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Accepted
RoyalTS
Asked: 2024-09-18 17:04:07 +0800 CST

添加到 curly-curly 参数

  • 772

我试图用这个典型例子来说明卷曲运算符的工作原理

mean_by <- function(data, by, var) {
  data %>%
    dplyr::group_by({{ by }}) %>%
    dplyr::summarise(avg = mean({{ var }}, na.rm = TRUE))
}

mtcars %>% mean_by(by = cyl, var = disp)

并通过向分组添加一个额外的变量来修改它。唉，我可能永远都不明白这个操作符到底做了什么；我尝试做的事情如下

mean_by <- function(data, by, var) {
  data %>%
    dplyr::group_by({{ c(by, hp) }}) %>%
    dplyr::summarise(avg = mean({{ var }}, na.rm = TRUE))
}

mtcars %>% mean_by(by = cyl, var = disp)

显然不起作用

2 个回答

  1. 几年前dplyr添加了pick允许人们传递{{}}数据屏蔽功能的功能。所以你可以做这样的事情。

    library(dplyr)

group_mean = \(data, by, var, ...) {
  data |> 
   group_by(pick({{by}}), ...) |> 
   summarise(mean = mean({{var}}))
}

group_mean(starwars, by = c(homeworld, species), sex, var = mass)
#> `summarise()` has grouped output by 'homeworld', 'species'. You can override
#> using the `.groups` argument.
#> # A tibble: 64 × 4
#> # Groups:   homeworld, species [57]
#>    homeworld      species   sex     mean
#>    <chr>          <chr>     <chr>  <dbl>
#>  1 Alderaan       Human     female    49
#>  2 Alderaan       Human     male      NA
#>  3 Aleen Minor    Aleena    male      15
#>  4 Bespin         Human     male      79
#>  5 Bestine IV     <NA>      <NA>     110
#>  6 Cato Neimoidia Neimodian male      90
#>  7 Cerea          Cerean    male      82
#>  8 Champala       Chagrian  male      NA
#>  9 Chandrila      Human     female    NA
#> 10 Concord Dawn   Human     male      79
#> # ℹ 54 more rows

    创建于 2024-09-18，使用reprex v2.1.0

    • 4
  2. Best Answer

    可以使用以下方法rlang::enquos()来化解多个表达式：

    dplyr::group_by(hp, !!!rlang::enquos(by))

    然而，正如这篇 Tidyverse博客文章所述：

    如果你想将多个参数传递给数据屏蔽动词，请...直接传递

    因此，在您的情况下，按分组hp以及调用者传递的其他内容进行分组：

    mean_by_hp <- function(data, ..., var) {
    data |>
        dplyr::group_by(hp, ...) |>
        dplyr::summarise(avg = mean({{ var }}, na.rm = TRUE))
}


mean_by_hp(mtcars, cyl, var = disp)
#       hp   cyl   avg
#    <dbl> <dbl> <dbl>
#  1    52     4  75.7
#  2    62     4 147.
#  3    65     4  71.1
#  4    66     4  78.8
#  5    91     4 120.
#  6    93     4 108
#  7    95     4 141.
#  8    97     4 120.
#  9   105     6 225
# 10   109     4 121
    • 3

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