Replace a value NA with the value from another column in R

Question

I want to replace the NA value in dfABy from the column A, with the value from the column B, based on the year of column year. For example, my df is:

                 >dfABy 
                 A    B   Year
                 56   75  1921
                 NA   45  1921
                 NA   77  1922
                 67   41  1923
                 NA   65  1923

The result what I will attend is:

                 > dfABy
                 A    B   Year
                 56   75  1921
                *45*  45  1921
                *77*  77  1922
                 67   41  1923
                *65*  65  1923

P.S: with the * the value replacing in column A from column B for every year

Posture

Strongly related: https://mcmap.net/q/152311/-how-to-implement-coalesce-efficiently-in-r/903061 (maybe consider as duplicate?) – Lallans

Answer 1

Perhaps the easiest to read/understand answer in R lexicon is to use ifelse. So borrowing Richard's dataframe we could do:

df <- structure(list(A = c(56L, NA, NA, 67L, NA),
                     B = c(75L, 45L, 77L, 41L, 65L),
                     Year = c(1921L, 1921L, 1922L, 1923L, 1923L)),
                .Names = c("A", "B", "Year"),
                class = "data.frame",
                row.names = c(NA, -5L))

df$A <- ifelse(is.na(df$A), df$B, df$A)

Marmoreal

Answer 2

Now corrected per @Max. (original worked with initial implementation)

The new dplyr function, coalesce, can really simplify these situations.

library(dplyr)

dfABy %>% 
  mutate(A = coalesce(A, B))

Psychosexual

Doesn't work but the other answers corrected it – Horseback

now corrected - indeed we shouldn't leave obsolete answers hanging around – Psychosexual

Answer 3

The solution provided by GGAnderson did return an error message. Using it inside mutate() however worked fine.

df <- structure(list(A = c(56L, NA, NA, 67L, NA),
                     B = c(75L, 45L, 77L, 41L, 65L),
                     Year = c(1921L, 1921L, 1922L, 1923L, 1923L)),
                .Names = c("A", "B", "Year"), 
                class = "data.frame", 
                row.names = c(NA, -5L))
df
df %>% 
  coalesce(A, B) # returns error

df %>%
  mutate(A = coalesce(A, B)) # works

Brevier

Answer 4

You could use simple replacement with [<-, subsetting for the NA elements.

df$A[is.na(df$A)] <- df$B[is.na(df$A)]

Or alternatively, within()

within(df, A[is.na(A)] <- B[is.na(A)])

Both give

   A  B Year
1 56 75 1921
2 45 45 1921
3 77 77 1922
4 67 41 1923
5 65 65 1923

Data:

df <- structure(list(A = c(56L, NA, NA, 67L, NA), B = c(75L, 45L, 77L, 
41L, 65L), Year = c(1921L, 1921L, 1922L, 1923L, 1923L)), .Names = c("A", 
"B", "Year"), class = "data.frame", row.names = c(NA, -5L))

Zia

Answer 5

Easy

library(dplyr)

dfABy %>%
  mutate(A_new = 
           A %>% 
             is.na %>%
             ifelse(B, A) )

Toscanini

Why add a new column? If you did mutate(A = ...) instead of A_new you will replace the values in the first column, which is what OP wants. – Zia

I in general tend to program with immutability in mind. – Toscanini