使用帶有多變數函式的summary(across(...,.fns=...))

我的問題要求我跨多列匯總資料，但每列必須由其他三列的多變數函式匯總。

我有一個包含數百列的資料框，其中包含有關資料集的不同統計資訊。這是一個類似結構的較小資料框。

df <- data.frame(a1_Avg = rnorm(10), 
                 a1_Std = runif(10), 
                 a2_Avg = rnorm(10), 
                 a2_Std = runif(10), 
                 Hour = c(1.0, 1.5, 2.0, 2.25, 2.5, 2.75, 3.0, 4.0, 4.5, 5.0),
                 Measurements = c(3, 3, 6, 6, 6, 6, 10, 7, 7, 2)) %>%

資料需要壓縮成匯總一小時塊的行。總結平均值很容易：我可以簡單地對它們求平均值，因為在一小時內測量的數量是一致的。

  group_by(Hour) %>%
  summarize(across(matches("a._Avg"), ~ mean(.x), .names = "combined_{col}"),

但是合并標準差比較棘手，因為我需要來自三個獨立列的資訊來計算它。手動，我會這樣做：

            combined_a1_Std = sqrt((1/n())*sum(a1_Std^2   (a1_Avg - combined_a1_Avg)^2)),
            combined_a2_Std = sqrt((1/n())*sum(a2_Std^2   (a2_Avg - combined_a2_Avg)^2)))

但這對于數百列是不可行的。

有沒有一種簡單的方法可以做到這一點？

這是上面的完整代碼，以及所需的輸出：

set.seed(1)
df <- data.frame(a1_Avg = rnorm(10), 
                 a1_Std = runif(10), 
                 a2_Avg = rnorm(10), 
                 a2_Std = runif(10), 
                 Hour = c(1.0, 1.5, 2.0, 2.25, 2.5, 2.75, 3.0, 4.0, 4.5, 5.0),
                 Measurements = c(3, 3, 6, 6, 6, 6, 10, 7, 7, 2)) %>%
  mutate(Hour = floor(Hour)) %>%
  group_by(Hour) %>%
  summarize(across(matches("a._Avg"), ~ mean(.x), .names = "combined_{col}"),
            combined_a1_Std = sqrt((1/n())*sum(a1_Std^2   (a1_Avg - combined_a1_Avg)^2)),
            combined_a2_Std = sqrt((1/n())*sum(a2_Std^2   (a2_Avg - combined_a2_Avg)^2)))

df

   Hour combined_a1_Avg combined_a2_Avg combined_a1_Std combined_a2_Std
  <dbl>           <dbl>           <dbl>           <dbl>           <dbl>
1     1         -0.221          -0.0306           0.859           0.859
2     2          0.0672          0.819            1.17            1.17 
3     3          0.487           0.782            0.116           0.116
4     4          0.657          -0.957            0.795           0.795
5     5         -0.305           0.620            0.583           0.583

uj5u.com熱心網友回復：

一種選擇是回圈遍歷一組列，然后get通過替換列名稱中的子字串來回圈另一組

library(dplyr)
library(stringr)
out2 <- df %>% 
   mutate(Hour = floor(Hour)) %>%
   group_by(Hour) %>%
   summarize(across(matches("a\\d _Avg"), ~ mean(.x),
    .names = "combined_{col}"), 
         across(matches('^a\\d _Avg$'),
     ~ sqrt((1/n())*sum(get(str_replace(cur_column(), "Avg", "Std"))  
                   (. - get(str_c( "combined_", cur_column() )))^2)), 
      .names = "combined_{str_replace(.col, 'Avg', 'Std')}"))

- 檢查 OP 的手動方法

out1 <- df %>%
   mutate(Hour = floor(Hour)) %>%
  group_by(Hour) %>%
  summarize(across(matches("a._Avg"), ~ mean(.x), .names = "combined_{col}"),
            combined_a1_Std = sqrt((1/n())*sum(a1_Std   (a1_Avg - combined_a1_Avg)^2)),
            combined_a2_Std = sqrt((1/n())*sum(a2_Std   (a2_Avg - combined_a2_Avg)^2)))
identical(out1, out2)
[1] TRUE

資料

set.seed(1)
df <- data.frame(a1_Avg = rnorm(10), 
                 a1_Std = runif(10), 
                 a2_Avg = rnorm(10), 
                 a2_Std = runif(10), 
                 Hour = c(1.0, 1.5, 2.0, 2.25, 2.5, 2.75, 3.0, 4.0, 4.5, 5.0),
                 Measurements = c(3, 3, 6, 6, 6, 6, 10, 7, 7, 2))

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