10.1 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

10.2 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.

10.3 Getting Started

10.3.1 Installing and launching R packages

In this exercise, ggstatsplot and tidyverse will be used.

Code

pacman::p_load(ggstatsplot, tidyverse)

10.3.2 Importing data

Code

exam <- read_csv("Exam_data.csv")

10.3.3 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.

Code

set.seed(1234)

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

10.3.4 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.
The Schwarz criterion is one of the easiest ways to calculate rough approximation of the Bayes Factor.

10.3.6 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. ‘np’ stands for non-parametric.

Code

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

10.3.7 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.

Code

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

10.3.8 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.

Code

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

10.3.9 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().

Code

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.

Code

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

--- title: "Hands On Exercise 4B" subtitle: "Visual Statistical Analysis" format: html date: 05/09/2025 date-format: long date-modified: last-modified editor: visual execute: eval: true echo: true warning: false freeze: true --- # 10.1 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 # 10.2 Visual Statistical Analysis with ggstatsplot [**ggstatsplot**](https://indrajeetpatil.github.io/ggstatsplot/index.html) is an extension of [**ggplot2**](https://ggplot2.tidyverse.org/) package for creating graphics with details from statistical tests included in the information-rich plots themselves. # 10.3 Getting Started ## 10.3.1 Installing and launching R packages In this exercise, **ggstatsplot** and **tidyverse** will be used. ```{r} pacman::p_load(ggstatsplot, tidyverse) ``` ## 10.3.2 Importing data ```{r} exam <- read_csv("Exam_data.csv") ``` # 10.3.3 One-sample test: *gghistostats()* method In the code chunk below, [*gghistostats()*](https://indrajeetpatil.github.io/ggstatsplot/reference/gghistostats.html) is used to to build an visual of one-sample test on English scores. ```{r} set.seed(1234) gghistostats( data = exam, x = ENGLISH, type = "bayes", test.value = 60, xlab = "English scores" ) ``` # 10.3.4 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. - The [**Schwarz criterion**](https://www.statisticshowto.com/bayesian-information-criterion/) is one of the easiest ways to calculate rough approximation of the Bayes Factor. # 10.3.6 Two-sample mean test: *ggbetweenstats()* In the code chunk below, [*ggbetweenstats()*](https://indrajeetpatil.github.io/ggstatsplot/reference/ggbetweenstats.html) is used to build a visual for two-sample mean test of Maths scores by gender. 'np' stands for non-parametric. ```{r} ggbetweenstats( data = exam, x = GENDER, y = MATHS, type = "p", messages = FALSE ) ``` # 10.3.7 Oneway ANOVA Test: *ggbetweenstats()* method In the code chunk below, [*ggbetweenstats()*](https://indrajeetpatil.github.io/ggstatsplot/reference/ggbetweenstats.html) is used to build a visual for One-way ANOVA test on English score by race. ```{r} ggbetweenstats( data = exam, x = RACE, y = ENGLISH, type = "p", mean.ci = TRUE, pairwise.comparisons = TRUE, pairwise.display = "s", p.adjust.method = "fdr", messages = FALSE ) ``` # 10.3.8 Significant Test of Correlation: *ggscatterstats()* In the code chunk below, [*ggscatterstats()*](https://indrajeetpatil.github.io/ggstatsplot/reference/ggscatterstats.html) is used to build a visual for Significant Test of Correlation between Maths scores and English scores. ```{r} ggscatterstats( data = exam, x = MATHS, y = ENGLISH, marginal = TRUE, ) ``` # 10.3.9 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()*](https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/cut). ```{r} exam1 <- exam %>% mutate(MATHS_bins = cut(MATHS, breaks = c(0,60,75,85,100)) ) ``` In this code chunk below [*ggbarstats()*](https://indrajeetpatil.github.io/ggstatsplot/reference/ggbarstats.html) is used to build a visual for Significant Test of Association. ```{r} ggbarstats(exam1, x = MATHS_bins, y = GENDER) ```