summarise_at对不同变量使用不同的函数

问题描述

当我使用group_by并在dplyr中进行汇总时，我自然可以将不同的汇总函数应用于不同的变量。例如：

When I use group_by and summarise in dplyr, I can naturally apply different summary functions to different variables. For instance:

    library(tidyverse)

    df <- tribble(
      ~category,   ~x,  ~y,  ~z,
      #----------------------
          'a',      4,   6,   8,
          'a',      7,   3,   0,
          'a',      7,   9,   0,
          'b',      2,   8,   8,
          'b',      5,   1,   8,
          'b',      8,   0,   1,
          'c',      2,   1,   1,
          'c',      3,   8,   0,
          'c',      1,   9,   1
     )

    df %>% group_by(category) %>% summarize(
      x=mean(x),
      y=median(y),
      z=first(z)
    )

输出结果：

    # A tibble: 3 x 4
      category     x     y     z
         <chr> <dbl> <dbl> <dbl>
    1        a     6     6     8
    2        b     5     1     8
    3        c     2     8     1

我的问题是，我怎么用summarise_at来做到这一点？显然，对于此示例而言，这是不必要的，但是假设我有很多我想取均值的变量，很多中位数，等等。

My question is, how would I do this with summarise_at? Obviously for this example it's unnecessary, but assume I have lots of variables that I want to take the mean of, lots of medians, etc.

我是否一次失去了此功能我搬到summarise_at吗？我必须在所有变量组上使用所有函数，然后丢弃那些我不需要的函数吗？

Do I lose this functionality once I move to summarise_at? Do I have to use all functions on all groups of variables and then throw away the ones I don't want?

也许我只是想念一些东西，但是我可以还没弄清楚，在文档中也看不到任何示例。感谢您的帮助。

Perhaps I'm just missing something, but I can't figure it out, and I don't see any examples of this in the documentation. Any help is appreciated.

推荐答案

这是一个主意。

library(tidyverse)

df_mean <- df %>%
  group_by(category) %>%
  summarize_at(vars(x), funs(mean(.)))

df_median <- df %>%
  group_by(category) %>%
  summarize_at(vars(y), funs(median(.)))

df_first <- df %>%
  group_by(category) %>%
  summarize_at(vars(z), funs(first(.)))

df_summary <- reduce(list(df_mean, df_median, df_first),
                     left_join, by = "category")

就像您说的，在此示例中无需使用 summarise_at 。但是，如果您有很多列需​​要按不同功能进行汇总，则此策略可能会起作用。您需要为每个 summarize_at 指定 vars（...）中的列。规则与 dplyr :: select 函数相同。

Like you said, there is no need to use summarise_at for this example. However, if you have a lot of columns need to be summarized by different functions, this strategy may work. You will need to specify the columns in the vars(...) for each summarize_at. The rule is the same as the dplyr::select function.

这是另一个想法。定义一个修改 summarise_at 函数的函数，然后使用 map2 将该函数与查找列表一起显示变量和要应用的关联函数。在此示例中，我将平均值应用于 x 和 y 列和中位数到 z 。

Here is another idea. Define a function which modifies the summarise_at function, and then use map2 to apply this function with a look-up list showing variables and associated functions to apply. In this example, I applied mean to x and y column and median to z.

# Define a function
summarise_at_fun <- function(variable, func, data){
  data2 <- data %>%
    summarise_at(vars(variable), funs(get(func)(.)))
  return(data2)
}

# Group the data
df2 <- df %>% group_by(category)

# Create a look-up list with function names and variable to apply
look_list <- list(mean = c("x", "y"),
                  median = "z")

# Apply the summarise_at_fun
map2(look_list, names(look_list), summarise_at_fun, data = df2) %>%
  reduce(left_join, by = "category")

# A tibble: 3 x 4
  category     x     y     z
     <chr> <dbl> <dbl> <dbl>
1        a     6     6     0
2        b     5     3     8
3        c     2     6     1

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