Ridge plot for plotting logistic regression data

ridge_plot_example.R

library(dplyr)
library(ggplot2)
library(ggridges)
dat <- select(psych::bfi, age, education, gender) %>% 
  mutate(education = as.factor(education), 
         gender = as.factor(gender),
         gender_edu = paste(education, gender)) %>% 
  na.omit()

ggplot(dat, aes(x = age, 
                y = gender_edu, 
                group = gender_edu,
                color = education,
                fill = education
                )) +
  geom_density_ridges(aes(alpha = gender)) +
  scale_alpha_discrete(range = c(.2, .6)) +
  geom_point(alpha = .3)
  

ggplot(dat, aes(x = age, 
                y = gender_edu, 
                group = gender_edu,
                color = education,
                fill = education
)) +
  geom_density_ridges(aes(alpha = gender), scale = 1) + # Change scale to adjust amount of density overlap
  scale_alpha_discrete(range = c(.2, .6)) +
  geom_jitter(width = 0, height = .2, alpha = .3)

mod <- glm(gender ~ age + education, family = "binomial", data = dat)

newdat <- bind_cols(dat, pred = predict(mod, type = "response"))

# Scatterplot using predicted probabilities and point shape
ggplot(newdat, aes(x = age, y = pred)) +
  geom_point(aes(alpha = gender, color = education)) +
  geom_point(aes(color = education), shape = 1) +
  scale_alpha_discrete(range = c(0, 1))