Hands-on Exercise 4 (b) :
Visual Statistical Analysis

Author

Li Jianyi

Published

May 8, 2025



Learning Outcome

In this hands-on exercise, you will gain hands-on experience on using:

  • ggstatsplot package to create visual graphics with rich statistical information,

  • performance package to visualise model diagnostics, and

  • parameters package to visualise model parameters

Visual Statistical Analysis with ggstatsplot

ggstatsplot is an extension of ggplot2 package for creating graphics with details from statistical tests included in the information-rich plots themselves.

-   To provide alternative statistical inference methods by default.
-   To follow best practices for statistical reporting. For all statistical tests reported in the plots, the default template abides by the [APA](https://my.ilstu.edu/~jhkahn/apastats.html) gold standard for statistical reporting. For example, here are results from a robust t-test:

Getting Started

Installing and launching R packages

In this exercise, ggstatsplot and tidyverse will be used.

pacman::p_load(ggstatsplot, tidyverse)

Importing data

Do-It-Yourself

Importing Exam.csv data by using appropriate tidyverse package.

# A tibble: 322 × 7
   ID         CLASS GENDER RACE    ENGLISH MATHS SCIENCE
   <chr>      <chr> <chr>  <chr>     <dbl> <dbl>   <dbl>
 1 Student321 3I    Male   Malay        21     9      15
 2 Student305 3I    Female Malay        24    22      16
 3 Student289 3H    Male   Chinese      26    16      16
 4 Student227 3F    Male   Chinese      27    77      31
 5 Student318 3I    Male   Malay        27    11      25
 6 Student306 3I    Female Malay        31    16      16
 7 Student313 3I    Male   Chinese      31    21      25
 8 Student316 3I    Male   Malay        31    18      27
 9 Student312 3I    Male   Malay        33    19      15
10 Student297 3H    Male   Indian       34    49      37
# ℹ 312 more rows

One-sample test: gghistostats() method

In the code chunk below, gghistostats() is used to to build an visual of one-sample test on English scores.

set.seed(1234)

gghistostats(
  data = exam,
  x = ENGLISH,
  type = "bayes",
  test.value = 60,
  xlab = "English scores"
)

Default information: - statistical details - Bayes Factor - sample sizes - distribution summary

Unpacking the Bayes Factor

  • A Bayes factor is the ratio of the likelihood of one particular hypothesis to the likelihood of another. It can be interpreted as a measure of the strength of evidence in favor of one theory among two competing theories.

  • That’s because the Bayes factor gives us a way to evaluate the data in favor of a null hypothesis, and to use external information to do so. It tells us what the weight of the evidence is in favor of a given hypothesis.

  • When we are comparing two hypotheses, H1 (the alternate hypothesis) and H0 (the null hypothesis), the Bayes Factor is often written as B10. It can be defined mathematically as

  • The Schwarz criterion is one of the easiest ways to calculate rough approximation of the Bayes Factor.

How to interpret Bayes Factor

A Bayes Factor can be any positive number. One of the most common interpretations is this one—first proposed by Harold Jeffereys (1961) and slightly modified by Lee and Wagenmakers in 2013:

Two-sample mean test: ggbetweenstats()

In the code chunk below, ggbetweenstats() is used to build a visual for two-sample mean test of Maths scores by gender.

ggbetweenstats(
  data = exam,
  x = GENDER, 
  y = MATHS,
  type = "np",
  messages = FALSE
)

Default information: - statistical details - Bayes Factor - sample sizes - distribution summary

Oneway ANOVA Test: ggbetweenstats() method

In the code chunk below, ggbetweenstats() is used to build a visual for One-way ANOVA test on English score by race.

ggbetweenstats(
  data = exam,
  x = RACE, 
  y = ENGLISH,
  type = "p",
  mean.ci = TRUE, 
  pairwise.comparisons = TRUE, 
  pairwise.display = "s",
  p.adjust.method = "fdr",
  messages = FALSE
)

  • “ns” → only non-significant
  • “s” → only significant
  • “all” → everything

ggbetweenstats - Summary of tests

Significant Test of Correlation: ggscatterstats()

In the code chunk below, ggscatterstats() is used to build a visual for Significant Test of Correlation between Maths scores and English scores.

ggscatterstats(
  data = exam,
  x = MATHS,
  y = ENGLISH,
  marginal = FALSE,
  )

Significant Test of Association (Depedence) : ggbarstats() methods

In the code chunk below, the Maths scores is binned into a 4-class variable by using cut().

exam1 <- exam %>% 
  mutate(MATHS_bins = 
           cut(MATHS, 
               breaks = c(0,60,75,85,100))
)

In this code chunk below ggbarstats() is used to build a visual for Significant Test of Association

ggbarstats(exam1, 
           x = MATHS_bins, 
           y = GENDER)