R两端仅找到正值或负值tidyverse [英] R across find only positive or only negative values tidyverse
本文介绍了R两端仅找到正值或负值tidyverse的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
在dplyr中,按列操作具有以下示例:
In dplyr Column-wise operations has this example:
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
如果我们稍微改变一下小标题:
if we change a bit the tibble:
df <- tibble(x = c("a", "b", "c"), y = c(1, 1, -1), z = c(-1, 1, -1))
我们想为两列都取负值或正值,我们需要命名这些列:
and we want to get negative or positive values for both columns we need to name the columns:
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
与 across()
一起怎么做?
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
推荐答案
我们必须从诸如之类的一组条件中检查所有
, TRUE
或所有 FALSE
> c(T,T) c(T,F)
和 c(F,F)
.现在-
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)
. Now -
-
if_all
将过滤c(T,T)
-
!if_any
将从!
中再次过滤c(T,T)
!即剩余值取反 - 这两个将由
|
加入,即 OR - 因此,我们只有
c(T,T)
&c(F,F)
if_all
will filterc(T, T)
!if_any
will filter againc(T, T)
from!
i.e. negation of remaining values- these two will be joined by a
|
i.e. OR - Thus, we will have only
c(T, T)
&c(F, F)
这样,就可以了
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
替代
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
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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