Explaining teaching score with gender

Mô hình hóa với dữ liệu trong Tidyverse

Albert Y. Kim

Assistant Professor of Statistical and Data Sciences

Exploratory data visualization

library(ggplot2)
library(dplyr)
library(moderndive)

ggplot(evals, aes(x = gender, y = score)) +
  geom_boxplot() +
  labs(x = "gender", y = "score")

Boxplot of score over gender

Facetted histogram

library(ggplot2)
library(dplyr)
library(moderndive)

ggplot(evals, aes(x = score)) +
  geom_histogram(binwidth = 0.25) +
  facet_wrap(~gender) +
  labs(x = "gender", y = "score")

Facetted histogram

Fitting a regression model

# Fit regression model
model_score_3 <- lm(score ~ gender, data = evals)

# Get regression table
get_regression_table(model_score_3)

# A tibble: 2 x 7
  term       estimate std_error statistic p_value...
  <chr>         <dbl>     <dbl>     <dbl>   <dbl>...
1 intercept     4.09      0.039    106.     0...
2 gendermale    0.142     0.051      2.78   0.006...

Fitting a regression model

# Compute group means based on gender
evals %>% 
  group_by(gender) %>% 
  summarize(avg_score = mean(score))

# A tibble: 2 x 2
  gender avg_score
  <fct>      <dbl>
1 female      4.09
2 male        4.23

A different categorical explanatory variable: rank

evals %>% 
  group_by(rank) %>% 
  summarize(n = n())

# A tibble: 3 x 2
  rank             n
  <fct>        <int>
1 teaching       102
2 tenure track   108
3 tenured        253

Let's practice!

Mô hình hóa với dữ liệu trong Tidyverse