Remove columns from dataframe where some of values are NA

Question

I have a dataframe where some of the values are NA. I would like to remove these columns.

My data.frame looks like this

    v1   v2 
1    1   NA 
2    1    1 
3    2    2 
4    1    1 
5    2    2 
6    1   NA

I tried to estimate the col mean and select the column means !=NA. I tried this statement, it does not work.

data=subset(Itun, select=c(is.na(colMeans(Itun))))

I got an error,

error : 'x' must be an array of at least two dimensions

Can anyone give me some help?

Answer 1

The data:

Itun <- data.frame(v1 = c(1,1,2,1,2,1), v2 = c(NA, 1, 2, 1, 2, NA)) 

This will remove all columns containing at least one NA:

Itun[ , colSums(is.na(Itun)) == 0]

An alternative way is to use apply:

Itun[ , apply(Itun, 2, function(x) !any(is.na(x)))]

Answer 2

Here's a convenient way to do using dplyr function select_if(). Combine not (!), any() and is.na(), which is the equivalent of selecting all columns that don't contain any NA values.

library(dplyr)
Itun %>%
    select_if(~ !any(is.na(.))

Answer 3

You can use transpose twice:

newdf <- t(na.omit(t(df)))

Answer 4

data[,!apply(is.na(data), 2, any)]

Answer 5

A base R method related to the apply answers is

Itun[!unlist(vapply(Itun, anyNA, logical(1)))]
  v1
1  1
2  1
3  2
4  1
5  2
6  1

Here, vapply is used as we are operating on a list, and, apply, it does not coerce the object into a matrix. Also, since we know that the output will be logical vector of length 1, we can feed this to vapply and potentially get a little speed boost. For the same reason, I used anyNA instead of any(is.na()).

Answer 6

Another alternative with the dplyr package would be to make use of the Filter function

Filter(function(x) !any(is.na(x)), Itun)

with data.table would be a little more cumbersome

setDT(Itun)[,.SD,.SDcols=setdiff((1:ncol(Itun)),
                                which(colSums(is.na(Itun))>0))]