Determine the number of NA values in a column

Question

You're over-thinking the problem:

sum(is.na(df$col))

If you are looking for NA counts for each column in a dataframe then:

na_count <-sapply(x, function(y) sum(length(which(is.na(y)))))

should give you a list with the counts for each column.

na_count <- data.frame(na_count)

Should output the data nicely in a dataframe like:

----------------------
| row.names | na_count
------------------------
| column_1  | count

Try the colSums function

df <- data.frame(x = c(1,2,NA), y = rep(NA, 3))

colSums(is.na(df))

#x y 
#1 3 

If you are looking to count the number of NAs in the entire dataframe you could also use

sum(is.na(df))

A quick and easy Tidyverse solution to get a NA count for all columns is to use summarise_all() which I think makes a much easier to read solution than using purrr or sapply

library(tidyverse)
# Example data
df <- tibble(col1 = c(1, 2, 3, NA), 
             col2 = c(NA, NA, "a", "b"))

df %>% summarise_all(~ sum(is.na(.)))
#> # A tibble: 1 x 2
#>    col1  col2
#>   <int> <int>
#> 1     1     2

Or using the more modern across() function:

df %>% summarise(across(everything(), ~ sum(is.na(.))))

In the summary() output, the function also counts the NAs so one can use this function if one wants the sum of NAs in several variables.