library(tidyverse)

data = read_csv("https://uoepsy.github.io/data/dapr3_2324_report.csv")

data1 = data %>% 
  pivot_longer(cols = 2:6, names_to = "type", values_to = "value") %>% 
  mutate(x = replace(x, x == -99, NA)) %>% 
  filter(flyer == 0|flyer == 1) %>% 
  mutate(flyer = factor(flyer), stars = factor(stars))
  
data1[data1==-99] = NA

mod1 = lm(enj ~ value, data = data1)
summary(mod1)

ggplot(data = data1, aes(x = value, y = enj, color = flyer))+
  geom_point()+
  geom_smooth(method = "lm")

library(lme4)

mod2= lmer(enj  ~ value + flyer + (1 + flyer | stars), data = data1,control = lmerControl(optimizer="bobyqa"))
summary(mod2)
bruceR::model_summary(mod2)

library(broom.mixed)
glance(mod2)

tidy(mod2)

augment(mod2)

augment(mod2) %>%
  ggplot(aes(x = value, y = .fitted, col = stars)) + 
  geom_line()

augment(mod2) %>%
  ggplot(aes(x = value, y = .fitted, col = stars)) + 
  geom_point(data = data1, aes(value, enj))+
  geom_abline(intercept = fixef(mod2)[1], slope = fixef(mod2)[2], lwd = 2)+
  geom_line()

bruceR::model_summary(mod2)

plot(mod2, type=c("p","smooth"))

plot(mod2,
     form = sqrt(abs(resid(.))) ~ fitted(.),
     type = c("p","smooth"))

library(sjPlot)
tab_model(mod2)

bruceR::HLM_summary(mod2)