我正在编写一个自定义函数，我希望其中一个参数获取变量列表。我已设法使用rlang以及对...的一些初步了解，以便在函数中正确阅读此列表。但我不知道如何将此列表作为参数分配给另一个函数（如dplyr::group_by）。我完全可以重复下面的例子以及我想要的最终结果。
# loading the needed libraries
library(dplyr)
library(rlang)
library(datasets)
# defining the custom function
prac.fn <- function(data, vars = ..., measure) {
# getting the dataframe ready
df <-
dplyr::select(.data = data,
!!rlang::enquo(vars),
!!rlang::enquo(measure))
# print to see if all variables are included
print(head(df))
# summarize by specified grouping variables
df %>%
dplyr::group_by(.data = ., c(!!rlang::enquo(vars))) %>%
dplyr::summarise(mean = mean(!!rlang::enquo(measure)))
}
# use the function (doesn't work)
prac.fn(data = mtcars,
vars = c(cyl, am),
measure = wt)
#> cyl am wt
#> Mazda RX4 6 1 2.620
#> Mazda RX4 Wag 6 1 2.875
#> Datsun 710 4 1 2.320
#> Hornet 4 Drive 6 0 3.215
#> Hornet Sportabout 8 0 3.440
#> Valiant 6 0 3.460
#> Error in mutate_impl(.data, dots): Column `c(c(cyl, am))` must be length 32 (the number of rows) or one, not 64
# output I want
mtcars %>%
dplyr::group_by(cyl, am) %>%
dplyr::summarise(mean = mean(wt))
#> # A tibble: 6 x 3
#> # Groups: cyl [?]
#> cyl am mean
#> <dbl> <dbl> <dbl>
#> 1 4.00 0 2.94
#> 2 4.00 1.00 2.04
#> 3 6.00 0 3.39
#> 4 6.00 1.00 2.76
#> 5 8.00 0 4.10
#> 6 8.00 1.00 3.37
由reprex package（v0.2.0）创建于2018-02-17。
答案 0 :(得分：2)
在group_by中，在将'vars'转换为quosure（enquo）之后，使用quo_squash展开表达式，将其转换为list（{{1} }）并删除第一个元素即。 as.list，然后c对其进行评估
!!!-testing
prac.fn <- function(data, vars, measure) {
data %>%
select(!!rlang::enquo(vars),
!!rlang::enquo(measure)) %>%
dplyr::group_by(!!! as.list(quo_squash(rlang::enquo(vars)))[-1]) %>%
dplyr::summarise(mean = mean(!!rlang::enquo(measure)))
}
检查更多组
prac.fn(data = mtcars,
vars = c(cyl, am),
measure = wt)
# A tibble: 6 x 3
# Groups: cyl [?]
# cyl am mean
# <dbl> <dbl> <dbl>
#1 4.00 0 2.94
#2 4.00 1.00 2.04
#3 6.00 0 3.39
#4 6.00 1.00 2.76
#5 8.00 0 4.10
#6 8.00 1.00 3.37
目前尚不清楚OP是否总是希望prac.fn(data = mtcars,
vars = c(cyl, am, gear),
measure = wt)
# A tibble: 10 x 4
# Groups: cyl, am [?]
# cyl am gear mean
# <dbl> <dbl> <dbl> <dbl>
# 1 4.00 0 3.00 2.46
# 2 4.00 0 4.00 3.17
# 3 4.00 1.00 4.00 2.11
# 4 4.00 1.00 5.00 1.83
# 5 6.00 0 3.00 3.34
# 6 6.00 0 4.00 3.44
# 7 6.00 1.00 4.00 2.75
# 8 6.00 1.00 5.00 2.77
# 9 8.00 0 3.00 4.10
#10 8.00 1.00 5.00 3.37
用于c()参数，即如果存在单个分组变量，则该函数在传递参数的行为相同时有效
vars但是，如果我们必须改变行为，即prac.fn(data = mtcars,
vars = c(cyl),
measure = wt)
#<quosure>
# expr: ^c(cyl)
# env: global
# A tibble: 3 x 2
# cyl mean
# <dbl> <dbl>
#1 4.00 2.29
#2 6.00 3.12
#3 8.00 4.00
而没有vars = cyl，则需要用c()声明来解决，即
if/else-testing
prac.fnN <- function(data, vars, measure) {
vars <- as.list(quo_squash(enquo(vars)))
vars <- if(length(vars) ==1) vars else vars[-1]
data %>%
select(!!! vars,
!!rlang::enquo(measure)) %>%
dplyr::group_by(!!! vars) %>%
dplyr::summarise(mean = mean(!!rlang::enquo(measure)))
}
除了上述方法之外，自然选项是将参数传递为prac.fnN(data = mtcars,
vars = cyl,
measure = wt)
# A tibble: 3 x 2
# cyl mean
# <dbl> <dbl>
#1 4.00 2.29
#2 6.00 3.12
#3 8.00 4.00
prac.fnN(data = mtcars,
vars = c(cyl),
measure = wt)
# A tibble: 3 x 2
# cyl mean
# <dbl> <dbl>
#1 4.00 2.29
#2 6.00 3.12
#3 8.00 4.00
prac.fnN(data = mtcars,
vars = c(cyl, am),
measure = wt)
# A tibble: 6 x 3
# Groups: cyl [?]
# cyl am mean
# <dbl> <dbl> <dbl>
#1 4.00 0 2.94
#2 4.00 1.00 2.04
#3 6.00 0 3.39
#4 6.00 1.00 2.76
#5 8.00 0 4.10
#6 8.00 1.00 3.37
，然后我们就不必考虑quos/quo和其他enquo
if/else-testing
prac.fnQ <- function(data, vars, measure) {
stopifnot(is_quosures(vars))
stopifnot(is_quosure(measure))
data %>%
select(!!! vars, !! measure) %>%
dplyr::group_by(!!! vars) %>%
dplyr::summarise(mean = mean(!! measure))
}
如果我们还需要检查'度量'变量（假设我们有多个'度量'变量）是prac.fnQ(data = mtcars,
vars = quos(cyl, am),
measure = quo(wt))
# A tibble: 6 x 3
# Groups: cyl [?]
# cyl am mean
# <dbl> <dbl> <dbl>
#1 4.00 0 2.94
#2 4.00 1.00 2.04
#3 6.00 0 3.39
#4 6.00 1.00 2.76
#5 8.00 0 4.10
#6 8.00 1.00 3.37
numeric