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How to select columns of an R data frame that are not in a vector?
An R data frame can have so many columns and we might want to select them except a few. In this situation, it is better to extract columns by deselecting the columns that are not needed instead of selecting the columns that we need because the number of columns needed are more than the columns that are not needed. This can be done easily with the help of ! sign and single square brackets.
Example
Consider the below data frame −
> Age<-sample(20:50,20)
> Gender<-rep(c("Male","Female"),times=10)
> Salary<-sample(20000:40000,20)
> ID<-1:20
> Education<-rep(c("Grad","Post-Grad","PhD","Highschool"),times=5)
> Experience<-sample(1:5,20,replace=TRUE)
> df<-data.frame(ID,Gender,Age,Salary,Experience,Education)
> dfOutput
ID Gender Age Salary Experience Education 1 1 Male 35 38245 4 Grad 2 2 Female 43 21995 5 Post-Grad 3 3 Male 21 38941 4 PhD 4 4 Female 26 36599 2 Highschool 5 5 Male 50 27477 2 Grad 6 6 Female 28 25281 2 Post-Grad 7 7 Male 33 20310 4 PhD 8 8 Female 24 30171 2 Highschool 9 9 Male 38 28779 3 Grad 10 10 Female 46 31213 3 Post-Grad 11 11 Male 36 27697 4 PhD 12 12 Female 41 36929 2 Highschool 13 13 Male 42 35367 2 Grad 14 14 Female 29 28711 1 Post-Grad 15 15 Male 22 29253 3 PhD 16 16 Female 30 28982 5 Highschool 17 17 Male 39 39458 4 Grad 18 18 Female 27 31891 2 Post-Grad 19 19 Male 48 29931 2 PhD 20 20 Female 31 34817 2 Highschool
Selecting columns except Gender and Age −
> df[,!names(df)%in%c("Gender","Age")]Output
ID Salary Experience Education 1 1 38245 4 Grad 2 2 21995 5 Post-Grad 3 3 38941 4 PhD 4 4 36599 2 Highschool 5 5 27477 2 Grad 6 6 25281 2 Post-Grad 7 7 20310 4 PhD 8 8 30171 2 Highschool 9 9 28779 3 Grad 10 10 31213 3 Post-Grad 11 11 27697 4 PhD 12 12 36929 2 Highschool 13 13 35367 2 Grad 14 14 28711 1 Post-Grad 15 15 29253 3 PhD 16 16 28982 5 Highschool 17 17 39458 4 Grad 18 18 31891 2 Post-Grad 19 19 29931 2 PhD 20 20 34817 2 Highschool
Selecting columns except ID and Education −
> df[,!names(df)%in%c("ID","Education")]Output
Gender Age Salary Experience 1 Male 35 38245 4 2 Female 43 21995 5 3 Male 21 38941 4 4 Female 26 36599 2 5 Male 50 27477 2 6 Female 28 25281 2 7 Male 33 20310 4 8 Female 24 30171 2 9 Male 38 28779 3 10 Female 46 31213 3 11 Male 36 27697 4 12 Female 41 36929 2 13 Male 42 35367 2 14 Female 29 28711 1 15 Male 22 29253 3 16 Female 30 28982 5 17 Male 39 39458 4 18 Female 27 31891 2 19 Male 48 29931 2 20 Female 31 34817 2
Selecting columns except ID, Education, and Gender −
> df[,!names(df)%in%c("ID","Education","Gender")]Output
Age Salary Experience 1 35 38245 4 2 43 21995 5 3 21 38941 4 4 26 36599 2 5 50 27477 2 6 28 25281 2 7 33 20310 4 8 24 30171 2 9 38 28779 3 10 46 31213 3 11 36 27697 4 12 41 36929 2 13 42 35367 2 14 29 28711 1 15 22 29253 3 16 30 28982 5 17 39 39458 4 18 27 31891 2 19 48 29931 2 20 31 34817 2
Selecting columns except ID, Age, and Gender −
> df[,!names(df)%in%c("ID","Age","Gender")]Salary Experience Education 1 38245 4 Grad 2 21995 5 Post-Grad 3 38941 4 PhD 4 36599 2 Highschool 5 27477 2 Grad 6 25281 2 Post-Grad 7 20310 4 PhD 8 30171 2 Highschool 9 28779 3 Grad 10 31213 3 Post-Grad 11 27697 4 PhD 12 36929 2 Highschool 13 35367 2 Grad 14 28711 1 Post-Grad 15 29253 3 PhD 16 28982 5 Highschool 17 39458 4 Grad 18 31891 2 Post-Grad 19 29931 2 PhD 20 34817 2 Highschool
Updated on: 04-Sep-2020
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