為什么 table() function 給我的是長數據而不是寬數據？

Question

我無法讓我的數據集與table() function 一起使用。我的數據最初看起來像這樣：

學生_種族	Pre_DAS種族
白色的	黑色的
白色的	白色的
亞洲人	黑色的
黑色的	白色的
白色的	黑色的

但是，我希望它像這樣制表：

學生_種族	白色的	黑色的	亞洲人
白色的	1個	2個	0
黑色的	1個	0	0
亞洲人	0	1個	0

我之前運行過這段代碼：

PreDASEthnicityPredictor <- table(Predict_DAS$Student_Ethnicity, Predict_DAS$PreDAS_Ethnicity)

但是，當數據制成表格時，它看起來像這樣：

學生_種族	Pre_DAS種族	頻率
白色的	白色的	1個
白色的	黑色的	2個
白色的	亞洲人	0
黑色的	白色的	1個
黑色的	黑色的	0
黑色的	亞洲人	0
亞洲人	白色的	0
亞洲人	黑色的	1個
亞洲人	亞洲人	0

發生這種情況是否有特定原因，我該如何正確制作此表格？ 獲取此表格格式以進行卡方分析非常重要，我稍后會對此進行分析。

這是我正在使用的實際數據集：

structure(list(Predict_DAS.Student_Ethnicity = c("White/Caucasian", 
"White/Caucasian", "White/Caucasian", "White/Caucasian", "Other", 
"White/Caucasian", "White/Caucasian", "Multiple", "White/Caucasian", 
"White/Caucasian", "Black/African American", "White/Caucasian", 
"White/Caucasian", "Black/African American", "Hispanic/Latinx", 
"Black/African American", "White/Caucasian", "Other", "Other", 
"Hispanic/Latinx", "Hispanic/Latinx", "Other", "Native Hawaiian or Pacific Islander", 
"Black/African American", "Black/African American", "Hispanic/Latinx", 
"White/Caucasian", "White/Caucasian", "White/Caucasian", "Black/African American", 
"Black/African American", "White/Caucasian", "Black/African American", 
"Hispanic/Latinx", "Black/African American", "White/Caucasian", 
"Black/African American", "White/Caucasian", "Black/African American", 
"White/Caucasian", "White/Caucasian", "Other", "Native Hawaiian or Pacific Islander", 
"Black/African American", "White/Caucasian", "Other", "Hispanic/Latinx", 
"No Selection", "White/Caucasian", "American Indian or Alaskan Native", 
"Hispanic/Latinx", "White/Caucasian", "Hispanic/Latinx", "Black/African American", 
"American Indian or Alaskan Native", "White/Caucasian", "White/Caucasian", 
"Multiple", "White/Caucasian", "American Indian or Alaskan Native", 
"No Selection", "Asian", "White/Caucasian", "Black/African American", 
"Native Hawaiian or Pacific Islander", "Native Hawaiian or Pacific Islander", 
"Hispanic/Latinx", "American Indian or Alaskan Native", "No Selection", 
"Asian", "Black/African American", "Black/African American", 
"Black/African American", "White/Caucasian", "American Indian or Alaskan Native", 
"Black/African American", "Black/African American", "White/Caucasian", 
"Black/African American", "Black/African American", "Multiple", 
"White/Caucasian", "Hispanic/Latinx", "White/Caucasian", "Asian", 
"Multiple", "White/Caucasian", "White/Caucasian", "Black/African American", 
"White/Caucasian", "No Selection", "White/Caucasian", "White/Caucasian", 
"Multiple", "Black/African American", "White/Caucasian", "White/Caucasian", 
"Black/African American", "Other", "Black/African American", 
"Multiple", "Black/African American", "Hispanic/Latinx", "White/Caucasian", 
"White/Caucasian", "White/Caucasian", "Black/African American", 
"White/Caucasian", "White/Caucasian", "White/Caucasian", "Black/African American", 
"Black/African American", "Hispanic/Latinx", "Multiple", "Black/African American", 
"Black/African American", "Asian", "White/Caucasian", "White/Caucasian", 
"Black/African American", "White/Caucasian", "Black/African American", 
"White/Caucasian", "Other", "Multiple", "Multiple", "Multiple", 
"No Selection", "Asian", "No Selection", "White/Caucasian", "White/Caucasian", 
"No Selection", "Other", "White/Caucasian", "American Indian or Alaskan Native", 
"White/Caucasian", "Hispanic/Latinx", "Multiple", "Hispanic/Latinx"
), Predict_DAS.PreDAS_Ethnicity = c("White/Caucasian", "White/Caucasian", 
"Asian", "White/Caucasian", "Other", "No Selection", "White/Caucasian", 
"No Selection", "No Selection", "White/Caucasian", "Black/African American", 
"White/Caucasian", "No Selection", "No Selection", "Hispanic/Latinx", 
"Black/African American", "No Selection", "Asian", "Other", "No Selection", 
"No Selection", "No Selection", "White/Caucasian", "No Selection", 
"No Selection", "Hispanic/Latinx", "No Selection", "White/Caucasian", 
"No Selection", "Black/African American", "Black/African American", 
"White/Caucasian", "White/Caucasian", "Hispanic/Latinx", "No Selection", 
"Hispanic/Latinx", "No Selection", "White/Caucasian", "Black/African American", 
"White/Caucasian", "White/Caucasian", "No Selection", "Asian", 
"White/Caucasian", "White/Caucasian", "No Selection", "Hispanic/Latinx", 
"Other", "White/Caucasian", "Black/African American", "No Selection", 
"White/Caucasian", "No Selection", "Black/African American", 
"No Selection", "White/Caucasian", "Asian", "Multiple", "White/Caucasian", 
"White/Caucasian", "No Selection", "No Selection", "No Selection", 
"Black/African American", "No Selection", "No Selection", "No Selection", 
"White/Caucasian", "Black/African American", "Other", "Black/African American", 
"No Selection", "Black/African American", "White/Caucasian", 
"No Selection", "Black/African American", "No Selection", "No Selection", 
"No Selection", "Black/African American", "Multiple", "Black/African American", 
"Hispanic/Latinx", "Black/African American", "Asian", "Multiple", 
"White/Caucasian", "White/Caucasian", "Black/African American", 
"White/Caucasian", "No Selection", "No Selection", "White/Caucasian", 
"No Selection", "Asian", "No Selection", "White/Caucasian", "Black/African American", 
"No Selection", "Black/African American", "Black/African American", 
"Black/African American", "No Selection", "White/Caucasian", 
"No Selection", "White/Caucasian", "Black/African American", 
"White/Caucasian", "No Selection", "White/Caucasian", "No Selection", 
"Black/African American", "No Selection", "Other", "No Selection", 
"No Selection", "No Selection", "White/Caucasian", "White/Caucasian", 
"Black/African American", "White/Caucasian", "Black/African American", 
"White/Caucasian", "Other", "White/Caucasian", "White/Caucasian", 
"No Selection", "No Selection", "White/Caucasian", "No Selection", 
"White/Caucasian", "Black/African American", "Black/African American", 
"Black/African American", "Other", "No Selection", "White/Caucasian", 
"Black/African American", "No Selection", "No Selection")), class = "data.frame", row.names = c(NA, 
-140L))

Answer 1

假設您的數據集名為df ，您可以使用tidyverse包執行以下操作：

library(tidyverse)

result <- df %>% 
  count(Predict_DAS.Student_Ethnicity, Predict_DAS.PreDAS_Ethnicity) %>% 
  pivot_wider(names_from = Predict_DAS.PreDAS_Ethnicity, values_from = n, values_fill = 0)

  Predict_DAS.Student_Ethnicity   `Black/African…` `No Selection` `White/Caucasi…` Asian Other
  <chr>                                      <int>          <int>            <int> <int> <int>
1 American Indian or Alaskan Nat…                1              3                2     0     0
2 Asian                                          0              2                1     1     1
3 Black/African American                        19             11                2     1     0
4 Hispanic/Latinx                                1              8                0     0     0
5 Multiple                                       1              4                2     0     1
6 Native Hawaiian or Pacific Isl…                0              2                1     1     0
7 No Selection                                   2              4                0     0     1
8 Other                                          1              4                0     1     3
9 White/Caucasian                                3             12               32     2     1
# … with 2 more variables: `Hispanic/Latinx` <int>, Multiple <int>