How to select data.table object columns based on their class in R?

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

• First of all, create a data.table object.

• Then, use str function to check the structure of the object.

• After that, use select_if function from dplyr package to select the columns based on their class.

Example 1

Create the data.table object

Let’s create a data.table object as shown below −

library(data.table) x1<-sample(LETTERS[1:4],25,replace=TRUE) x2<-rnorm(25) x3<-rpois(25,5) DT1<-data.table(x1,x2,x3) DT1

Output

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

    x1    x2 x3 1:  D -1.03911797 6 2:  C -0.18664587 3 3:  D -1.36429362 3 4:  A -2.26587126 8 5:  B  0.10786571 3 6:  C  0.01271455 4 7:  D  1.33889909 8 8:  C  2.24053400 9 9:  B  1.45987567 9 10: B  0.16747607 7 11: C -0.39258915 3 12: A  0.22643666 7 13: A -0.19909780 7 14: D -1.37518544 7 15: D -1.47161101 4 16: C  0.95574993 7 17: B  0.86833240 5 18: A  0.24516224 5 19: C -1.25342994 6 20: A  1.46559041 2 21: C  0.34863015 2 22: D -0.33877737 5 23: B  0.26071352 4 24: D -0.61747246 4 25: A -0.35990471 7   x1 x2 x3

Check the structure of data.table object

Using str function to check the structure of the data.table object DT1 −

library(data.table) x1<-sample(LETTERS[1:4],25,replace=TRUE) x2<-rnorm(25) x3<-rpois(25,5) DT1<-data.table(x1,x2,x3) str(DT1)

Output

Classes ‘data.table’ and 'data.frame': 25 obs. of 3 variables: $ x1: chr "D" "C" "D" "A" ... $ x2: num -1.039 -0.187 -1.364 -2.266 0.108 ... $ x3: int 6 3 3 8 3 4 8 9 9 7 ... - attr(*, ".internal.selfref")=<externalptr>

Select columns based on their class

Using select_if function from dplyr package to select integer columns as shown below −

library(data.table) x1<-sample(LETTERS[1:4],25,replace=TRUE) x2<-rnorm(25) x3<-rpois(25,5) DT1<-data.table(x1,x2,x3) str(DT1) library(dplyr) DT1 %>% select_if(is.integer)

Output

 x3 1: 6 2: 3 3: 3 4: 8 5: 3 6: 4 7: 8 8: 9 9: 9 10: 7 11: 3 12: 7 13: 7 14: 7 15: 4 16: 7 17: 5 18: 5 19: 6 20: 2 21: 2 22: 5 23: 4 24: 4 25: 7 x3

Example 2

Create the data.table object

Let’s create a data.table object as shown below −

library(data.table) y1<-sample(c(TRUE,FALSE),25,replace=TRUE) y2<-factor(sample(c("I","II","III"),25,replace=TRUE)) DT2<-data.table(y1,y2) DT2

Output

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

 y1 y2 1: TRUE III 2: FALSE III 3: TRUE I 4: FALSE II 5: FALSE III 6: FALSE I 7: TRUE II 8: FALSE II 9: TRUE II 10: FALSE III 11: TRUE I 12: TRUE I 13: TRUE II 14: FALSE II 15: TRUE I 16: FALSE III 17: FALSE I 18: FALSE II 19: TRUE II 20: FALSE II 21: TRUE III 22: TRUE I 23: TRUE I 24: TRUE II 25: FALSE III y1 y2

Select columns based on their class

Using select_if function from dplyr package to select logical columns as shown below −

library(data.table) y1<-sample(c(TRUE,FALSE),25,replace=TRUE) y2<-factor(sample(c("I","II","III"),25,replace=TRUE)) DT2<-data.table(y1,y2) library(dplyr) DT2 %>% select_if(is.logical)

Output

 y1 1: TRUE 2: FALSE 3: TRUE 4: FALSE 5: FALSE 6: FALSE 7: TRUE 8: FALSE 9: TRUE 10: FALSE 11: TRUE 12: TRUE 13: TRUE 14: FALSE 15: TRUE 16: FALSE 17: FALSE 18: FALSE 19: TRUE 20: FALSE 21: TRUE 22: TRUE 23: TRUE 24: TRUE 25: FALSE y1