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.

Im trying to do this with:

    bothbeams.data = within(bothbeams.data, {       bothbeams.data$x.x = ifelse(is.na(bothbeams.data$x.x) == TRUE, 0, bothbeams.data$x.x)       bothbeams.data$x.y = ifelse(is.na(bothbeams.data$x.y) == TRUE, 0, bothbeams.data$x.y)     }) 

Where $x.x is one column and $x.y is the other of course, but this doesn't seem to work.

Answer 1

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 2

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  0 0 0 2  1 0 0 3  2 0 2 4  3 0 5 5  0 0 2 6  0 0 1 7  1 0 1 8  2 0 5 9  3 0 2 10 0 0 4 11 0 0 3 12 1 0 5 13 2 0 5 14 3 0 0 15 0 0 1 

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  0    0 0 2  1 <NA> 0 3  2    0 2 4  3 <NA> 5 5  0    0 2 6  0 <NA> 1 7  1    0 1 8  2 <NA> 5 9  3    0 2 10 0 <NA> 4 11 0    0 3 12 1 <NA> 5 13 2    0 5 14 3 <NA> 0 15 0    0 1 

Update

with dplyr 1.0.0, across is introduced:

library(dplyr) # Replace `NA` for all columns df %>%   mutate(across(everything(), ~ ifelse(is.na(.), 0, .)))  # Replace `NA` for numeric columns df %>%   mutate(across(where(is.numeric), ~ ifelse(is.na(.), 0, .))) 

---

Data:

set.seed(123) df <- data.frame(A=rep(c(0:3, NA), 3),                   B=rep(c("0", NA), length.out = 15),                   C=sample(c(0:5, NA), 15, replace = TRUE))