How to substitute NA by 0 in 20 columns?

Question

I want to substitute NA by 0 in 20 columns. I found this approach for 2 columns, however I guess it\'s not optimal if the number of columns is 20. Is there any alternative a

Answer 1

We can use NAer from qdap to convert the NA to 0. If there are multiple column, loop using lapply.

library(qdap)
nm1 <- c('b', 'c', 'e', 'f')
mydata[nm1] <- lapply(mydata[nm1], NAer)
mydata
#  a b c  d e f g d.1
#1 1 0 0  2 3 4 7   6
#2 2 g 3 NA 4 5 4   Y
#3 3 r 4  4 0 t 5   5

---

Or using dplyr

library(dplyr)
mydata %>% 
   mutate_each_(funs(replace(., which(is.na(.)), 0)), nm1)
#  a b c  d e f g d.1
#1 1 0 0  2 3 4 7   6
#2 2 g 3 NA 4 5 4   Y
#3 3 r 4  4 0 t 5   5

Answer 2

Another option:

library(tidyr)
v <- c('b', 'c', 'e', 'f')
replace_na(df, as.list(setNames(rep(0, length(v)), v)))

Which gives:

#  a b c  d e f g d.1
#1 1 0 0  2 3 4 7   6
#2 2 g 3 NA 4 5 4   Y
#3 3 r 4  4 0 t 5   5

Answer 3

The replace_na function from tidyr can be applied over a vector as well as a dataframe (http://tidyr.tidyverse.org/reference/replace_na.html).

Use it with a mutate_at variation from dplyr to apply it to multiple columns at the same time:

my_data %>% mutate_at(vars(b,c,e,f), replace_na, 0)

or

my_data %>% mutate_at(c('b','c','e','f'), replace_na, 0)

Answer 4

Here is a tidyverse way to replace NA with different values based on the data type of the column.

library(tidyverse)

dataset %>% mutate_if(is.numeric, replace_na, 0) %>%  
    mutate_if(is.character, replace_na, "")