How to remove rows that have 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