The answer is
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
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 elave columns where only some vlaues 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.
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")
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))
)
)
Another option with Filter
Filter(function(x) !all(is.na(x)), df)
NOTE: Data from @Simon O'Hanlon's post.
Another option is the janitor package:
df <- remove_empty_cols(df)
Source: Stackoverflow.com