R语言【dplyr】——case_when()是一般向量化的 if-else()，该函数允许您将多个 if_else() 语句矢量化

Package dplyr version 1.1.4

---

Parameters

case_when(..., .default = NULL, .ptype = NULL, .size = NULL)

参数【...】：<dynamic-dots> 一组两面公式（two-sided formulas）。

• 公式左边（left hand side，LHS）决定了哪些值符合这种情况。
• 公式右边（right hand side，RHS）提供了替换值。
• LHS 输入的结果必须是逻辑向量。
• RHS 输入将被强制转换为通用类型。
• 所有输入的数据都将被回收利用，恢复到其平常大小。尽管如此，我们还是鼓励所有 LHS 输入的大小相同。
• 循环主要适用于 RHS 输入，在这种情况下，您可能会提供一个大小为 1 的输入，它将被循环为 LHS 输入的大小。
• 输入 NULL 将被忽略。

参数【.default】：当所有 LHS 输入返回 FALSE 或 NA 时使用的值。

• 参数【.default】的大小必须为 1 或与参数【...】计算出的通用大小相同。
• 参数【.default】与 RHS 输入一起参与通用类型的计算。
• LHS 条件中的 NA 值将被视为 FALSE，这意味着这些位置的结果将被分配为参数【.default】值。要以不同的方式处理条件中的缺失值，必须在它们落入参数【.default】之前明确地用另一个条件来捕获它们。这通常涉及 is.na(x) ~ value 的一些变体，以适应您对 case_when() 的使用。
• 如果为 NULL（默认值），将使用缺失值。

参数【.ptype】：一个可选的原型，用于声明所需的输出类型。如果提供，将覆盖 RHS 输入的通用类型。

参数【.size】：一个可选的大小，用于声明所需的输出大小。如果提供，它将覆盖从参数【...】计算出的通用大小。

---

Value

一个向量，其大小与参数【...】中输入值计算出的共同大小相同，类型与参数【...】中 RHS 输入值的共同类型相同。

---

Examples

1. 最简单的例子

x <- 1:70
case_when(
  x %% 35 == 0 ~ "fizz buzz",
  x %% 5 == 0 ~ "fizz",
  x %% 7 == 0 ~ "buzz",
  .default = as.character(x)
)

 [1] "1"         "2"         "3"         "4"         "fizz"      "6"        
 [7] "buzz"      "8"         "9"         "fizz"      "11"        "12"       
[13] "13"        "buzz"      "fizz"      "16"        "17"        "18"       
[19] "19"        "fizz"      "buzz"      "22"        "23"        "24"       
[25] "fizz"      "26"        "27"        "buzz"      "29"        "fizz"     
[31] "31"        "32"        "33"        "34"        "fizz buzz" "36"       
[37] "37"        "38"        "39"        "fizz"      "41"        "buzz"     
[43] "43"        "44"        "fizz"      "46"        "47"        "48"       
[49] "buzz"      "fizz"      "51"        "52"        "53"        "54"       
[55] "fizz"      "buzz"      "57"        "58"        "59"        "fizz"     
[61] "61"        "62"        "buzz"      "64"        "fizz"      "66"       
[67] "67"        "68"        "69"        "fizz buzz"

2. 与 if语句一样，条件会按顺序进行检测，所以您应该将条件按照最严格到最宽松排列，否则会出现以下情况

x <- 1:70
case_when(
  x %%  5 == 0 ~ "fizz",
  x %%  7 == 0 ~ "buzz",
  x %% 35 == 0 ~ "fizz buzz",
  .default = as.character(x)
)

 [1] "1"    "2"    "3"    "4"    "fizz" "6"    "buzz" "8"    "9"   
[10] "fizz" "11"   "12"   "13"   "buzz" "fizz" "16"   "17"   "18"  
[19] "19"   "fizz" "buzz" "22"   "23"   "24"   "fizz" "26"   "27"  
[28] "buzz" "29"   "fizz" "31"   "32"   "33"   "34"   "fizz" "36"  
[37] "37"   "38"   "39"   "fizz" "41"   "buzz" "43"   "44"   "fizz"
[46] "46"   "47"   "48"   "buzz" "fizz" "51"   "52"   "53"   "54"  
[55] "fizz" "buzz" "57"   "58"   "59"   "fizz" "61"   "62"   "buzz"
[64] "64"   "fizz" "66"   "67"   "68"   "69"   "fizz"

3. 如果元素不符合任何条件，那么就会触发参数【.default】，默认为NA

x <- 1:70
case_when(
  x %% 35 == 0 ~ "fizz buzz",
  x %% 5 == 0 ~ "fizz",
  x %% 7 == 0 ~ "buzz",
)

 [1] NA          NA          NA          NA          "fizz"     
 [6] NA          "buzz"      NA          NA          "fizz"     
[11] NA          NA          NA          "buzz"      "fizz"     
[16] NA          NA          NA          NA          "fizz"     
[21] "buzz"      NA          NA          NA          "fizz"     
[26] NA          NA          "buzz"      NA          "fizz"     
[31] NA          NA          NA          NA          "fizz buzz"
[36] NA          NA          NA          NA          "fizz"     
[41] NA          "buzz"      NA          NA          "fizz"     
[46] NA          NA          NA          "buzz"      "fizz"     
[51] NA          NA          NA          NA          "fizz"     
[56] "buzz"      NA          NA          NA          "fizz"     
[61] NA          NA          "buzz"      NA          "fizz"     
[66] NA          NA          NA          NA          "fizz buzz"

4. 请注意，LHS 上的 NA 值将被视为 FALSE，并触发参数【.default】。如果要使用不同的值，必须明确处理它们。处理缺失值的具体方法取决于您使用的 LHS 条件集

x <- 1:70
x[2:4] <- NA_real_
case_when(
  x %% 35 == 0 ~ "fizz buzz",
  x %% 5 == 0 ~ "fizz",
  x %% 7 == 0 ~ "buzz",
  is.na(x) ~ "nope",
  .default = as.character(x)
)

 [1] "1"         "nope"      "nope"      "nope"      "fizz"     
 [6] "6"         "buzz"      "8"         "9"         "fizz"     
[11] "11"        "12"        "13"        "buzz"      "fizz"     
[16] "16"        "17"        "18"        "19"        "fizz"     
[21] "buzz"      "22"        "23"        "24"        "fizz"     
[26] "26"        "27"        "buzz"      "29"        "fizz"     
[31] "31"        "32"        "33"        "34"        "fizz buzz"
[36] "36"        "37"        "38"        "39"        "fizz"     
[41] "41"        "buzz"      "43"        "44"        "fizz"     
[46] "46"        "47"        "48"        "buzz"      "fizz"     
[51] "51"        "52"        "53"        "54"        "fizz"     
[56] "buzz"      "57"        "58"        "59"        "fizz"     
[61] "61"        "62"        "buzz"      "64"        "fizz"     
[66] "66"        "67"        "68"        "69"        "fizz buzz"

5. case_when() 对所有 RHS 表达式进行求值，然后通过提取所选（通过 LHS 表达式）部分来构建结果

y <- seq(-2, 2, by = .5)
case_when(
  y >= 0 ~ sqrt(y),
  .default = y
)

[1] -2.0000000 -1.5000000 -1.0000000 -0.5000000  0.0000000  0.7071068
[7]  1.0000000  1.2247449  1.4142136
Warning message:
In sqrt(y) : 产生了NaNs

6. 当你想创建一个依赖于现有变量复杂组合的新变量时，**case_when()**在 mutate() 中特别有用

starwars

# A tibble: 87 × 14
   name       height  mass hair_color skin_color eye_color birth_year
   <chr>       <int> <dbl> <chr>      <chr>      <chr>          <dbl>
 1 Luke Skyw...    172    77 blond      fair       blue            19  
 2 C-3PO         167    75 NA         gold       yellow         112  
 3 R2-D2          96    32 NA         white, bl... red             33  
 4 Darth Vad...    202   136 none       white      yellow          41.9
 5 Leia Orga...    150    49 brown      light      brown           19  
 6 Owen Lars     178   120 brown, gr... light      blue            52  
 7 Beru Whit...    165    75 brown      light      blue            47  
 8 R5-D4          97    32 NA         white, red red             NA  
 9 Biggs Dar...    183    84 black      light      brown           24  
10 Obi-Wan K...    182    77 auburn, w... fair       blue-gray       57  
# ℹ 77 more rows
# ℹ 7 more variables: sex <chr>, gender <chr>, homeworld <chr>,
#   species <chr>, films <list>, vehicles <list>, starships <list>
# ℹ Use `print(n = ...)` to see more rows

starwars %>%
  select(name:mass, gender, species) %>%
  mutate(
    type = case_when(
      height > 200 | mass > 200 ~ "large",
      species == "Droid" ~ "robot",
      .default = "other"
    )
  )

# A tibble: 87 × 6
   name               height  mass gender    species type 
   <chr>               <int> <dbl> <chr>     <chr>   <chr>
 1 Luke Skywalker        172    77 masculine Human   other
 2 C-3PO                 167    75 masculine Droid   robot
 3 R2-D2                  96    32 masculine Droid   robot
 4 Darth Vader           202   136 masculine Human   large
 5 Leia Organa           150    49 feminine  Human   other
 6 Owen Lars             178   120 masculine Human   other
 7 Beru Whitesun Lars    165    75 feminine  Human   other
 8 R5-D4                  97    32 masculine Droid   robot
 9 Biggs Darklighter     183    84 masculine Human   other
10 Obi-Wan Kenobi        182    77 masculine Human   other
# ℹ 77 more rows
# ℹ Use `print(n = ...)` to see more rows

7. case_when() 不是一个整洁的条件函数。如果想重复使用相同的模式，请在自定义函数中调用 case_when()

case_character_type <- function(height, mass, species) {
  case_when(
    height > 200 | mass > 200 ~ "large",
    species == "Droid" ~ "robot",
    .default = "other"
  )
}

case_character_type(150, 250, "Droid")
case_character_type(150, 150, "Droid")

[1] "large"
[1] "robot"

8. 上述函数也可在 mutate() 中使用

starwars %>%
  mutate(type = case_character_type(height, mass, species)) %>%
  pull(type)

 [1] "other" "robot" "robot" "large" "other" "other" "other" "robot"
 [9] "other" "other" "other" "other" "large" "other" "other" "large"
[17] "other" "other" "other" "other" "other" "robot" "other" "other"
[25] "other" "other" "other" "other" "other" "other" "other" "other"
[33] "other" "other" "other" "large" "large" "other" "other" "other"
[41] "other" "other" "other" "other" "other" "other" "other" "other"
[49] "other" "other" "other" "other" "other" "other" "other" "large"
[57] "other" "other" "other" "other" "other" "other" "other" "other"
[65] "other" "other" "other" "other" "other" "other" "large" "large"
[73] "other" "robot" "other" "other" "other" "large" "large" "other"
[81] "other" "large" "other" "other" "other" "robot" "other"

9. **case_when()**忽略 NULL 输入。当你想只在特定条件下使用模式时，这很有用。在这里，我们将利用 if 在没有 else 子句时返回 NULL 这一事实。

case_character_type <- function(height, mass, species, robots = TRUE) {
  case_when(
    height > 200 | mass > 200 ~ "large",
    if (robots) species == "Droid" ~ "robot",
    .default = "other"
  )
}

starwars %>%
  mutate(type = case_character_type(height, mass, species, robots = FALSE)) %>%
  pull(type)

 [1] "other" "other" "other" "large" "other" "other" "other" "other"
 [9] "other" "other" "other" "other" "large" "other" "other" "large"
[17] "other" "other" "other" "other" "other" "other" "other" "other"
[25] "other" "other" "other" "other" "other" "other" "other" "other"
[33] "other" "other" "other" "large" "large" "other" "other" "other"
[41] "other" "other" "other" "other" "other" "other" "other" "other"
[49] "other" "other" "other" "other" "other" "other" "other" "large"
[57] "other" "other" "other" "other" "other" "other" "other" "other"
[65] "other" "other" "other" "other" "other" "other" "large" "large"
[73] "other" "other" "other" "other" "other" "large" "large" "other"
[81] "other" "large" "other" "other" "other" "other" "other"

每种情况都按顺序进行检测，每个元素的第一个匹配值决定了其在输出向量中的相应值。如果没有匹配的情况，则使用 参数【.default】 作为最后的 "else "声明。