# four fold plots based on Frinedly's 2000 Visualizging Categorical Data
values = c(1198, 557, 1493, 1278)
dim(values) = c(2,2)
rownames(values) =  c("Male", "Female")
colnames(values) =  c("Admitted", "Rejected")
fourfoldplot(values[,])

data(UCBAdmissions)
x <- aperm(UCBAdmissions, c(2, 1, 3))
dimnames(x)[[2]] <- c("Yes", "No")
names(dimnames(x)) <- c("Sex", "Admit?", "Department")
fourfoldplot(x[,,])

# Mosaic plot based on Frinedly's 2000 Visualizging Categorical Data
# Note that values are not exactly what is in his book.  Buy it to get the real values.
values = c(6, 30, 15, 17, 16, 55, 15, 11, 21, 85, 18, 95, 69, 120, 27, 8);
values = matrix(values, nrow = 4, byrow=TRUE)
rownames(values) =  c("Green", "Hazel", "Blue", "Brown")
colnames(values) =  c("Black", "Brown", "Red", "Blond")
mosaicplot(values, shade = TRUE)

# Association plot based on Frinedly's 2000 Visualizging Categorical Data
# Note that values are not exactly what is in his book.  Buy it to get the real values.
values = c(6, 30, 15, 17, 16, 55, 15, 11, 21, 85, 18, 95, 69, 120, 27, 8);
values = matrix(values, nrow = 4, byrow=TRUE)
rownames(values) =  c("Green", "Hazel", "Blue", "Brown")
colnames(values) =  c("Black", "Brown", "Red", "Blond")
marg <- margin.table(values, c(1, 2))
assocplot(marg, main = "Relation between hair and eye color")