Quick Reference Guide for jjstatsplot

jjstatsplot Quick Reference

This quick reference provides essential information for using jjstatsplot efficiently in both jamovi and R.

Function Overview

Function	Purpose	Data Type	jamovi Location
`jjhistostats()`	Distribution analysis	1 continuous	Continuous → Histogram
`jjscatterstats()`	Bivariate relationships	2 continuous	Continuous vs Continuous → Scatter Plot
`jjcorrmat()`	Multiple correlations	2+ continuous	Continuous vs Continuous → Correlation Matrix
`jjbetweenstats()`	Group comparisons	1 continuous + 1 categorical	Categorical vs Continuous → Box-Violin (Between)
`jjdotplotstats()`	Group means	1 continuous + 1 categorical	Categorical vs Continuous → Dot Chart
`jjwithinstats()`	Paired comparisons	2+ continuous (paired)	Categorical vs Continuous → Box-Violin (Within)
`jjbarstats()`	Categorical associations	1-2 categorical	Categorical vs Categorical → Bar Charts
`jjpiestats()`	Proportional data	1 categorical	Categorical vs Categorical → Pie Charts
`waffle()`	Distribution visualization	1 categorical	Distribution → Waffle Charts

Quick Start Templates

jamovi Workflow

1. Data → Open file
2. JJStatsPlot → [Category] → [Analysis]
3. Drag variables to appropriate boxes
4. Adjust options as needed
5. Export → Copy/Save plot

R Workflow

library(ClinicoPath)

# Basic pattern
result <- jj[function](data = your_data, dep = "variable")
result$plot  # Main plot

# With grouping
result <- jj[function](data = your_data, dep = "variable", grvar = "group")
result$plot2  # Grouped plots

Essential R Code Snippets

Data Preparation

# Load and check data
library(ClinicoPath)
data(mtcars)
str(mtcars)

# Convert to factors
mtcars$cyl <- factor(mtcars$cyl)
mtcars$am <- factor(mtcars$am, labels = c("Auto", "Manual"))

# Check missing data
summary(mtcars)

Single Variable Analysis

# Histogram
hist_result <- jjhistostats(data = mtcars, dep = "mpg", grvar = NULL)
hist_result$plot

# Multiple variables
multi_hist <- jjhistostats(data = mtcars, dep = c("mpg", "hp", "wt"), grvar = NULL)
multi_hist$plot

Bivariate Analysis

# Scatter plot
scatter_result <- jjscatterstats(data = mtcars, dep = "mpg", group = "hp")
scatter_result$plot

# Correlation matrix
corr_result <- jjcorrmat(data = mtcars, dep = c("mpg", "hp", "wt"))
corr_result$plot

Group Comparisons

# Between groups
between_result <- jjbetweenstats(data = mtcars, dep = "mpg", group = "cyl")
between_result$plot

# Dot chart
dot_result <- jjdotplotstats(data = mtcars, dep = "mpg", group = "cyl")
dot_result$plot

Categorical Analysis

# Bar chart
bar_result <- jjbarstats(data = mtcars, dep = "cyl", group = "am")
bar_result$plot

# Pie chart
pie_result <- jjpiestats(data = mtcars, dep = "cyl")
pie_result$plot

Grouped Analysis

# Any function with grvar parameter
grouped_result <- jjhistostats(
  data = mtcars, 
  dep = "mpg", 
  grvar = "cyl"
)
grouped_result$plot2  # Note: plot2 for grouped plots

Common Options and Customizations

jamovi Interface Elements

Dependent Variable(s): Main variables to analyze
Grouping Variable: Split data by categories
Grouping Variable for Plots: Create separate plots
Statistical Options: Test types, confidence levels
Plot Options: Themes, colors, labels

R Function Parameters

# Common parameters across functions
jj[function](
  data = your_data,           # Data frame
  dep = "variable",           # Dependent variable(s)
  group = "grouping_var",     # Grouping variable
  grvar = "plot_grouping",    # Separate plots by group
  # Function-specific options vary
)

Statistical Tests by Function

Function	Default Test	Alternative Tests
`jjhistostats()`	Shapiro-Wilk normality	Anderson-Darling, Kolmogorov-Smirnov
`jjscatterstats()`	Pearson correlation	Spearman, robust correlation
`jjbetweenstats()`	Welch’s ANOVA	Student’s t-test, Kruskal-Wallis
`jjdotplotstats()`	Similar to between-stats	Non-parametric alternatives
`jjwithinstats()`	Paired t-test	Wilcoxon signed-rank
`jjbarstats()`	Chi-square test	Fisher’s exact test
`jjcorrmat()`	Pearson correlations	Spearman, partial correlations

Troubleshooting Checklist

Before Analysis

Check variable types (str(data))
Verify sufficient sample sizes (table(group_var))
Handle missing values (summary(data))
Ensure appropriate data format

Common Errors

“No data to plot”: Check variable selection and missing values
“Object not found”: Verify variable names (case-sensitive)
Blank plots: Use result$plot2 for grouped analyses
Statistical test failures: Check assumptions and sample sizes

Performance Issues

Large datasets: Sample or subset data
Complex grouping: Reduce number of groups
Memory problems: Clean workspace with rm() and gc()

Export and Reporting

jamovi Export

Right-click plot → Copy
File → Export → [Format]
Results panel → Copy statistical output

R Export

# Save plot
result <- jjhistostats(data = mtcars, dep = "mpg")
ggsave("histogram.png", result$plot, width = 8, height = 6, dpi = 300)

# Extract for further modification
my_plot <- result$plot + 
  ggplot2::labs(title = "Custom Title") +
  ggplot2::theme_minimal()

Statistical Reporting Template

When reporting results, include:

Sample size: n = X
Test type: “Welch’s ANOVA was conducted…”
Test statistic: F(df1, df2) = X.XX
p-value: p < .001 or exact value
Effect size: η² = .XX or Cohen’s d = X.XX
Confidence intervals: 95% CI [X.XX, X.XX]

Example: “A Welch’s ANOVA revealed a significant difference in fuel efficiency across cylinder groups, F(2, 18.03) = 39.70, p < .001, η² = .70, suggesting a large effect.”

Documentation Links

Complete jamovi Guide: vignette("jamovi-user-guide", package = "jjstatsplot")
R Programming Guide: vignette("r-programming-guide", package = "jjstatsplot")
Analysis Gallery: vignette("analysis-gallery", package = "jjstatsplot")
Troubleshooting: vignette("troubleshooting-faq", package = "jjstatsplot")
ggstatsplot Documentation: indrajeetpatil.github.io/ggstatsplot

Getting Help

Quick Checks

Try with built-in data (e.g., mtcars, iris)
Simplify analysis (remove grouping variables)
Check for recent updates
Restart jamovi/R session

Report Issues

Include data example, error message, and steps
GitHub: sbalci/ClinicoPathJamoviModule/issues
Email: drserdarbalci@gmail.com

This quick reference covers the essentials for efficient jjstatsplot usage. For detailed examples and advanced usage, consult the comprehensive vignettes listed above.

ClinicoPath Development Team

2025-06-30