476027,, +South-Eastern Asia (SDG),,2019,2.4730477,, +South-Eastern Asia (SDG),,2020,2.3996496,, +South-Eastern Asia (SDG),,2021,2.3289108,, +Southern Asia (SDG),,1990,12.694868,, +Southern Asia (SDG),,1991,12.316481,, +Southern Asia (SDG),,1992,11.960709,, +Southern Asia (SDG),,1993,11.627183,, +Southern Asia (SDG),,1994,11.313504,, +Southern Asia (SDG),,1995,10.982104,, +Southern Asia (SDG),,1996,10.641321,, +Southern Asia (SDG),,1997,10.287654,, +Southern Asia (SDG),,1998,9.925081,, +Southern Asia (SDG),,1999,9.557598,, +Southern Asia (SDG),,2000,9.192766,, +Southern Asia (SDG),,2001,8.836355,, +Southern Asia (SDG),,2002,8.493515,, +Southern Asia (SDG),,2003,8.167643,, +Southern Asia (SDG),,2004,7.8643913,, +Southern Asia (SDG),,2005,7.5424323,, +Southern Asia (SDG),,2006,7.2385306,, +Southern Asia (SDG),,2007,6.9438043,, +Southern Asia (SDG),,2008,6.6497297,, +Southern Asia (SDG),,2009,6.3737383,, +Southern Asia (SDG),,2010,6.08883,, +Southern Asia (SDG),,2011,5.8157544,, +Southern Asia (SDG),,2012,5.5470552,, +Southern Asia (SDG),,2013,5.283538,, +Southern Asia (SDG),,2014,5.0285363,, +Southern Asia (SDG),,2015,4.782811,, +Southern Asia (SDG),,2016,4.5501847,, +Southern Asia (SDG),,2017,4.336833,, +Southern Asia (SDG),,2018,4.138192,, +Southern Asia (SDG),,2019,3.9597328,, +Southern Asia (SDG),,2020,3.7907383,, +Southern Asia (SDG),,2021,3.623916,, +Spain,ESP,1950,10.343167,28069696, +Spain,ESP,1951,9.229969,28207082, +Spain,ESP,1952,8.348163,28404774, +Spain,ESP,1953,7.670057,28633540, +Spain,ESP,1954,7.1657386,28862382, +Spain,ESP,1955,6.795508,29095376, +Spain,ESP,1956,6.5024557,29329782, +Spain,ESP,1957,6.24009,29577444, +Spain,ESP,1958,5.9842334,29857444, +Spain,ESP,1959,5.727705,30150510, +Spain,ESP,1960,5.4688087,30415594, +Spain,ESP,1961,5.204925,30711868, +Spain,ESP,1962,4.9408464,31052630, 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Lanka,LKA,1971,6.9287925,12654656, +Sri Lanka,LKA,1972,6.851472,12914457, +Sri Lanka,LKA,1973,6.7544417,13169738, +Sri Lanka,LKA,1974,6.605806,13418276, +Sri Lanka,LKA,1975,6.395994,13662872, +Sri Lanka,LKA,1976,6.112982,13910170, +Sri Lanka,LKA,1977,5.765926,14159059, +Sri Lanka,LKA,1978,5.3833256,14414817, +Sri Lanka,LKA,1979,4.9892616,14680394, +Sri Lanka,LKA,1980,4.6198473,14943644, +Sri Lanka,LKA,1981,4.297747,15198920, +Sri Lanka,LKA,1982,3.9975648,15438754, +Sri Lanka,LKA,1983,3.6815553,15658448, +Sri Lanka,LKA,1984,3.3430722,15872577, +Sri Lanka,LKA,1985,3.0186534,16092339, +Sri Lanka,LKA,1986,2.738053,16317998, +Sri Lanka,LKA,1987,2.520922,16547143, +Sri Lanka,LKA,1988,2.3815045,16773736, +Sri Lanka,LKA,1989,2.7069669,16990582, +Sri Lanka,LKA,1990,2.2824516,17204098, +Sri Lanka,LKA,1991,2.2478883,17416596, +Sri Lanka,LKA,1992,2.199601,17624462, +Sri Lanka,LKA,1993,2.1388702,17825610, +Sri Lanka,LKA,1994,2.0673838,18011746, +Sri Lanka,LKA,1995,1.9923639,18177570, +Sri Lanka,LKA,1996,1.9196752,18322504, +Sri Lanka,LKA,1997,1.8501318,18449120, +Sri Lanka,LKA,1998,1.7812107,18560806, +Sri Lanka,LKA,1999,1.7134587,18670408, +Sri Lanka,LKA,2000,1.6521295,18776374, +Sri Lanka,LKA,2001,1.600301,18920278, +Sri Lanka,LKA,2002,1.5559708,19110706, +Sri Lanka,LKA,2003,1.5139736,19303182, +Sri Lanka,LKA,2004,2.8857045,19490434, +Sri Lanka,LKA,2005,1.4104508,19673866, +Sri Lanka,LKA,2006,1.3356377,19870706, +Sri Lanka,LKA,2007,1.2576369,20078652, +Sri Lanka,LKA,2008,1.1952604,20285640, +Sri Lanka,LKA,2009,2.0934803,20482484, +Sri Lanka,LKA,2010,1.1218226,20668556, +Sri Lanka,LKA,2011,1.083713,20859746, +Sri Lanka,LKA,2012,1.0374973,21017150, +Sri Lanka,LKA,2013,0.9851939,21131762, +Sri Lanka,LKA,2014,0.9306739,21239448, +Sri Lanka,LKA,2015,0.87841284,21336692,Asia +Sri Lanka,LKA,2016,0.83246964,21425494, +Sri Lanka,LKA,2017,0.7930187,21506816, +Sri Lanka,LKA,2018,0.7584399,21580712, +Sri Lanka,LKA,2019,0.7277492,21649664, +Sri Lanka,LKA,2020,0.70039,21715076, 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(SDG),,2011,9.898929,, +Sub-Saharan Africa (SDG),,2012,9.567301,, +Sub-Saharan Africa (SDG),,2013,9.263441,, +Sub-Saharan Africa (SDG),,2014,8.99519,, +Sub-Saharan Africa (SDG),,2015,8.748624,, +Sub-Saharan Africa (SDG),,2016,8.502775,, +Sub-Saharan Africa (SDG),,2017,8.272789,, +Sub-Saharan Africa (SDG),,2018,8.031673,, +Sub-Saharan Africa (SDG),,2019,7.804345,, +Sub-Saharan Africa (SDG),,2020,7.585541,, +Sub-Saharan Africa (SDG),,2021,7.3721404,, +Sub-Saharan Africa (UNICEF),,1990,17.884157,, +Sub-Saharan Africa (UNICEF),,1991,17.72996,, +Sub-Saharan Africa (UNICEF),,1992,17.57673,, +Sub-Saharan Africa (UNICEF),,1993,17.441462,, +Sub-Saharan Africa (UNICEF),,1994,17.429136,, +Sub-Saharan Africa (UNICEF),,1995,17.00034,, +Sub-Saharan Africa (UNICEF),,1996,16.714348,, +Sub-Saharan Africa (UNICEF),,1997,16.401009,, +Sub-Saharan Africa (UNICEF),,1998,16.02787,, +Sub-Saharan Africa (UNICEF),,1999,15.589394,, +Sub-Saharan Africa (UNICEF),,2000,15.109472,, +Sub-Saharan Africa (UNICEF),,2001,14.588048,, +Sub-Saharan Africa (UNICEF),,2002,14.041888,, +Sub-Saharan Africa (UNICEF),,2003,13.491101,, +Sub-Saharan Africa (UNICEF),,2004,12.94467,, +Sub-Saharan Africa (UNICEF),,2005,12.418845,, +Sub-Saharan Africa (UNICEF),,2006,11.925297,, +Sub-Saharan Africa (UNICEF),,2007,11.441687,, +Sub-Saharan Africa (UNICEF),,2008,10.977786,, +Sub-Saharan Africa (UNICEF),,2009,10.543542,, +Sub-Saharan Africa (UNICEF),,2010,10.157123,, +Sub-Saharan Africa (UNICEF),,2011,9.807321,, +Sub-Saharan Africa (UNICEF),,2012,9.481583,, +Sub-Saharan Africa (UNICEF),,2013,9.181646,, +Sub-Saharan Africa (UNICEF),,2014,8.915307,, +Sub-Saharan Africa (UNICEF),,2015,8.6694355,, +Sub-Saharan Africa (UNICEF),,2016,8.424507,, +Sub-Saharan Africa (UNICEF),,2017,8.195444,, +Sub-Saharan Africa (UNICEF),,2018,7.9561334,, +Sub-Saharan Africa (UNICEF),,2019,7.7302117,, +Sub-Saharan Africa (UNICEF),,2020,7.512834,, +Sub-Saharan Africa (UNICEF),,2021,7.301496,, +Sudan,SDN,1950,,6191328, 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States,USA,2005,0.7961977,296842656, +United States,USA,2006,0.78607786,299753088, +United States,USA,2007,0.77434266,302743392, +United States,USA,2008,0.7613894,305694912, +United States,USA,2009,0.7475947,308512032, +United States,USA,2010,0.7336587,311182848, +United States,USA,2011,0.72034514,313876608, +United States,USA,2012,0.7082055,316651328, +United States,USA,2013,0.697497,319375168, +United States,USA,2014,0.6879688,322033952, +United States,USA,2015,0.67921585,324607776,North America +United States,USA,2016,0.6707162,327210208, +United States,USA,2017,0.6620765,329791232, +United States,USA,2018,0.65311426,332140032, +United States,USA,2019,0.6439842,334319680, +United States,USA,2020,0.634569,335942016, +United States,USA,2021,0.624298,336997632, +United States Minor Outlying Islands,UMI,2015,,,Oceania +United States Virgin Islands,VIR,1950,,27100, +United States Virgin Islands,VIR,1951,,27710, +United States Virgin Islands,VIR,1952,,28091, +United States Virgin Islands,VIR,1953,,28460, +United States Virgin Islands,VIR,1954,,28877, +United States Virgin Islands,VIR,1955,,29329, +United States Virgin Islands,VIR,1956,,29823, +United States Virgin Islands,VIR,1957,,30373, +United States Virgin Islands,VIR,1958,,30974, +United States Virgin Islands,VIR,1959,,31626, +United States Virgin Islands,VIR,1960,,32883, +United States Virgin Islands,VIR,1961,,35018, +United States Virgin Islands,VIR,1962,,37572, +United States Virgin Islands,VIR,1963,,40285, +United States Virgin Islands,VIR,1964,,43132, +United States Virgin Islands,VIR,1965,,46304, +United States Virgin Islands,VIR,1966,,49722, +United States Virgin Islands,VIR,1967,,53180, +United States Virgin Islands,VIR,1968,,56635, +United States Virgin Islands,VIR,1969,,60031, +United States Virgin Islands,VIR,1970,,63464, +United States Virgin Islands,VIR,1971,,67131, +United States Virgin Islands,VIR,1972,,70912, +United States Virgin Islands,VIR,1973,,74551, +United States Virgin Islands,VIR,1974,,78012, +United States Virgin Islands,VIR,1975,,81383, +United States Virgin Islands,VIR,1976,,84723, +United States Virgin Islands,VIR,1977,,88002, +United States Virgin Islands,VIR,1978,,91159, +United States Virgin Islands,VIR,1979,,94250, +United States Virgin Islands,VIR,1980,,96654, +United States Virgin Islands,VIR,1981,,98168, +United States Virgin Islands,VIR,1982,,99412, +United States Virgin Islands,VIR,1983,,100557, +United States Virgin Islands,VIR,1984,,101460, +United States Virgin Islands,VIR,1985,,101923, +United States Virgin Islands,VIR,1986,,101934, +United States Virgin Islands,VIR,1987,,101593, +United States Virgin Islands,VIR,1988,,101129, +United States Virgin Islands,VIR,1989,,100760, +United States Virgin Islands,VIR,1990,,100699, +United States Virgin Islands,VIR,1991,,100991, +United States Virgin Islands,VIR,1992,,101546, +United States Virgin Islands,VIR,1993,,102157, +United States Virgin Islands,VIR,1994,,102653, +United States Virgin Islands,VIR,1995,,103468, +United States Virgin Islands,VIR,1996,,104648, +United States Virgin Islands,VIR,1997,,105801, +United States Virgin Islands,VIR,1998,,106879, +United States Virgin Islands,VIR,1999,,107842, +United States Virgin Islands,VIR,2000,,108199, +United States Virgin Islands,VIR,2001,,108019, +United States Virgin Islands,VIR,2002,,107742, +United States Virgin Islands,VIR,2003,,107303, +United States Virgin Islands,VIR,2004,,106758, +United States Virgin Islands,VIR,2005,,106499, +United States Virgin Islands,VIR,2006,,106531, +United States Virgin Islands,VIR,2007,,106524, +United States Virgin Islands,VIR,2008,,106493, +United States Virgin Islands,VIR,2009,,106476, +United States Virgin Islands,VIR,2010,,106154, +United States Virgin Islands,VIR,2011,,105491, +United States Virgin Islands,VIR,2012,,104780, +United States Virgin Islands,VIR,2013,,104070, +United States Virgin Islands,VIR,2014,,103393, +United States Virgin Islands,VIR,2015,,102808,North America +United States Virgin Islands,VIR,2016,,102330, +United States Virgin Islands,VIR,2017,,101860, +United States Virgin Islands,VIR,2018,,101377, +United States Virgin Islands,VIR,2019,,100837, +United States Virgin Islands,VIR,2020,,100452, +United States Virgin Islands,VIR,2021,,100100, +Upper-middle-income countries,,1950,,918865980, +Upper-middle-income countries,,1951,,936488346, +Upper-middle-income countries,,1952,,956135860, +Upper-middle-income countries,,1953,,977180204, +Upper-middle-income countries,,1954,,998585321, +Upper-middle-income countries,,1955,,1021053483, +Upper-middle-income countries,,1956,,1043522165, +Upper-middle-income countries,,1957,,1067110863, +Upper-middle-income countries,,1958,,1090929731, +Upper-middle-income countries,,1959,,1109551013, +Upper-middle-income countries,,1960,,1122079651, +Upper-middle-income countries,,1961,,1134055566, +Upper-middle-income countries,,1962,,1154442670, +Upper-middle-income countries,,1963,,1184787557, +Upper-middle-income countries,,1964,,1216528861, +Upper-middle-income countries,,1965,,1246756365, +Upper-middle-income countries,,1966,,1276824449, +Upper-middle-income countries,,1967,,1305914467, +Upper-middle-income countries,,1968,,1336327932, +Upper-middle-income countries,,1969,,1368462120, +Upper-middle-income countries,,1970,,1400588420, +Upper-middle-income countries,,1971,,1432397676, +Upper-middle-income countries,,1972,,1463253261, +Upper-middle-income countries,,1973,,1493577365, +Upper-middle-income countries,,1974,,1522998449, +Upper-middle-income countries,,1975,,1550620448, +Upper-middle-income countries,,1976,,1577158789, +Upper-middle-income countries,,1977,,1602899568, +Upper-middle-income countries,,1978,,1627539626, +Upper-middle-income countries,,1979,,1652710622, +Upper-middle-income countries,,1980,,1679054459, +Upper-middle-income countries,,1981,,1706088992, +Upper-middle-income countries,,1982,,1734572338, +Upper-middle-income countries,,1983,,1763046849, +Upper-middle-income countries,,1984,,1790931059, +Upper-middle-income countries,,1985,,1819847676, +Upper-middle-income countries,,1986,,1850542202, +Upper-middle-income countries,,1987,,1883081918, +Upper-middle-income countries,,1988,,1915432562, +Upper-middle-income countries,,1989,,1946975318, +Upper-middle-income countries,,1990,5.2167654,1978674629, +Upper-middle-income countries,,1991,5.099152,2007371520, +Upper-middle-income countries,,1992,4.9640083,2031690983, +Upper-middle-income countries,,1993,4.81438,2054704372, +Upper-middle-income countries,,1994,4.642042,2076865377, +Upper-middle-income countries,,1995,4.462596,2097969970, +Upper-middle-income countries,,1996,4.2802,2118266453, +Upper-middle-income countries,,1997,4.0937376,2137669038, +Upper-middle-income countries,,1998,3.9060326,2156208218, +Upper-middle-income countries,,1999,3.7176576,2174058070, +Upper-middle-income countries,,2000,3.5267277,2191949480, +Upper-middle-income countries,,2001,3.3253398,2209701540, +Upper-middle-income countries,,2002,3.126473,2226823411, +Upper-middle-income countries,,2003,2.9356256,2243462002, +Upper-middle-income countries,,2004,2.7604795,2260098040, +Upper-middle-income countries,,2005,2.5998068,2276903910, +Upper-middle-income countries,,2006,2.4474108,2293429307, +Upper-middle-income countries,,2007,2.290219,2309893739, +Upper-middle-income countries,,2008,2.1426587,2327278207, +Upper-middle-income countries,,2009,1.9974457,2345622340, +Upper-middle-income countries,,2010,1.8660583,2364144387, +Upper-middle-income countries,,2011,1.757644,2382503938, +Upper-middle-income countries,,2012,1.6463058,2402146692, +Upper-middle-income countries,,2013,1.5668387,2423354868, +Upper-middle-income countries,,2014,1.4849609,2444604237, +Upper-middle-income countries,,2015,1.4213719,2464672367, +Upper-middle-income countries,,2016,1.3516481,2482967003, +Upper-middle-income countries,,2017,1.279889,2500616723, +Upper-middle-income countries,,2018,1.257701,2516785800, +Upper-middle-income countries,,2019,1.2193989,2530546049, +Upper-middle-income countries,,2020,1.2012602,2541463877, +Upper-middle-income countries,,2021,1.1750195,2548718303, +Uruguay,URY,1950,6.6637297,2234152, +Uruguay,URY,1951,6.3644743,2256080, +Uruguay,URY,1952,6.149493,2280664, +Uruguay,URY,1953,6.024707,2307492, +Uruguay,URY,1954,5.9938283,2336183, +Uruguay,URY,1955,6.0500917,2366211, +Uruguay,URY,1956,6.161397,2397448, +Uruguay,URY,1957,6.2912745,2429788, +Uruguay,URY,1958,6.3953238,2462879, +Uruguay,URY,1959,6.423552,2496167, +Uruguay,URY,1960,6.351777,2529028, +Uruguay,URY,1961,6.218919,2561156, +Uruguay,URY,1962,6.070071,2592448, +Uruguay,URY,1963,5.9333434,2622951, +Uruguay,URY,1964,5.8316765,2652381, +Uruguay,URY,1965,5.7731605,2680426, +Uruguay,URY,1966,5.7355747,2707032, +Uruguay,URY,1967,5.691098,2731850, +Uruguay,URY,1968,5.6238866,2754425, +Uruguay,URY,1969,5.521319,2773964, +Uruguay,URY,1970,5.3981266,2790270, +Uruguay,URY,1971,5.315561,2805497, 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Futuna,WLF,1979,,10928, +Wallis and Futuna,WLF,1980,,11341, +Wallis and Futuna,WLF,1981,,11764, +Wallis and Futuna,WLF,1982,,12215, +Wallis and Futuna,WLF,1983,,12533, +Wallis and Futuna,WLF,1984,,12690, +Wallis and Futuna,WLF,1985,,12815, +Wallis and Futuna,WLF,1986,,12942, +Wallis and Futuna,WLF,1987,,13066, +Wallis and Futuna,WLF,1988,,13195, +Wallis and Futuna,WLF,1989,,13335, +Wallis and Futuna,WLF,1990,,13479, +Wallis and Futuna,WLF,1991,,13618, +Wallis and Futuna,WLF,1992,,13765, +Wallis and Futuna,WLF,1993,,13924, +Wallis and Futuna,WLF,1994,,14069, +Wallis and Futuna,WLF,1995,,14199, +Wallis and Futuna,WLF,1996,,14313, +Wallis and Futuna,WLF,1997,,14412, +Wallis and Futuna,WLF,1998,,14513, +Wallis and Futuna,WLF,1999,,14617, +Wallis and Futuna,WLF,2000,,14748, +Wallis and Futuna,WLF,2001,,14872, +Wallis and Futuna,WLF,2002,,15012, +Wallis and Futuna,WLF,2003,,15059, +Wallis and Futuna,WLF,2004,,14875, +Wallis and Futuna,WLF,2005,,14574, +Wallis and Futuna,WLF,2006,,14283, 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math4mad +code-fold: true +--- + + +:::{.callout-note title="ref"} +[Dumbbell plot in ggplot2](https://r-charts.com/distribution/dumbbell-plot-ggplot2/) +::: + +```{julia} +include("utils.jl") +``` + + +```{julia} +R""" +# Seed +set.seed(1) + +# Data +customers <- sample(50:150, 10) +potential_customers <- sample(150:500, 10) +company <- LETTERS[1:10] + +# Data frame +df <- data.frame(company = company, + x = customers, + y = potential_customers) +""" +df=@rget df +rename!(df,:x=>:customers,:y=>:potential_customers) +sort(df,:company) +#df2=@pivot_longer(df, customers:potential_customers) + +``` + + + +```{julia} + fig=Figure() + ax=Axis(fig[1,1]) +# for (idx,row) in enumerate(eachrow(df)) + +# hlines!(ax,[idx]; xmin =[row.customers], xmax = [row.potential_customers]) + +# end +# hlines!(ax, 1:10;xmin=[df.customers...], xmax =[df.potential_customers...], color = :blue) + fig +``` \ No newline at end of file diff --git a/vis/utils.jl b/vis/utils.jl index efed576..2ffaaee 100644 --- a/vis/utils.jl +++ b/vis/utils.jl @@ -3,6 +3,7 @@ using MakieThemes using StatsBase using AlgebraOfGraphics using RCall +using ScientificTypes Makie.set_theme!(ggthemr(:dust))