将多列从函数的参数传递给group_by

Question

考虑以下示例：

library(tidyverse)

df <- tibble(
  cat = rep(1:2, times = 4, each = 2),
  loc = rep(c("a", "b"), each = 8),
  value = rnorm(16)
)

df %>% 
  group_by(cat, loc) %>% 
  summarise(mean = mean(value), .groups = "drop")

# # A tibble: 4 x 3
# cat loc     mean
# * <int> <chr>  <dbl>
# 1     1 a     -0.563
# 2     1 b     -0.394
# 3     2 a      0.159
# 4     2 b      0.212

我想为最后两行创建一个函数，它采用

group

参数将多列传递给

group_by

。

这里有一个虚拟函数，它通过一组列计算

mean

值作为示例：

group_mean <- function(data, col_value, group) {
  data %>% 
    group_by(across(all_of(group))) %>% 
    summarise(mean = mean({{col_value}}), .groups = "drop")
}

group_mean(df, value, c("cat", "loc"))

# # A tibble: 4 x 3
# cat loc     mean
# * <int> <chr>  <dbl>
# 1     1 a     -0.563
# 2     1 b     -0.394
# 3     2 a      0.159
# 4     2 b      0.212

该函数有效，但我更喜欢使用

tidyselect

/

rlang

方法来避免引用列名称，如下所示：

group_mean(df, value, c(cat, loc))

# Error: Problem adding computed columns in `group_by()`.
# x Problem with `mutate()` input `..1`.
# x object 'loc' not found
# ℹ Input `..1` is `across(all_of(c(cat, loc)))`.

将

group

括在

{{}}

中适用于单列，但不适用于多列。我怎样才能做到这一点？

Answer 1

考虑使用

...

，然后在使用

sym

 转换为 

ensym

bol 后，我们可以选择使用带引号或不带引号的

group_mean <- function(data, col_value, ...) {
   data %>% 
     group_by(!!! ensyms(...)) %>% 
     summarise(mean = mean({{col_value}}), .groups = "drop")
 }

-测试

> group_mean(df, value, cat, loc)
# A tibble: 4 x 3
    cat loc     mean
  <int> <chr>  <dbl>
1     1 a      0.327
2     1 b     -0.291
3     2 a     -0.382
4     2 b     -0.320
> group_mean(df, value, 'cat', 'loc')
# A tibble: 4 x 3
    cat loc     mean
  <int> <chr>  <dbl>
1     1 a      0.327
2     1 b     -0.291
3     2 a     -0.382
4     2 b     -0.320

---

如果我们已经使用

...

作为其他参数，那么一个选项是

group_mean <- function(data, col_value, group) {
  grp_lst <- as.list(substitute(group))
  if(length(grp_lst)> 1) grp_lst <- grp_lst[-1]
  grps <- purrr::map_chr(grp_lst, rlang::as_string)
  data %>% 
     group_by(across(all_of(grps))) %>% 
     summarise(mean = mean({{col_value}}), .groups = "drop")
}

-测试

> group_mean(df, value, c(cat, loc))
# A tibble: 4 x 3
    cat loc     mean
  <int> <chr>  <dbl>
1     1 a      0.327
2     1 b     -0.291
3     2 a     -0.382
4     2 b     -0.320

Answer 2

现在使用 across() 更快更容易。 以你为例：

group_mean <- function(data, col_value, group) {
  data %>% 
    group_by(across({{group}})) %>% 
    summarise(mean = mean({{col_value}}), .groups = "drop")
}

group_mean(df, value, group=c(cat, loc))