# load the ggplot2 library to get the diamonds dataset
library(ggplot2)
# look at the diamonds dataset
diamonds## # A tibble: 53,940 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
## # ... with 53,930 more rows
You can see here that it prints out a portion of the dataset, this is because this is an tibble object, which is an extension of the core data.frame object in R.
If you work with any package that Hadley Whickham has written, be prepared to see tibbles.
They (for the most part) will work exactly the same as data.frame objects
# get 1 column from the dataframe
diamonds$caratI'm not showing the output for the code becuse there are too many values to print to the screen The results of using the $ notation for subsetting will be a vector of values
# get 1 column using bracket notation
# bracket noation [row, columns]
diamonds[, 'carat']## # A tibble: 53,940 x 1
## carat
## <dbl>
## 1 0.23
## 2 0.21
## 3 0.23
## 4 0.29
## 5 0.31
## 6 0.24
## 7 0.24
## 8 0.26
## 9 0.22
## 10 0.23
## # ... with 53,930 more rows
You can visually see the difference between the [ ] and $ notation of subsetting. [ ] will return a dataframe (or in this case, a tibble) back
# get multiple columns from the dataset
diamonds[, c('carat', 'price')]## # A tibble: 53,940 x 2
## carat price
## <dbl> <int>
## 1 0.23 326
## 2 0.21 326
## 3 0.23 327
## 4 0.29 334
## 5 0.31 335
## 6 0.24 336
## 7 0.24 336
## 8 0.26 337
## 9 0.22 337
## 10 0.23 338
## # ... with 53,930 more rows
# various ways of getting rows from data
diamonds[1, ]## # A tibble: 1 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
diamonds[10, ]## # A tibble: 1 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39
diamonds[c(1, 2), ]## # A tibble: 2 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
diamonds[c(11:20), ]## # A tibble: 10 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.30 Good J SI1 64.0 55 339 4.25 4.28 2.73
## 2 0.23 Ideal J VS1 62.8 56 340 3.93 3.90 2.46
## 3 0.22 Premium F SI1 60.4 61 342 3.88 3.84 2.33
## 4 0.31 Ideal J SI2 62.2 54 344 4.35 4.37 2.71
## 5 0.20 Premium E SI2 60.2 62 345 3.79 3.75 2.27
## 6 0.32 Premium E I1 60.9 58 345 4.38 4.42 2.68
## 7 0.30 Ideal I SI2 62.0 54 348 4.31 4.34 2.68
## 8 0.30 Good J SI1 63.4 54 351 4.23 4.29 2.70
## 9 0.30 Good J SI1 63.8 56 351 4.23 4.26 2.71
## 10 0.30 Very Good J SI1 62.7 59 351 4.21 4.27 2.66
diamonds[11:20, ]## # A tibble: 10 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.30 Good J SI1 64.0 55 339 4.25 4.28 2.73
## 2 0.23 Ideal J VS1 62.8 56 340 3.93 3.90 2.46
## 3 0.22 Premium F SI1 60.4 61 342 3.88 3.84 2.33
## 4 0.31 Ideal J SI2 62.2 54 344 4.35 4.37 2.71
## 5 0.20 Premium E SI2 60.2 62 345 3.79 3.75 2.27
## 6 0.32 Premium E I1 60.9 58 345 4.38 4.42 2.68
## 7 0.30 Ideal I SI2 62.0 54 348 4.31 4.34 2.68
## 8 0.30 Good J SI1 63.4 54 351 4.23 4.29 2.70
## 9 0.30 Good J SI1 63.8 56 351 4.23 4.26 2.71
## 10 0.30 Very Good J SI1 62.7 59 351 4.21 4.27 2.66
# getting rows and columns from your data
diamonds[11:20, c('cut', 'color')]## # A tibble: 10 x 2
## cut color
## <ord> <ord>
## 1 Good J
## 2 Ideal J
## 3 Premium F
## 4 Ideal J
## 5 Premium E
## 6 Premium E
## 7 Ideal I
## 8 Good J
## 9 Good J
## 10 Very Good J
Tibbles are nice in that they don't print all the rows out to the screen by default. If you end up working with other dataframe-like objects, they might print out more rows of data you will care to look at
# get the first 6 rows of the dataset
head(diamonds)## # A tibble: 6 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
# get the last 6 rows of the dataset
tail(diamonds)## # A tibble: 6 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.72 Premium D SI1 62.7 59 2757 5.69 5.73 3.58
## 2 0.72 Ideal D SI1 60.8 57 2757 5.75 5.76 3.50
## 3 0.72 Good D SI1 63.1 55 2757 5.69 5.75 3.61
## 4 0.70 Very Good D SI1 62.8 60 2757 5.66 5.68 3.56
## 5 0.86 Premium H SI2 61.0 58 2757 6.15 6.12 3.74
## 6 0.75 Ideal D SI2 62.2 55 2757 5.83 5.87 3.64
# get numner of rows and columns
dim(diamonds)## [1] 53940 10
nrow(diamonds)## [1] 53940
ncol(diamonds)## [1] 10
# get the number of columns from dim
dim(diamonds)[2]## [1] 10
# look at the built in r dataset, cars
head(cars)## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
# use the class function to see what you have
class(diamonds)## [1] "tbl_df" "tbl" "data.frame"
class(cars)## [1] "data.frame"
# the mtcars datset
head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
# get a specific value out of the data
mtcars[1, 'wt']## [1] 2.62
names(mtcars)## [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
## [11] "carb"
row.names(mtcars)## [1] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710"
## [4] "Hornet 4 Drive" "Hornet Sportabout" "Valiant"
## [7] "Duster 360" "Merc 240D" "Merc 230"
## [10] "Merc 280" "Merc 280C" "Merc 450SE"
## [13] "Merc 450SL" "Merc 450SLC" "Cadillac Fleetwood"
## [16] "Lincoln Continental" "Chrysler Imperial" "Fiat 128"
## [19] "Honda Civic" "Toyota Corolla" "Toyota Corona"
## [22] "Dodge Challenger" "AMC Javelin" "Camaro Z28"
## [25] "Pontiac Firebird" "Fiat X1-9" "Porsche 914-2"
## [28] "Lotus Europa" "Ford Pantera L" "Ferrari Dino"
## [31] "Maserati Bora" "Volvo 142E"
# specifying row and columns by name
mtcars["Mazda RX4", 'wt']## [1] 2.62
# create new column called cars
mtcars$cars <- row.names(mtcars)
mtcars$cyl_wt <- mtcars$cyl / mtcars$wtsummary(mtcars)
The reason why you would not want to use attach in your code is becuase it mucks up the variables you have access to in your R code.
It can be very easy to forget which what the column names are in your dataset.
# dont do this
attach(mtcars)## The following object is masked from package:ggplot2:
##
## mpg
## The following object is masked from package:datasets:
##
## cars
mpg## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2
## [15] 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4
## [29] 15.8 19.7 15.0 21.4
detach(mtcars)
# do this idead, if you want
with(mtcars, mpg)## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2
## [15] 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4
## [29] 15.8 19.7 15.0 21.4
We can perform conditional subsetting by using the way we subset dataframes: [ , ]
summary(mtcars)## mpg cyl disp hp
## Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
## 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
## Median :19.20 Median :6.000 Median :196.3 Median :123.0
## Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
## 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
## Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
## drat wt qsec vs
## Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
## 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
## Median :3.695 Median :3.325 Median :17.71 Median :0.0000
## Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
## 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
## Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
## am gear carb cars
## Min. :0.0000 Min. :3.000 Min. :1.000 Length:32
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000 Class :character
## Median :0.0000 Median :4.000 Median :2.000 Mode :character
## Mean :0.4062 Mean :3.688 Mean :2.812
## 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :1.0000 Max. :5.000 Max. :8.000
## cyl_wt
## Min. :1.254
## 1st Qu.:1.732
## Median :2.074
## Mean :1.963
## 3rd Qu.:2.241
## Max. :2.644
Let's subset our data and look for all the cars where the mpg is less than 15.43
mtcars[mtcars$mpg < 15.43, ]## mpg cyl disp hp drat wt qsec vs am gear carb
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## cars cyl_wt
## Duster 360 Duster 360 2.240896
## Merc 450SLC Merc 450SLC 2.116402
## Cadillac Fleetwood Cadillac Fleetwood 1.523810
## Lincoln Continental Lincoln Continental 1.474926
## Chrysler Imperial Chrysler Imperial 1.496726
## AMC Javelin AMC Javelin 2.328967
## Camaro Z28 Camaro Z28 2.083333
## Maserati Bora Maserati Bora 2.240896
The reason why this works is becuase the row subsetter returns a vector of TRUE/FALSE values
mtcars$mpg < 15.43## [1] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
## [23] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
When you give a dataframe row or column slicer a vector of booleans (i.e., TRUE/FALSE), it will return the rows and/or columns that have a TRUE.
Subsetting and slicing dataframs can be weird. when you subset a data.frame that ends up with 1 column it will come back as a vector and not a data.frame
Note, tibble objects don't do this.
You can prevent this automatic conversion to a vector by passing in the drop = FALSE into the [ ] subsetter
mtcars[mtcars$mpg < 15.43, 'cyl', drop = FALSE]## cyl
## Duster 360 8
## Merc 450SLC 8
## Cadillac Fleetwood 8
## Lincoln Continental 8
## Chrysler Imperial 8
## AMC Javelin 8
## Camaro Z28 8
## Maserati Bora 8
Otherwise you will have to manually convert the vector back to a data.frame by using as.data.frame
as.data.frame(mtcars[mtcars$mpg < 15.43, 'cyl'])## mtcars[mtcars$mpg < 15.43, "cyl"]
## 1 8
## 2 8
## 3 8
## 4 8
## 5 8
## 6 8
## 7 8
## 8 8
But then you'll have to end up changing the column name
bool_df <- as.data.frame(mtcars[mtcars$mpg < 15.43, 'cyl'])
names(bool_df) <- 'cyl'
bool_df## cyl
## 1 8
## 2 8
## 3 8
## 4 8
## 5 8
## 6 8
## 7 8
## 8 8
You can use the | or & to have an OR or AND operator in your boolean condition, respectively.
mtcars[mtcars$mpg < 15.43 | mtcars$cyl == 4, ]## mpg cyl disp hp drat wt qsec vs am gear carb
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## cars cyl_wt
## Datsun 710 Datsun 710 1.724138
## Duster 360 Duster 360 2.240896
## Merc 240D Merc 240D 1.253918
## Merc 230 Merc 230 1.269841
## Merc 450SLC Merc 450SLC 2.116402
## Cadillac Fleetwood Cadillac Fleetwood 1.523810
## Lincoln Continental Lincoln Continental 1.474926
## Chrysler Imperial Chrysler Imperial 1.496726
## Fiat 128 Fiat 128 1.818182
## Honda Civic Honda Civic 2.476780
## Toyota Corolla Toyota Corolla 2.179837
## Toyota Corona Toyota Corona 1.622718
## AMC Javelin AMC Javelin 2.328967
## Camaro Z28 Camaro Z28 2.083333
## Fiat X1-9 Fiat X1-9 2.067183
## Porsche 914-2 Porsche 914-2 1.869159
## Lotus Europa Lotus Europa 2.643754
## Maserati Bora Maserati Bora 2.240896
## Volvo 142E Volvo 142E 1.438849
mtcars[mtcars$mpg < 15.43 & mtcars$cyl == 4, ]## [1] mpg cyl disp hp drat wt qsec vs am gear
## [11] carb cars cyl_wt
## <0 rows> (or 0-length row.names)
Missing values in R are coded as an NA value
NA## [1] NA
You can't use the == operator to check for missing values
NA == NA## [1] NA
NA == TRUE## [1] NA
NA == FALSE## [1] NA
NA == 3## [1] NA
You have to use a special function to check for missing values
is.na(NA)## [1] TRUE