Set NA to 0 in R

Question

After merging a dataframe with another im left with random NA\'s for the occasional row. I\'d like to set these NA\'s to 0 so I can perform calculations with them.

Answer 1

Why not try this

  na.zero <- function (x) {
        x[is.na(x)] <- 0
        return(x)
    }
    na.zero(df)

Answer 2

You can just use the output of is.na to replace directly with subsetting:

bothbeams.data[is.na(bothbeams.data)] <- 0

Or with a reproducible example:

dfr <- data.frame(x=c(1:3,NA),y=c(NA,4:6))
dfr[is.na(dfr)] <- 0
dfr
  x y
1 1 0
2 2 4
3 3 5
4 0 6

However, be careful using this method on a data frame containing factors that also have missing values:

> d <- data.frame(x = c(NA,2,3),y = c("a",NA,"c"))
> d[is.na(d)] <- 0
Warning message:
In `[<-.factor`(`*tmp*`, thisvar, value = 0) :
  invalid factor level, NA generated

It "works":

> d
  x    y
1 0    a
2 2 <NA>
3 3    c

...but you likely will want to specifically alter only the numeric columns in this case, rather than the whole data frame. See, eg, the answer below using dplyr::mutate_if.

Answer 3

A solution using mutate_all from dplyr in case you want to add that to your dplyr pipeline:

library(dplyr)
df %>%
  mutate_all(funs(ifelse(is.na(.), 0, .)))

Result:

   A B C
1  1 1 2
2  2 2 5
3  3 1 2
4  0 2 0
5  1 1 0
6  2 2 0
7  3 1 3
8  0 2 0
9  1 1 3
10 2 2 3
11 3 1 0
12 0 2 3
13 1 1 4
14 2 2 4
15 3 1 0
16 0 2 0
17 1 1 1
18 2 2 0
19 3 1 2
20 0 2 0

If in any case you only want to replace the NA's in numeric columns, which I assume it might be the case in modeling, you can use mutate_if:

library(dplyr)
df %>%
  mutate_if(is.numeric, funs(ifelse(is.na(.), 0, .)))

or in base R:

replace(is.na(df), 0)

Result:

   A  B C
1  1  0 2
2  2 NA 5
3  3  0 2
4  0 NA 0
5  1  0 0
6  2 NA 0
7  3  0 3
8  0 NA 0
9  1  0 3
10 2 NA 3
11 3  0 0
12 0 NA 3
13 1  0 4
14 2 NA 4
15 3  0 0
16 0 NA 0
17 1  0 1
18 2 NA 0
19 3  0 2
20 0 NA 0

Data:

set.seed(123)
df <- data.frame(A=rep(c(0:3, NA), 5), B=rep(c("0", "NA"), 10), C=c(sample(c(0:5, NA), 20, replace = TRUE)))

Answer 4

To add to James's example, it seems you always have to create an intermediate when performing calculations on NA-containing data frames.

For instance, adding two columns (A and B) together from a data frame dfr:

temp.df <- data.frame(dfr) # copy the original
temp.df[is.na(temp.df)] <- 0
dfr$C <- temp.df$A + temp.df$B # or any other calculation
remove('temp.df')

When I do this I throw away the intermediate afterwards with remove/rm.