From daff6891f08c900f34f82fa3113768c07f41cd20 Mon Sep 17 00:00:00 2001 From: Joachim Gassen Date: Sat, 5 Dec 2020 17:19:15 +0100 Subject: [PATCH] Documentation for 0.5.2 --- _pkgdown.yml | 69 ++++- docs/LICENSE-text.html | 2 +- docs/articles/ExPanD_notebook.html | 19 +- .../figure-html/bar_chart-1.png | Bin 80960 -> 80947 bytes .../figure-html/scatter_plot1-1.png | Bin 537247 -> 536315 bytes .../figure-html/scatter_plot2-1.png | Bin 531554 -> 530451 bytes .../lightable-0.0.1/lightable.css | 272 ++++++++++++++++++ docs/articles/ExPanDaR-functions.html | 240 +++------------- .../figure-html/bgtg_plot-1.png | Bin 0 -> 129748 bytes .../figure-html/missing_obs-1.png | Bin 68936 -> 69095 bytes .../figure-html/quantile_plot-1.png | Bin 79341 -> 83454 bytes .../figure-html/scatter_plot-1.png | Bin 710446 -> 709329 bytes .../figure-html/time_trend_plot-1.png | Bin 60802 -> 60713 bytes .../lightable-0.0.1/lightable.css | 272 ++++++++++++++++++ docs/articles/customize_ExPanD.html | 14 +- docs/articles/index.html | 2 +- docs/articles/use_ExPanD.html | 16 +- docs/authors.html | 2 +- docs/index.html | 4 +- docs/news/index.html | 32 ++- docs/reference/ExPanD.html | 42 ++- .../reference/ExPanD_config_russell_3000.html | 2 +- docs/reference/ExPanD_config_worldbank.html | 2 +- docs/reference/ExPanDaR.html | 2 +- docs/reference/index.html | 10 +- .../reference/prepare_by_group_bar_graph.html | 12 +- .../prepare_by_group_trend_graph-1.png | Bin 0 -> 189641 bytes .../prepare_by_group_trend_graph.html | 238 +++++++++++++++ .../prepare_by_group_violin_graph.html | 12 +- docs/reference/prepare_correlation_graph.html | 2 +- docs/reference/prepare_correlation_table.html | 5 +- docs/reference/prepare_descriptive_table.html | 9 +- docs/reference/prepare_ext_obs_table.html | 14 +- .../prepare_missing_values_graph-1.png | Bin 75586 -> 75813 bytes .../prepare_missing_values_graph-2.png | Bin 69196 -> 69187 bytes .../prepare_missing_values_graph.html | 5 +- .../prepare_quantile_trend_graph-1.png | Bin 122170 -> 170885 bytes .../prepare_quantile_trend_graph-2.png | Bin 127070 -> 133073 bytes .../prepare_quantile_trend_graph.html | 17 +- docs/reference/prepare_regression_table.html | 78 +++-- docs/reference/prepare_scatter_plot-1.png | Bin 178028 -> 177823 bytes docs/reference/prepare_scatter_plot.html | 13 +- docs/reference/prepare_trend_graph-1.png | Bin 105959 -> 105776 bytes docs/reference/prepare_trend_graph.html | 9 +- docs/reference/russell_3000-1.png | Bin 75586 -> 75813 bytes docs/reference/russell_3000.html | 4 +- docs/reference/russell_3000_data_def.html | 2 +- docs/reference/treat_outliers.html | 5 +- docs/reference/worldbank-1.png | Bin 153782 -> 154586 bytes docs/reference/worldbank.html | 12 +- docs/reference/worldbank_data_def.html | 2 +- docs/reference/worldbank_var_def.html | 2 +- 52 files changed, 1100 insertions(+), 343 deletions(-) create mode 100644 docs/articles/ExPanD_notebook_files/lightable-0.0.1/lightable.css create mode 100644 docs/articles/ExPanDaR-functions_files/figure-html/bgtg_plot-1.png create mode 100644 docs/articles/ExPanDaR-functions_files/lightable-0.0.1/lightable.css create mode 100644 docs/reference/prepare_by_group_trend_graph-1.png create mode 100644 docs/reference/prepare_by_group_trend_graph.html diff --git a/_pkgdown.yml b/_pkgdown.yml index a86a8aa..e844bdb 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -12,21 +12,68 @@ navbar: href: reference/index.html - text: News href: news/index.html + - text: Articles + menu: + - text: ExPanD + - text: Using ExPanD for Panel Data Exploration + href: articles/use_ExPanD.html + - text: Customize ExPanD + href: articles/customize_ExPanD.html + - text: Using the ExPanD Notebook function + href: articles/ExPanD_notebook.html + - text: ------- + - text: "'ExPanDaR' functions" + - text: Using the functions of the 'ExPanDaR' package + href: articles/ExPanDaR-functions.html right: - text: Code repository icon: fa-github - href: https://www.github.com/joachim-gassen/tidycovid19 + href: https://www.github.com/joachim-gassen/ExPanDaR + +articles: +- title: ExPanD + desc: These vignettes describe how to use and customize the ExPanD app + contents: + - use_ExPanD + - customize_ExPanD + - ExPanD_notebook +- title: "'ExPanDaR' Functions" + desc: A vignette that features the underlying functions included in the 'ExPanDaR' package + contents: + - ExPanDaR-functions reference: -- title: Data functions - desc: Functions to download Covid-19 related data from authorative sources +- title: ExPanD app + desc: Shiny-based app for interactive exploratory data analysis + contents: + - ExPanD +- title: Workflow functions + desc: Functions, mostly also used by ExPanD, that facilitate a typical exploratory data analysis workflow contents: - - download_jhu_csse_covid19_data - - download_acaps_npi_data - - download_google_trends_data - - download_wbank_data - - download_merged_data -- title: Visualization function - desc: A customizable function to display the spread of Covid-19, inspired by the displays by John Burn-Murdoch from the Financial Times + - prepare_missing_values_graph + - treat_outliers + - prepare_descriptive_table + - prepare_ext_obs_table + - prepare_by_group_bar_graph + - prepare_by_group_trend_graph + - prepare_by_group_violin_graph + - prepare_trend_graph + - prepare_quantile_trend_graph + - prepare_correlation_table + - prepare_correlation_graph + - prepare_scatter_plot + - prepare_regression_table +- title: Data + desc: Data set used to showcase the functionality of ExPanD. + 'russell_3000' is a set of financial accounting and stock return data + for a sample of U.S. firms that are members of the Russell 3000 index. + 'worldbank' is a country year panel of macro-economic data provided + by the World Bank API. contents: - - plot_covid19_spread + - russell_3000 + - russell_3000_data_def + - ExPanD_config_russell_3000 + - worldbank + - worldbank_data_def + - worldbank_var_def + - ExPanD_config_worldbank diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index ed78cea..1548e7e 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -73,7 +73,7 @@ ExPanDaR - 0.5.1 + 0.5.2 diff --git a/docs/articles/ExPanD_notebook.html b/docs/articles/ExPanD_notebook.html index e68127c..4fc6491 100644 --- a/docs/articles/ExPanD_notebook.html +++ b/docs/articles/ExPanD_notebook.html @@ -37,7 +37,7 @@ ExPanDaR - 0.5.1 + 0.5.2 @@ -97,13 +97,14 @@ -
+ +
+
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Let’s assume that in addition you want to see how cash flows from operations relate to stock returns. Easy! Just replace "nioa", the name of the net income variable with "cfoa", the name of the cash flow from operations variable.

-
df <- smp
-df <- df[, c("coid", "coname", "period", "cfoa", "return", "sector", "toas")]
-df <- df[complete.cases(df), ]
-df$sector <- as.factor(df$sector)
-prepare_scatter_plot(df, "cfoa", "return", color = "sector", size = "toas", loess = 1)
+
df <- smp
+df <- df[, c("coid", "coname", "period", "cfoa", "return", "sector", "toas")]
+df <- df[complete.cases(df), ]
+df$sector <- as.factor(df$sector)
+prepare_scatter_plot(df, "cfoa", "return", color = "sector", size = "toas", loess = 1)
+
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Looks very similar, right? Now you are all set. Feel free to modify and extend your analysis along all possible dimensions. Code away and enjoy!

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  • @@ -97,13 +97,14 @@ -
    + +
    @@ -1149,18 +1154,18 @@

    Regression Tables

    Finally, if you happen to be a fan of starred numbers, you can also quickly produce regression tables by using the function prepare_regression_table() that calls lfe::felm() for OLS and glm() for binary logit models. The tables are then constructed by calling stargazer::stargazer(), allowing for plain text, html and latex output.

    You can construct tables by mixing different models…

    -
    dvs <- c("return", "return", "return", "return", "return", "return")
    -idvs <- list(c("nioa"), 
    -             c("cfoa"), 
    -             c("accoa"), 
    -             c("cfoa", "accoa"), 
    -             c("nioa", "accoa"), 
    -             c("nioa", "accoa")) 
    -feffects <- list("period", "period", "period", 
    -                 c("period", "coid"), c("period", "coid"), c("period", "coid"))
    -clusters <- list("", "", "", c("coid"), c("coid"), c("period", "coid"))
    -t <- prepare_regression_table(r3win, dvs, idvs, feffects, clusters)
    -htmltools::HTML(t$table)
    +
    dvs <- c("return", "return", "return", "return", "return", "return")
    +idvs <- list(c("nioa"), 
    +             c("cfoa"), 
    +             c("accoa"), 
    +             c("cfoa", "accoa"), 
    +             c("nioa", "accoa"), 
    +             c("nioa", "accoa")) 
    +feffects <- list("period", "period", "period", 
    +                 c("coid", "period"), c("coid", "period"), c("coid", "period"))
    +clusters <- list("", "", "", "coid", "coid", c("coid", "period"))
    +t <- prepare_regression_table(r3win, dvs, idvs, feffects, clusters)
    +htmltools::HTML(t$table)
    @@ -1199,7 +1204,7 @@

    - @@ -1259,7 +1264,8 @@

    - + @@ -1268,7 +1274,7 @@

    - + @@ -1294,9 +1300,9 @@

    - - - + + + @@ -1305,7 +1311,7 @@

    - + @@ -1319,22 +1325,22 @@

    - - - - - - + + + + + + - - - - - - + + + + + + @@ -1344,161 +1350,9 @@

    1.794*** 1.794** +1.794***
    -0.890*** -0.890-0.890** +
    (0.116) (0.303) (0.314)(0.440)(0.439)
    period period periodperiod, coidperiod, coidperiod, coidcoid, periodcoid, periodcoid, period
    Std. errors clusteredNo coid coidperiod, coidcoid, period
    Observations
    R2 0.0540.0470.0350.3410.3440.3440.0230.0150.0040.0130.0180.018
    Adjusted R2 0.0540.0460.035-0.044-0.039-0.0390.0230.0150.003-0.564-0.556-0.556

    … or by applying one model on different sub-samples.

    -
    t <- prepare_regression_table(r3win, "return", c("nioa", "accoa"), byvar="period")
    -htmltools::HTML(t$table)
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Dependent variable:
    return
    Full SampleFY2014FY2015FY2016
    (1)(2)(3)(4)
    nioa0.801*** -0.234* -0.607*** -1.375*** -
    (0.074)(0.139)(0.123)(0.119)
    accoa-0.0670.0140.638*** --1.224*** -
    (0.132)(0.240)(0.205)(0.233)
    Constant0.082*** -0.130*** -0.0140.096*** -
    (0.006)(0.011)(0.011)(0.011)
    Estimatorolsolsolsols
    Fixed effectsNoneNoneNoneNone
    Std. errors clusteredNoNoNoNo
    Observations6,0021,8172,0332,152
    R2 -0.0230.0020.0330.058
    Adjusted R2 -0.0230.0010.0320.057
    Note: -*p<0.1; **p<0.05; ***p<0.01
    +
    t <- prepare_regression_table(r3win, "return", c("nioa", "accoa"), byvar="period")
    +htmltools::HTML(t$table)
    + % Error: Unrecognized object type. % Error: Unrecognized object type. % Error: Unrecognized object type. % Error: Unrecognized object type.

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    @@ -103,7 +103,7 @@

    Customize ExPanD

    Joachim Gassen

    -

    2020-01-29

    +

    2020-12-05

    @@ -193,14 +193,18 @@

    A graph that visualizes the distribution of one chosen variable over time +by_group_trend_graph +A grouped time trend graph that visualizes the development of one variable across groups over time + + corrplot A visual representation of Spearman and Pearson correlations of the numerical and logical variables in the sample. Exact correlations are displayed when you hover above the respective cell with your mouse - + scatter_plot A scatter plot, where you can present up to 4 dimensions of your data (x, y, size, color). You can choose whether you want a LOESS line to be displayed and whether you want your sample to be limited to 1,000 observations - + regression A regression component where you can estimate a linear or logit regression, with up to two fixed effect levels and standard error clustering @@ -488,7 +492,7 @@

    df_def = df_def, config_list = clist, html_blocks = html_blocks)

    -

    This is it. Now we have a customized version of ExPanD focusing on communicating the robustness of the Preston Curve. If you do not want to run the code yourself, you can take a quick look at the customized version here.

    +

    This is it. Now we have a customized version of ExPanD focusing on communicating the robustness of the Preston Curve. If you do not want to run the code yourself, you can take a quick look at the customized version here.

    If you want to take customization to the next level, you can always fork the code of the ‘ExPanDaR’ package on GitHub. The code for the shiny app is in the inst/app folder of the package.

    Enjoy.

    diff --git a/docs/articles/index.html b/docs/articles/index.html index 62c2c14..3e0d2c0 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -73,7 +73,7 @@ ExPanDaR - 0.5.1 + 0.5.2
    diff --git a/docs/articles/use_ExPanD.html b/docs/articles/use_ExPanD.html index 190b067..107d95e 100644 --- a/docs/articles/use_ExPanD.html +++ b/docs/articles/use_ExPanD.html @@ -37,7 +37,7 @@ ExPanDaR - 0.5.1 + 0.5.2 @@ -103,7 +103,7 @@

    Using ExPanD for Panel Data Exploration

    Joachim Gassen

    -

    2020-01-29

    +

    2020-12-05

    @@ -119,17 +119,17 @@

    2020-01-29

  • provide users the opportunity to assess the robustness of your findings without providing them access to the underlying data.
  • This vignette will guide you through the process of using ExPanD by discussing three use cases. While the first two use macro-economic data to explore the association of gross domestic production (GDP) per capita with life expectancy at birth, the last use case explores the association between financial accounting performance measures and concurrent stock returns.

    -

    If you do not use R you can still use the ExPanD app to explore panel data! In this case, access the hosted variant of the ExPanD app here and follow the advice below on how to upload a suitable panel data file for online exploration. No worries: Your data won’t be stored on the server and will be deleted from memory once the connection to the server is closed.

    +

    If you do not use R you can still use the ExPanD app to explore panel data! In this case, access the hosted variant of the ExPanD app here and follow the advice below on how to upload a suitable panel data file for online exploration. No worries: Your data won’t be stored on the server and will be deleted from memory once the connection to the server is closed.

    Starting ExPanD to upload a local file containing panel data

    The easiest way to start using ExPanD is to use it with a local data file containing panel data. ExPanD supports Stata, SAS, CSV, Excel and R file formats.

    For using ExPanD from within R, you have to install the ExPanDaR package and start ExPanD.

    -
    devtools::install_github("joachim-gassen/ExPanDaR")
    +
    devtools::install_github("joachim-gassen/ExPanDaR")
     library(ExPanDaR)
     
     ExPanD()
    -

    Alternatively, you can simply access the online hosted version of the ExPanD app here (no R required).

    +

    Alternatively, you can simply access the online hosted version of the ExPanD app here (no R required).

    After starting ExPanD, you will be greeted with a bare bones file upload dialog.

    Now you need a file to explore. Feel free to use whatever you want but for our first use case I will use the well-known gapminder dataset provided by the gapminder package (click here if you do not know the gapminder initiative).

    @@ -166,7 +166,7 @@

    Starting ExPanD with a data frame containing panel data

    Alternatively, if you are using R you can bypass the file upload dialog by specifying a data frame and its cross-sectional as well as time-series identifiers.

    -
    devtools::install_github("joachim-gassen/ExPanDaR")
    +
    devtools::install_github("joachim-gassen/ExPanDaR")
     library(ExPanDaR)
     library(gapminder)
     
    @@ -179,7 +179,7 @@ 

    As can be seen by the bar chart, the gapminder dataset provides a balanced panel of 142 countries with 12 observations per country. The missing values graph shows no missing data across all variables. When you scroll down, you will see that the dataset contains three numerical variables. Play around with the histogram and the extreme observation table to learn more about these. The time trend graph and the quantile time trend communicate good news: the life expectancy is increasing over time world-wide. You can verify that the same holds true for the population of the sample countries and for their GDP per capita. You will also notice that for the latter two the cross-country distribution widens over time.

    The gapminder dataset is often used to document the strong positive association between GDP per capita and life expectancy. You can see this association in the correlation plot. The blue ellipsoid above (below) the diagonal visualizes the positive Pearson (Spearman) correlation of the two variables. If you are interested in the exact correlation values, hover over the ellipsoid with your mouse.

    -

    The scatter plot and the regression analysis section allow you to explore this association in a little bit more detail. Below you will see a screenshot where I prepared a “Hans Rosling” scatter plot (click here if you do not know the name). In addition, I estimated a by region OLS model with country fixed effects and standard errors clustered by country to verify that the association is not just driven by unobservable time-constant country heterogeneity.

    +

    The scatter plot and the regression analysis section allow you to explore this association in a little bit more detail. Below you will see a screenshot where I prepared a “Hans Rosling” scatter plot (click here if you do not know the name). In addition, I estimated a by region OLS model with country fixed effects and standard errors clustered by country to verify that the association is not just driven by unobservable time-constant country heterogeneity.

    Looking at the scatter plot you notice that there are some observations with extremely high GDP per capita that trigger the LOESS smoother line to get a negative slope. If you hover over the dots with your mouse you will see that these are observations from Kuwait. To what extent are our regression results affected by these extreme observations? To figure this out, scroll up and select to winsorize your data at the 1 % level. After doing this, the figure from above now looks like this.

    @@ -286,7 +286,7 @@

    Making ExPanD available to users without providing them raw data access

    In many instances, you as a researcher want to provide readers, editors and/or reviewers the opportunity to assess the robustness of your findings but are unable to provide them with the underlying data as this data is subject to commercial licensing or other usage restrictions. By uploading your ExPanD instance on a public server, you can make your results accessible without sharing the data. As shiny apps can be hosted on dedicated servers, this is easily achieved. Maybe you noticed the “Publish” icon in the top-right corner of your ExPanD window. Click on it to learn more about your options.

    -

    As an example, you can access an ExPanD-based extension of a current working paper of our team here.

    +

    As an example, you can access an ExPanD-based extension of a current working paper of our team here.

    diff --git a/docs/authors.html b/docs/authors.html index 0771628..9b8c4c7 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -73,7 +73,7 @@ ExPanDaR - 0.5.1 + 0.5.2
    diff --git a/docs/index.html b/docs/index.html index f59113a..ec126fd 100644 --- a/docs/index.html +++ b/docs/index.html @@ -44,7 +44,7 @@ ExPanDaR - 0.5.1 + 0.5.2
    @@ -141,7 +141,7 @@

    if (!require("devtools")) {
       install.packages("devtools")
     }
    -devtools::install_github("joachim-gassen/ExPanDaR")
    +devtools::install_github("joachim-gassen/ExPanDaR")
     library(ExPanDaR)
    diff --git a/docs/news/index.html b/docs/news/index.html index f8c1ff0..8569631 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -73,7 +73,7 @@ ExPanDaR - 0.5.1 + 0.5.2
    @@ -141,9 +141,32 @@

    Changelog

    +
    +

    +ExPanDaR 0.5.2 Unreleased +

    +

    Extensions:

    + +

    Bug fixes:

    +
      +
    • Fixed notebook code for sub sampling (failed on sampling variables containing NAs)

    • +
    • Fixed a small issue in the Notebook generation code for the time trend and quantile trend chunks

    • +
    • Fixed display of prepare_regression_table() for logit model by group tables with fixed effects

    • +
    +

    Minor Issues:

    +
      +
    • Replaced lfe with plm package for fixed effect models as lfe has been removed from CRAN

    • +
    • Removed wbstats from Suggests as it is currently not on CRAN

    • +
    • Removed tidyquant package from Suggests as it depends on a package that requires R >= 3.5.0

    • +
    • Spelling

    • +
    +

    -ExPanDaR 0.5.1 Unreleased +ExPanDaR 0.5.1 2020-01-29

    Extensions:

      @@ -157,7 +180,7 @@

      • Updated World Bank data

      • Allowed ExPanD() to be called with ts_id == NULL and cs_id != NULL so that data frame variables can be specified as cross-sectional identifiers in the cross-sectional mode

      • -
      • Removed some artefacts in cross-sectional mode where cross-sectional and time series indentifiers were still provided as variable options in the ExPanD() app

      • +
      • Removed some artefacts in cross-sectional mode where cross-sectional and time series identifiers were still provided as variable options in the ExPanD() app

      • Included manual zip package import in NAMESPACE to make devtoools::check() happy on various CRAN systems

      • Changed shinyapps.io URLs in vignettes to plain HTTP as win-builder seems to have an issue with the SSL handshake of shinyapps.io

      @@ -228,7 +251,7 @@

    • Added packages used by ExPanD() to NAMESPACE to make CRAN checks happy

    • UI cleanups

    • Fixed the definition of oint_ta in r3 dataset

    • -
    • Added a check to verify that ts_ids provided as ordered vectors

    • +
    • Added a check to verify that ts_id is provided as an ordered vector

    • Fixed a typo in worldbank_var_def

    • Removed the drop_undersore parameter in prepare_regression_table() (no longer needed)

    • Work-around for special characters in stargazer column.labels

    • @@ -251,6 +274,7 @@

      Contents

      @@ -154,18 +154,30 @@

      Explore Your Data (ExPanD)

    -
    ExPanD(df = NULL, cs_id = NULL, ts_id = NULL, df_def = NULL,
    -  var_def = NULL, config_list = NULL,
    -  title = "ExPanD - Explore your data!", abstract = NULL,
    -  df_name = deparse(substitute(df)), long_def = TRUE,
    -  factor_cutoff = 10L, components = c(sample_selection = TRUE,
    -  subset_factor = TRUE, grouping = TRUE, bar_chart = TRUE, missing_values =
    -  TRUE, udvars = TRUE, descriptive_table = TRUE, histogram = TRUE, ext_obs
    -  = TRUE, by_group_bar_graph = TRUE, by_group_violin_graph = TRUE,
    -  trend_graph = TRUE, quantile_trend_graph = TRUE, corrplot = TRUE,
    -  scatter_plot = TRUE, regression = TRUE), html_blocks = NULL,
    -  export_nb_option = FALSE, store_encrypted = FALSE,
    -  key_phrase = "What a wonderful key", debug = FALSE, ...)
    +
    ExPanD(
    +  df = NULL,
    +  cs_id = NULL,
    +  ts_id = NULL,
    +  df_def = NULL,
    +  var_def = NULL,
    +  config_list = NULL,
    +  title = "ExPanD - Explore your data!",
    +  abstract = NULL,
    +  df_name = deparse(substitute(df)),
    +  long_def = TRUE,
    +  factor_cutoff = 10L,
    +  components = c(sample_selection = TRUE, subset_factor = TRUE, grouping = TRUE,
    +    bar_chart = TRUE, missing_values = TRUE, udvars = TRUE, descriptive_table = TRUE,
    +    histogram = TRUE, ext_obs = TRUE, by_group_bar_graph = TRUE, by_group_violin_graph =
    +    TRUE, trend_graph = TRUE, quantile_trend_graph = TRUE, by_group_trend_graph = TRUE,
    +    corrplot = TRUE, scatter_plot = TRUE, regression = TRUE),
    +  html_blocks = NULL,
    +  export_nb_option = FALSE,
    +  store_encrypted = FALSE,
    +  key_phrase = "What a wonderful key",
    +  debug = FALSE,
    +  ...
    +)

    Arguments

    @@ -192,7 +204,7 @@

    Arg ExPanD will verify that it has the same levels for each data frame and throw an error otherwise. If cs_id and ts_id are not provided either directly of by df_def, the data is treated as -cross-sectional,observations are identifed by row names and +cross-sectional,observations are identified by row names and only appropriate displays are included. df_def overrides if provided.

    @@ -295,7 +307,7 @@

    Arg

    +runApp.

    ...

    Additional parameters that are passed on to -runApp.

    diff --git a/docs/reference/ExPanD_config_russell_3000.html b/docs/reference/ExPanD_config_russell_3000.html index 8403b8a..2d62657 100644 --- a/docs/reference/ExPanD_config_russell_3000.html +++ b/docs/reference/ExPanD_config_russell_3000.html @@ -75,7 +75,7 @@ ExPanDaR - 0.5.1 + 0.5.2 diff --git a/docs/reference/ExPanD_config_worldbank.html b/docs/reference/ExPanD_config_worldbank.html index 9923044..82dd56c 100644 --- a/docs/reference/ExPanD_config_worldbank.html +++ b/docs/reference/ExPanD_config_worldbank.html @@ -75,7 +75,7 @@ ExPanDaR - 0.5.1 + 0.5.2 diff --git a/docs/reference/ExPanDaR.html b/docs/reference/ExPanDaR.html index 9e586b5..be93d9f 100644 --- a/docs/reference/ExPanDaR.html +++ b/docs/reference/ExPanDaR.html @@ -77,7 +77,7 @@ ExPanDaR - 0.5.1 + 0.5.2 diff --git a/docs/reference/index.html b/docs/reference/index.html index da2dfd4..d9208e4 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -73,7 +73,7 @@ ExPanDaR - 0.5.1 + 0.5.2 @@ -201,6 +201,12 @@

    prepare_by_group_trend_graph()

    + +

    Prepares a By Group Trend Graph

    + +

    prepare_by_group_violin_graph()

    @@ -272,7 +278,7 @@

    <

    worldbank

    -

    A Snapshot of Macroeconomic Data as Provided by the World Bank API (1960 - 2016)

    +

    A Snapshot of Macroeconomic Data as Provided by the World Bank API (1960 - 2018)

    diff --git a/docs/reference/prepare_by_group_bar_graph.html b/docs/reference/prepare_by_group_bar_graph.html index bb96f14..05932c0 100644 --- a/docs/reference/prepare_by_group_bar_graph.html +++ b/docs/reference/prepare_by_group_bar_graph.html @@ -77,7 +77,7 @@ ExPanDaR - 0.5.1 + 0.5.2 @@ -154,8 +154,14 @@

    Prepares a by Group Bar Graph

    -
    prepare_by_group_bar_graph(df, by_var, var, stat_fun = mean,
    -  order_by_stat = FALSE, color = "red")
    +
    prepare_by_group_bar_graph(
    +  df,
    +  by_var,
    +  var,
    +  stat_fun = mean,
    +  order_by_stat = FALSE,
    +  color = "red"
    +)

    Arguments

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    +
    + + + +
    + +
    +
    + + +
    + +

    Reads a data frame and line plots the selected variables (which need to be numeric) + by group and an ordered factor (normally the time-series indicator).

    + +
    + +
    prepare_by_group_trend_graph(
    +  df,
    +  ts_id,
    +  group_var,
    +  var,
    +  points = TRUE,
    +  error_bars = FALSE
    +)
    + +

    Arguments

    +
    + + + + + + + + + + + + + + + + + + + + + + + + + +
    df

    Data frame containing the ordered factor and a set of numerical variables +to be plotted.

    ts_id

    a string containing the column name of the ordered factor +(normally the time-series indicator).

    group_var

    a variable coercible into a factor to group the data on.

    var

    The name of the variable that you want to plot.

    points

    Do you want points to indicate the observations? Defaults to TRUE.

    error_bars

    Do you want error bars to be plotted? Defaults to FALSE.

    + +

    Value

    + +

    A list containing two items:

    +
    "df"

    A data frame containing the plotted means and standard errors by group

    +
    "plot

    The plot as returned by ggplot

    +
    + + + +

    Examples

    +
    df <- worldbank +df$gdp_capita <- worldbank$NY.GDP.PCAP.KD +graph <- prepare_by_group_trend_graph(df, "year", "region", "gdp_capita") +graph$plot
    + + + + +
    + + +
    +

    Site built with pkgdown 1.3.0.

    +
    +
    + + + + + + + diff --git a/docs/reference/prepare_by_group_violin_graph.html b/docs/reference/prepare_by_group_violin_graph.html index 12cd014..ea3f612 100644 --- a/docs/reference/prepare_by_group_violin_graph.html +++ b/docs/reference/prepare_by_group_violin_graph.html @@ -76,7 +76,7 @@ ExPanDaR - 0.5.1 + 0.5.2 @@ -152,8 +152,14 @@

    Prepares a by Group Violin Graph

    -
    prepare_by_group_violin_graph(df, by_var, var, order_by_mean = FALSE,
    -  group_on_y = TRUE, ...)
    +
    prepare_by_group_violin_graph(
    +  df,
    +  by_var,
    +  var,
    +  order_by_mean = FALSE,
    +  group_on_y = TRUE,
    +  ...
    +)

    Arguments

    diff --git a/docs/reference/prepare_correlation_graph.html b/docs/reference/prepare_correlation_graph.html index 40069a9..7cc70b3 100644 --- a/docs/reference/prepare_correlation_graph.html +++ b/docs/reference/prepare_correlation_graph.html @@ -77,7 +77,7 @@ ExPanDaR - 0.5.1 + 0.5.2 diff --git a/docs/reference/prepare_correlation_table.html b/docs/reference/prepare_correlation_table.html index 052f567..636ea04 100644 --- a/docs/reference/prepare_correlation_table.html +++ b/docs/reference/prepare_correlation_table.html @@ -76,7 +76,7 @@ ExPanDaR - 0.5.1 + 0.5.2 @@ -152,8 +152,7 @@

    Prepares a Correlation Table

    -
    prepare_correlation_table(df, digits = 2, bold = 0.05,
    -  format = "html", ...)
    +
    prepare_correlation_table(df, digits = 2, bold = 0.05, format = "html", ...)

    Arguments

    diff --git a/docs/reference/prepare_descriptive_table.html b/docs/reference/prepare_descriptive_table.html index 287942b..93d1a9b 100644 --- a/docs/reference/prepare_descriptive_table.html +++ b/docs/reference/prepare_descriptive_table.html @@ -78,7 +78,7 @@ ExPanDaR - 0.5.1 + 0.5.2 @@ -156,8 +156,11 @@

    Prepares a Table of Descriptive Statistics

    -
    prepare_descriptive_table(df, digits = c(0, 3, 3, 3, 3, 3, 3, 3),
    -  format = "html")
    +
    prepare_descriptive_table(
    +  df,
    +  digits = c(0, 3, 3, 3, 3, 3, 3, 3),
    +  format = "html"
    +)

    Arguments

    diff --git a/docs/reference/prepare_ext_obs_table.html b/docs/reference/prepare_ext_obs_table.html index fc041ad..eca568d 100644 --- a/docs/reference/prepare_ext_obs_table.html +++ b/docs/reference/prepare_ext_obs_table.html @@ -75,7 +75,7 @@ ExPanDaR - 0.5.1 + 0.5.2 @@ -150,9 +150,15 @@

    Prepares a Table Displaying Extreme Observations

    -
    prepare_ext_obs_table(df, n = 5, cs_id = NULL, ts_id = NULL,
    -  var = utils::tail(colnames(df[sapply(df, is.numeric) & (!colnames(df)
    -  %in% c(cs_id, ts_id))]), n = 1), ...)
    +
    prepare_ext_obs_table(
    +  df,
    +  n = 5,
    +  cs_id = NULL,
    +  ts_id = NULL,
    +  var = utils::tail(colnames(df[sapply(df, is.numeric) & (!colnames(df) %in% c(cs_id,
    +    ts_id))]), n = 1),
    +  ...
    +)

    Arguments

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    Prepares a Graph Displaying Missing Values in Panel Data

    -
    prepare_missing_values_graph(df, ts_id, no_factors = FALSE,
    -  binary = FALSE)
    +
    prepare_missing_values_graph(df, ts_id, no_factors = FALSE, binary = FALSE)

    Arguments

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z17`rgb^?d@=_CCU7NoyTt3hK~-?f(C;%{d35G>gTj^T-E`KUSMh$ZT<wR!M5OyoWE(5b{0`h9HD;XBr0XGd@qio- z|N9e4_wu;~CHL!fnz-I~N!4=Xh3wK+B-LW-7GpQe_cJI(2*8)1!*IFMeL?kmhPl;ISz;$numnXZlbC&rt)_br_?7YBv2|H$ql z&uyNILY4yobqxRYe0Ie;N6;=@LPoy$M7Sie?@O#t4vjxwpyxdYcdG`k+K2SWyL?Tx zpkYcrVcyMVSisKgzcnOPbx9W>^&lA*3)z}^^#S?HBe_DShzFockueIkR` z!dclIGL=9{=EzUTb&_D9P%}WRU?xEbDt7cIpSIpTL7UsMPbrgr#GxuYt3A>@*$W42 z1Z;t|(z-v(*

    Prepares a Quantile Trend Graph

    -
    prepare_quantile_trend_graph(df, ts_id, quantiles = c(0.05, 0.25, 0.5,
    -  0.75, 0.95), var = utils::tail(colnames(df[sapply(df, is.numeric) &
    -  colnames(df) != ts_id]), n = 1))
    +
    prepare_quantile_trend_graph(
    +  df,
    +  ts_id,
    +  quantiles = c(0.05, 0.25, 0.5, 0.75, 0.95),
    +  var = utils::tail(colnames(df[sapply(df, is.numeric) & colnames(df) != ts_id]), n =
    +    1),
    +  points = TRUE
    +)

    Arguments

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b/docs/reference/prepare_quantile_trend_graph.html index 487275f..b1c95c7 100644 --- a/docs/reference/prepare_quantile_trend_graph.html +++ b/docs/reference/prepare_quantile_trend_graph.html @@ -77,7 +77,7 @@ ExPanDaR - 0.5.1 + 0.5.2 @@ -154,9 +154,14 @@

    @@ -181,6 +186,10 @@

    Arg to be plotted. Defaults to the last numerical variable of the data frame that is not ts_id.

    +

    + + +
    points

    Do you want points to indicate the statistics? Defaults to TRUE.

    Value

    diff --git a/docs/reference/prepare_regression_table.html b/docs/reference/prepare_regression_table.html index e9c5987..6fbd05d 100644 --- a/docs/reference/prepare_regression_table.html +++ b/docs/reference/prepare_regression_table.html @@ -75,7 +75,7 @@ ExPanDaR - 0.5.1 + 0.5.2 @@ -150,9 +150,16 @@

    Prepares a Regression Table

    -
    prepare_regression_table(df, dvs, idvs, feffects = rep("", length(dvs)),
    -  clusters = rep("", length(dvs)), models = rep("auto", length(dvs)),
    -  byvar = "", format = "html")
    +
    prepare_regression_table(
    +  df,
    +  dvs,
    +  idvs,
    +  feffects = rep("", length(dvs)),
    +  clusters = rep("", length(dvs)),
    +  models = rep("auto", length(dvs)),
    +  byvar = "",
    +  format = "html"
    +)

    Arguments

    @@ -202,15 +209,20 @@

    Value

    Details

    -

    This is a wrapper function calling the stargazer package. Depending on whether the dependent variable - is numeric, logical or a factor with two levels, the models are estimated - using felm (for numeric dependent variables) - or glm (with family = binomial(link="logit")) (for two-level factors or logical variables). - You can override this behavior by specifying the model with the models parameter. - Multinomial logit models are not supported. - For glm, clustered standard errors are estimated using - cluster.vcov. - If run with byvar, only levels that have more observations than coefficients are estimated.

    +

    This is a wrapper function calling the stargazer package. For numeric + dependent variables the models are estimated using lm + for models without and plm for models with fixed + effects. Binary dependent variable models are estimated using + glm (with family = binomial(link="logit")). + You can override this behavior by specifying the model with the + models parameter. Multinomial logit models are not supported. + Clustered standard errors are estimated using plm's robust + covariance matrix estimators for OLS and + cluster.vcov for logit models. + Only up to two dimensions are supported for fixed effects and standard + error clusters need to be also present as fixed effects. + If run with byvar, only levels that have more observations than + coefficients are estimated.

    Examples

    @@ -237,7 +249,7 @@

    Examp #> [11] "DAX 0.864*** 0.664*** -0.188 " #> [12] " (0.169) (0.141) (0.180) " #> [13] " " -#> [14] "CAC 0.735*** -0.204 0.188 " +#> [14] "CAC 0.735*** -0.204 0.188* " #> [15] " (0.111) (0.218) (0.102) " #> [16] " " #> [17] "FTSE -0.124 0.722*** 0.112 " @@ -248,39 +260,15 @@

    Examp #> [22] "Fixed effects year year year year " #> [23] "Std. errors clustered year year year year " #> [24] "Observations 1,860 1,860 1,860 1,860 " -#> [25] "R2 0.994 0.995 0.981 0.989 " -#> [26] "Adjusted R2 0.994 0.995 0.981 0.989 " +#> [25] "R2 0.900 0.885 0.811 0.747 " +#> [26] "Adjusted R2 0.900 0.884 0.810 0.746 " #> [27] "=========================================================" #> [28] "Note: *p<0.1; **p<0.05; ***p<0.01"
    t <- prepare_regression_table(df, "DAX", c("SMI", "CAC", "FTSE"), byvar="year", format = "text") -print(t$table)
    #> [1] "" -#> [2] "==================================================================================================================================" -#> [3] " Dependent variable: " -#> [4] " ------------------------------------------------------------------------------------------------------------" -#> [5] " DAX " -#> [6] " Full Sample 1991 1992 1993 1994 1995 1996 1997 1998 " -#> [7] " (1) (2) (3) (4) (5) (6) (7) (8) (9) " -#> [8] "----------------------------------------------------------------------------------------------------------------------------------" -#> [9] "SMI 0.493*** 0.634*** -0.283*** 0.445*** -0.265*** 0.333*** 0.189*** 0.258*** 0.244*** " -#> [10] " (0.015) (0.068) (0.056) (0.021) (0.039) (0.043) (0.020) (0.026) (0.043) " -#> [11] " " -#> [12] "CAC 0.496*** -0.052 0.952*** 0.516*** 0.721*** 0.304*** 0.577*** 1.019*** 1.182*** " -#> [13] " (0.015) (0.045) (0.026) (0.033) (0.052) (0.060) (0.039) (0.075) (0.054) " -#> [14] " " -#> [15] "FTSE -0.017 0.066 -0.069** 0.134*** -0.085** 0.017 0.389*** 0.195*** -0.378*** " -#> [16] " (0.021) (0.046) (0.035) (0.040) (0.039) (0.049) (0.027) (0.045) (0.045) " -#> [17] " " -#> [18] "Constant -175.946*** 458.772*** 575.676*** -688.784*** 1,623.337*** 567.057*** -803.680*** -1,353.434*** 1,146.634***" -#> [19] " (44.666) (72.607) (72.806) (56.941) (65.786) (105.476) (68.069) (110.585) (174.146) " -#> [20] " " -#> [21] "----------------------------------------------------------------------------------------------------------------------------------" -#> [22] "Estimator ols ols ols ols ols ols ols ols ols " -#> [23] "Fixed effects None None None None None None None None None " -#> [24] "Std. errors clustered No No No No No No No No No " -#> [25] "Observations 1,860 131 260 260 260 260 260 260 169 " -#> [26] "R2 0.990 0.669 0.850 0.973 0.560 0.720 0.932 0.964 0.978 " -#> [27] "Adjusted R2 0.990 0.661 0.848 0.973 0.555 0.717 0.931 0.964 0.978 " -#> [28] "==================================================================================================================================" -#> [29] "Note: *p<0.1; **p<0.05; ***p<0.01"
    +print(t$table)
    #> [1] "" "% Error: Unrecognized object type." +#> [3] "% Error: Unrecognized object type." "% Error: Unrecognized object type." +#> [5] "% Error: Unrecognized object type." "% Error: Unrecognized object type." +#> [7] "% Error: Unrecognized object type." "% Error: Unrecognized object type." +#> [9] "% Error: Unrecognized object type." "% Error: Unrecognized object type."
    @@ -150,8 +150,15 @@

    Prepares a Scatter Plot

    -
    prepare_scatter_plot(df, x, y, color = "", size = "", loess = 0,
    -  alpha = min(1, 1/((1 + (max(0, log(nrow(df)) - log(100)))))))
    +
    prepare_scatter_plot(
    +  df,
    +  x,
    +  y,
    +  color = "",
    +  size = "",
    +  loess = 0,
    +  alpha = min(1, 1/((1 + (max(0, log(nrow(df)) - log(100))))))
    +)

    Arguments

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    Prepares a Trend Graph

    -
    prepare_trend_graph(df, ts_id, var = colnames(df[sapply(df, is.numeric) &
    -  colnames(df) != ts_id]))
    +
    prepare_trend_graph(
    +  df,
    +  ts_id,
    +  var = colnames(df[sapply(df, is.numeric) & colnames(df) != ts_id])
    +)

    Arguments

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    FormatSource

    -

    Has been collected using the tq_get function family in Summer 2017. +

    Has been collected using the tidyquant::tq_get function family in Summer 2017. The code to generate this data is available in the github repository of this package. As the Google Finance API providing financial statement data is currently unavailable, diff --git a/docs/reference/russell_3000_data_def.html b/docs/reference/russell_3000_data_def.html index d25dc5f..2bebb3e 100644 --- a/docs/reference/russell_3000_data_def.html +++ b/docs/reference/russell_3000_data_def.html @@ -77,7 +77,7 @@ ExPanDaR - 0.5.1 + 0.5.2 diff --git a/docs/reference/treat_outliers.html b/docs/reference/treat_outliers.html index 9e5e033..e158c2f 100644 --- a/docs/reference/treat_outliers.html +++ b/docs/reference/treat_outliers.html @@ -75,7 +75,7 @@ ExPanDaR - 0.5.1 + 0.5.2 @@ -150,8 +150,7 @@

    Treats Outliers in Numerical Data

    -
    treat_outliers(x, percentile = 0.01, truncate = FALSE, by = NULL,
    -  ...)
    +
    treat_outliers(x, percentile = 0.01, truncate = FALSE, by = NULL, ...)

    Arguments

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    @@ -158,8 +158,8 @@

    FormatSource

    -

    Has been collected using the wb function - from the World Bank API in March 2018. +

    Has been collected using the wbstats::wb() function + from the World Bank API in Dec 2020. The code to generate this data is available in the github repository of this package. Use in scientific studies is not advised without prior cleaning/checking.

    diff --git a/docs/reference/worldbank_data_def.html b/docs/reference/worldbank_data_def.html index fab4270..bd31426 100644 --- a/docs/reference/worldbank_data_def.html +++ b/docs/reference/worldbank_data_def.html @@ -77,7 +77,7 @@ ExPanDaR - 0.5.1 + 0.5.2
    diff --git a/docs/reference/worldbank_var_def.html b/docs/reference/worldbank_var_def.html index 54ffba5..98a7d33 100644 --- a/docs/reference/worldbank_var_def.html +++ b/docs/reference/worldbank_var_def.html @@ -76,7 +76,7 @@ ExPanDaR - 0.5.1 + 0.5.2