0

我正在尝试编写一个函数，该函数允许我通过对数据框中的多个因素进行分组来生成描述性统计数据。我花了太多时间试图让我的函数识别我选择的变量。

这是假数据：

grouping1 <- c("red", "blue", "blue", "green", "red", "blue", "red", "green")                 
grouping2 <- c("high", "high", "low", "medium", "low", "high", "medium", "high")                  
value <- c(22,40,72,41,36,16,88,99)

fake_df <- data.frame(grouping1, grouping2, value)

假代码示例：

library(dplyr)

by_group_fun <- function(fun.data.in, fun.grouping.factor){
  fake_df2 <- fun.data.in %>%
    group_by(fun.grouping.factor) %>%
    summarize(mean = mean(value), median = median(value))
  fake_df2
}
by_group_fun(fake_df, grouping1) 
by_group_fun(fake_df, grouping2)

这给了我：

 Error in grouped_df_impl(data, unname(vars), drop) : 
  Column `fun.grouping.factor` is unknown

第二次尝试

我试图将函数中选择的 by 变量分配给一个新变量并继续执行。

假代码示例（第二次尝试）：

by_group_fun2 <- function(fun.data.in, fun.grouping.factor){
  fun.data.in$by_var <- fun.data.in$fun.grouping.factor

  fake_df2 <- fun.data.in %>%
    group_by(by_var) %>%
    summarize(mean = mean(value), median = median(value))
  fake_df2
}

by_group_fun2(fake_df, grouping1) 
by_group_fun2(fake_df, grouping2)

这，第二次尝试，给了我：

 Error in grouped_df_impl(data, unname(vars), drop) : 
  Column `by_var` is unknown
4

2 回答 2

2

用这个例子来指导你

myfun <- function(df, thesecols) {
              require(dplyr)
              thesecols <- enquo(thesecols)    # need to quote
              df %>%
                group_by_at(vars(!!thesecols))  # !! unquotes
         }

myfun(fake_df, grouping1)

输出

# A tibble: 8 x 3
# Groups:   grouping1 [3]
  grouping1 grouping2 value
     <fctr>    <fctr> <dbl>
1       red      high    22
2      blue      high    40
3      blue       low    72
4     green    medium    41
5       red       low    36
6      blue      high    16
7       red    medium    88
8     green      high    99
于 2017-09-13T01:20:57.613 回答
2

在不借助 dplyr 编程的情况下获得相同输出的一种非常简单的方法是将分组列收集为长格式。按结果键和值列分组将获得您要求的所有组合，而不会超出单个 data.frame：

library(tidyverse)

fake_df <- data_frame(grouping1 = c("red", "blue", "blue", "green", "red", "blue", "red", "green"),
                      grouping2 = c("high", "high", "low", "medium", "low", "high", "medium", "high"),
                      value = c(22,40,72,41,36,16,88,99))

fake_df %>% 
    gather(group_var, group_val, -value) %>% 
    group_by(group_var, group_val) %>% 
    summarise(mean = mean(value), 
              median = median(value))
#> # A tibble: 6 x 4
#> # Groups:   group_var [?]
#>   group_var group_val     mean median
#>       <chr>     <chr>    <dbl>  <dbl>
#> 1 grouping1      blue 42.66667   40.0
#> 2 grouping1     green 70.00000   70.0
#> 3 grouping1       red 48.66667   36.0
#> 4 grouping2      high 44.25000   31.0
#> 5 grouping2       low 54.00000   54.0
#> 6 grouping2    medium 64.50000   64.5
于 2017-09-13T01:46:04.623 回答