I'm having trouble getting my data set to work with the table function in r. 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 that it is in 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))
CodePudding user response：
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>
CodePudding user response：
I suspect the problem is that the 'levels' of your factor (PreDAS_Ethnicity) don't match your expected outcome. Here is a potential solution specifying the levels before 'table()ing' the data:
Predict_DAS <- read.table(text = "Student_Ethnicity PreDAS_Ethnicity
White Black
White White
Asian Black
Black White
White Black", header = TRUE)
Predict_DAS$Student_Ethnicity <- factor(Predict_DAS$Student_Ethnicity, levels = unique(Predict_DAS$Student_Ethnicity))
Predict_DAS$PreDAS_Ethnicity <- factor(Predict_DAS$PreDAS_Ethnicity, levels = unique(Predict_DAS$Student_Ethnicity))
PreDASEthnicityPredictor <- table(Predict_DAS$Student_Ethnicity, Predict_DAS$PreDAS_Ethnicity)
PreDASEthnicityPredictor
#>
#> White Asian Black
#> White 1 0 2
#> Asian 0 0 1
#> Black 1 0 0
# With your example data:
df <- 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))
df$Predict_DAS.Student_Ethnicity <- factor(df$Predict_DAS.Student_Ethnicity, levels = unique(df$Predict_DAS.Student_Ethnicity))
df$Predict_DAS.PreDAS_Ethnicity <- factor(df$Predict_DAS.PreDAS_Ethnicity, levels = unique(df$Predict_DAS.Student_Ethnicity))
PreDASEthnicityPredictor <- table(df$Predict_DAS.Student_Ethnicity,
df$Predict_DAS.PreDAS_Ethnicity)
PreDASEthnicityPredictor
#>
#> White/Caucasian Other Multiple
#> White/Caucasian 32 1 0
#> Other 0 3 0
#> Multiple 2 1 3
#> Black/African American 2 0 0
#> Hispanic/Latinx 0 0 0
#> Native Hawaiian or Pacific Islander 1 0 0
#> No Selection 0 1 0
#> American Indian or Alaskan Native 2 0 0
#> Asian 1 1 0
#>
#> Black/African American Hispanic/Latinx
#> White/Caucasian 3 1
#> Other 1 0
#> Multiple 1 0
#> Black/African American 19 0
#> Hispanic/Latinx 1 5
#> Native Hawaiian or Pacific Islander 0 0
#> No Selection 2 0
#> American Indian or Alaskan Native 1 0
#> Asian 0 0
#>
#> Native Hawaiian or Pacific Islander
#> White/Caucasian 0
#> Other 0
#> Multiple 0
#> Black/African American 0
#> Hispanic/Latinx 0
#> Native Hawaiian or Pacific Islander 0
#> No Selection 0
#> American Indian or Alaskan Native 0
#> Asian 0
#>
#> No Selection
#> White/Caucasian 12
#> Other 4
#> Multiple 4
#> Black/African American 11
#> Hispanic/Latinx 8
#> Native Hawaiian or Pacific Islander 2
#> No Selection 4
#> American Indian or Alaskan Native 3
#> Asian 2
#>
#> American Indian or Alaskan Native Asian
#> White/Caucasian 0 2
#> Other 0 1
#> Multiple 0 0
#> Black/African American 0 1
#> Hispanic/Latinx 0 0
#> Native Hawaiian or Pacific Islander 0 1
#> No Selection 0 0
#> American Indian or Alaskan Native 0 0
#> Asian 0 1
Created on 2022-03-21 by the reprex package (v2.0.1)