Why table() function is giving me long data instead of wide?

Question

I'm having trouble getting my data set to work with the table() function. My data originally looks something like this:

Student_Ethnicity	Pre_DAS Ethnicity
White	Black
White	White
Asian	Black
Black	White
White	Black

But, I want it to tabulate like this:

Student_Ethnicity	White	Black	Asian
White	1	2	0
Black	1	0	0
Asian	0	1	0

I previously ran this code:

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

But, when the data tabulated it looked like this:

Student_Ethnicity	Pre_DAS Ethnicity	Frq
White	White	1
White	Black	2
White	Asian	0
Black	White	1
Black	Black	0
Black	Asian	0
Asian	White	0
Asian	Black	1
Asian	Asian	0

Is there a specific reason this could be happening and how can I make this tabulate correctly? It is important to get this table format for a chi-squared analysis I would like to do on this later.

Here is the actual dataset I am working with:

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

Assuming your data set is called df, you could do the following with the tidyverse packages:

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>