I want to filter my data if all of the values in a subset of columns are NA
.
I found an answer here that works brilliantly for all columns, but in this case I want to exclude "wrapper" columns from the filter operation.
library(dplyr)
df <- tibble(a = letters[1:3], b = c(NA, NA, 3), c = c(NA, 2, 3), d = letters[1:3])
# works, but I've lost my surrounding columns
df %>%
select(-one_of(c("a", "d"))) %>%
filter_all(any_vars(!is.na(.)))
# only works if all columns are all not NA (or vice versa), I've lost my partially NA rows
df %>%
filter(across(-one_of(c("a", "d")),
~ !is.na(.)))
Desired outcome:
> df[2:3,]
# A tibble: 2 x 4
a b c d
<chr> <dbl> <dbl> <chr>
1 b NA 2 b
2 c 3 3 c
In the previous version of dplyr
you could use filter_at
in this way :
library(dplyr)
df %>% filter_at(vars(-one_of(c("a", "d"))), any_vars(!is.na(.)))
across
do not have direct replacement of any_vars
so one way would be to use Reduce
here :
df %>% filter(Reduce(`|`, across(-one_of(c("a", "d")),~!is.na(.))))
# A tibble: 2 x 4
# a b c d
# <chr> <dbl> <dbl> <chr>
#1 b NA 2 b
#2 c 3 3 c
One option could be:
df %>%
rowwise() %>%
filter(sum(!is.na(c_across(-all_of(c("a", "d"))))) != 0)
a b c d
<chr> <dbl> <dbl> <chr>
1 b NA 2 b
2 c 3 3 c
I found a similar example in vignette("colwise", package = "dplyr")
rowAny <- function(x) rowSums(x) > 0
df %>% filter(rowAny(across(-c(a,d), ~ !is.na(.))))
# A tibble: 2 x 4
a b c d
<chr> <dbl> <dbl> <chr>
1 b NA 2 b
2 c 3 3 c
But I would use this because it's a bit more readable:
rowAll <- function(x) rowSums(x) == length(x)
df %>% filter(!rowAll(across(-c(a,d), is.na)))
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