How to delete columns that contain ONLY NAs?

Solution 1:

One way of doing it:

df[, colSums(is.na(df)) != nrow(df)]

If the count of NAs in a column is equal to the number of rows, it must be entirely NA.

Or similarly

df[colSums(!is.na(df)) > 0]

Solution 2:

Here is a dplyr solution:

df %>% select_if(~sum(!is.na(.)) > 0)

Update: The summarise_if() function is superseded as of dplyr 1.0. Here are two other solutions that use the where() tidyselect function:

df %>% 
  select(
    where(
      ~sum(!is.na(.x)) > 0
    )
  )

df %>% 
  select(
    where(
      ~!all(is.na(.x))
    )
  )

Solution 3:

Another option is the janitor package:

df <- remove_empty_cols(df)

https://github.com/sfirke/janitor

Solution 4:

It seeems like you want to remove ONLY columns with ALL NAs, leaving columns with some rows that do have NAs. I would do this (but I am sure there is an efficient vectorised soution:

#set seed for reproducibility
set.seed <- 103
df <- data.frame( id = 1:10 , nas = rep( NA , 10 ) , vals = sample( c( 1:3 , NA ) , 10 , repl = TRUE ) )
df
#      id nas vals
#   1   1  NA   NA
#   2   2  NA    2
#   3   3  NA    1
#   4   4  NA    2
#   5   5  NA    2
#   6   6  NA    3
#   7   7  NA    2
#   8   8  NA    3
#   9   9  NA    3
#   10 10  NA    2

#Use this command to remove columns that are entirely NA values, it will leave columns where only some values are NA
df[ , ! apply( df , 2 , function(x) all(is.na(x)) ) ]
#      id vals
#   1   1   NA
#   2   2    2
#   3   3    1
#   4   4    2
#   5   5    2
#   6   6    3
#   7   7    2
#   8   8    3
#   9   9    3
#   10 10    2

If you find yourself in the situation where you want to remove columns that have any NA values you can simply change the all command above to any.

Solution 5:

An intuitive script: dplyr::select_if(~!all(is.na(.))). It literally keeps only not-all-elements-missing columns. (to delete all-element-missing columns).

> df <- data.frame( id = 1:10 , nas = rep( NA , 10 ) , vals = sample( c( 1:3 , NA ) , 10 , repl = TRUE ) )

> df %>% glimpse()
Observations: 10
Variables: 3
$ id   <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
$ nas  <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
$ vals <int> NA, 1, 1, NA, 1, 1, 1, 2, 3, NA

> df %>% select_if(~!all(is.na(.))) 
   id vals
1   1   NA
2   2    1
3   3    1
4   4   NA
5   5    1
6   6    1
7   7    1
8   8    2
9   9    3
10 10   NA