Reputation: 169
I have a data frame with some columns with missing values. Is there a way (using dplyr) to efficiently calculate the percentage of each column that is missing i.e. NA. Sought of like a colSum equivalent. So I dont have to calculate each column percentage missing individually ?
Upvotes: 7
Views: 15690
Reputation: 311
Loving the concision of purrr::map
for this type of thing:
x %>% map(~ mean(is.na(.)))
Upvotes: 8
Reputation: 589
First, I created a test data for you:
a<- c(1,NA,NA,4)
b<- c(NA,2,3,4)
x<- data.frame(a,b)
x
# a b
# 1 1 NA
# 2 NA 2
# 3 NA 3
# 4 4 4
Then you can use colMeans(is.na(x))
:
colMeans(is.na(x))
# a b
# 0.50 0.25
Upvotes: 18
Reputation: 887078
We can use summarise_each
library(dplyr)
x %>%
summarise_each(funs(100*mean(is.na(.))))
Upvotes: 17