Reputation: 1775

How to delete columns that contain ONLY NAs?

I have a data.frame containing some columns with all NA values. How can I delete them from the data.frame?

Can I use the function,

na.omit(...)

specifying some additional arguments?

Upvotes: 114

Answers (9)

jsta

Reputation: 3393

Another option is the janitor package:

df <- janitor::remove_empty(df, which = "cols")

https://github.com/sfirke/janitor

Upvotes: 45

Quinten

Reputation: 41225

Another option using the map_lgl function from the purrr package, which returns a logical vector and using the [ to remove the columns with all NA. Here is a reproducible example:

set.seed(7)
df <- data.frame(id = 1:5 , nas = rep(NA, 5) , vals = sample(c(1:3,NA), 5, repl = TRUE))
df
#>   id nas vals
#> 1  1  NA    2
#> 2  2  NA    3
#> 3  3  NA    3
#> 4  4  NA   NA
#> 5  5  NA    3
library(purrr)
df[!map_lgl(df, ~ all(is.na(.)))]
#>   id vals
#> 1  1    2
#> 2  2    3
#> 3  3    3
#> 4  4   NA
#> 5  5    3

^{Created on 2022-08-28 with reprex v2.0.2}

Upvotes: 0

user2110417

Reputation:

Try as follows:

df <- df[,colSums(is.na(df))<nrow(df)]

Upvotes: 0

Simon O'Hanlon

Reputation: 59970

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.

Upvotes: 28

Brad Cannell

Reputation: 3200

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Update: The summarise_if() function is superseded as of dplyr 1.0.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))
    )
  )

Upvotes: 101

DJV

Reputation: 4863

Because performance was really important for me, I benchmarked all the functions above.

NOTE: Data from @Simon O'Hanlon's post. Only with size 15000 instead of 10.

library(tidyverse)
library(microbenchmark)

set.seed(123)
df <- data.frame(id = 1:15000,
                 nas = rep(NA, 15000), 
                 vals = sample(c(1:3, NA), 15000,
                               repl = TRUE))
df

MadSconeF1 <- function(x) x[, colSums(is.na(x)) != nrow(x)]

MadSconeF2 <- function(x) x[colSums(!is.na(x)) > 0]

BradCannell <- function(x) x %>% select_if(~sum(!is.na(.)) > 0)

SimonOHanlon <- function(x) x[ , !apply(x, 2 ,function(y) all(is.na(y)))]

jsta <- function(x) janitor::remove_empty(x)

SiboJiang <- function(x) x %>% dplyr::select_if(~!all(is.na(.)))

akrun <- function(x) Filter(function(y) !all(is.na(y)), x)

mbm <- microbenchmark(
  "MadSconeF1" = {MadSconeF1(df)},
  "MadSconeF2" = {MadSconeF2(df)},
  "BradCannell" = {BradCannell(df)},
  "SimonOHanlon" = {SimonOHanlon(df)},
  "SiboJiang" = {SiboJiang(df)},
  "jsta" = {jsta(df)}, 
  "akrun" = {akrun(df)},
  times = 1000)

mbm

Results:

Unit: microseconds
         expr    min      lq      mean  median      uq      max neval  cld
   MadSconeF1  154.5  178.35  257.9396  196.05  219.25   5001.0  1000 a   
   MadSconeF2  180.4  209.75  281.2541  226.40  251.05   6322.1  1000 a   
  BradCannell 2579.4 2884.90 3330.3700 3059.45 3379.30  33667.3  1000    d
 SimonOHanlon  511.0  565.00  943.3089  586.45  623.65 210338.4  1000  b  
    SiboJiang 2558.1 2853.05 3377.6702 3010.30 3310.00  89718.0  1000    d
         jsta 1544.8 1652.45 2031.5065 1706.05 1872.65  11594.9  1000   c 
        akrun   93.8  111.60  139.9482  121.90  135.45   3851.2  1000 a


autoplot(mbm)

mbm %>% 
  tbl_df() %>%
  ggplot(aes(sample = time)) + 
  stat_qq() + 
  stat_qq_line() +
  facet_wrap(~expr, scales = "free")

Upvotes: 19

SJ9

Reputation: 497

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

Upvotes: 24