—: "jjscatterstats: Comprehensive Scatter Plot Analysis": "ClinicoPath": "2025-07-13": html: >%\VignetteIndexEntry{jjscatterstats: Comprehensive Scatter Plot Analysis}%\VignetteEngine{quarto::html}%\VignetteEncoding{UTF-8}—{r, include = FALSE}\nknitr::opts_chunk$set(\n collapse = TRUE,\n comment = \"#>\",\n fig.width = 8,\n fig.height = 6,\n warning = FALSE,\n message = FALSE\n)\n# Introduction to jjscatterstatsjjscatterstats function is a powerful wrapper around ggstatsplot::ggscatterstats() and ggstatsplot::grouped_ggscatterstats() that provides comprehensive scatter plot analysis with statistical testing capabilities. This function is particularly useful for exploring relationships between continuous variables in clinical and research settings.## Key Features- Statistical Analysis: Automatic correlation analysis with multiple statistical approaches- Flexible Grouping: Support for grouped analysis across categorical variables- Multiple Statistical Types: Parametric, non-parametric, robust, and Bayesian statistics- Customizable Visualization: Extensive theming and labeling options- Performance Optimized: Enhanced caching and data preparation for faster rendering## Loading Required Libraries{r setup}\nlibrary(ClinicoPath)\nlibrary(ggplot2)\nlibrary(dplyr)\n\n# Load example datasets\ndata(iris)\ndata(mtcars)\n# Basic Scatter Plot Analysis## Simple Correlation Analysis’s start with a basic scatter plot analyzing the relationship between sepal length and petal length in the iris dataset:{r basic-example}\n# Basic scatter plot with parametric correlation\nresult_basic <- jjscatterstats(\n data = iris,\n dep = \"Sepal.Length\",\n group = \"Petal.Length\",\n typestatistics = \"parametric\",\n mytitle = \"Sepal Length vs Petal Length\",\n xtitle = \"Sepal Length (cm)\",\n ytitle = \"Petal Length (cm)\"\n)\n\nprint(result_basic)\n## Different Statistical Approaches### Non-parametric Analysis{r nonparametric}\n# Non-parametric correlation (Spearman's rho)\nresult_nonparam <- jjscatterstats(\n data = iris,\n dep = \"Sepal.Length\",\n group = \"Petal.Width\",\n typestatistics = \"nonparametric\",\n mytitle = \"Non-parametric Correlation Analysis\"\n)\n\nprint(result_nonparam)\n### Robust Statistics{r robust}\n# Robust correlation analysis\nresult_robust <- jjscatterstats(\n data = iris,\n dep = \"Sepal.Width\",\n group = \"Petal.Length\",\n typestatistics = \"robust\",\n mytitle = \"Robust Correlation Analysis\"\n)\n\nprint(result_robust)\n### Bayesian Analysis{r bayesian}\n# Bayesian correlation analysis\nresult_bayes <- jjscatterstats(\n data = iris,\n dep = \"Sepal.Length\",\n group = \"Sepal.Width\",\n typestatistics = \"bayes\",\n mytitle = \"Bayesian Correlation Analysis\"\n)\n\nprint(result_bayes)\n# Grouped Analysis## Scatter Plot by Speciesof the most powerful features is the ability to create grouped scatter plots:{r grouped-analysis}\n# Grouped scatter plot by species\nresult_grouped <- jjscatterstats(\n data = iris,\n dep = \"Sepal.Length\",\n group = \"Petal.Length\",\n grvar = \"Species\",\n typestatistics = \"parametric\",\n mytitle = \"Correlation Analysis by Species\"\n)\n\nprint(result_grouped)\n## Multiple Group Analysis with mtcars{r mtcars-grouped}\n# Prepare mtcars data\nmtcars_modified <- mtcars %>%\n mutate(\n transmission = factor(am, levels = c(0, 1), labels = c(\"Automatic\", \"Manual\")),\n cylinders = factor(cyl)\n )\n\n# Grouped analysis of mpg vs horsepower by transmission type\nresult_mtcars <- jjscatterstats(\n data = mtcars_modified,\n dep = \"hp\",\n group = \"mpg\",\n grvar = \"transmission\",\n typestatistics = \"parametric\",\n mytitle = \"MPG vs Horsepower by Transmission Type\",\n xtitle = \"Horsepower\",\n ytitle = \"Miles per Gallon\"\n)\n\nprint(result_mtcars)\n# Clinical Research Applications## Using Clinical Test Data{r clinical-example, eval=FALSE}\n# Example with clinical data (if available)\n# This demonstrates typical clinical research scenarios\n\n# Load clinical test data\ndata(jjscatterstats_clinical)\n\n# Analyze tumor size vs Ki67 percentage\nclinical_result <- jjscatterstats(\n data = jjscatterstats_clinical,\n dep = \"tumor_size_mm\",\n group = \"ki67_percentage\",\n grvar = \"stage\",\n typestatistics = \"parametric\",\n mytitle = \"Tumor Size vs Ki67 by Cancer Stage\",\n xtitle = \"Tumor Size (mm)\",\n ytitle = \"Ki67 Percentage (%)\"\n)\n\nprint(clinical_result)\n## Biomarker Correlation Analysis{r biomarker-example, eval=FALSE}\n# Biomarker correlation analysis\nbiomarker_result <- jjscatterstats(\n data = jjscatterstats_clinical,\n dep = \"mutation_count\",\n group = \"survival_months\",\n grvar = \"histology\",\n typestatistics = \"nonparametric\",\n mytitle = \"Mutation Count vs Survival by Histology\",\n xtitle = \"Mutation Count\",\n ytitle = \"Survival (months)\"\n)\n\nprint(biomarker_result)\n# Advanced Customization## Theme Customization{r theme-custom}\n# Using original ggstatsplot theme\nresult_original_theme <- jjscatterstats(\n data = iris,\n dep = \"Sepal.Length\",\n group = \"Petal.Length\",\n originaltheme = TRUE,\n mytitle = \"Original ggstatsplot Theme\"\n)\n\nprint(result_original_theme)\n## Custom Labels and Titles{r custom-labels}\n# Comprehensive labeling\nresult_labeled <- jjscatterstats(\n data = mtcars,\n dep = \"wt\",\n group = \"mpg\",\n typestatistics = \"parametric\",\n mytitle = \"Vehicle Weight vs Fuel Efficiency\",\n xtitle = \"Weight (1000 lbs)\",\n ytitle = \"Miles per Gallon\",\n resultssubtitle = TRUE\n)\n\nprint(result_labeled)\n## Controlling Statistical Results Display{r results-control}\n# Hide statistical results subtitle\nresult_no_stats <- jjscatterstats(\n data = iris,\n dep = \"Sepal.Width\",\n group = \"Petal.Width\",\n typestatistics = \"parametric\",\n mytitle = \"Clean Plot Without Statistics\",\n resultssubtitle = FALSE\n)\n\nprint(result_no_stats)\n# Performance Considerations## Large Dataset Handlingfunction includes several performance optimizations:{r performance-demo, eval=FALSE}\n# Performance test with larger dataset\ndata(jjscatterstats_performance)\n\n# This should render efficiently due to optimizations\nstart_time <- Sys.time()\nperformance_result <- jjscatterstats(\n data = jjscatterstats_performance,\n dep = \"measurement_1\",\n group = \"measurement_2\",\n grvar = \"lab_id\",\n typestatistics = \"parametric\",\n mytitle = \"Performance Test - Large Dataset\"\n)\nend_time <- Sys.time()\n\ncat(\"Rendering time:\", difftime(end_time, start_time, units = \"secs\"), \"seconds\\n\")\nprint(performance_result)\n## Data Caching and Optimizationfunction implements several performance enhancements:. Data Preparation Caching: Processed data is cached to avoid recomputation. Option Preprocessing: Common option processing is done once and cached. Hash-based Change Detection: Only reprocesses data when inputs change. Efficient Memory Usage: Minimizes data copying and transformation overhead# Edge Cases and Data Validation## Handling Missing Values{r missing-values}\n# Create data with missing values\niris_with_na <- iris\niris_with_na[1:10, \"Sepal.Length\"] <- NA\niris_with_na[15:20, \"Petal.Length\"] <- NA\n\n# Function automatically handles missing values\nresult_na <- jjscatterstats(\n data = iris_with_na,\n dep = \"Sepal.Length\",\n group = \"Petal.Length\",\n typestatistics = \"parametric\",\n mytitle = \"Handling Missing Values\"\n)\n\nprint(result_na)\n## Input Validation{r validation-demo, error=TRUE}\n# Example of error handling\ntry({\n result_error <- jjscatterstats(\n data = data.frame(), # Empty dataset\n dep = \"nonexistent\",\n group = \"variable\",\n typestatistics = \"parametric\"\n )\n})\n# Best Practices and Recommendations## Statistical Type Selection- Parametric: Use when data is normally distributed (Pearson correlation)- Non-parametric: Use for non-normal data or ordinal variables (Spearman correlation)- Robust: Use when data has outliers (percentage bend correlation)- Bayesian: Use for incorporating prior knowledge or uncertainty quantification## Visualization Guidelines. Choose appropriate variables: Both x and y should be continuous. Consider grouping: Use grouping variables to reveal subgroup patterns. Label clearly: Always provide meaningful titles and axis labels. Statistical reporting: Include statistical results unless specifically hiding them## Performance Tips. Large datasets: The function is optimized for datasets up to several thousand rows. Multiple analyses: Reuse data objects when possible to benefit from caching. Grouping variables: Limit grouping variables to reasonable numbers of levels (< 10)# Troubleshooting Common Issues## Variable Type Issues{r variable-types}\n# Ensure variables are numeric\nstr(iris[c(\"Sepal.Length\", \"Petal.Length\")])\n\n# The function automatically converts to numeric, but it's good practice to check\n## Memory and Performance{r memory-tips, eval=FALSE}\n# For very large datasets, consider:\n# 1. Sampling your data first\nset.seed(123)\nsampled_data <- iris[sample(nrow(iris), 100), ]\n\nresult_sampled <- jjscatterstats(\n data = sampled_data,\n dep = \"Sepal.Length\",\n group = \"Petal.Length\",\n typestatistics = \"parametric\"\n)\n\n# 2. Using simpler statistical methods for initial exploration\nresult_simple <- jjscatterstats(\n data = iris,\n dep = \"Sepal.Length\",\n group = \"Petal.Length\",\n typestatistics = \"parametric\",\n resultssubtitle = FALSE # Faster rendering\n)\n# Summaryjjscatterstats function provides a comprehensive solution for scatter plot analysis with:- Multiple statistical testing approaches- Flexible grouping capabilities- Performance optimizations for larger datasets- Extensive customization options- Robust error handling and validationmakes it an excellent choice for exploratory data analysis, clinical research, and statistical reporting in the jamovi environment.## Function Referencecomplete parameter documentation, see:- ggstatsplot::ggscatterstats- ggstatsplot::grouped_ggscatterstats{r session-info}\n# Session information\nsessionInfo()\n