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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
Updated on: 09-Nov-2021
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