In-Class Exercise 4

Import Packages and Data

pacman::p_load(rstatix, gt, patchwork, tidyverse)

exam_data <- read_csv('data/Exam_data.csv')

Visualising a Normal Distribution

First Attempt: QQ Plot

We can use a QQ plot to visualise whether a distribution is normal or not. In the plot below, the points deviate significantly from the straight line, indicating that the data is not normally distributed.

The Plot

The Code Chunk

ggplot(data = exam_data,
       aes(sample=ENGLISH)) +
  stat_qq() +
  stat_qq_line()

Note

We use stat_qq() and stat_qq_line() methods to plot the QQ plot. Note that here aes takes an argument called sample instead of typical x and/or y.

Second Attempt: QQ Plot + Statistical Test Table

We can add a table showing the results of a formal statistical test for normality. Here we use the Shapiro-Wilk Test.

The Plot

The Code Chunk

qq <- ggplot(data = exam_data,
             aes(sample=ENGLISH)) +
  stat_qq() +
  stat_qq_line()

sw_t <- exam_data %>%
  shapiro_test(ENGLISH) %>%
  gt()

tmp <- tempfile(fileext = '.png')
gtsave(sw_t, tmp)
table_png <- png::readPNG(tmp, native=TRUE)

qq + table_png