Professional Waffle Charts with jwaffle

Introduction

The jwaffle function provides professional waffle chart visualizations for proportional data display. Waffle charts (also known as square pie charts) use a grid of squares to represent data proportions, making them particularly effective for displaying percentages, survey responses, and categorical distributions.

What are Waffle Charts?

Waffle charts are a powerful alternative to traditional pie charts that offer several advantages:

Easy to read: Grid-based layout is more intuitive than pie slices
Precise comparison: Individual squares make exact proportions clear
Flexible scaling: Can represent different scales (each square = 1%, 10 cases, etc.)
Professional appearance: Clean, modern aesthetic suitable for presentations
Accessible design: Color patterns are easier to distinguish than pie segments

Key Features of jwaffle

Flexible Data Input: Works with counts, frequencies, or case-level data
Advanced Customization: Multiple color palettes, themes, and styling options
Faceting Support: Create multi-panel charts for complex data comparison
Performance Optimized: Intelligent caching for large datasets and repeated analyses
Professional Styling: Publication-ready output with customizable themes

Installation and Setup

# Load required libraries
library(ClinicoPath)

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library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)

# Set options for better output
options(digits = 3)
knitr::opts_chunk$set(
  fig.width = 12,
  fig.height = 8,
    dpi = 300,
  echo = TRUE,
  eval = FALSE,
  out.width = "100%"
)

# Check if required packages are available
if (!requireNamespace("waffle", quietly = TRUE)) {
  message("Note: waffle package required for waffle chart creation")
}

if (!requireNamespace("scales", quietly = TRUE)) {
  message("Note: scales package recommended for enhanced formatting")
}

Data Preparation

Let’s create diverse datasets to demonstrate different waffle chart applications:

Market Research Data

# Create market research dataset
set.seed(123)
market_data <- data.frame(
  # Core product preferences
  preferred_brand = factor(sample(c("Brand_A", "Brand_B", "Brand_C", "Brand_D", "Brand_E"), 
                                 500, replace = TRUE, prob = c(0.35, 0.25, 0.20, 0.15, 0.05))),
  
  purchase_frequency = sample(1:12, 500, replace = TRUE, 
                             prob = c(0.3, 0.2, 0.15, 0.1, 0.08, 0.07, rep(0.02, 6))),
  
  # Demographic segmentation
  age_group = factor(sample(c("18-25", "26-35", "36-45", "46-55", "55+"), 
                           500, replace = TRUE, prob = c(0.15, 0.25, 0.25, 0.20, 0.15))),
  
  region = factor(sample(c("North", "South", "East", "West"), 
                        500, replace = TRUE)),
  
  income_level = factor(sample(c("Low", "Middle", "High"), 
                              500, replace = TRUE, prob = c(0.25, 0.50, 0.25))),
  
  # Customer behavior
  loyalty_program = factor(sample(c("Member", "Non_Member"), 
                                 500, replace = TRUE, prob = c(0.4, 0.6))),
  
  purchase_channel = factor(sample(c("Online", "In_Store", "Mobile", "Phone"), 
                                  500, replace = TRUE, prob = c(0.45, 0.35, 0.15, 0.05)))
)

# Display structure
str(market_data)
head(market_data)

Survey Response Data

# Create employee satisfaction survey data
survey_data <- data.frame(
  # Satisfaction ratings (Likert scale)
  job_satisfaction = factor(sample(c("Very_Satisfied", "Satisfied", "Neutral", "Dissatisfied", "Very_Dissatisfied"), 
                                  400, replace = TRUE, prob = c(0.20, 0.35, 0.25, 0.15, 0.05)),
                           levels = c("Very_Dissatisfied", "Dissatisfied", "Neutral", "Satisfied", "Very_Satisfied")),
  
  work_life_balance = factor(sample(c("Excellent", "Good", "Fair", "Poor"), 
                                   400, replace = TRUE, prob = c(0.15, 0.45, 0.30, 0.10)),
                            levels = c("Poor", "Fair", "Good", "Excellent")),
  
  # Employee demographics
  department = factor(sample(c("Engineering", "Sales", "Marketing", "HR", "Finance", "Operations"), 
                            400, replace = TRUE, prob = c(0.25, 0.20, 0.15, 0.10, 0.15, 0.15))),
  
  tenure_years = factor(sample(c("Less_than_1", "1-2", "3-5", "5-10", "More_than_10"), 
                              400, replace = TRUE, prob = c(0.15, 0.25, 0.30, 0.20, 0.10))),
  
  employment_type = factor(sample(c("Full_Time", "Part_Time", "Contract"), 
                                 400, replace = TRUE, prob = c(0.80, 0.15, 0.05))),
  
  remote_work = factor(sample(c("Fully_Remote", "Hybrid", "On_Site"), 
                             400, replace = TRUE, prob = c(0.30, 0.45, 0.25)))
)

str(survey_data)

Healthcare Data

# Create healthcare outcome data
healthcare_data <- data.frame(
  # Treatment outcomes
  treatment_response = factor(sample(c("Complete_Response", "Partial_Response", "Stable_Disease", "Progressive_Disease"), 
                                    300, replace = TRUE, prob = c(0.25, 0.35, 0.25, 0.15))),
  
  # Patient characteristics
  age_category = factor(sample(c("Under_40", "40-60", "60-75", "Over_75"), 
                              300, replace = TRUE, prob = c(0.15, 0.35, 0.35, 0.15))),
  
  disease_stage = factor(sample(c("Early", "Intermediate", "Advanced"), 
                               300, replace = TRUE, prob = c(0.30, 0.45, 0.25))),
  
  treatment_type = factor(sample(c("Surgery", "Chemotherapy", "Radiation", "Immunotherapy", "Combination"), 
                                300, replace = TRUE, prob = c(0.20, 0.25, 0.20, 0.15, 0.20))),
  
  hospital_type = factor(sample(c("Academic", "Community", "Cancer_Center"), 
                               300, replace = TRUE, prob = c(0.35, 0.40, 0.25))),
  
  insurance_type = factor(sample(c("Private", "Medicare", "Medicaid"), 
                                300, replace = TRUE, prob = c(0.60, 0.30, 0.10)))
)

str(healthcare_data)

Basic Usage

Simple Waffle Chart

The most basic waffle chart shows the distribution of a categorical variable:

# Basic waffle chart
basic_chart <- jwaffle(
  data = market_data,
  groups = "preferred_brand"
)

# Display the result
print("Basic waffle chart created successfully")

This creates a 10x10 grid (100 squares) where each square represents approximately 1% of the data.

Understanding Waffle Chart Components

A waffle chart consists of:

Squares: Individual units that make up the grid
Colors: Different colors represent different categories
Legend: Explains what each color represents
Caption: Shows what each square represents (e.g., “Each square represents 5 cases”)

Customizing Rows and Layout

# Different grid sizes
waffle_5x20 <- jwaffle(
  data = market_data,
  groups = "preferred_brand",
  rows = 5  # Creates 5x20 grid (still 100 squares)
)

waffle_8x12 <- jwaffle(
  data = market_data,
  groups = "preferred_brand", 
  rows = 8  # Creates 8x12 grid (approximately 100 squares)
)

# Flipped orientation
waffle_flipped <- jwaffle(
  data = market_data,
  groups = "preferred_brand",
  rows = 10,
  flip = TRUE
)

print("Custom layout waffle charts created")

Using Count Variables

When you have aggregated data with counts:

# Aggregate data first
brand_summary <- market_data %>%
  group_by(preferred_brand) %>%
  summarise(customer_count = n(), .groups = "drop")

print(brand_summary)

# Create waffle chart with counts
count_waffle <- jwaffle(
  data = brand_summary,
  groups = "preferred_brand",
  counts = "customer_count"
)

print("Count-based waffle chart created")

Advanced Features

Faceting for Multi-Panel Charts

Create separate waffle charts for different subgroups:

# Faceted waffle chart
faceted_chart <- jwaffle(
  data = survey_data,
  groups = "job_satisfaction",
  facet = "department"
)

print("Faceted waffle chart created")

# Faceting with geographic data
regional_chart <- jwaffle(
  data = market_data,
  groups = "preferred_brand",
  facet = "region"
)

print("Regional comparison waffle chart created")

Faceting is particularly useful for: - Comparing patterns across different groups - Showing changes over time periods - Analyzing subpopulations separately

Color Palettes for Professional Presentation

# Default palette
default_palette <- jwaffle(
  data = healthcare_data,
  groups = "treatment_response",
  color_palette = "default"
)

# Colorblind-friendly palette
colorblind_palette <- jwaffle(
  data = healthcare_data,
  groups = "treatment_response",
  color_palette = "colorblind"
)

# Professional business palette
professional_palette <- jwaffle(
  data = healthcare_data,
  groups = "treatment_response",
  color_palette = "professional"
)

# Presentation palette (high contrast)
presentation_palette <- jwaffle(
  data = healthcare_data,
  groups = "treatment_response",
  color_palette = "presentation"
)

# Journal publication palette
journal_palette <- jwaffle(
  data = healthcare_data,
  groups = "treatment_response",
  color_palette = "journal"
)

# Pastel colors
pastel_palette <- jwaffle(
  data = healthcare_data,
  groups = "treatment_response",
  color_palette = "pastel"
)

# Dark theme colors
dark_palette <- jwaffle(
  data = healthcare_data,
  groups = "treatment_response",
  color_palette = "dark"
)

print("Multiple color palette waffle charts created")

Color Palette Guidelines

Default: General purpose, good balance of visibility and aesthetics
Colorblind: Optimized for accessibility, distinguishable by all users
Professional: Business-appropriate colors for corporate presentations
Presentation: High contrast colors for projector displays
Journal: Grayscale-friendly for academic publications
Pastel: Soft colors for informal or creative presentations
Dark: Bold colors that work well on dark backgrounds

Titles and Labels

# Comprehensive labeling
labeled_chart <- jwaffle(
  data = survey_data,
  groups = "work_life_balance",
  facet = "employment_type",
  mytitle = "Work-Life Balance by Employment Type",
  legendtitle = "Balance Rating",
  color_palette = "professional",
  show_legend = TRUE
)

print("Labeled waffle chart created")

# Minimal labeling (no legend)
minimal_chart <- jwaffle(
  data = market_data,
  groups = "purchase_channel",
  mytitle = "Customer Purchase Channel Preferences", 
  show_legend = FALSE,
  color_palette = "presentation"
)

print("Minimal waffle chart created")

Real-World Applications

Market Research Analysis

Brand Preference Study

# Overall brand preference
brand_preference <- jwaffle(
  data = market_data,
  groups = "preferred_brand",
  mytitle = "Overall Brand Preference Distribution",
  legendtitle = "Brand",
  color_palette = "professional",
  show_legend = TRUE
)

# Brand preference by age group
age_brand_analysis <- jwaffle(
  data = market_data,
  groups = "preferred_brand",
  facet = "age_group",
  mytitle = "Brand Preference by Age Group",
  legendtitle = "Brand",
  color_palette = "presentation",
  rows = 8
)

# Purchase channel analysis
channel_analysis <- jwaffle(
  data = market_data,
  groups = "purchase_channel",
  facet = "income_level",
  mytitle = "Purchase Channel Preference by Income Level",
  legendtitle = "Channel",
  color_palette = "colorblind"
)

print("Market research waffle charts created")

Customer Segmentation

# Loyalty program membership
loyalty_chart <- jwaffle(
  data = market_data,
  groups = "loyalty_program",
  facet = "region",
  mytitle = "Loyalty Program Membership by Region",
  legendtitle = "Membership",
  color_palette = "professional",
  rows = 6
)

print("Customer segmentation chart created")

Employee Satisfaction Survey

Job Satisfaction Analysis

# Overall job satisfaction
overall_satisfaction <- jwaffle(
  data = survey_data,
  groups = "job_satisfaction",
  mytitle = "Employee Job Satisfaction Distribution",
  legendtitle = "Satisfaction Level",
  color_palette = "journal",
  rows = 10
)

# Satisfaction by department
dept_satisfaction <- jwaffle(
  data = survey_data,
  groups = "job_satisfaction",
  facet = "department",
  mytitle = "Job Satisfaction by Department",
  legendtitle = "Satisfaction",
  color_palette = "professional"
)

# Work-life balance analysis
balance_analysis <- jwaffle(
  data = survey_data,
  groups = "work_life_balance",
  facet = "remote_work",
  mytitle = "Work-Life Balance by Work Arrangement",
  legendtitle = "Balance Rating",
  color_palette = "colorblind",
  rows = 8
)

print("Employee satisfaction charts created")

Tenure and Employment Analysis

# Employment type distribution
employment_dist <- jwaffle(
  data = survey_data,
  groups = "employment_type",
  mytitle = "Employment Type Distribution",
  legendtitle = "Employment Type",
  color_palette = "presentation",
  rows = 6
)

# Tenure analysis
tenure_analysis <- jwaffle(
  data = survey_data,
  groups = "tenure_years",
  facet = "department",
  mytitle = "Employee Tenure by Department",
  legendtitle = "Tenure",
  color_palette = "pastel"
)

print("Tenure analysis charts created")

Healthcare Outcomes Research

Treatment Response Analysis

# Overall treatment response
treatment_overall <- jwaffle(
  data = healthcare_data,
  groups = "treatment_response",
  mytitle = "Treatment Response Distribution",
  legendtitle = "Response Type",
  color_palette = "professional",
  rows = 10
)

# Response by treatment type
response_by_treatment <- jwaffle(
  data = healthcare_data,
  groups = "treatment_response",
  facet = "treatment_type",
  mytitle = "Treatment Response by Treatment Type", 
  legendtitle = "Response",
  color_palette = "journal"
)

# Response by disease stage
response_by_stage <- jwaffle(
  data = healthcare_data,
  groups = "treatment_response",
  facet = "disease_stage",
  mytitle = "Treatment Response by Disease Stage",
  legendtitle = "Response",
  color_palette = "colorblind",
  rows = 8
)

print("Healthcare outcome charts created")

Healthcare System Analysis

# Hospital type distribution
hospital_dist <- jwaffle(
  data = healthcare_data,
  groups = "hospital_type",
  mytitle = "Patient Distribution by Hospital Type",
  legendtitle = "Hospital Type",
  color_palette = "presentation"
)

# Insurance analysis
insurance_analysis <- jwaffle(
  data = healthcare_data,
  groups = "insurance_type",
  facet = "age_category",
  mytitle = "Insurance Type by Age Category",
  legendtitle = "Insurance",
  color_palette = "professional",
  rows = 6
)

print("Healthcare system charts created")

Advanced Customization

Complex Multi-Variable Analysis

# Create complex segmented data
complex_data <- market_data %>%
  mutate(
    customer_segment = case_when(
      loyalty_program == "Member" & income_level == "High" ~ "Premium_Customer",
      loyalty_program == "Member" & income_level == "Middle" ~ "Standard_Member", 
      loyalty_program == "Member" & income_level == "Low" ~ "Budget_Member",
      loyalty_program == "Non_Member" & income_level == "High" ~ "High_Value_Prospect",
      TRUE ~ "Standard_Prospect"
    )
  )

# Complex segmentation analysis
segment_analysis <- jwaffle(
  data = complex_data,
  groups = "customer_segment",
  facet = "purchase_channel",
  mytitle = "Customer Segmentation by Purchase Channel",
  legendtitle = "Customer Segment",
  color_palette = "professional",
  rows = 8
)

print("Complex segmentation analysis created")

Weighted Analysis with Purchase Frequency

# Use purchase frequency as weights
weighted_analysis <- jwaffle(
  data = market_data,
  groups = "preferred_brand",
  counts = "purchase_frequency",
  facet = "region",
  mytitle = "Brand Preference Weighted by Purchase Frequency",
  legendtitle = "Brand",
  color_palette = "presentation"
)

print("Weighted analysis chart created")

Time-Based Analysis

# Create quarterly data
quarterly_data <- data.frame(
  quarter = rep(c("Q1", "Q2", "Q3", "Q4"), each = 100),
  satisfaction = factor(sample(c("Satisfied", "Neutral", "Dissatisfied"), 
                              400, replace = TRUE, prob = c(0.6, 0.25, 0.15))),
  product_line = factor(sample(c("Product_A", "Product_B", "Product_C"), 
                              400, replace = TRUE))
)

# Quarterly satisfaction trends
quarterly_satisfaction <- jwaffle(
  data = quarterly_data,
  groups = "satisfaction",
  facet = "quarter",
  mytitle = "Customer Satisfaction Trends by Quarter",
  legendtitle = "Satisfaction",
  color_palette = "journal",
  rows = 10
)

print("Quarterly trend analysis created")

Performance and Optimization

Large Dataset Handling

# Create large dataset for performance testing
large_data <- do.call(rbind, replicate(5, market_data, simplify = FALSE))
large_data$customer_id <- 1:nrow(large_data)

# Performance timing
system.time({
  large_chart1 <- jwaffle(
    data = large_data,
    groups = "preferred_brand",
    color_palette = "professional"
  )
})

# Second run should be faster due to caching
system.time({
  large_chart2 <- jwaffle(
    data = large_data,
    groups = "preferred_brand", 
    color_palette = "professional"
  )
})

print("Large dataset analysis completed")

Caching Performance Benefits

The jwaffle function includes intelligent caching that provides several performance benefits:

Data Caching: Prepared data is cached to avoid reprocessing
Plot Caching: Generated plots are cached for identical parameters
Palette Caching: Color palettes are cached based on options and group count
Hash-Based Invalidation: Cache is automatically invalidated when data or options change

# Demonstrate caching with repeated calls
for (i in 1:3) {
  start_time <- Sys.time()
  
  result <- jwaffle(
    data = survey_data,
    groups = "job_satisfaction",
    color_palette = "professional"
  )
  
  end_time <- Sys.time()
  cat("Run", i, "completed in", round(as.numeric(end_time - start_time), 3), "seconds\n")
}

Best Practices

When to Use Waffle Charts

Waffle charts are ideal for:

Proportional data: When showing percentages or parts of a whole
Survey responses: Likert scales and categorical responses
Market share: Brand preference and competitive analysis
Demographics: Population breakdowns and characteristics
Binary outcomes: Yes/no, success/failure, member/non-member

# Create best practices guide
best_practices <- data.frame(
  Scenario = c(
    "Survey Results",
    "Market Share",
    "Demographics", 
    "Binary Outcomes",
    "Progress Tracking",
    "Budget Allocation"
  ),
  Waffle_Advantage = c(
    "Easy to see exact proportions",
    "Clear competitive positioning",
    "Intuitive population representation",
    "Simple visual comparison",
    "Progress visualization", 
    "Resource distribution clarity"
  ),
  Key_Settings = c(
    "Use colorblind palette, show legend",
    "Professional palette, custom title",
    "Facet by subgroups, clean layout",
    "High contrast colors, minimal legend",
    "Sequential colors, progress labels",
    "Professional theme, clear categories"
  )
)

knitr::kable(best_practices, caption = "Waffle Chart Best Practices by Use Case")

Design Guidelines

Color Selection

# Create color guideline examples
color_guide <- data.frame(
  Palette = c("Default", "Colorblind", "Professional", "Presentation", "Journal", "Pastel", "Dark"),
  Best_For = c(
    "General purpose presentations",
    "Accessible design, inclusive audiences", 
    "Business meetings, corporate reports",
    "Projector displays, conference presentations",
    "Academic papers, grayscale printing",
    "Creative projects, informal presentations",
    "Dark themes, modern designs"
  ),
  Avoid_When = c(
    "Accessibility is critical",
    "Creative expression is primary goal",
    "Informal or creative contexts",
    "Fine detail distinction needed",
    "Color emphasis is important",
    "Professional formality required",
    "Light backgrounds are used"
  )
)

knitr::kable(color_guide, caption = "Color Palette Selection Guide")

Layout Recommendations

# Layout guidelines
layout_guide <- data.frame(
  Grid_Size = c("5 rows", "8 rows", "10 rows", "12+ rows"),
  Best_For = c(
    "Simple data (2-4 categories)",
    "Moderate complexity (4-6 categories)",
    "Standard presentations (any categories)",
    "Detailed analysis (many categories)"
  ),
  Considerations = c(
    "Large squares, easy to count",
    "Good balance of detail and clarity",
    "Standard 10x10 grid, familiar format",
    "Smaller squares, harder to count individually"
  )
)

knitr::kable(layout_guide, caption = "Grid Size Selection Guide")

Accessibility Considerations

# Accessible waffle chart example
accessible_chart <- jwaffle(
  data = survey_data,
  groups = "job_satisfaction",
  color_palette = "colorblind",  # Colorblind-friendly
  mytitle = "Employee Job Satisfaction Survey Results (N=400)",  # Descriptive title
  legendtitle = "Satisfaction Level",  # Clear legend
  show_legend = TRUE,  # Always show legend for accessibility
  rows = 10  # Standard grid for easy counting
)

print("Accessible waffle chart created")

Accessibility best practices: - Use colorblind-friendly palettes - Include descriptive titles and legends - Provide sample size information - Use standard grid sizes when possible - Consider alternative text descriptions for digital formats

Integration with Research Workflows

Statistical Analysis Integration

# Combine waffle charts with statistical tests
library(dplyr)

# Chi-square test for independence
if (requireNamespace("stats", quietly = TRUE)) {
  # Test brand preference vs age group
  contingency_table <- table(market_data$preferred_brand, market_data$age_group)
  chi_test <- chisq.test(contingency_table)
  
  cat("Chi-square test results:\n")
  cat("Chi-square =", round(chi_test$statistic, 3), "\n")
  cat("p-value =", round(chi_test$p.value, 4), "\n")
  cat("Significant association:", ifelse(chi_test$p.value < 0.05, "Yes", "No"), "\n")
}

# Combine with waffle visualization
statistical_chart <- jwaffle(
  data = market_data,
  groups = "preferred_brand",
  facet = "age_group",
  mytitle = paste("Brand Preference by Age Group",
                  ifelse(chi_test$p.value < 0.05, "(p < 0.05)", "(n.s.)")),
  legendtitle = "Brand",
  color_palette = "journal"
)

print("Statistical analysis with waffle chart created")

Report Generation

# Function to generate waffle chart reports
generate_waffle_report <- function(data, group_var, facet_var = NULL, title_prefix = "") {
  
  # Basic summary statistics
  summary_stats <- data %>%
    group_by(.data[[group_var]]) %>%
    summarise(
      count = n(),
      percentage = round(n() / nrow(data) * 100, 1),
      .groups = "drop"
    )
  
  # Create waffle chart
  chart <- jwaffle(
    data = data,
    groups = group_var,
    facet = facet_var,
    mytitle = paste(title_prefix, "Distribution of", gsub("_", " ", group_var)),
    legendtitle = gsub("_", " ", group_var),
    color_palette = "professional"
  )
  
  # Return summary and chart
  return(list(
    summary = summary_stats,
    chart = chart,
    total_n = nrow(data)
  ))
}

# Generate reports
brand_report <- generate_waffle_report(
  data = market_data,
  group_var = "preferred_brand",
  title_prefix = "Market Research:"
)

satisfaction_report <- generate_waffle_report(
  data = survey_data,
  group_var = "job_satisfaction",
  facet_var = "department",
  title_prefix = "Employee Survey:"
)

# Display summaries
cat("Brand Preference Summary:\n")
print(brand_report$summary)
cat("\nTotal respondents:", brand_report$total_n, "\n")

cat("\nJob Satisfaction Summary:\n")
print(satisfaction_report$summary)
cat("\nTotal employees:", satisfaction_report$total_n, "\n")

Troubleshooting

Common Issues and Solutions

Data Validation

# Function to validate waffle chart data
validate_waffle_data <- function(data, groups, counts = NULL, facet = NULL) {
  issues <- c()
  
  # Check if data is not empty
  if (nrow(data) == 0) {
    issues <- c(issues, "Data frame is empty")
  }
  
  # Check if group variable exists
  if (!groups %in% names(data)) {
    issues <- c(issues, paste("Group variable", groups, "not found in data"))
  }
  
  # Check if count variable exists (if specified)
  if (!is.null(counts) && !counts %in% names(data)) {
    issues <- c(issues, paste("Count variable", counts, "not found in data"))
  }
  
  # Check if facet variable exists (if specified)
  if (!is.null(facet) && !facet %in% names(data)) {
    issues <- c(issues, paste("Facet variable", facet, "not found in data"))
  }
  
  # Check for sufficient data in groups
  if (groups %in% names(data)) {
    group_counts <- table(data[[groups]])
    if (any(group_counts == 0)) {
      issues <- c(issues, "Some groups have zero observations")
    }
  }
  
  # Check for missing values
  if (groups %in% names(data)) {
    missing_groups <- sum(is.na(data[[groups]]))
    if (missing_groups > 0) {
      issues <- c(issues, paste(missing_groups, "missing values in group variable"))
    }
  }
  
  return(issues)
}

# Test validation
validation_result <- validate_waffle_data(
  data = market_data,
  groups = "preferred_brand",
  facet = "region"
)

if (length(validation_result) == 0) {
  cat("✓ Data validation passed\n")
} else {
  cat("⚠ Data validation issues:\n")
  for (issue in validation_result) {
    cat("-", issue, "\n")
  }
}

Handling Missing Data

# Create data with missing values
missing_data <- market_data
missing_data$preferred_brand[sample(1:500, 25)] <- NA
missing_data$region[sample(1:500, 15)] <- NA

# Check missing data impact
cat("Original data:", nrow(market_data), "rows\n")
cat("Data with missing values:", nrow(missing_data), "rows\n")
cat("Complete cases:", sum(complete.cases(missing_data)), "rows\n")

# Create waffle chart (automatically handles missing data)
missing_chart <- jwaffle(
  data = missing_data,
  groups = "preferred_brand",
  facet = "region",
  mytitle = "Brand Preference with Missing Data Handling",
  legendtitle = "Brand",
  color_palette = "colorblind"
)

print("Missing data chart created (missing values automatically excluded)")

Performance Optimization Tips

# Performance optimization guidelines
perf_tips <- data.frame(
  Scenario = c(
    "Large datasets (>1000 rows)",
    "Many categories (>8 groups)",
    "Frequent updates",
    "Multiple facets",
    "Presentation mode"
  ),
  Recommendation = c(
    "Use caching, avoid unnecessary recalculation",
    "Consider grouping rare categories",
    "Minimize data changes between calls",
    "Limit number of facet levels",
    "Use high-contrast palettes"
  ),
  Implementation = c(
    "Keep same data object, change options only",
    "Create 'Other' category for small groups",
    "Batch process multiple charts",
    "Filter data to key comparisons",
    "Use 'presentation' color palette"
  )
)

knitr::kable(perf_tips, caption = "Performance Optimization Guide")

Summary

The jwaffle function provides a comprehensive solution for creating professional waffle charts. Key advantages include:

Core Visualization Capabilities

Intuitive Design: Grid-based layout that’s easier to interpret than pie charts
Flexible Input: Works with raw data, counts, or aggregated summaries
Professional Output: Publication and presentation-ready visualizations
Accessibility: Colorblind-friendly palettes and clear visual hierarchy

Advanced Features

Faceting Support: Multi-panel comparisons across subgroups
Custom Styling: Multiple color palettes and theme options
Performance Optimization: Intelligent caching for large datasets
Error Handling: Graceful handling of edge cases and missing data

Real-World Applications

Market Research: Brand preference, customer segmentation, purchase behavior
Survey Analysis: Employee satisfaction, customer feedback, opinion polls
Healthcare Research: Treatment outcomes, patient demographics, quality metrics
Business Intelligence: Resource allocation, performance metrics, KPI tracking

Integration Benefits

Statistical Workflow: Seamless integration with statistical testing
Report Generation: Automated chart creation for regular reporting
Data Validation: Built-in checks for data quality and completeness
Accessibility: Support for inclusive design and universal access

Next Steps

Explore different applications for your specific data visualization needs
Experiment with color palettes and styling for your organization’s brand
Try the performance optimization features with your datasets
Consider jwaffle for standardizing proportional data visualization

sessionInfo()

Proportional Data Visualization Using Grid-Based Charts

ClinicoPath

last-modified

Introduction

What are Waffle Charts?

Key Features of jwaffle

Installation and Setup

Data Preparation

Market Research Data

Survey Response Data

Healthcare Data

Basic Usage

Simple Waffle Chart

Understanding Waffle Chart Components

Customizing Rows and Layout

Using Count Variables

Advanced Features

Faceting for Multi-Panel Charts

Color Palettes for Professional Presentation

Color Palette Guidelines

Titles and Labels

Real-World Applications

Market Research Analysis

Brand Preference Study

Customer Segmentation

Employee Satisfaction Survey

Job Satisfaction Analysis

Tenure and Employment Analysis

Healthcare Outcomes Research

Treatment Response Analysis

Healthcare System Analysis

Advanced Customization

Complex Multi-Variable Analysis

Weighted Analysis with Purchase Frequency

Time-Based Analysis

Performance and Optimization

Large Dataset Handling

Caching Performance Benefits

Best Practices

When to Use Waffle Charts

Design Guidelines

Color Selection

Layout Recommendations

Accessibility Considerations

Integration with Research Workflows

Statistical Analysis Integration

Report Generation

Troubleshooting

Common Issues and Solutions

Data Validation

Handling Missing Data

Performance Optimization Tips

Summary

Core Visualization Capabilities

Advanced Features

Real-World Applications

Integration Benefits

Next Steps