Advanced Raincloud Plots - Enhanced Distribution Visualization • jjstatsplot

Overview

The advancedraincloud module provides enhanced raincloud visualization using the modern ggrain package, offering advanced features beyond standard raincloud plots. This module is specifically designed for complex research scenarios requiring longitudinal connections, Likert scale support, and flexible positioning.

Key Advantages Over Standard Raincloud

Feature	Standard Raincloud (ggdist)	Advanced Raincloud (ggrain)
Package	ggdist	ggrain
Longitudinal Connections	❌	✅ Connect repeated measures
Likert Scale Support	❌	✅ Y-axis jittering for ordinal data
Positioning Options	Fixed	✅ Left/Right/Flanking
Covariate Mapping	❌	✅ Point color remapping
Clinical Applications	Basic distribution	✅ Complex research designs

When to Use Advanced Raincloud

Longitudinal Studies: Before/after or repeated measures data
Survey Analysis: Likert scales and ordinal responses
Clinical Trials: Treatment response with individual tracking
Complex Comparisons: Multi-group analysis with covariates

Dataset Overview

We’ll demonstrate using the comprehensive histopathology dataset:

# Load the histopathology dataset
data("histopathology")

# Dataset structure for raincloud plots
str(histopathology[c("Group", "Age", "OverallTime", "Sex", "Grade_Level", "ID")])
#> spc_tbl_ [250 × 6] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
#>  $ Group      : chr [1:250] "Control" "Treatment" "Control" "Treatment" ...
#>  $ Age        : num [1:250] 27 36 65 51 58 53 33 26 25 68 ...
#>  $ OverallTime: num [1:250] 3.5 3.1 3.1 4.9 3.3 9.3 6.3 9 5.8 9.9 ...
#>  $ Sex        : chr [1:250] "Male" "Female" "Male" "Male" ...
#>  $ Grade_Level: chr [1:250] "high" "low" "low" "high" ...
#>  $ ID         : num [1:250] 1 2 3 4 5 6 7 8 9 10 ...
#>  - attr(*, "spec")=List of 3
#>   ..$ cols   :List of 38
#>   .. ..$ ID                  : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ Name                : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ Sex                 : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ Age                 : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ Race                : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ PreinvasiveComponent: list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ LVI                 : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ PNI                 : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ LastFollowUpDate    : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ Death               : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ Group               : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ Grade               : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ TStage              : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ Anti-X-intensity    : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ Anti-Y-intensity    : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ LymphNodeMetastasis : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ Valid               : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ Smoker              : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ Grade_Level         : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ SurgeryDate         : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ DeathTime           : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ int                 : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ OverallTime         : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ Outcome             : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ Mortality5yr        : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ Rater 1             : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ Rater 2             : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ Rater 3             : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ Rater A             : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ Rater B             : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ New Test            : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ Golden Standart     : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ MeasurementA        : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ MeasurementB        : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ Disease Status      : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   .. ..$ Measurement1        : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ Measurement2        : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#>   .. ..$ Outcome2            : list()
#>   .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#>   ..$ default: list()
#>   .. ..- attr(*, "class")= chr [1:2] "collector_guess" "collector"
#>   ..$ skip   : num 1
#>   ..- attr(*, "class")= chr "col_spec"

# Key variables for advanced raincloud plots
cat("Variables suitable for advanced raincloud visualization:\n")
#> Variables suitable for advanced raincloud visualization:
cat("Y-axis (continuous):", paste(c("Age", "OverallTime", "MeasurementA", "MeasurementB"), collapse = ", "), "\n")
#> Y-axis (continuous): Age, OverallTime, MeasurementA, MeasurementB
cat("X-axis (grouping):", paste(c("Group", "Sex", "Grade_Level"), collapse = ", "), "\n")
#> X-axis (grouping): Group, Sex, Grade_Level
cat("Fill variable:", paste(c("Sex", "Grade_Level"), collapse = ", "), "\n")
#> Fill variable: Sex, Grade_Level
cat("ID variable:", "ID (for longitudinal connections)\n")
#> ID variable: ID (for longitudinal connections)

# Check for repeated measures (simulated)
set.seed(123)
histopathology$ID <- rep(1:125, 2)  # Simulate paired data
histopathology$Time <- rep(c("Baseline", "Follow-up"), each = 125)
cat("Simulated longitudinal data: 125 patients with baseline and follow-up measurements\n")
#> Simulated longitudinal data: 125 patients with baseline and follow-up measurements

Basic Advanced Raincloud Plot

Let’s start with a basic advanced raincloud plot:

# Basic advanced raincloud plot
advancedraincloud(
  data = histopathology,
  y_var = "Age",
  x_var = "Group",
  plot_title = "Age Distribution by Treatment Group",
  show_statistics = TRUE,
  show_interpretation = TRUE
)

R Code Example for Basic Plot

# Reproducible R code using ggrain
library(ggrain)
library(ggplot2)

# Filter out missing values
histopathology_clean <- histopathology %>%
  filter(!is.na(Group) & !is.na(Age))

# Basic advanced raincloud with ggrain
basic_plot <- ggplot(histopathology_clean, aes(x = Group, y = Age, fill = Group)) +
  geom_rain(rain.side = "l") +
  scale_fill_manual(values = c("#2E86AB", "#A23B72")) +
  theme_minimal() +
  labs(
    title = "Advanced Raincloud Plot - Basic Example",
    x = "Treatment Group",
    y = "Age (years)"
  ) +
  theme(legend.position = "none")

print(basic_plot)

Longitudinal Connections

The most powerful feature of advanced raincloud plots is connecting repeated observations:

# Advanced raincloud with longitudinal connections
advancedraincloud(
  data = histopathology,
  y_var = "OverallTime", 
  x_var = "Time",
  id_var = "ID",
  show_longitudinal = TRUE,
  plot_title = "Overall Survival Time: Baseline vs Follow-up",
  x_label = "Time Point",
  y_label = "Overall Time (months)"
)

R Code Example for Longitudinal Connections

# Create simulated longitudinal data
longitudinal_data <- histopathology %>%
  filter(!is.na(OverallTime)) %>%
  slice_head(n = 50) %>%  # Smaller sample for clearer visualization
  select(-Time) %>%  # Remove existing Time column to avoid duplicates
  mutate(
    Baseline = OverallTime,
    Follow_up = OverallTime + rnorm(n(), 2, 1),  # Simulated follow-up
    ID = row_number()
  ) %>%
  tidyr::pivot_longer(
    cols = c(Baseline, Follow_up),
    names_to = "Time",
    values_to = "Measurement"
  )

# Advanced raincloud with longitudinal connections
longitudinal_plot <- ggplot(longitudinal_data, aes(x = Time, y = Measurement, fill = Time)) +
  geom_rain(
    id.long.var = "ID",  # Connect observations by ID
    rain.side = "f"      # Flanking rainclouds
  ) +
  scale_fill_manual(values = c("#1f77b4", "#ff7f0e")) +
  theme_minimal() +
  labs(
    title = "Advanced Raincloud with Longitudinal Connections",
    x = "Time Point",
    y = "Measurement Value",
    caption = "Lines connect repeated observations from the same subjects"
  )

print(longitudinal_plot)

Raincloud Positioning Options

Advanced raincloud plots offer flexible positioning:

# Left-side raincloud (default)
advancedraincloud(
  data = histopathology,
  y_var = "Age",
  x_var = "Sex", 
  rain_rain.side = "l",
  plot_title = "Left-side Raincloud"
)

# Right-side raincloud
advancedraincloud(
  data = histopathology,
  y_var = "Age",
  x_var = "Sex",
  rain_rain.side = "r", 
  plot_title = "Right-side Raincloud"
)

# Flanking rainclouds (both sides)
advancedraincloud(
  data = histopathology,
  y_var = "Age",
  x_var = "Sex",
  rain_rain.side = "f",
  plot_title = "Flanking Rainclouds"
)

R Code Examples for Different Positions

# Create comparison plots
library(gridExtra)

# Left-side raincloud
histopathology_clean <- histopathology %>%
  filter(!is.na(Sex) & !is.na(Age))
left_plot <- ggplot(histopathology_clean, aes(x = Sex, y = Age, fill = Sex)) +
  geom_rain(rain.side = "l") +
  scale_fill_manual(values = c("#e74c3c", "#3498db")) +
  theme_minimal() +
  labs(title = "Left-side Raincloud", x = "Sex", y = "Age") +
  theme(legend.position = "none")

# Right-side raincloud  
right_plot <- ggplot(histopathology_clean, aes(x = Sex, y = Age, fill = Sex)) +
  geom_rain(rain.side = "r") +
  scale_fill_manual(values = c("#e74c3c", "#3498db")) +
  theme_minimal() +
  labs(title = "Right-side Raincloud", x = "Sex", y = "Age") +
  theme(legend.position = "none")

# Flanking rainclouds
flanking_plot <- ggplot(histopathology_clean, aes(x = Sex, y = Age, fill = Sex)) +
  geom_rain(rain.side = "f") +
  scale_fill_manual(values = c("#e74c3c", "#3498db")) +
  theme_minimal() +
  labs(title = "Flanking Rainclouds", x = "Sex", y = "Age") +
  theme(legend.position = "none")

# Display plots
grid.arrange(left_plot, right_plot, flanking_plot, ncol = 3)

Likert Scale Support

For ordinal survey data, advanced raincloud plots offer specialized support:

# Simulate Likert scale data
histopathology$Satisfaction <- sample(1:5, nrow(histopathology), replace = TRUE,
                                     prob = c(0.1, 0.2, 0.4, 0.2, 0.1))

# Advanced raincloud for Likert data
advancedraincloud(
  data = histopathology,
  y_var = "Satisfaction",
  x_var = "Group",
  likert_mode = TRUE,  # Enable Likert mode
  plot_title = "Patient Satisfaction by Treatment Group",
  y_label = "Satisfaction Score (1-5)"
)

R Code Example for Likert Data

# Create Likert scale data
likert_data <- histopathology %>%
  mutate(
    Satisfaction = sample(1:5, n(), replace = TRUE, prob = c(0.1, 0.2, 0.4, 0.2, 0.1)),
    Satisfaction_Factor = factor(Satisfaction, levels = 1:5, 
                                labels = c("Very Poor", "Poor", "Average", "Good", "Excellent"))
  )

# Advanced raincloud for Likert data with jittering
likert_plot <- ggplot(likert_data, aes(x = Group, y = Satisfaction, fill = Group)) +
  geom_rain(
    likert = TRUE,  # Enable Likert mode for Y-axis jittering
    rain.side = "l"
  ) +
  scale_fill_manual(values = c("#2E86AB", "#A23B72")) +
  scale_y_continuous(breaks = 1:5, labels = c("Very Poor", "Poor", "Average", "Good", "Excellent")) +
  theme_minimal() +
  labs(
    title = "Patient Satisfaction by Treatment Group - Likert Scale",
    x = "Treatment Group",
    y = "Satisfaction Score",
    caption = "Y-axis jittering applied for discrete ordinal responses"
  ) +
  theme(legend.position = "none")

print(likert_plot)

Covariate Mapping

Advanced raincloud plots support point color remapping based on additional variables:

# Advanced raincloud with covariate mapping
advancedraincloud(
  data = histopathology,
  y_var = "OverallTime",
  x_var = "Group",
  cov_var = "Age",  # Map point colors to age
  plot_title = "Overall Time by Group with Age Mapping",
  show_statistics = TRUE
)

R Code Example for Covariate Mapping

# Advanced raincloud with covariate mapping
histopathology_cov <- histopathology %>%
  filter(!is.na(Group) & !is.na(OverallTime) & !is.na(Age))
covariate_plot <- ggplot(histopathology_cov, aes(x = Group, y = OverallTime, fill = Group)) +
  geom_rain(
    rain.side = "l",
    cov = "Age"  # Map point colors to age covariate
  ) +
  scale_fill_manual(values = c("#2E86AB", "#A23B72")) +
  scale_color_viridis_c(name = "Age") +  # Continuous color scale for age
  theme_minimal() +
  labs(
    title = "Overall Time by Group with Age Covariate Mapping",
    x = "Treatment Group", 
    y = "Overall Time (months)",
    caption = "Point colors represent patient age"
  )

print(covariate_plot)

Color Palettes

The module includes 6 professional color palettes:

# Clinical palette (default)
advancedraincloud(data = histopathology, y_var = "Age", x_var = "Grade_Level", 
                 color_palette = "clinical")

# Viridis palette
advancedraincloud(data = histopathology, y_var = "Age", x_var = "Grade_Level",
                 color_palette = "viridis")

# Pastel palette
advancedraincloud(data = histopathology, y_var = "Age", x_var = "Grade_Level",
                 color_palette = "pastel")

R Code Examples for Color Palettes

# Create palette comparison
grade_data <- histopathology %>% filter(!is.na(Grade_Level))

# Clinical palette
clinical_colors <- c("#2E86AB", "#A23B72", "#F18F01")
clinical_plot <- ggplot(grade_data, aes(x = Grade_Level, y = Age, fill = Grade_Level)) +
  geom_rain() +
  scale_fill_manual(values = clinical_colors) +
  theme_minimal() +
  labs(title = "Clinical Palette") +
  theme(legend.position = "none")

# Viridis palette
viridis_plot <- ggplot(grade_data, aes(x = Grade_Level, y = Age, fill = Grade_Level)) +
  geom_rain() +
  scale_fill_viridis_d() +
  theme_minimal() +
  labs(title = "Viridis Palette") +
  theme(legend.position = "none")

# Pastel palette  
pastel_colors <- c("#FFB3BA", "#BAFFC9", "#BAE1FF")
pastel_plot <- ggplot(grade_data, aes(x = Grade_Level, y = Age, fill = Grade_Level)) +
  geom_rain() +
  scale_fill_manual(values = pastel_colors) +
  theme_minimal() +
  labs(title = "Pastel Palette") +
  theme(legend.position = "none")

# Display palette comparison
grid.arrange(clinical_plot, viridis_plot, pastel_plot, ncol = 3)

Statistical Integration

Advanced raincloud plots include comprehensive statistical analysis:

# With summary statistics and group comparisons
advancedraincloud(
  data = histopathology,
  y_var = "Age", 
  x_var = "Group",
  show_statistics = TRUE,      # Summary statistics table
  show_comparisons = TRUE,     # Statistical tests
  show_interpretation = TRUE   # Feature guide
)

Statistical Test Details

The module automatically selects appropriate tests:

2 Groups: Wilcoxon rank-sum test (robust, non-parametric)
>2 Groups: Kruskal-Wallis test (robust, non-parametric)

# Demonstrate statistical testing
group_data <- histopathology %>% filter(Group %in% c("Control", "Treatment"))

# Wilcoxon test for two groups
wilcox_result <- wilcox.test(Age ~ Group, data = group_data)

cat("Statistical Test Results:\n")
cat("Wilcoxon rank-sum test\n")
cat("W =", wilcox_result$statistic, "\n")
cat("p-value =", format.pval(wilcox_result$p.value, digits = 3), "\n")

# Summary statistics by group
summary_stats <- group_data %>%
  group_by(Group) %>%
  summarise(
    n = n(),
    mean = round(mean(Age, na.rm = TRUE), 1),
    median = round(median(Age, na.rm = TRUE), 1),
    sd = round(sd(Age, na.rm = TRUE), 1),
    .groups = 'drop'
  )

print(summary_stats)

Customization Options

Advanced raincloud plots offer extensive customization:

# Highly customized advanced raincloud
advancedraincloud(
  data = histopathology,
  y_var = "OverallTime",
  x_var = "Group", 
  fill_var = "Sex",
  point_size = 2.0,          # Larger points
  point_alpha = 0.8,         # Point transparency
  violin_alpha = 0.6,        # Violin transparency
  boxplot_width = 0.15,      # Boxplot width
  plot_title = "Customized Advanced Raincloud Plot",
  x_label = "Treatment Group",
  y_label = "Overall Survival Time (months)"
)

R Code Example for Customization

# Load required libraries
library(ggrain)
library(ggplot2)

# Filter out any missing values to prevent errors
histopathology_clean <- histopathology %>%
  filter(!is.na(Group) & !is.na(OverallTime) & !is.na(Sex))

# Highly customized plot
custom_plot <- ggplot(histopathology_clean, aes(x = Group, y = OverallTime, fill = Sex)) +
  geom_rain(
    rain.side = "l",  # Changed from side to side
    point.args = list(size = 2.0, alpha = 0.8),      # Custom points
    violin.args = list(alpha = 0.6),                  # Custom violin
    boxplot.args = list(width = 0.15)                 # Custom boxplot
  ) +
  scale_fill_manual(values = c("#e74c3c", "#3498db")) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
    axis.title = element_text(size = 12),
    legend.position = "bottom"
  ) +
  labs(
    title = "Customized Advanced Raincloud Plot",
    x = "Treatment Group",
    y = "Overall Survival Time (months)",
    fill = "Sex"
  )

print(custom_plot)

Clinical Research Applications

1. Before/After Treatment Analysis

# Simulate before/after treatment data
treatment_data <- data.frame(
  Patient_ID = rep(1:30, 2),
  Time = rep(c("Before", "After"), each = 30),
  Biomarker = c(
    rnorm(30, 100, 15),                    # Before treatment
    rnorm(30, 85, 12)                      # After treatment (improvement)
  ),
  Treatment = rep(c("Drug A", "Drug B"), each = 15, times = 2)
)

# Advanced raincloud with connections
before_after_plot <- ggplot(treatment_data, aes(x = Time, y = Biomarker, fill = Treatment)) +
  geom_rain(
    id.long.var = "Patient_ID",  # Connect before/after for same patients
    rain.side = "f"              # Flanking for comparison
  ) +
  scale_fill_manual(values = c("#2E86AB", "#A23B72")) +
  theme_minimal() +
  labs(
    title = "Before/After Treatment Analysis with Patient Connections",
    x = "Time Point",
    y = "Biomarker Level",
    fill = "Treatment",
    caption = "Lines connect measurements from the same patients"
  )

print(before_after_plot)

2. Multi-Group Clinical Trial

# Simulate clinical trial data
trial_data <- histopathology %>%
  mutate(
    Treatment_Arm = case_when(
      Group == "Control" ~ "Placebo",
      Group == "Treatment" ~ sample(c("Low Dose", "High Dose"), n(), replace = TRUE)
    ),
    Response_Score = case_when(
      Treatment_Arm == "Placebo" ~ rnorm(n(), 3, 1),
      Treatment_Arm == "Low Dose" ~ rnorm(n(), 5, 1.2),
      Treatment_Arm == "High Dose" ~ rnorm(n(), 7, 1.5)
    )
  ) %>%
  filter(!is.na(Treatment_Arm))

# Multi-group comparison
trial_plot <- ggplot(trial_data, aes(x = Treatment_Arm, y = Response_Score, fill = Treatment_Arm)) +
  geom_rain(rain.side = "l") +
  scale_fill_manual(values = c("#95a5a6", "#3498db", "#e74c3c")) +
  theme_minimal() +
  labs(
    title = "Clinical Trial: Treatment Response by Dose",
    x = "Treatment Arm",
    y = "Response Score",
    caption = "Advanced raincloud plot showing distribution differences"
  ) +
  theme(legend.position = "none")

print(trial_plot)

Best Practices

1. Choosing the Right Approach

Standard Raincloud: Simple group comparisons, basic distribution analysis
Advanced Raincloud: Repeated measures, longitudinal data, complex designs

2. Data Preparation

# Always check your data structure
cat("Data preparation checklist:\n")
#> Data preparation checklist:
cat("✓ Y-variable: continuous/numeric\n")
#> ✓ Y-variable: continuous/numeric
cat("✓ X-variable: categorical/factor\n")
#> ✓ X-variable: categorical/factor
cat("✓ ID variable: unique identifier for each subject\n")
#> ✓ ID variable: unique identifier for each subject
cat("✓ No excessive missing values\n")
#> ✓ No excessive missing values

# Example data check
data_check <- histopathology %>%
  summarise(
    n_total = n(),
    n_complete = sum(complete.cases(Group, Age)),
    missing_pct = round((n_total - n_complete) / n_total * 100, 1)
  )

cat("\nData completeness:", data_check$n_complete, "of", data_check$n_total, 
    "cases (", data_check$missing_pct, "% missing)\n")
#> 
#> Data completeness: 248 of 250 cases ( 0.8 % missing)

3. Interpretation Guidelines

cat("Advanced Raincloud Plot Interpretation:\n")
#> Advanced Raincloud Plot Interpretation:
cat("📊 Violin: Shows probability density distribution\n")
#> 📊 Violin: Shows probability density distribution
cat("📦 Box: Median, quartiles, and outliers\n") 
#> 📦 Box: Median, quartiles, and outliers
cat("🔵 Points: Individual observations\n")
#> 🔵 Points: Individual observations
cat("📈 Connections: Longitudinal relationships (when enabled)\n")
#> 📈 Connections: Longitudinal relationships (when enabled)
cat("🎨 Colors: Group or covariate distinctions\n")
#> 🎨 Colors: Group or covariate distinctions

Package Dependencies

The advanced raincloud module requires:

# Core dependencies
library(ggrain)      # Enhanced raincloud plots
library(ggplot2)     # Graphics
library(dplyr)       # Data manipulation
library(jmvcore)     # jamovi framework

# Optional enhancements
library(viridis)     # Color palettes
library(RColorBrewer) # Additional palettes

Conclusion

Advanced raincloud plots represent a significant enhancement over standard raincloud visualization, providing:

Key Benefits

Longitudinal Connections: Track individual subjects across time/conditions
Likert Scale Support: Specialized handling for ordinal survey data
Flexible Positioning: Optimal layout for different research designs
Covariate Integration: Multi-dimensional data visualization
Statistical Integration: Robust non-parametric testing
Clinical Focus: Designed for medical and clinical research

When to Use

Repeated Measures Studies: Before/after, pre/post designs
Survey Research: Likert scales, ordinal responses
Clinical Trials: Treatment comparisons with individual tracking
Longitudinal Analysis: Multi-timepoint studies
Complex Visualizations: Multi-group, multi-variable analyses

Next Steps

Explore the interactive features in jamovi
Experiment with different positioning options
Try longitudinal connections with your repeated measures data
Use covariate mapping for multi-dimensional analysis

For additional statistical plotting modules in ClinicoPath: - raincloud for standard raincloud plots - jjscatterstats for correlation visualization - jjbarstats for categorical data analysis - jjhistostats for distribution analysis

Advanced Raincloud Plots - Enhanced Distribution Visualization

ClinicoPath Team

2025-07-29