Remove Rows with Missing Values in R Data Frame

If we want to remove rows containing missing values based on a particular column then we should select that column by ignoring the missing values. This can be done by using is.na function. For example, if we have a data frame df that contains column x, y, z and each of the columns have some missing values then rows of x without missing values can be selected as df[!is.na(df$x),].

Example

Consider the below data frame −

 Live Demo

x1<−sample(c(NA,1,2,3,4),20,replace=TRUE)
x2<−sample(c(NA,5,10),20,replace=TRUE)
x3<−sample(c(NA,3,12,21,30),20,replace=TRUE)
x4<−sample(c(NA,54,65),20,replace=TRUE)
x5<−sample(c(NA,101,125,111),20,replace=TRUE)
x6<−sample(c(NA,500),20,replace=TRUE)
df<−data.frame(x1,x2,x3,x4,x5,x6)
df

Output

  x1 x2 x3 x4 x5 x6
1  4 10 21 54 NA NA
2  4 NA 21 65 NA 500
3 NA  5 NA NA 101 NA
4  3  5 NA NA NA NA
5  1  5 21 65 101 NA
6 NA 10 NA 65 111 500
7  2 NA NA NA NA NA
8 NA  5 NA NA 125 500
9  4 10 NA 54 NA NA
10 1 NA 12 NA 101 NA
11 4 NA 12 NA 101 NA
12 3  5 NA 65 111 NA
13 4 10 30 54 101 500
14 4  5 30 54 111 NA
15 3  5 NA 65 111 NA
16 1 NA 30 65 125 NA
17 1  5 3 65 125 500
18 3  5 NA NA 125 NA
19 NA NA 12 65 101 500
20 2  NA 21 54 111 NA

Selecting rows of x1 that does not contain missing values −

Example

df[!is.na(df$x1),]

Output

x1 x2 x3 x4 x5 x6
1 4 10 21 54 NA NA
2 4 NA 21 65 NA 500
4 3 5 NA NA NA NA
5 1 5 21 65 101 NA
7 2 NA NA NA NA NA
9 4 10 NA 54 NA NA
10 1 NA 12 NA 101 NA
11 4 NA 12 NA 101 NA
12 3 5 NA 65 111 NA
13 4 10 30 54 101 500
14 4 5 30 54 111 NA
15 3 5 NA 65 111 NA
16 1 NA 30 65 125 NA
17 1 5 3 65 125 500
18 3 5 NA NA 125 NA
20 2 NA 21 54 111 NA

Selecting rows of x2 that does not contain missing values −

Example

df[!is.na(df$x2),]

Output

x1 x2 x3 x4 x5 x6
1 4 10 21 54 NA NA
3 NA 5 NA NA 101 NA
4 3 5 NA NA NA NA
5 1 5 21 65 101 NA
6 NA 10 NA 65 111 500
8 NA 5 NA NA 125 500
9 4 10 NA 54 NA NA
12 3 5 NA 65 111 NA
13 4 10 30 54 101 500
14 4 5 30 54 111 NA
15 3 5 NA 65 111 NA
17 1 5 3 65 125 500
18 3 5 NA NA 125 NA

Selecting rows of x3 that does not contain missing values −

Example

df[!is.na(df$x3),]

Output

x1 x2 x3 x4 x5 x6
1 4 10 21 54 NA NA
2 4 NA 21 65 NA 500
5 1 5 21 65 101 NA
10 1 NA 12 NA 101 NA
11 4 NA 12 NA 101 NA
13 4 10 30 54 101 500
14 4 5 30 54 111 NA
16 1 NA 30 65 125 NA
17 1 5 3 65 125 500
19 NA NA 12 65 101 500
20 2 NA 21 54 111 NA

Selecting rows of x4 that does not contain missing values −

Example

df[!is.na(df$x4),]

Output

x1 x2 x3 x4 x5 x6
1 4 10 21 54 NA NA
2 4 NA 21 65 NA 500
5 1 5 21 65 101 NA
6 NA 10 NA 65 111 500
9 4 10 NA 54 NA NA
12 3 5 NA 65 111 NA
13 4 10 30 54 101 500
14 4 5 30 54 111 NA
15 3 5 NA 65 111 NA
16 1 NA 30 65 125 NA
17 1 5 3 65 125 500
19 NA NA 12 65 101 500
20 2 NA 21 54 111 NA

Selecting rows of x5 that does not contain missing values −

Example

df[!is.na(df$x5),]

Output

x1 x2 x3 x4 x5 x6
3 NA 5 NA NA 101 NA
5 1 5 21 65 101 NA
6 NA 10 NA 65 111 500
8 NA 5 NA NA 125 500
10 1 NA 12 NA 101 NA
11 4 NA 12 NA 101 NA
12 3 5 NA 65 111 NA
13 4 10 30 54 101 500
14 4 5 30 54 111 NA
15 3 5 NA 65 111 NA
16 1 NA 30 65 125 NA
17 1 5 3 65 125 500
18 3 5 NA NA 125 NA
19 NA NA 12 65 101 500
20 2 NA 21 54 111 NA

Selecting rows of x6 that does not contain missing values −

Example

df[!is.na(df$x6),]

Output

 x1 x2 x3 x4 x5 x6
2 4 NA 21 65 NA 500
6 NA 10 NA 65 111 500
8 NA 5 NA NA 125 500
13 4 10 30 54 101 500
17 1 5 3 65 125 500
19 NA NA 12 65 101 500