[英]R across find only positive or only negative values tidyverse
In dplyr Column-wise operations has this example:在 dplyr 中,按列操作有这个例子:
df <- tibble(x = c("a", "b"), y = c(1, 1), z = c(-1, 1))
# Find all rows where EVERY numeric variable is greater than zero
df %>% filter(across(where(is.numeric), ~ .x > 0))
#> # A tibble: 1 x 3
#> x y z
#> <chr> <dbl> <dbl>
#> 1 b 1 1
if we change a bit the tibble:如果我们稍微改变一下小标题:
df <- tibble(x = c("a", "b", "c"), y = c(1, 1, -1), z = c(-1, 1, -1))
and we want to get negative or positive values for both columns we need to name the columns:我们想要为两列获取负值或正值,我们需要命名列:
df %>% filter((y > 0 & z > 0) | (y < 0 & z < 0))
#> # A tibble: 2 x 3
#> x y z
#> <chr> <dbl> <dbl>
#> 1 b 1 1
#> 2 c -1 -1
with across()
how can this be done?使用across()
如何做到这一点?
df %>% filter(across(where(is.numeric), ~ .x > 0 | .x < 0))
#> # A tibble: 3 x 3
#> x y z
#> <chr> <dbl> <dbl>
#> 1 a 1 -1
#> 2 b 1 1
#> 3 c -1 -1
I think since you are dealing with 2 variables in a row-wise operation, it would be much easier to use map2
from purrr
package:我认为由于您在逐行操作中处理 2 个变量,因此使用purrr
package 中的map2
会容易得多:
library(dplyr)
library(purrr)
df <- tibble(x = c("a", "b", "c"), y = c(1, 1, -1), z = c(-1, 1, -1))
df %>%
filter(map2_lgl(y, z, ~ (.x > 0 & .y > 0) | (.x < 0 & .y < 0)))
# A tibble: 2 x 3
x y z
<chr> <dbl> <dbl>
1 b 1 1
2 c -1 -1
We have to check for either all TRUE
or all FALSE
from a set of conditionals like c(T, T)
, c(T, F)
and c(F, F)
.我们必须从一组条件(如c(T, T)
、 c(T, F)
和c(F, F)
中检查所有TRUE
或所有FALSE
。 Now -现在 -
if_all
will filter c(T, T)
if_all
将过滤c(T, T)
!if_any
will filter again c(T, T)
from !
!if_any
将再次过滤来自!
的c(T, T)
ie negation of remaining values即剩余值的否定|
这两个将由一个|
加入ie OR即或c(T, T)
& c(F, F)
因此,我们将只有c(T, T)
& c(F, F)
Thus, this will do因此,这会做
df %>% filter(if_all(where(is.numeric), ~ .x > 0) | !if_any(where(is.numeric), ~ .x < 0))
# A tibble: 2 x 3
x y z
<chr> <dbl> <dbl>
1 b 1 1
2 c -1 -1
Alternative选择
df %>% filter(if_all(where(is.numeric), ~ .x > 0) | across(where(is.numeric), ~ .x < 0))
# A tibble: 2 x 3
x y z
<chr> <dbl> <dbl>
1 b 1 1
2 c -1 -1
Let's check on bigger example让我们看看更大的例子
set.seed(201)
df <- data.frame(A = LETTERS[1:10], x = rnorm(10), y = rnorm(10), z = -1*rnorm(10))
> df
A x y z
1 A 0.28606069 0.69329617 0.24400084
2 B -0.34454603 0.22380936 0.98825314
3 C 0.32576373 0.39845694 -1.24206048
4 D -1.69658097 1.01347438 1.68266603
5 E -1.28548252 -0.64785307 -1.44289063
6 F -0.07503189 0.64845271 0.46543975
7 G 0.26693735 0.20734270 -0.69366150
8 H 0.05593404 0.06439014 0.08772557
9 I -2.30403431 0.66938092 0.95508038
10 J 0.18900414 -0.37425445 -0.17010088
> df %>% filter(if_all(where(is.numeric), ~ .x > 0) | !if_any(where(is.numeric), ~ .x < 0))
A x y z
1 A 0.28606069 0.69329617 0.24400084
2 E -1.28548252 -0.64785307 -1.44289063
3 H 0.05593404 0.06439014 0.08772557
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