R Replace NA for all Columns Except *

Question

library(tidyverse)
df <- tibble(Date = c(rep(as.Date("2020-01-01"), 3), NA),
             col1 = 1:4,
             thisCol = c(NA, 8, NA, 3),
             thatCol = 25:28,
             col999 = rep(99, 4))
#> # A tibble: 4 x 5
#>   Date        col1  thisCol thatCol col999
#>   <date>     <int>    <dbl>   <int>  <dbl>
#> 1 2020-01-01     1       NA      25     99
#> 2 2020-01-01     2        8      26     99
#> 3 2020-01-01     3       NA      27     99
#> 4 NA             4        3      28     99

My actual R data frame has hundreds of columns that aren't neatly named, but can be approximated by the df data frame above.

I want to replace all values of NA with 0, with the exception of several columns (in my example I want to leave out the Date column and the thatCol column. I'd want to do it in this sort of fashion:

df %>% replace(is.na(.), 0)
#> Error: Assigned data `values` must be compatible with existing data.
#> i Error occurred for column `Date`.
#> x Can't convert <double> to <date>.
#> Run `rlang::last_error()` to see where the error occurred.

And my unsuccessful ideas for accomplishing the "everything except" replace NA are shown below.

df %>% replace(is.na(c(., -c(Date, thatCol)), 0))
df %>% replace_na(list([, c(2:3, 5)] = 0))
df %>% replace_na(list(everything(-c(Date, thatCol)) = 0))

Is there a way to select everything BUT in the way I need to? There's hundred of columns, named inconsistently, so typing them one by one is not a practical option.

Ronak Shah · Accepted Answer · 2020-05-01T14:22:10.020

You can use mutate_at :

library(dplyr)

Remove them by Name

df %>% mutate_at(vars(-c(Date, thatCol)), ~replace(., is.na(.), 0))

Remove them by position

df %>% mutate_at(-c(1,4), ~replace(., is.na(.), 0))

Select them by name

df %>% mutate_at(vars(col1, thisCol, col999), ~replace(., is.na(.), 0))

Select them by position

df %>% mutate_at(c(2, 3, 5), ~replace(., is.na(.), 0))

If you want to use replace_na

df %>% mutate_at(vars(-c(Date, thatCol)), tidyr::replace_na, 0)

Note that mutate_at is soon going to be replaced by across in dplyr 1.0.0.

score 2 · Answer 2 · answered May 01 '20 at 14:13

You have several options here based on data.table.

One of the coolest options: setnafill (version >= 1.12.4):

library(data.table)
setDT(df)

data.table::setnafill(df,fill = 0, cols = colnames(df)[!(colnames(df) %in% c("Date", thatCol)]))

Note that your dataframe is updated by reference.

NelsonGon · Answer 3 · 2020-05-01T14:58:41.953

Another base solution:

 to_change<-grep("^(this|col)",names(df))
   df[to_change]<- sapply(df[to_change],function(x) replace(x,is.na(x),0))
    df
    # A tibble: 4 x 5
      Date        col1 thisCol thatCol col999
      <date>     <dbl>   <dbl>   <int>  <dbl>
    1 2020-01-01     1       0      25     99
    2 2020-01-01     2       8      26     99
    3 2020-01-01     3       0      27     99
    4 NA             0       3      28     99

Data(I changed one value):

df <- structure(list(Date = structure(c(18262, 18262, 18262, NA), class = "Date"), 
    col1 = c(1L, 2L, 3L, NA), thisCol = c(NA, 8, NA, 3), thatCol = 25:28, 
    col999 = c(99, 99, 99, 99)), row.names = c(NA, -4L), class = c("tbl_df", 
"tbl", "data.frame"))

score 1 · Answer 4 · answered May 01 '20 at 19:02

replace works on a data.frame, so we can just do the replacement by index and update the original dataset

df[-c(1, 4)] <- replace(df[-c(1, 4)], is.na(df[-c(1, 4)]), 0)

Or using replace_na with across (from the new dplyr)

library(dplyr)
library(tidyr)
df %>% 
     mutate(across(-c(Date, thatCol), ~ replace_na(., 0)))

score 0 · Answer 5 · answered May 01 '20 at 14:20

If you know the ones that you don't want to change, you could do it like this:

df <- tibble(Date = c(rep(as.Date("2020-01-01"), 3), NA),
             col1 = 1:4,
             thisCol = c(NA, 8, NA, 3),
             thatCol = 25:28,
             col999 = rep(99, 4))


#dplyr
df_nonreplace <- select(df, c("Date", "thatCol"))

df_replace <- df[ ,!names(df) %in% names(df_nonreplace)]

df_replace[is.na(df_replace)] <- 0

df <- cbind(df_nonreplace, df_replace)


> head(df)
        Date thatCol col1 thisCol col999
1 2020-01-01      25    1       0     99
2 2020-01-01      26    2       8     99
3 2020-01-01      27    3       0     99
4       <NA>      28    4       3     99

R Replace NA for all Columns Except *

5 Answers5