[英]Why does dplyr's filter drop NA values from a factor variable?
When I use filter
from the dplyr
package to drop a level of a factor variable, filter
also drops the NA
values.当我使用
filter
从所述dplyr
包下降的一个因素可变的电平, filter
也下降的NA
值。 Here's an example:下面是一个例子:
library(dplyr)
set.seed(919)
(dat <- data.frame(var1 = factor(sample(c(1:3, NA), size = 10, replace = T))))
# var1
# 1 <NA>
# 2 3
# 3 3
# 4 1
# 5 1
# 6 <NA>
# 7 2
# 8 2
# 9 <NA>
# 10 1
filter(dat, var1 != 1)
# var1
# 1 3
# 2 3
# 3 2
# 4 2
This does not seem ideal -- I only wanted to drop rows where var1 == 1
.这似乎并不理想——我只想删除
var1 == 1
行。
It looks like this is occurring because any comparison with NA
returns NA
, which filter
then drops.看起来这是因为与
NA
任何比较都会返回NA
,然后filter
会下降。 So, for example, filter(dat, !(var1 %in% 1))
produces the correct results.因此,例如,
filter(dat, !(var1 %in% 1))
产生正确的结果。 But is there a way to tell filter
not to drop the NA
values?但是有没有办法告诉
filter
不要删除NA
值?
You could use this:你可以用这个:
filter(dat, var1 != 1 | is.na(var1))
var1
1 <NA>
2 3
3 3
4 <NA>
5 2
6 2
7 <NA>
And it won't.它不会。
Also just for completion, dropping NAs is the intended behavior of filter
as you can see from the following:同样只是为了完成,删除 NA 是
filter
的预期行为,如下所示:
test_that("filter discards NA", {
temp <- data.frame(
i = 1:5,
x = c(NA, 1L, 1L, 0L, 0L)
)
res <- filter(temp, x == 1)
expect_equal(nrow(res), 2L)
})
This test above was taken from the tests for filter
from github .上面的这个测试取自github 的
filter
测试。
The answers previously given are good, but when your filter statement involves a function of many fields, the work around might not be so great.之前给出的答案很好,但是当您的过滤器语句涉及多个字段的函数时,解决方法可能不会那么好。 Also, who wants to use
mapply
the non-vectorized identical
.另外,谁想使用
mapply
非矢量化的identical
. Here is another somewhat simpler solution using coalesce
这是另一个使用
coalesce
更简单的解决方案
filter(dat, coalesce( var1 != 1, TRUE))
I often map identical
with mapply
...我经常映射与
mapply
identical
...
(note: I believe because of changes in R 3.6.0, set.seed
and sample
end up with different test data) (注意:我相信因为 R 3.6.0 的变化,
set.seed
和sample
最终得到不同的测试数据)
library(dplyr, warn.conflicts = FALSE)
set.seed(919)
(dat <- data.frame(var1 = factor(sample(c(1:3, NA), size = 10, replace = T))))
#> var1
#> 1 3
#> 2 1
#> 3 <NA>
#> 4 3
#> 5 1
#> 6 3
#> 7 2
#> 8 3
#> 9 2
#> 10 1
filter(dat, var1 != 1)
#> var1
#> 1 3
#> 2 3
#> 3 3
#> 4 2
#> 5 3
#> 6 2
filter(dat, !mapply(identical, as.numeric(var1), 1))
#> var1
#> 1 3
#> 2 <NA>
#> 3 3
#> 4 3
#> 5 2
#> 6 3
#> 7 2
it works for numerics and strings as well (probably more common use case)...它也适用于数字和字符串(可能更常见的用例)...
library(dplyr, warn.conflicts = FALSE)
set.seed(919)
(dat <- data.frame(var1 = sample(c(1:3, NA), size = 10, replace = T),
var2 = letters[sample(c(1:3, NA), size = 10, replace = T)],
stringsAsFactors = FALSE))
#> var1 var2
#> 1 3 <NA>
#> 2 1 a
#> 3 NA a
#> 4 3 b
#> 5 1 b
#> 6 3 <NA>
#> 7 2 a
#> 8 3 c
#> 9 2 <NA>
#> 10 1 b
filter(dat, !mapply(identical, var1, 1L))
#> var1 var2
#> 1 3 <NA>
#> 2 NA a
#> 3 3 b
#> 4 3 <NA>
#> 5 2 a
#> 6 3 c
#> 7 2 <NA>
filter(dat, !mapply(identical, var2, 'a'))
#> var1 var2
#> 1 3 <NA>
#> 2 3 b
#> 3 1 b
#> 4 3 <NA>
#> 5 3 c
#> 6 2 <NA>
#> 7 1 b
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