Dataframe is commonly used in data analysis, and sometimes we need to select several columns of a dataframe. This can be done with []
We’ll use the “palmerpenguins” packages (https://allisonhorst.github.io/palmerpenguins/) to address this question. You’ll need to install the package with install.packages(“palmerpenguins”) if you have not done so before, call library("“palmerpenguins”), and load the data with data(penguins)
#install.packages("palmerpenguins")
library(palmerpenguins)## Warning: package 'palmerpenguins' was built under R version 4.1.2data(penguins)
penguins## # A tibble: 344 x 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750
## 2 Adelie Torgersen 39.5 17.4 186 3800
## 3 Adelie Torgersen 40.3 18 195 3250
## 4 Adelie Torgersen NA NA NA NA
## 5 Adelie Torgersen 36.7 19.3 193 3450
## 6 Adelie Torgersen 39.3 20.6 190 3650
## 7 Adelie Torgersen 38.9 17.8 181 3625
## 8 Adelie Torgersen 39.2 19.6 195 4675
## 9 Adelie Torgersen 34.1 18.1 193 3475
## 10 Adelie Torgersen 42 20.2 190 4250
## # ... with 334 more rows, and 2 more variables: sex <fct>, year <int>At this point, “penguins” is already a dataframe, and we can use [] to select one single column
penguins[3:4]## # A tibble: 344 x 2
## bill_length_mm bill_depth_mm
## <dbl> <dbl>
## 1 39.1 18.7
## 2 39.5 17.4
## 3 40.3 18
## 4 NA NA
## 5 36.7 19.3
## 6 39.3 20.6
## 7 38.9 17.8
## 8 39.2 19.6
## 9 34.1 18.1
## 10 42 20.2
## # ... with 334 more rowspenguins[,3:4]## # A tibble: 344 x 2
## bill_length_mm bill_depth_mm
## <dbl> <dbl>
## 1 39.1 18.7
## 2 39.5 17.4
## 3 40.3 18
## 4 NA NA
## 5 36.7 19.3
## 6 39.3 20.6
## 7 38.9 17.8
## 8 39.2 19.6
## 9 34.1 18.1
## 10 42 20.2
## # ... with 334 more rows[3:4] and [,3:4] gives the same output. The first one means “column #3 to #4” and the other is saying “every row in column 3 to 4”
if we are selecting seperate columns, use a vector of column indexes
penguins[c(3,5)]## # A tibble: 344 x 2
## bill_length_mm flipper_length_mm
## <dbl> <int>
## 1 39.1 181
## 2 39.5 186
## 3 40.3 195
## 4 NA NA
## 5 36.7 193
## 6 39.3 190
## 7 38.9 181
## 8 39.2 195
## 9 34.1 193
## 10 42 190
## # ... with 334 more rowsFor more information on this topic, see https://dzone.com/articles/learn-r-how-extract-rows
data.frame
[]
“,”
c()
palmerpenguins