In sbalci/ClinicoPathJamoviModule: Analysis for Clinicopathological Research

knitr::opts_chunk$set(comment = "#>", collapse = TRUE)
library(dplyr)
library(jjstatsplot)
library(ggstatsplot)
data(mtcars)

# Convert relevant variables to factors for categorical analysis
mtcars$cyl <- as.factor(mtcars$cyl)
mtcars$am <- as.factor(mtcars$am)
mtcars$vs <- as.factor(mtcars$vs)

This vignette demonstrates the categorical plot functions available in the jjstatsplot package. These functions are designed to work within the jamovi interface, but we can demonstrate their underlying functionality using the ggstatsplot functions they wrap.

Bar charts with `jjbarstats()`

The jjbarstats() function creates bar charts and performs chi-squared tests to compare categorical variables. It wraps ggstatsplot::ggbarstats().

# Underlying function that jjbarstats() wraps
ggstatsplot::ggbarstats(
  data = mtcars,
  x = cyl,
  y = am,
  title = "Cylinders by Transmission Type"
)

Pie charts with `jjpiestats()`

The jjpiestats() function creates pie charts for categorical data visualization. It wraps ggstatsplot::ggpiestats().

# Underlying function that jjpiestats() wraps
ggstatsplot::ggpiestats(
  data = mtcars,
  x = cyl,
  y = am,
  title = "Distribution of Cylinders by Transmission"
)

Dot charts with `jjdotplotstats()`

The jjdotplotstats() function shows group comparisons using dot plots. It wraps ggstatsplot::ggdotplotstats().

# Underlying function that jjdotplotstats() wraps
ggstatsplot::ggdotplotstats(
  data = mtcars,
  x = hp,
  y = vs,
  title = "Horsepower by Engine Configuration"
)

Within-group comparisons with `jjwithinstats()`

The jjwithinstats() function compares repeated measurements within groups. It wraps ggstatsplot::ggwithinstats().

# Create long format data for within-group comparison
library(tidyr)
mtcars_long <- mtcars %>%
  select(mpg, hp, wt, qsec) %>%
  mutate(id = row_number()) %>%
  pivot_longer(cols = c(mpg, hp, wt, qsec), 
               names_to = "measure", 
               values_to = "value") %>%
  # Standardize values for comparison
  group_by(measure) %>%
  mutate(value = scale(value)[,1]) %>%
  ungroup()

# Underlying function that jjwithinstats() wraps
ggstatsplot::ggwithinstats(
  data = mtcars_long,
  x = measure,
  y = value,
  paired = TRUE,
  id = id,
  title = "Comparison of Standardized Car Measurements"
)