Remove Rows with NA in R Data Frames Stored in a List

To remove rows that have NA in R data frames stored in a list, we can use lapply function along with na.omit function.

For example, if we have a list called LIST that contains some data frames each containing few missing values then the removal of rows having missing values from these data frames can be done by using the command given below −

lapply(LIST,na.omit)

Check out the below given example to understand how it works.

Example

Following snippet creates a sample data frame −

df1<-data.frame(x1=rnorm(20),x2=sample(c(NA,2,5),20,replace=TRUE),x3=rnorm(20))
df2<-data.frame(y1=rpois(20,2),x2=rpois(20,5),x3=sample(c(NA,3,1),20,replace=TRUE))
df3<-data.frame(z1=sample(c(NA,35,40),20,replace=TRUE),z2=rpois(20,1),z3=rpois(20,3))
List<-list(df1,df2,df3)
List

Output

The following dataframes are created −

[[1]]
     x1        x2     x3
1   0.6335449  2  -0.22124641
2   0.3349365  5  -0.54942283
3   0.9501903 NA   0.16557880
4   1.5303796  2  -2.08566142
5   0.4384521 NA  -0.67675747
6  -2.7142501  2  -0.12101118
7  -0.7043597  5  -0.56138587
8   0.5291248 NA  -1.41423100
9  -1.4908927  2  -1.02494189
10  0.7306274 NA  -0.27786609
11  0.2796165  2   1.09441626
12  0.1193108  2  -0.44195048
13  0.2438589 NA   0.79697076
14  0.7290668 NA  -0.89008018
15 -1.2803967  5   1.24610229
16 -0.4271407 NA   0.12830629
17 -0.2131141  5   0.06398567
18  1.0359205  2   0.65339733
19  0.1116563  2  -2.34618128
20 -0.3049548  5  -0.45742539

[[2]]
   y1 x2 x3
1  0  8  3
2  2  7  3
3  2  6  3
4  0  9 NA
5  2  5  3
6  2  8 NA
7  1  2  3
8  0  6  1
9  1  8 NA
10 0  5  3
11 3  5 NA
12 3  6 NA
13 3  5  3
14 0  7  1
15 2 10  1
16 1  7  1
17 3  0  1
18 0  4  1
19 1  4  3
20 0  4 NA

[[3]]
   z1  z2 z3
1  40  2  3
2  NA  0  1
3  35  1  2
4  40  2  3
5  35  2  1
6  NA  0  3
7  35  1  6
8  NA  1  2
9  35  2  4
10 35  2  0
11 40  2  2
12 35  1  2
13 NA  2  3
14 NA  2  5
15 NA  1  2
16 NA  1  2
17 40  1  2
18 35  1  3
19 40  0  0
20 40  2  5

Add the following code to the above snippet −

df1<-data.frame(x1=rnorm(20),x2=sample(c(NA,2,5),20,replace=TRUE),x3=rnorm(20))
df2<-data.frame(y1=rpois(20,2),x2=rpois(20,5),x3=sample(c(NA,3,1),20,replace=TRUE))
df3<-data.frame(z1=sample(c(NA,35,40),20,replace=TRUE),z2=rpois(20,1),z3=rpois(20,3))
List<-list(df1,df2,df3)
lapply(List,na.omit)

Output

If you execute all the above given snippets as a single program, it generates the following Output −

[[1]]
    x1         x2     x3
1   0.6335449  2  -0.22124641
2   0.3349365  5  -0.54942283
4   1.5303796  2  -2.08566142
6  -2.7142501  2  -0.12101118
7  -0.7043597  5  -0.56138587
9  -1.4908927  2  -1.02494189
11  0.2796165  2   1.09441626
12  0.1193108  2  -0.44195048
15 -1.2803967  5   1.24610229
17 -0.2131141  5   0.06398567
18  1.0359205  2   0.65339733
19  0.1116563  2  -2.34618128
20 -0.3049548  5  -0.45742539

[[2]]
   y1 x2 x3
1  0  8  3
2  2  7  3
3  2  6  3
5  2  5  3
7  1  2  3
8  0  6  1
10 0  5  3
13 3  5  3
14 0  7  1
15 2 10  1
16 1  7  1
17 3  0  1
18 0  4  1
19 1  4  3

[[3]]
   z1  z2  z3
1  40  2   3
3  35  1   2
4  40  2   3
5  35  2   1
7  35  1   6
9  35  2   4
10 35  2   0
11 40  2   2
12 35  1   2
17 40  1   2
18 35  1   3
19 40  0   0
20 40  2   5