Replace all 0 values to NA

Question

I have a dataframe with some numeric columns. Some row has a 0 value which should be considered as null in statistical analysis. What is the fastest way to replace all the 0

Answer 1

An alternative way without the [<- function:

A sample data frame dat (shamelessly copied from @Chase's answer):

dat

  x y
1 0 2
2 1 2
3 1 1
4 2 1
5 0 0

Zeroes can be replaced with NA by the is.na<- function:

is.na(dat) <- !dat


dat

   x  y
1 NA  2
2  1  2
3  1  1
4  2  1
5 NA NA

Answer 2

Because someone asked for the Data.Table version of this, and because the given data.frame solution does not work with data.table, I am providing the solution below.

Basically, use the := operator --> DT[x == 0, x := NA]

library("data.table")

status = as.data.table(occupationalStatus)

head(status, 10)
    origin destination  N
 1:      1           1 50
 2:      2           1 16
 3:      3           1 12
 4:      4           1 11
 5:      5           1  2
 6:      6           1 12
 7:      7           1  0
 8:      8           1  0
 9:      1           2 19
10:      2           2 40


status[N == 0, N := NA]

head(status, 10)
    origin destination  N
 1:      1           1 50
 2:      2           1 16
 3:      3           1 12
 4:      4           1 11
 5:      5           1  2
 6:      6           1 12
 7:      7           1 NA
 8:      8           1 NA
 9:      1           2 19
10:      2           2 40