如何使用跨函數重寫相同的代碼

小統計

我編寫了以下代碼

out %>% group_by(tests0, GROUP) %>% 
  summarise(
            mean0 = mean(score0, na.rm = T),
            stderr0 = std.error(score0, na.rm = T), 
            mean7 = mean(score7, na.rm = T), 
            stederr7 = std.error(score7, na.rm = T),
            diff.std.mean = t.test(score0, score7, paired = T)$estimate, 
            p.value = t.test(score0, score7, paired = T)$p.value, 
            )

我獲得了以下輸出

 tests0     GROUP    mean0 stderr0 mean7 stederr7 diff.std.mean p.value
   <fct>      <fct>    <dbl>   <dbl> <dbl>    <dbl>         <dbl>   <dbl>
 1 ADAS_CogT0 CONTROL   12.6   0.525  13.6    0.662        -1.15  0.00182
 2 ADAS_CogT0 TRAINING  14.0   0.613  12.6    0.570         1.40  0.00295
 3 PVF_T0     CONTROL   32.1   1.22   31.3    1.45          0.498 0.636  
 4 PVF_T0     TRAINING  31.6   1.37   34.3    1.51         -2.48  0.0102 
 5 ROCF_CT0   CONTROL   29.6   0.893  30.3    0.821        -0.180 0.835  
 6 ROCF_CT0   TRAINING  30.1   0.906  29.5    0.929         0.489 0.615  
 7 ROCF_IT0   CONTROL   12.8   0.563  12.2    0.683         0.580 0.356  
 8 ROCF_IT0   TRAINING  10.9   0.735  12.3    0.768        -1.44  0.0238 
 9 ROCF_RT0   CONTROL   12.1   0.725  12.5    0.797        -0.370 0.598  
10 ROCF_RT0   TRAINING  10.5   0.746  10.9    0.742        -0.534 0.370  
11 SVF_T0     CONTROL   35.5   1.05   34      1.15          1.42  0.107  
12 SVF_T0     TRAINING  34.1   1.04   32.9    1.16          0.962 0.231

如果我想通過做同樣的事情across function，我應該怎麼做才能達到相同的結果，顯示在上面的代碼中？實際上，我遇到了麻煩，因為我正在從這個問題下發布的答案中得出一些例子Reproduce a complex table with double headeresrs，但我無法正確地適應它。

這裡的數據集

> dput(head(out, 100))
structure(list(ID = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 
29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 
45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 
77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 
93, 94, 95, 96, 97, 98, 99, 100), GROUP = structure(c(2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L), .Label = c("CONTROL", "TRAINING"), class = "factor"), 
    Gender = structure(c(1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 
    2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 
    2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 
    2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 
    1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 
    2L), .Label = c("M", "F"), class = "factor"), Age = c(74, 
    76, 81, 74, 69, 72, 75, 83, 78, 72, 82, 68, 72, 72, 73, 80, 
    69, 72, 70, 80, 75, 80, 78, 74, 82, 74, 80, 82, 78, 81, 66, 
    71, 70, 79, 78, 73, 72, 77, 77, 71, 83, 74, 70, 71, 77, 69, 
    67, 64, 79, 71, 77, 77, 73, 67, 68, 79, 81, 67, 84, 75, 80, 
    73, 68, 74, 77, 79, 79, 72, 73, 78, 76, 78, 77, 74, 78, 77, 
    77, 82, 77, 70, 77, 81, 79, 75, 74, 78, 69, 77, 73, 77, 70, 
    79, 70, 72, 77, 72, 71, 71, 73, 81), Education = c(18, 4, 
    8, 5, 8, 11, 5, 5, 4, 8, 8, 12, 5, 18, 13, 5, 13, 13, 5, 
    5, 13, 5, 3, 8, 17, 5, 8, 5, 5, 8, 17, 8, 18, 18, 13, 13, 
    13, 13, 15, 17, 8, 5, 5, 13, 8, 5, 11, 13, 8, 8, 8, 5, 13, 
    8, 5, 17, 8, 12, 13, 5, 8, 8, 8, 5, 3, 8, 18, 5, 8, 13, 8, 
    5, 17, 8, 5, 17, 5, 8, 11, 8, 8, 5, 12, 3, 8, 8, 8, 13, 5, 
    5, 8, 8, 13, 5, 5, 8, 13, 5, 8, 12), tests0 = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("ADAS_CogT0", 
    "PVF_T0", "ROCF_CT0", "ROCF_IT0", "ROCF_RT0", "SVF_T0"), class = "factor"), 
    score0 = c(14.66, 15.33, 17.33, 19, 7.66, 12.6, 18.67, 14.99, 
    17.99, 17.33, 13.66, 16.99, 10.66, 9.66, 14.99, 15.66, 13.33, 
    4.33, 14.33, 15.99, 16.33, 10.66, 14.66, 10.66, 19.33, 17.66, 
    15.99, 20.66, 20.6, 17, 10.33, 6.33, 6.66, 19.99, 13.33, 
    24.33, 12.33, 10.33, 12.33, 9.66, 10.99, 13.99, 23, 6.32, 
    11.32, 13.99, 14.66, 8.99, 14.33, 9.99, 7.33, 15.66, 14, 
    7.99, 23.32, 14.66, 9.99, 5.66, 6.99, 11.66, 10.33, 6.99, 
    19.32, NA, 10, 17.66, 13.66, 10.32, NA, NA, 8.66, 9, 6.99, 
    14.99, 9.66, 13.66, 15.32, 12, 14, 13.66, 11.99, 15.66, 16, 
    15, 16.99, 20, 11, 7.99, 8.33, 8.32, 14.99, 18.66, 10.33, 
    11.99, 9.32, 17, 14.33, 14.66, 16.6, 9.99), tests7 = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("ADAS_CogT7", 
    "PVF_T7", "ROCF_CT7", "ROCF_IT7", "ROCF_RT7", "SVF_T7"), class = "factor"), 
    score7 = c(16, 9.32, 21.33, 17, 8.32, 11, 14.99, 10.99, 17, 
    18.33, 13.32, 14.34, 8.99, 7, 11.99, 15.33, 6.99, 5.33, 12.32, 
    13, 21.32, 7.99, 13.33, 11.99, 17.32, 16.32, 16.33, 14.66, 
    18.99, 17.33, 7.99, 9.33, 10.99, NA, 12.99, 16.33, 21.66, 
    9, 9.34, 8.66, 8.33, 13.66, 15.66, 6.66, 10.99, 13.33, 13.33, 
    7.99, 11.99, 11.32, 7.33, 9.66, 6.99, NA, 15.99, 15.66, 14.66, 
    6.32, 7, 11, 14, 10.33, 24.66, NA, 14.99, NA, 15.99, 9.32, 
    NA, NA, 9.99, 9.33, 7.66, 17.33, 10.32, 16, 17, 12.99, 15, 
    14.33, 10, 14.99, 19, 13.99, 19.33, NA, 10, 6.99, 11.66, 
    6.66, 14.33, 16, 8.66, 10, NA, 20, 14.99, 19.66, 26.66, 8.99
    )), row.names = c(NA, -100L), class = c("tbl_df", "tbl", 
"data.frame"))
>

您可以在下面找到我想獲得相同的方式。它是一種需要 .x 操作的方法。

out %>%    group_by(across(all_of(by))) %>%    summarise(across(starts_with('score'),                         list(            mean = ~ mean(.x,na.rm = T),            stderr = ~ std.error(.x, na.rm = TRUE),            diff.std.mean = ~ t.test(.x, get(str_replace(cur_column(), "score", "score7")))$estimate,              p.value = ~ t.test(.x, get(str_replace(cur_column(), "score0", "score7")))$p.value            )     ),     .groups = "drop")

馬林加

您可以在以下參數.names中使用該參數across()：

library(dplyr)
out %>%
  group_by(tests0, GROUP) %>%
  summarize(across(c(score0, score7), sd, na.rm = TRUE, .names = "sd_{.col}"), 
            across(c(score0, score7), mean, na.rm = TRUE, .names = "mean_{.col}"), 
            diff.std.mean = t.test(score0, score7, paired = T)$estimate, 
            p.value = t.test(score0, score7, paired = T)$p.value) %>%
  ungroup()
#> `summarise()` has grouped output by 'tests0'. You can override using the `.groups` argument.
#> # A tibble: 2 x 8
#>   tests0 GROUP sd_score0 sd_score7 mean_score0 mean_score7 diff.std.mean p.value
#>   <fct>  <fct>     <dbl>     <dbl>       <dbl>       <dbl>         <dbl>   <dbl>
#> 1 ADAS_~ CONT~      3.72      4.81        12.5        13.5         -1.24 0.00471
#> 2 ADAS_~ TRAI~      4.55      4.15        14.0        12.6          1.40 0.00295

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