Plot the equivalent of correlation matrix for factors (categorical data)? And mixed types?

Question

Actually there are 2 questions, one is more advanced than the other.

Q1: I am looking for a method that similar to corrplot() but can deal with factors.

I originally tried to use chisq.test() then calculate the p-value and Cramer's V as correlation, but there too many columns to figure out. So could anyone tell me if there is a quick way to create a "corrplot" that each cell contains the value of Cramer's V, while the colour is rendered by p-value. Or any other kind of similar plot.

Regarding Cramer's V, let's say tbl is a 2-dimensional factor data frame.

chi2 <- chisq.test(tbl, correct=F)
Cramer_V <- sqrt(chi2$/nrow(tbl)) 

I prepared a test data frame with factors:

df <- data.frame(
group = c('A', 'A', 'A', 'A', 'A', 'B', 'C'),
student = c('01', '01', '01', '02', '02', '01', '02'),
exam_pass = c('Y', 'N', 'Y', 'N', 'Y', 'Y', 'N'),
subject = c('Math', 'Science', 'Japanese', 'Math', 'Science', 'Japanese', 'Math')
) 

Q2: Then I would like to compute a correlation/association matrix on a mixed-types dataframe e.g.:

df <- data.frame(
group = c('A', 'A', 'A', 'A', 'A', 'B', 'C'),
student = c('01', '01', '01', '02', '02', '01', '02'),
exam_pass = c('Y', 'N', 'Y', 'N', 'Y', 'Y', 'N'),
subject = c('Math', 'Science', 'Japanese', 'Math', 'Science', 'Japanese', 'Math')
) 
df$group <- factor(df$group, levels = c('A', 'B', 'C'), ordered = T)
df$student <- as.integer(df$student)

Answer 1

If you want to have a genuine correlation plot for factors or mixed-type, you can also use model.matrix to one-hot encode all non-numeric variables. This is quite different than calculating Cramér's V as it will consider your factor as separate variables, as many regression models do.

You can then use your favorite correlation-plot library. I personally like ggcorrplot for its ggplot2 compatibility.

Here is an example with your dataset:

library(ggcorrplot)
model.matrix(~0+., data=df) %>% 
  cor(use="pairwise.complete.obs") %>% 
  ggcorrplot(show.diag = F, type="lower", lab=TRUE, lab_size=2)