How to select data frame columns based on their class in R?

To select data frame columns based on their class in R, we can follow the below steps −

• First of all, create a data frame or consider an inbuilt data set.

• Then, use select_if function of dplyr package with class function.

Example 1

str(CO2)

Output

On executing, the above script generates the below output(this output will vary on your system due to randomization) −

Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and 'data.frame':84 obs. of 5 variables: $ Plant : Ord.factor w/ 12 levels "Qn1"<"Qn2"<"Qn3"<..: 1 1 1 1 1 1 1 2 2 2 ... $ Type : Factor w/ 2 levels "Quebec","Mississippi": 1 1 1 1 1 1 1 1 1 1 ... $ Treatment: Factor w/ 2 levels "nonchilled","chilled": 1 1 1 1 1 1 1 1 1 1 ... $ conc : num 95 175 250 350 500 675 1000 95 175 250 ... $ uptake : num 16 30.4 34.8 37.2 35.3 39.2 39.7 13.6 27.3 37.1 ... - attr(*, "formula")=Class 'formula' language uptake ~ conc | Plant .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv> - attr(*, "outer")=Class 'formula' language ~Treatment * Type .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv> - attr(*, "labels")=List of 2 ..$ x: chr "Ambient carbon dioxide concentration" ..$ y: chr "CO2 uptake rate" - attr(*, "units")=List of 2 ..$ x: chr "(uL/L)" ..$ y: chr "(umol/m^2 s)"

Select columns in CO2 based on class

Using select_if function from dplyr package to select factor columns in CO2 data frame −

library(dplyr) CO2 %>% select_if(is.factor)

Output

On executing, the above script generates the below output(this output will vary on your system due to randomization) −

   Plant Type     Treatment 1  Qn1  Quebec   nonchilled 2  Qn1  Quebec   nonchilled 3  Qn1  Quebec   nonchilled 4  Qn1  Quebec   nonchilled 5  Qn1  Quebec     nonchilled 6  Qn1  Quebec      nonchilled 7  Qn1  Quebec      nonchilled 8  Qn2  Quebec      nonchilled 9  Qn2  Quebec      nonchilled 10 Qn2  Quebec      nonchilled 11 Qn2  Quebec      nonchilled 12 Qn2  Quebec      nonchilled 13 Qn2  Quebec      nonchilled 14 Qn2  Quebec      nonchilled 15 Qn3  Quebec      nonchilled 16 Qn3  Quebec      nonchilled 17 Qn3  Quebec      nonchilled 18 Qn3  Quebec      nonchilled 19 Qn3  Quebec      nonchilled 20 Qn3  Quebec      nonchilled 21 Qn3  Quebec      nonchilled 22 Qc1  Quebec      chilled 23 Qc1  Quebec      chilled 24 Qc1  Quebec      chilled 25 Qc1  Quebec      chilled 26 Qc1  Quebec      chilled 27 Qc1  Quebec      chilled 28 Qc1  Quebec      chilled 29 Qc2  Quebec      chilled 30 Qc2  Quebec      chilled 31 Qc2  Quebec      chilled 32 Qc2  Quebec      chilled 33 Qc2  Quebec      chilled 34 Qc2  Quebec      chilled 35 Qc2  Quebec      chilled 36 Qc3  Quebec      chilled 37 Qc3  Quebec      chilled 38 Qc3  Quebec      chilled 39 Qc3  Quebec      chilled 40 Qc3  Quebec      chilled 41 Qc3  Quebec      chilled 42 Qc3  Quebec      chilled 43 Mn1  Mississippi nonchilled 44 Mn1  Mississippi nonchilled 45 Mn1  Mississippi nonchilled 46 Mn1  Mississippi nonchilled 47 Mn1  Mississippi nonchilled 48 Mn1  Mississippi nonchilled 49 Mn1  Mississippi nonchilled 50 Mn2  Mississippi nonchilled 51 Mn2  Mississippi nonchilled 52 Mn2  Mississippi nonchilled 53 Mn2  Mississippi nonchilled 54 Mn2  Mississippi nonchilled 55 Mn2  Mississippi nonchilled 56 Mn2  Mississippi nonchilled 57 Mn3  Mississippi nonchilled 58 Mn3  Mississippi nonchilled 59 Mn3  Mississippi nonchilled 60 Mn3  Mississippi nonchilled 61 Mn3  Mississippi nonchilled 62 Mn3  Mississippi nonchilled 63 Mn3  Mississippi nonchilled 64 Mc1  Mississippi chilled 65 Mc1  Mississippi chilled 66 Mc1  Mississippi chilled 67 Mc1  Mississippi chilled 68 Mc1  Mississippi chilled 69 Mc1  Mississippi chilled 70 Mc1  Mississippi chilled 71 Mc2  Mississippi chilled 72 Mc2  Mississippi chilled 73 Mc2  Mississippi chilled 74 Mc2  Mississippi chilled 75 Mc2  Mississippi chilled 76 Mc2  Mississippi chilled 77 Mc2  Mississippi chilled 78 Mc3  Mississippi chilled 79 Mc3  Mississippi chilled 80 Mc3  Mississippi chilled 81 Mc3  Mississippi chilled 82 Mc3  Mississippi chilled 83 Mc3  Mississippi chilled 84 Mc3  Mississippi chilled

Example 2

Consider the PlantGrowth data frame in base R and check it’s structure with str function −

str(PlantGrowth) 

Output

On executing, the above script generates the below output(this output will vary on your system due to randomization) −

$Rscript main.r 'data.frame':30 obs. of 2 variables: $ weight: num 4.17 5.58 5.18 6.11 4.5 4.61 5.17 4.53 5.33 5.14 ... $ group : Factor w/ 3 levels "ctrl","trt1",..: 1 1 1 1 1 1 1 1 1 1 ...

Select columns in PlantGrowth based on class

Using select_if function from dplyr package to select numeric columns in PlantGrowth data frame −

library(dplyr) PlantGrowth %>% select_if(is.numeric)

Output

 weight 1 4.17 2 5.58 3 5.18 4 6.11 5 4.50 6 4.61 7 5.17 8 4.53 9 5.33 10 5.14 11 4.81 12 4.17 13 4.41 14 3.59 15 5.87 16 3.83 17 6.03 18 4.89 19 4.32 20 4.69 21 6.31 22 5.12 23 5.54 24 5.50 25 5.37 26 5.29 27 4.92 28 6.15 29 5.80 30 5.26