一个真正的问题。每当我需要编写 dplyr 函数时，我都在旁听。
我知道 curl-curly 运算符可以简化很多任务。
https://www.tidyverse.org/blog/2019/06/rlang-0-4-0/
和
https://www.tidyverse.org/blog/2020/02/glue-strings-and-tidy-eval/
我不清楚什么时候使用简单的“=”和海象运算符“:=”。
例如，考虑帖子末尾的片段。
函数mean_by 和mean_by2 的不同只是因为前者依赖于“=”，后者依赖于“:=”，但结果是一样的。
但是，如果我尝试编写一个依赖于 mutate 来添加新列的函数，如果我在创建新列时使用“=”而不是“:=”，我会收到一条错误消息。
有人可以向我澄清为什么不同吗？这是否意味着使用 Walrus 运算符而不是“=”更安全？
谢谢!
library(tidyverse)
mean_by <- function(data, by, var) {
data %>%
group_by({{ by }}) %>%
summarise(avg = mean({{ var }}, na.rm = TRUE))
}
mean_by2 <- function(data, by, var) {
data %>%
group_by({{ by }}) %>%
summarise(avg := mean({{ var }}, na.rm = TRUE))
}
add_new_col <- function(data, old_col, new_col){
data %>%
mutate({{new_col}}:={{old_col}})
}
iris %>% mean_by(Species, Sepal.Width)
#> # A tibble: 3 x 2
#> Species avg
#> <fct> <dbl>
#> 1 setosa 3.43
#> 2 versicolor 2.77
#> 3 virginica 2.97
iris %>% mean_by2(Species, Sepal.Width)
#> # A tibble: 3 x 2
#> Species avg
#> <fct> <dbl>
#> 1 setosa 3.43
#> 2 versicolor 2.77
#> 3 virginica 2.97
iris %>% add_new_col(Species, New_species)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species New_species
#> 1 5.1 3.5 1.4 0.2 setosa setosa
#> 2 4.9 3.0 1.4 0.2 setosa setosa
#> 3 4.7 3.2 1.3 0.2 setosa setosa
#> 4 4.6 3.1 1.5 0.2 setosa setosa
#> 5 5.0 3.6 1.4 0.2 setosa setosa
#> 6 5.4 3.9 1.7 0.4 setosa setosa
#> 7 4.6 3.4 1.4 0.3 setosa setosa
#> 8 5.0 3.4 1.5 0.2 setosa setosa
#> 9 4.4 2.9 1.4 0.2 setosa setosa
#> 10 4.9 3.1 1.5 0.1 setosa setosa
#> 11 5.4 3.7 1.5 0.2 setosa setosa
#> 12 4.8 3.4 1.6 0.2 setosa setosa
#> 13 4.8 3.0 1.4 0.1 setosa setosa
#> 14 4.3 3.0 1.1 0.1 setosa setosa
#> 15 5.8 4.0 1.2 0.2 setosa setosa
#> 16 5.7 4.4 1.5 0.4 setosa setosa
#> 17 5.4 3.9 1.3 0.4 setosa setosa
#> 18 5.1 3.5 1.4 0.3 setosa setosa
#> 19 5.7 3.8 1.7 0.3 setosa setosa
#> 20 5.1 3.8 1.5 0.3 setosa setosa
#> 21 5.4 3.4 1.7 0.2 setosa setosa
#> 22 5.1 3.7 1.5 0.4 setosa setosa
#> 23 4.6 3.6 1.0 0.2 setosa setosa
#> 24 5.1 3.3 1.7 0.5 setosa setosa
#> 25 4.8 3.4 1.9 0.2 setosa setosa
#> 26 5.0 3.0 1.6 0.2 setosa setosa
#> 27 5.0 3.4 1.6 0.4 setosa setosa
#> 28 5.2 3.5 1.5 0.2 setosa setosa
#> 29 5.2 3.4 1.4 0.2 setosa setosa
#> 30 4.7 3.2 1.6 0.2 setosa setosa
#> 31 4.8 3.1 1.6 0.2 setosa setosa
#> 32 5.4 3.4 1.5 0.4 setosa setosa
#> 33 5.2 4.1 1.5 0.1 setosa setosa
#> 34 5.5 4.2 1.4 0.2 setosa setosa
#> 35 4.9 3.1 1.5 0.2 setosa setosa
#> 36 5.0 3.2 1.2 0.2 setosa setosa
#> 37 5.5 3.5 1.3 0.2 setosa setosa
#> 38 4.9 3.6 1.4 0.1 setosa setosa
#> 39 4.4 3.0 1.3 0.2 setosa setosa
#> 40 5.1 3.4 1.5 0.2 setosa setosa
#> 41 5.0 3.5 1.3 0.3 setosa setosa
#> 42 4.5 2.3 1.3 0.3 setosa setosa
#> 43 4.4 3.2 1.3 0.2 setosa setosa
#> 44 5.0 3.5 1.6 0.6 setosa setosa
#> 45 5.1 3.8 1.9 0.4 setosa setosa
#> 46 4.8 3.0 1.4 0.3 setosa setosa
#> 47 5.1 3.8 1.6 0.2 setosa setosa
#> 48 4.6 3.2 1.4 0.2 setosa setosa
#> 49 5.3 3.7 1.5 0.2 setosa setosa
#> 50 5.0 3.3 1.4 0.2 setosa setosa
#> 51 7.0 3.2 4.7 1.4 versicolor versicolor
#> 52 6.4 3.2 4.5 1.5 versicolor versicolor
#> 53 6.9 3.1 4.9 1.5 versicolor versicolor
#> 54 5.5 2.3 4.0 1.3 versicolor versicolor
#> 55 6.5 2.8 4.6 1.5 versicolor versicolor
#> 56 5.7 2.8 4.5 1.3 versicolor versicolor
#> 57 6.3 3.3 4.7 1.6 versicolor versicolor
#> 58 4.9 2.4 3.3 1.0 versicolor versicolor
#> 59 6.6 2.9 4.6 1.3 versicolor versicolor
#> 60 5.2 2.7 3.9 1.4 versicolor versicolor
#> 61 5.0 2.0 3.5 1.0 versicolor versicolor
#> 62 5.9 3.0 4.2 1.5 versicolor versicolor
#> 63 6.0 2.2 4.0 1.0 versicolor versicolor
#> 64 6.1 2.9 4.7 1.4 versicolor versicolor
#> 65 5.6 2.9 3.6 1.3 versicolor versicolor
#> 66 6.7 3.1 4.4 1.4 versicolor versicolor
#> 67 5.6 3.0 4.5 1.5 versicolor versicolor
#> 68 5.8 2.7 4.1 1.0 versicolor versicolor
#> 69 6.2 2.2 4.5 1.5 versicolor versicolor
#> 70 5.6 2.5 3.9 1.1 versicolor versicolor
#> 71 5.9 3.2 4.8 1.8 versicolor versicolor
#> 72 6.1 2.8 4.0 1.3 versicolor versicolor
#> 73 6.3 2.5 4.9 1.5 versicolor versicolor
#> 74 6.1 2.8 4.7 1.2 versicolor versicolor
#> 75 6.4 2.9 4.3 1.3 versicolor versicolor
#> 76 6.6 3.0 4.4 1.4 versicolor versicolor
#> 77 6.8 2.8 4.8 1.4 versicolor versicolor
#> 78 6.7 3.0 5.0 1.7 versicolor versicolor
#> 79 6.0 2.9 4.5 1.5 versicolor versicolor
#> 80 5.7 2.6 3.5 1.0 versicolor versicolor
#> 81 5.5 2.4 3.8 1.1 versicolor versicolor
#> 82 5.5 2.4 3.7 1.0 versicolor versicolor
#> 83 5.8 2.7 3.9 1.2 versicolor versicolor
#> 84 6.0 2.7 5.1 1.6 versicolor versicolor
#> 85 5.4 3.0 4.5 1.5 versicolor versicolor
#> 86 6.0 3.4 4.5 1.6 versicolor versicolor
#> 87 6.7 3.1 4.7 1.5 versicolor versicolor
#> 88 6.3 2.3 4.4 1.3 versicolor versicolor
#> 89 5.6 3.0 4.1 1.3 versicolor versicolor
#> 90 5.5 2.5 4.0 1.3 versicolor versicolor
#> 91 5.5 2.6 4.4 1.2 versicolor versicolor
#> 92 6.1 3.0 4.6 1.4 versicolor versicolor
#> 93 5.8 2.6 4.0 1.2 versicolor versicolor
#> 94 5.0 2.3 3.3 1.0 versicolor versicolor
#> 95 5.6 2.7 4.2 1.3 versicolor versicolor
#> 96 5.7 3.0 4.2 1.2 versicolor versicolor
#> 97 5.7 2.9 4.2 1.3 versicolor versicolor
#> 98 6.2 2.9 4.3 1.3 versicolor versicolor
#> 99 5.1 2.5 3.0 1.1 versicolor versicolor
#> 100 5.7 2.8 4.1 1.3 versicolor versicolor
#> 101 6.3 3.3 6.0 2.5 virginica virginica
#> 102 5.8 2.7 5.1 1.9 virginica virginica
#> 103 7.1 3.0 5.9 2.1 virginica virginica
#> 104 6.3 2.9 5.6 1.8 virginica virginica
#> 105 6.5 3.0 5.8 2.2 virginica virginica
#> 106 7.6 3.0 6.6 2.1 virginica virginica
#> 107 4.9 2.5 4.5 1.7 virginica virginica
#> 108 7.3 2.9 6.3 1.8 virginica virginica
#> 109 6.7 2.5 5.8 1.8 virginica virginica
#> 110 7.2 3.6 6.1 2.5 virginica virginica
#> 111 6.5 3.2 5.1 2.0 virginica virginica
#> 112 6.4 2.7 5.3 1.9 virginica virginica
#> 113 6.8 3.0 5.5 2.1 virginica virginica
#> 114 5.7 2.5 5.0 2.0 virginica virginica
#> 115 5.8 2.8 5.1 2.4 virginica virginica
#> 116 6.4 3.2 5.3 2.3 virginica virginica
#> 117 6.5 3.0 5.5 1.8 virginica virginica
#> 118 7.7 3.8 6.7 2.2 virginica virginica
#> 119 7.7 2.6 6.9 2.3 virginica virginica
#> 120 6.0 2.2 5.0 1.5 virginica virginica
#> 121 6.9 3.2 5.7 2.3 virginica virginica
#> 122 5.6 2.8 4.9 2.0 virginica virginica
#> 123 7.7 2.8 6.7 2.0 virginica virginica
#> 124 6.3 2.7 4.9 1.8 virginica virginica
#> 125 6.7 3.3 5.7 2.1 virginica virginica
#> 126 7.2 3.2 6.0 1.8 virginica virginica
#> 127 6.2 2.8 4.8 1.8 virginica virginica
#> 128 6.1 3.0 4.9 1.8 virginica virginica
#> 129 6.4 2.8 5.6 2.1 virginica virginica
#> 130 7.2 3.0 5.8 1.6 virginica virginica
#> 131 7.4 2.8 6.1 1.9 virginica virginica
#> 132 7.9 3.8 6.4 2.0 virginica virginica
#> 133 6.4 2.8 5.6 2.2 virginica virginica
#> 134 6.3 2.8 5.1 1.5 virginica virginica
#> 135 6.1 2.6 5.6 1.4 virginica virginica
#> 136 7.7 3.0 6.1 2.3 virginica virginica
#> 137 6.3 3.4 5.6 2.4 virginica virginica
#> 138 6.4 3.1 5.5 1.8 virginica virginica
#> 139 6.0 3.0 4.8 1.8 virginica virginica
#> 140 6.9 3.1 5.4 2.1 virginica virginica
#> 141 6.7 3.1 5.6 2.4 virginica virginica
#> 142 6.9 3.1 5.1 2.3 virginica virginica
#> 143 5.8 2.7 5.1 1.9 virginica virginica
#> 144 6.8 3.2 5.9 2.3 virginica virginica
#> 145 6.7 3.3 5.7 2.5 virginica virginica
#> 146 6.7 3.0 5.2 2.3 virginica virginica
#> 147 6.3 2.5 5.0 1.9 virginica virginica
#> 148 6.5 3.0 5.2 2.0 virginica virginica
#> 149 6.2 3.4 5.4 2.3 virginica virginica
#> 150 5.9 3.0 5.1 1.8 virginica virginica
创建于 2020-04-18 由 reprex package (v0.3.0)
最佳答案
经验法则很简单:如果您使用任何形式的 quasiquation (即 !! 和 {{ 运算符)在赋值的左侧，您需要海象运算符。
s = sym("abc")
## Quasi-quotation on the left-hand of the assignment
iris %>% mutate( !!s = Petal.Length * Petal.Width ) # Error: unexpected '='
## Using walrus fixes the issue
iris %>% mutate( !!s := Petal.Length * Petal.Width ) # Works, creates column abc
## No quasi-quotation on the left-hand side, so = is enough
iris %>% mutate( s = Petal.Length * Petal.Width ) # Also works, creates column s
请注意，海象运算符仅适用于支持准引用的函数。一般情况下不支持:
list( a := 5 ) ## Error
rlang::list2( a := 5 ) ## Works
关于r - Dplyr，非标准评估和海象算子和 curl curl ，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/61295654/