Using any() or all() with is.na() over multiple columns
I'd like to drop rows from my dataset that are all NAs (AKA keep rows with any non-NAs) for a list of columns. How could I update this code so that x & y are supplied as a vector? This would enable me to flexibly add and drop columns for inspection.
library(dplyr)
ds <-
tibble(
id = c(1:4),
x = c(NA, 1, NA, 4),
y = c(NA, NA , 3, 4)
)
ds %>%
rowwise() %>%
filter(
any(
!is.na(x),
!is.na(y)
)
) %>%
ungroup()
I'm trying to write something like any(!is.na(c(x,y))) but I'm not sure how to supply multiple arguments to is.na().
r
asked Nov 14 '18 at 19:09
JoeJoe
737616
add a comment |
I'd like to drop rows from my dataset that are all NAs (AKA keep rows with any non-NAs) for a list of columns. How could I update this code so that x & y are supplied as a vector? This would enable me to flexibly add and drop columns for inspection.
library(dplyr)
ds <-
tibble(
id = c(1:4),
x = c(NA, 1, NA, 4),
y = c(NA, NA , 3, 4)
)
ds %>%
rowwise() %>%
filter(
any(
!is.na(x),
!is.na(y)
)
) %>%
ungroup()
I'm trying to write something like any(!is.na(c(x,y))) but I'm not sure how to supply multiple arguments to is.na().
r
asked Nov 14 '18 at 19:09
JoeJoe
737616
add a comment |
I'd like to drop rows from my dataset that are all NAs (AKA keep rows with any non-NAs) for a list of columns. How could I update this code so that x & y are supplied as a vector? This would enable me to flexibly add and drop columns for inspection.
library(dplyr)
ds <-
tibble(
id = c(1:4),
x = c(NA, 1, NA, 4),
y = c(NA, NA , 3, 4)
)
ds %>%
rowwise() %>%
filter(
any(
!is.na(x),
!is.na(y)
)
) %>%
ungroup()
I'm trying to write something like any(!is.na(c(x,y))) but I'm not sure how to supply multiple arguments to is.na().
r
asked Nov 14 '18 at 19:09
JoeJoe
737616
I'd like to drop rows from my dataset that are all NAs (AKA keep rows with any non-NAs) for a list of columns. How could I update this code so that x & y are supplied as a vector? This would enable me to flexibly add and drop columns for inspection.
library(dplyr)
ds <-
tibble(
id = c(1:4),
x = c(NA, 1, NA, 4),
y = c(NA, NA , 3, 4)
)
ds %>%
rowwise() %>%
filter(
any(
!is.na(x),
!is.na(y)
)
) %>%
ungroup()
I'm trying to write something like any(!is.na(c(x,y))) but I'm not sure how to supply multiple arguments to is.na().
r
r
asked Nov 14 '18 at 19:09
JoeJoe
737616
asked Nov 14 '18 at 19:09
JoeJoe
737616
asked Nov 14 '18 at 19:09
JoeJoe
737616
asked Nov 14 '18 at 19:09
JoeJoe
737616
asked Nov 14 '18 at 19:09
JoeJoe
737616
737616
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
We can use filter_at with any_vars
ds %>%
filter_at(vars(x:y), any_vars(!is.na(.)))
# A tibble: 3 x 3
# id x y
# <int> <dbl> <dbl>
#1 2 1 NA
#2 3 NA 3
#3 4 4 4
answered Nov 14 '18 at 19:10
akrunakrun
415k13204278
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
We can use filter_at with any_vars
ds %>%
filter_at(vars(x:y), any_vars(!is.na(.)))
# A tibble: 3 x 3
# id x y
# <int> <dbl> <dbl>
#1 2 1 NA
#2 3 NA 3
#3 4 4 4
answered Nov 14 '18 at 19:10
akrunakrun
415k13204278
add a comment |
We can use filter_at with any_vars
ds %>%
filter_at(vars(x:y), any_vars(!is.na(.)))
# A tibble: 3 x 3
# id x y
# <int> <dbl> <dbl>
#1 2 1 NA
#2 3 NA 3
#3 4 4 4
answered Nov 14 '18 at 19:10
akrunakrun
415k13204278
add a comment |
We can use filter_at with any_vars
ds %>%
filter_at(vars(x:y), any_vars(!is.na(.)))
# A tibble: 3 x 3
# id x y
# <int> <dbl> <dbl>
#1 2 1 NA
#2 3 NA 3
#3 4 4 4
answered Nov 14 '18 at 19:10
akrunakrun
415k13204278
We can use filter_at with any_vars
ds %>%
filter_at(vars(x:y), any_vars(!is.na(.)))
# A tibble: 3 x 3
# id x y
# <int> <dbl> <dbl>
#1 2 1 NA
#2 3 NA 3
#3 4 4 4
answered Nov 14 '18 at 19:10
akrunakrun
415k13204278
answered Nov 14 '18 at 19:10
akrunakrun
415k13204278
answered Nov 14 '18 at 19:10
akrunakrun
415k13204278
answered Nov 14 '18 at 19:10
akrunakrun
415k13204278
415k13204278
add a comment |
add a comment |
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