r-如何用if.else替换if-else

Question

我想用if…else代替ifelse，因为我有完全相同的条件和很多效果。我看到其他人提出了类似问题14170778的问题，但我无法应用答案。我知道if语句不像ifelse那样矢量化

df <- mtcars %>% 
mutate(Tire = ifelse(mpg>18 & disp>160, "big",NA),
Bonus = ifelse(mpg>18 & disp>160, -100,NA),
Maintenance= ifelse(mpg>18 & disp>160, "expensive","cheap"))

我尝试了这样的方法，但它对1个突变不起作用，所以对3个…

df1 <- mtcars %>% 
{ if(mtcars$mpg>18 & mtcars$disp>160) mutate(.,Tire = "big") else . }

有没有其他方法可以避免同一行写10次？

Answer 1

由于您正在进行相同的逻辑比较以生成几个新的标签列，因此一个好的替代方法是将标签存储在一个单独的表中，然后进行一次比较，并连接标签：

labels <- data.frame(
value = c(TRUE, FALSE),
Tire = c('big', NA),
Bonus = c(-100, NA),
Maintenance = c('expensive', 'cheap')
)
value Tire Bonus Maintenance
1  TRUE  big  -100   expensive
2 FALSE <NA>    NA       cheap
df <- mtcars %>% 
mutate(value = mpg > 18 & disp > 160) %>% 
inner_join(labels)
mpg cyl disp  hp drat    wt  qsec vs am gear carb value Tire Bonus Maintenance
1 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 FALSE <NA>    NA       cheap
2 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 FALSE <NA>    NA       cheap
3 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 FALSE <NA>    NA       cheap
4 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1  TRUE  big  -100   expensive
5 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2  TRUE  big  -100   expensive
6 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1  TRUE  big  -100   expensive

Answer 2

如果我们想创建三列

library(dplyr)
library(tidyr)
library(stringr)
mtcars %>%
mutate(out = case_when(mpg > 18 & disp > 160 ~
list(tibble(Tire = "big", Bonus = -100, 
Maintenance = "expensive")),
TRUE  ~ list(tibble(Tire = NA_character_, Bonus = NA_real_, Maintenance = "cheap")))) %>%
unnest_wider(out, names_sep = "") %>% 
rename_with(~ str_remove(.x, "^out"))

-输出

# A tibble: 32 × 14
mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb Tire  Bonus Maintenance
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <chr>      
1  21       6  160    110  3.9   2.62  16.5     0     1     4     4 <NA>     NA cheap      
2  21       6  160    110  3.9   2.88  17.0     0     1     4     4 <NA>     NA cheap      
3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1 <NA>     NA cheap      
4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1 big    -100 expensive  
5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2 big    -100 expensive  
6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1 big    -100 expensive  
7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4 <NA>     NA cheap      
8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2 <NA>     NA cheap      
9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2 <NA>     NA cheap      
10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4 big    -100 expensive