3 Quantile–quantile plots

In-Class Exercise for Week 4

(First published: May 6, 2023)

1.Load the required packages

Show the code

pacman::p_load(rstatix,gt,patchwork,tidyverse,webshot2)

2.Load the data-set into R

Show the code

exam_data <- read_csv('data/Exam_data.csv', show_col_types = FALSE) 

3.Visualise Normal Distribution

Quantile–quantile (Q-Q) plots are a useful visualization when we want to determine to what extent the observed data points do or do not follow a given distribution. If the data is normally distributed, the points in a Q-Q plot will be on a straight diagonally line. Conversely, if the points deviate significantly from the straight diagonally line, then it’s less likely that the data is normally distributed.

The Plot

The Codes

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

Note

We can see that the points deviate significantly from the straight diagonal line. This is a clear indication that the set of data is not normally distributed.

4.Combining statistical graph and analysis table

We will need to install webshot2

Show the code

qq <-ggplot(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