[英]Avoiding writing a long if-else statement in R
I have run into a situation where I have a data like this: 我遇到过这样一种情况:我有这样的数据:
df <- data.frame(id = 1:1000,
x = sample(0:30, 1000, replace = T),
y = sample(50:10000, 1000, replace = T))
I want to assign another column called z
based on multiple conditions ie 我想基于多个条件分配另一个名为z
的列,即
if x <= 5 & y <= 100, z = 1
if x > 5 & x <= 10 & y <= 100, z = 2
if x > 10 & x <= 12 & y <= 100, z = 3
if x > 12 & x <= 20 & y <= 100, z = 4
if x > 20 & x <= 30 & y <= 100, z = 5
if x <= 5 & y > 100 & y <= 1000, z = 6
if x > 5 & x <= 10 & y > 100 & y <= 1000 z = 7
if x > 10 & x <= 12 & y > 100 & y <= 1000, z = 8
if x > 12 & x <= 20 & y > 100 & y <= 1000, z = 9
if x > 20 & x <= 30 & y > 100 & y <= 1000, z = 10
.
.
.
and so. I hope you get the drift.
The obvious solution for me to do is this to write a long ifelse
statement something like this; 对我来说,显而易见的解决方案是写一个长长的ifelse
语句,就像这样;
df %>% mutate(z = ifelse(x <= 5 & y <= 100, 1,
ifelse(x > 5 & x <= 10 & y <= 100, 2,
ifelse(x > 10 & x <= 12 & y <= 100, 3))),
........... and son on)
You would find that such scripts can be endlessly long and I wondered if there are other ways to achieve this without writing the long ifelse
statement. 您会发现这样的脚本可能会无休止地长,我想知道是否有其他方法可以实现这一点而无需编写长ifelse
语句。
If there is a pattern in the if else statements, we can create the set of expressions beforehand and use !!!
如果if else语句中有模式,我们可以事先创建表达式并使用!!!
to unqoute and splice them into arguments to case_when
: unqoute并将它们拼接成case_when
参数:
x_gt_cond <- rep(c(-Inf, 5, 10, 12, 20), 2)
x_le_cond <- rep(c(5, 10, 12, 20 ,30), 2)
y_gt_cond <- rep(c(-Inf, 100), each = 5)
y_le_cond <- rep(c(100, 1000), each = 5)
z <- 1:10
cases <- paste("x > ", x_gt_cond, "& x <= ", x_le_cond,
"& y > ", y_gt_cond, "& y <= ", y_le_cond, "~ ", z)
library(dplyr)
library(rlang)
df %>%
mutate(z = case_when(!!!parse_exprs(cases)))
The trick is to use -Inf
and Inf
for the lower and upper bounds so that you have balanced conditions for x
and y
. 诀窍是使用-Inf
和Inf
作为下限和上限,这样你就可以得到x
和y
平衡条件。 What's elegant about this solution is that you can add more conditions simply by altering the _cond
vectors. 这个解决方案的优雅之处在于,只需更改_cond
向量即可添加更多条件。
Output: 输出:
> cases
[1] "x > -Inf & x <= 5 & y > -Inf & y <= 100 ~ 1"
[2] "x > 5 & x <= 10 & y > -Inf & y <= 100 ~ 2"
[3] "x > 10 & x <= 12 & y > -Inf & y <= 100 ~ 3"
[4] "x > 12 & x <= 20 & y > -Inf & y <= 100 ~ 4"
[5] "x > 20 & x <= 30 & y > -Inf & y <= 100 ~ 5"
[6] "x > -Inf & x <= 5 & y > 100 & y <= 1000 ~ 6"
[7] "x > 5 & x <= 10 & y > 100 & y <= 1000 ~ 7"
[8] "x > 10 & x <= 12 & y > 100 & y <= 1000 ~ 8"
[9] "x > 12 & x <= 20 & y > 100 & y <= 1000 ~ 9"
[10] "x > 20 & x <= 30 & y > 100 & y <= 1000 ~ 10"
id x y z
1 1 13 8440 NA
2 2 3 1467 NA
3 3 5 2699 NA
4 4 24 5286 NA
5 5 5 2378 NA
6 6 16 268 9
7 7 19 2910 NA
8 8 19 706 9
9 9 24 6212 NA
10 10 7 6026 NA
...
It sounds like the case_when
function in dplyr
is what you're looking for. 这听起来像case_when
在功能dplyr
是你在找什么。 In your case, it might look something like this. 在你的情况下,它可能看起来像这样。
df %>% mutate(z = case_when(
x <= 5 & y <= 100 ~ 1,
x > 5 & x <= 10 & y <= 100 ~ 2,
x > 10 & x <=12 & y <= 100 ~ 3
)
)
edit: Changed answer to reflect that case_when
is in the dplyr
package. 编辑:更改了答案以反映该case_when
在dplyr
包中。 Thanks for comments below. 感谢下面的评论。
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.