How to change the position of missing values to the end of data frame in R?

R Programming Server Side Programming Programming

Most of the times we need to deal with missing values in data science projects and these missing values can be occurred at any position. We might want to change the position of these missing values and send them to the end of the columns in the data frame. This can be done with the help of lapply function as shown in the below examples.

Example1

Consider the below data frame −

Live Demo

> x1<-sample(c(NA,rpois(2,1)),20,replace=TRUE)
> x2<-sample(c(NA,rpois(2,1)),20,replace=TRUE)
> x3<-sample(c(NA,rpois(2,1)),20,replace=TRUE)
> df1<-data.frame(x1,x2,x3)
> df1

Output

   x1 x2 x3
1  0  0  2
2  1  1  NA
3  1  NA 0
4  0  NA 2
5  1  NA 2
6  NA NA NA
7  0  1  2
8  0  1  NA
9  1  0  2
10 0  1  0
11 0  1  2
12 0  1  2
13 0  1  NA
14 1  1  2
15 0  1  NA
16 NA 1  2
17 1  0  0
18 NA 1  NA
19 NA 0  NA
20 0  0  2

Sending the missing values to the end of df1 −

> df1[]<-lapply(df1,function(x) c(x[!is.na(x)], x[is.na(x)]))
> df1

Output

   x1 x2 x3
1  0  0  2
2  1  1  0
3  1  1  2
4  0  1  2
5  1  0  2
6  0  1  2
7  0  1  0
8  1  1  2
9  0  1  2
10 0  1  2
11 0  1  2
12 0  1  0
13 1  0  2
14 0  1  NA
15 1  0  NA
16 0  0  NA
17 NA NA NA
18 NA NA NA
19 NA NA NA
20 NA NA NA

Example2

Live Demo

> y1<-sample(c(NA,rnorm(3)),20,replace=TRUE)
> y2<-sample(c(NA,rnorm(3)),20,replace=TRUE)
> y3<-sample(c(NA,rnorm(3)),20,replace=TRUE)
> df2<-data.frame(y1,y2,y3)
> df2

Output

   y1           y2         y3
1  NA         NA           NA
2  1.3030960  NA         0.6250597
3  NA        -0.2795437  NA
4  NA        -0.2795437  0.6250597
5  NA         1.2997792  0.6250597
6  1.3030960  1.2997792  NA
7  0.5949615 -0.2795437  NA
8  0.1149380  NA         0.6250597
9  0.5949615 -0.2795437  NA
10 NA         NA        -0.6412672
11 NA         1.2997792 -0.6412672
12 1.3030960 -0.2795437 -0.6412672
13 0.1149380  1.2997792  NA
14 1.3030960 -0.2795437  NA
15 0.1149380 -0.2795437  0.6250597
16 0.1149380  NA         1.3271716
17 NA        -0.2795437 -0.6412672
18 NA         1.2481138  NA
19 0.5949615  1.2481138  NA
20 NA        -0.2795437  0.6250597

Sending the missing values to the end of df2 −

> df2[]<-lapply(df2,function(x) c(x[!is.na(x)], x[is.na(x)]))
> df2

Output

     y1          y2         y3
1  1.3030960  -0.2795437  0.6250597
2  1.3030960  -0.2795437  0.6250597
3  0.5949615   1.2997792  0.6250597
4  0.1149380   1.2997792  0.6250597
5  0.5949615  -0.2795437 -0.6412672
6  1.3030960  -0.2795437 -0.6412672
7  0.1149380   1.2997792 -0.6412672
8  1.3030960  -0.2795437  0.6250597
9  0.1149380   1.2997792  1.3271716
10 0.1149380  -0.2795437 -0.6412672
11 0.5949615  -0.2795437  0.6250597
12 NA         -0.2795437  NA
13 NA          1.2481138  NA
14 NA          1.2481138  NA
15 NA         -0.2795437  NA
16 NA          NA         NA
17 NA          NA         NA
18 NA          NA         NA
19 NA          NA         NA
20 NA          NA         NA

Nizamuddin Siddiqui

Updated on: 05-Mar-2021

67 Views

Kickstart Your Career

Get certified by completing the course

Get Started