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 −

 Live Demo

> 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)
> df

Output

 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