Reputation: 865
I have this dataframe in R
df <- structure(list(S.No = c(1L, 2L, 3L, 8L, 5L, 6L), A = c(8L, 8L,
5L, 2L, NA, 3L), B = c(5L, 8L, 8L, 2L, NA, 3L), C = c("8", "test",
"error", "3", NA, "3"), D = c(5L, 5L, 3L, 3L, NA, 8L), E = c("test",
"8", "8", "error", NA, "3")), class = "data.frame", row.names = c(NA,
-6L))
I need to find out if all values of the columns for each row have NA. It needs to be rowwise, but I can't get this to work. This is what I have tried so far
test.vars = c("A","B","C","D","E")
df %>% mutate(null_message = as.numeric(is.na(rowSums(.[test.vars]))))
This works if my columns have only numeric values. So, I tried to do something else:
df %>% mutate(null_message = any(is.na((.[test.vars]))))
but this doesn't work. It shows all rows as TRUE
, and I know why. I can use |
but it seems bit tedious to do is.na(A) | is.na(B) | is.na(C) | ...
. Is there a way I can get this done efficiently?
The expected output is the following:
Upvotes: 3
Views: 1342
Reputation: 6132
You could do:
df %>%
rowwise %>%
mutate(null_message = as.integer(all(c(A,B,C,D,E) %in% NA)))
# A tibble: 6 x 7
# Rowwise:
S.No A B C D E null_message
<int> <int> <int> <chr> <int> <chr> <int>
1 1 8 5 8 5 test 0
2 2 8 8 test 5 8 0
3 3 5 8 error 3 8 0
4 8 2 2 3 3 error 0
5 5 NA NA NA NA NA 1
6 6 3 3 3 8 3 0
Upvotes: 2
Reputation: 389355
You are on the right path :
library(dplyr)
test.vars = c("A","B","C","D","E")
df %>% mutate(null_message = as.numeric(rowSums(is.na(.[test.vars])) == length(test.vars)))
# S.No A B C D E null_message
#1 1 8 5 8 5 test 0
#2 2 8 8 test 5 8 0
#3 3 5 8 error 3 8 0
#4 8 2 2 3 3 error 0
#5 5 NA NA <NA> NA <NA> 1
#6 6 3 3 3 8 3 0
This reads assign 1 if number of NA
values in the row is same as length(test.vars)
.
Or in other way :
df %>% mutate(null_message = as.numeric(rowSums(!is.na(.[test.vars])) == 0))
This reads assign 1 if number of non-NA
value in the row is 0.
Upvotes: 3