如何在與分類級別變數一樣多的表中轉換小標題

我有以下小標題

structure(list(blockLabel = structure(c(1L, 2L, 3L, 4L, 5L, 1L, 
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L), .Label = c("auditory_only", 
"bimodal_focus_auditory", "bimodal_focus_visual", "divided", 
"visual_only"), class = "factor"), trial_resp.corr = structure(c(1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L), .Label = c("0", 
"1"), class = "factor"), participant = structure(c(1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("pilot01", 
"pilot02", "pilot03"), class = "factor"), Freq = c(0L, 1L, 3L, 
74L, 0L, 12L, 71L, 69L, 70L, 12L, 0L, 1L, 2L, 77L, 11L, 12L, 
71L, 70L, 67L, 1L, 1L, 1L, 3L, 75L, 0L, 11L, 71L, 69L, 69L, 12L
), tc = c(12, 72, 72, 144, 12, 12, 72, 72, 144, 12, 12, 72, 72, 
144, 12, 12, 72, 72, 144, 12, 12, 72, 72, 144, 12, 12, 72, 72, 
144, 12), freq = c(0, 1.38888888888889, 4.16666666666667, 51.3888888888889, 
0, 100, 98.6111111111111, 95.8333333333333, 48.6111111111111, 
100, 0, 1.38888888888889, 2.77777777777778, 53.4722222222222, 
91.6666666666667, 100, 98.6111111111111, 97.2222222222222, 46.5277777777778, 
8.33333333333333, 8.33333333333333, 1.38888888888889, 4.16666666666667, 
52.0833333333333, 0, 91.6666666666667, 98.6111111111111, 95.8333333333333, 
47.9166666666667, 100)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -30L))

我想根據參與者變數的級別創建三個不同的表。最終結果或多或少應該如下所示：

我已經開始撰寫以下代碼的腳本（因為我正在尋找通過 dplyr、應用系列、回圈或映射函式來執行此操作）

  list %>%  as_data_frame() %>%
  select(blockLabel, trial_resp.corr, participant, Freq, freq) %>% 
  map(~ flextable(.x)) 

但不幸的是，我收到以下錯誤代碼：

Error in flextable(.x) : is.data.frame(data) is not TRUE

我不是這種方法的專家，因此如果您對解決問題和分享知識以實作最終結果有什么建議，請告訴我（順便說一下，我指定 Trial_resp 的正確性對應于 1 和不正確性對應于 0 .corr 變數）

謝謝

uj5u.com熱心網友回復：

這是一種方法，我們將 'trial_resp.corr' 值修改為 'Incorrectness'、'Correctness' 基于 0、1 值，然后select是感興趣的列，split由 'participant' 列創建一個list資料集，回圈listwith map, 將資料從 long 調整為 Wide withpivot_wider然后轉換為flextable

library(dplyr)
library(tidyr)
library(purrr)
library(flextable)
library(stringr)
library(janitor)
lst1 <-  df1 %>% 
  mutate( trial_resp.corr = recode(trial_resp.corr, 
                    '0'= 'Incorrectness', '1' = "Correctness")) %>%
  select(blockLabel, trial_resp.corr, participant, Freq, `Freq%` = freq) %>%
  split(.$participant) %>% 
  map(~ .x  %>% 
        select(-participant)  %>% 
        pivot_wider(names_from = trial_resp.corr, 
                    values_from = c(Freq, `Freq%`), values_fill = 0, 
    names_glue ="{trial_resp.corr}{str_remove(.value, 'Freq' )}") %>%
        adorn_totals() %>%
        mutate(across(ends_with("%"),
    ~ replace(.x, n(), 100 *get(str_remove(cur_column(), 
   fixed("%")))[n()]/sum(Incorrectness[n()], Correctness[n()])))) %>%
          flextable)

如果我們想在全域環境中創建物件

list2env(lst1, .GlobalEnv)

-輸出

lst1[[1]]

uj5u.com熱心網友回復：

我會提出以下建議（對于 dplyr）：

library(dplyr)
dfs <- list()

for (prt in levels(df$participant)){
  dfs[[prt]] <- df %>% filter(participant == prt) %>% select(-participant)
}

您將獲得三個表以進行任何所需的進一步操作：

> dfs
$pilot01
# A tibble: 10 × 5
   blockLabel             trial_resp.corr  Freq    tc   freq
   <fct>                  <fct>           <int> <dbl>  <dbl>
 1 auditory_only          0                   0    12   0   
 2 bimodal_focus_auditory 0                   1    72   1.39
 3 bimodal_focus_visual   0                   3    72   4.17
 4 divided                0                  74   144  51.4 
 5 visual_only            0                   0    12   0   
 6 auditory_only          1                  12    12 100   
 7 bimodal_focus_auditory 1                  71    72  98.6 
 8 bimodal_focus_visual   1                  69    72  95.8 
 9 divided                1                  70   144  48.6 
10 visual_only            1                  12    12 100   

$pilot02
# A tibble: 10 × 5
   blockLabel             trial_resp.corr  Freq    tc   freq
   <fct>                  <fct>           <int> <dbl>  <dbl>
 1 auditory_only          0                   0    12   0   
 2 bimodal_focus_auditory 0                   1    72   1.39
 3 bimodal_focus_visual   0                   2    72   2.78
 4 divided                0                  77   144  53.5 
 5 visual_only            0                  11    12  91.7 
 6 auditory_only          1                  12    12 100   
 7 bimodal_focus_auditory 1                  71    72  98.6 
 8 bimodal_focus_visual   1                  70    72  97.2 
 9 divided                1                  67   144  46.5 
10 visual_only            1                   1    12   8.33

$pilot03
# A tibble: 10 × 5
   blockLabel             trial_resp.corr  Freq    tc   freq
   <fct>                  <fct>           <int> <dbl>  <dbl>
 1 auditory_only          0                   1    12   8.33
 2 bimodal_focus_auditory 0                   1    72   1.39
 3 bimodal_focus_visual   0                   3    72   4.17
 4 divided                0                  75   144  52.1 
 5 visual_only            0                   0    12   0   
 6 auditory_only          1                  11    12  91.7 
 7 bimodal_focus_auditory 1                  71    72  98.6 
 8 bimodal_focus_visual   1                  69    72  95.8 
 9 divided                1                  69   144  47.9 
10 visual_only            1                  12    12 100   

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