Count number of non-NA values for every column in a dataframe

Question

I have a big dataset that contains a lot of NAs and some non-Na values. At the moment I count my non-NA values for each column like this:

Answer 1

Try this:

nonNA_counts <- sapply(df, function(x) sum(!is.na(x)))

Answer 2

You can also call is.na on the entire data frame (implicitly coercing to a logical matrix) and call colSums on the inverted response:

# make sample data
set.seed(47)
df <- as.data.frame(matrix(sample(c(0:1, NA), 100*5, TRUE), 100))

str(df)
#> 'data.frame':    100 obs. of  5 variables:
#>  $ V1: int  NA 1 NA NA 1 NA 1 1 1 NA ...
#>  $ V2: int  NA NA NA 1 NA 1 0 1 0 NA ...
#>  $ V3: int  1 1 0 1 1 NA NA 1 NA NA ...
#>  $ V4: int  NA 0 NA 0 0 NA 1 1 NA NA ...
#>  $ V5: int  NA NA NA 0 0 0 0 0 NA NA ...

colSums(!is.na(df))
#> V1 V2 V3 V4 V5 
#> 69 55 62 60 70

Answer 3

With dplyr, that would be:

library(dplyr)

df %>%
summarise_all(funs(sum(!is.na(.)))

The advantage of that approach is that you can use group_by before, and that you don't need to care about column names (it just summarizes all of them).