Complete ggplot2 Visualization Guide: Mastering Beautiful Data Plots

data visualization
ggplot2
R
data science
A comprehensive guide to creating stunning visualizations with ggplot2, featuring custom themes, advanced techniques, and all major plot types with beautiful aesthetics.
Author

Krishna Kumar Shrestha

Published

July 17, 2025

Complete ggplot2 Visualization Guide: Mastering Beautiful Data Plots

Data visualization is the art of transforming numbers into stories. In this comprehensive guide, we’ll explore the power of ggplot2 to create stunning, publication-ready visualizations that not only convey information effectively but also captivate your audience with their aesthetic appeal.

Setup and Custom Theme

Let’s start by loading the necessary libraries and creating our custom theme that features a clean whitish background, no grid lines, and beautiful typography.

Show Code 
# Load required libraries
library(ggplot2)
library(dplyr)
library(viridis)
library(RColorBrewer)
library(scales)
library(gridExtra)
library(ggtext)
library(showtext)
library(patchwork)

# Add Google Fonts
font_add_google("Playfair Display", "playfair")
font_add_google("Source Sans Pro", "source")
font_add_google("Fira Code", "fira")
showtext_auto()

# Alternative fonts for Windows compatibility
if (.Platform$OS.type == "windows") {
  windowsFonts(
    playfair = windowsFont("Times New Roman"),
    source = windowsFont("Arial"),
    fira = windowsFont("Courier New")
  )
}


# Custom color palette (expanded to cover 12 months) - Dark & Sophisticated
custom_colors <- c("#1A1A2E", "#16213E", "#0F3460", "#533A71", "#6A0572", "#AB0E86", 
                   "#E91E63", "#FF5722", "#795548", "#607D8B", "#455A64", "#263238")

# Alternative dark color palettes for different uses
primary_colors <- c("#1A1A2E", "#16213E", "#0F3460", "#533A71")
accent_colors <- c("#6A0572", "#AB0E86", "#E91E63", "#FF5722")
dark_gradient <- c("#263238", "#37474F", "#455A64", "#546E7A", "#607D8B", "#78909C")
Show Code 
# Create our custom theme
theme_elegant <- function(base_size = 14, base_family = "source") {
  # Use fallback fonts on Windows
  title_family <- if (.Platform$OS.type == "windows") "Times New Roman" else "playfair"
  body_family <- if (.Platform$OS.type == "windows") "Arial" else "source"
  
  theme_minimal(base_size = base_size, base_family = body_family) +
    theme(
      # Background
      plot.background = element_rect(fill = "#FEFEFE", color = NA),
      panel.background = element_rect(fill = "#FEFEFE", color = NA),
      
      # Remove grid lines
      panel.grid = element_blank(),
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank(),
      
      # Axes
      axis.line = element_line(color = "#2C3E50", size = 0.5),
      axis.text = element_text(color = "#2C3E50", size = rel(0.9)),
      axis.title = element_text(color = "#2C3E50", size = rel(1.1), face = "bold"),
      
      # Title and subtitle
      plot.title = element_text(
        family = title_family, 
        size = rel(1.6), 
        face = "bold", 
        color = "#2C3E50",
        margin = margin(b = 20)
      ),
      plot.subtitle = element_text(
        family = body_family, 
        size = rel(1.1), 
        color = "#7F8C8D",
        margin = margin(b = 25)
      ),
      plot.caption = element_text(
        family = body_family, 
        size = rel(0.8), 
        color = "#95A5A6",
        hjust = 0,
        margin = margin(t = 15)
      ),
      
      # Legend
      legend.background = element_rect(fill = "white", color = NA),
      legend.key = element_rect(fill = "white", color = NA),
      legend.text = element_text(color = "#2C3E50", size = rel(0.9)),
      legend.title = element_text(color = "#2C3E50", size = rel(1), face = "bold"),
      legend.position = "right",
      
      # Facets
      strip.background = element_rect(fill = "#ECF0F1", color = NA),
      strip.text = element_text(color = "#2C3E50", face = "bold", size = rel(1)),
      
      # Margins
      plot.margin = margin(20, 20, 20, 20)
    )
}

# Set as default theme
theme_set(theme_elegant())

Sample Data Generation

Let’s create diverse datasets to showcase different types of visualizations:

Show Code 
# Set seed for reproducibility
set.seed(123)

# Dataset 1: Sales data
sales_data <- data.frame(
  month = factor(month.abb, levels = month.abb),
  revenue = c(45000, 52000, 48000, 61000, 55000, 67000, 
              72000, 69000, 58000, 63000, 71000, 78000),
  profit = c(12000, 15600, 14400, 18300, 16500, 20100,
             21600, 20700, 17400, 18900, 21300, 23400),
  region = rep(c("North", "South", "East", "West"), 3)
)

# Dataset 2: Customer demographics
customer_data <- data.frame(
  age = rnorm(500, 35, 12),
  income = rnorm(500, 50000, 15000),
  satisfaction = sample(1:10, 500, replace = TRUE),
  category = sample(c("Premium", "Standard", "Basic"), 500, replace = TRUE, prob = c(0.3, 0.5, 0.2)),
  gender = sample(c("Male", "Female", "Other"), 500, replace = TRUE, prob = c(0.45, 0.5, 0.05))
)

# Dataset 3: Time series data
time_series_data <- data.frame(
  date = seq(as.Date("2020-01-01"), as.Date("2024-12-31"), by = "month"),
  value = cumsum(rnorm(60, 5, 15)) + 100,
  trend = seq(100, 400, length.out = 60),
  category = rep(c("A", "B", "C"), 20)
)

# Dataset 4: Correlation matrix data
correlation_data <- data.frame(
  x = rnorm(200),
  y = rnorm(200),
  z = rnorm(200)
)
correlation_data$y <- correlation_data$x * 0.7 + correlation_data$y * 0.3
correlation_data$z <- correlation_data$x * -0.5 + correlation_data$z * 0.5

1. Bar Charts and Column Charts

Bar charts are perfect for comparing categories and showing distributions.

Show Code 
# Simple bar chart with custom colors
p1 <- ggplot(sales_data, aes(x = month, y = revenue, fill = month)) +
  geom_col(width = 0.7, alpha = 0.9) +
  scale_fill_manual(values = custom_colors) +
  scale_y_continuous(labels = scales::dollar_format(scale = 1e-3, suffix = "K")) +
  labs(
    title = "Monthly Revenue Performance",
    subtitle = "Consistent growth throughout the year",
    x = "Month",
    y = "Revenue (in thousands)",
    caption = "Data: Company Sales Report 2024"
  ) +
  theme(
    legend.position = "none",
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

# Alternative approach using viridis colors for months
p1_alt <- ggplot(sales_data, aes(x = month, y = revenue, fill = month)) +
  geom_col(width = 0.7, alpha = 0.9) +
  scale_fill_viridis_d(option = "plasma") +
  scale_y_continuous(labels = scales::dollar_format(scale = 1e-3, suffix = "K")) +
  labs(
    title = "Monthly Revenue Performance (Viridis Palette)",
    subtitle = "Perceptually uniform color progression",
    x = "Month",
    y = "Revenue (in thousands)",
    caption = "Data: Company Sales Report 2024"
  ) +
  theme(
    legend.position = "none",
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

# Grouped bar chart
p2 <- sales_data %>%
  select(month, revenue, profit) %>%
  tidyr::pivot_longer(cols = c(revenue, profit), names_to = "metric", values_to = "value") %>%
  ggplot(aes(x = month, y = value, fill = metric)) +
  geom_col(position = "dodge", width = 0.7, alpha = 0.8) +
  scale_fill_manual(
    values = c("revenue" = "#1A1A2E", "profit" = "#533A71"),
    labels = c("Profit", "Revenue")
  ) +
  scale_y_continuous(labels = scales::dollar_format(scale = 1e-3, suffix = "K")) +
  labs(
    title = "Revenue vs Profit Analysis",
    subtitle = "Monthly comparison of key financial metrics",
    x = "Month",
    y = "Amount (in thousands)",
    fill = "Metric"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

print(p1)

Show Code 
print(p1_alt)

Show Code 
print(p2)

2. Line Charts and Time Series

Line charts excel at showing trends over time and continuous relationships.

Show Code 
# Simple time series plot
p3 <- ggplot(time_series_data, aes(x = date, y = value)) +
  geom_line(color = "#1A1A2E", size = 1.2, alpha = 0.8) +
  geom_smooth(method = "loess", se = TRUE, color = "#533A71", fill = "#533A71", alpha = 0.2) +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year") +
  scale_y_continuous(labels = scales::comma_format()) +
  labs(
    title = "Business Growth Trajectory",
    subtitle = "5-year performance with trend analysis",
    x = "Year",
    y = "Performance Index",
    caption = "Includes LOESS smoothing with 95% confidence interval"
  )

# Multiple line chart
p4 <- time_series_data %>%
  group_by(category, year = lubridate::year(date)) %>%
  summarise(avg_value = mean(value), .groups = "drop") %>%
  ggplot(aes(x = year, y = avg_value, color = category)) +
  geom_line(size = 1.5, alpha = 0.9) +
  geom_point(size = 3, alpha = 0.8) +
  scale_color_manual(values = c("#1A1A2E", "#533A71", "#0F3460")) +
  scale_x_continuous(breaks = 2020:2024) +
  labs(
    title = "Category Performance Comparison",
    subtitle = "Annual trends across different business segments",
    x = "Year",
    y = "Average Performance",
    color = "Category"
  )

print(p3)

Show Code 
print(p4)

3. Scatter Plots and Correlation Analysis

Scatter plots reveal relationships between continuous variables.

Show Code 
# Basic scatter plot with regression line
p5 <- ggplot(customer_data, aes(x = age, y = income)) +
  geom_point(aes(color = category), size = 2.5, alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE, color = "#2C3E50", fill = "#95A5A6", alpha = 0.2) +
  scale_color_manual(values = c("#1A1A2E", "#533A71", "#0F3460")) +
  scale_y_continuous(labels = scales::dollar_format()) +
  labs(
    title = "Income vs Age Relationship",
    subtitle = "Customer segmentation analysis with linear trend",
    x = "Age (years)",
    y = "Annual Income",
    color = "Customer Category"
  )

# Bubble chart
p6 <- customer_data %>%
  group_by(category, gender) %>%
  summarise(
    avg_age = mean(age),
    avg_income = mean(income),
    count = n(),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = avg_age, y = avg_income, size = count, color = category)) +
  geom_point(alpha = 0.8) +
  scale_size_continuous(range = c(5, 20), guide = guide_legend(title = "Count")) +
  scale_color_manual(values = c("#1A1A2E", "#533A71", "#0F3460")) +
  scale_y_continuous(labels = scales::dollar_format()) +
  facet_wrap(~gender) +
  labs(
    title = "Customer Demographics Bubble Chart",
    subtitle = "Age, income, and count by category and gender",
    x = "Average Age",
    y = "Average Income",
    color = "Category",
    size = "Customer Count"
  )

print(p5)

Show Code 
print(p6)

4. Histograms and Density Plots

These plots show distributions and frequency patterns in your data.

Show Code 
# Histogram with density overlay
p7 <- ggplot(customer_data, aes(x = income)) +
  geom_histogram(aes(y = ..density..), bins = 30, fill = "#1A1A2E", alpha = 0.7, color = "white") +
  geom_density(color = "#533A71", size = 1.2) +
  scale_x_continuous(labels = scales::dollar_format()) +
  labs(
    title = "Income Distribution Analysis",
    subtitle = "Histogram with overlaid density curve",
    x = "Annual Income",
    y = "Density"
  )

# Faceted density plots
p8 <- ggplot(customer_data, aes(x = income, fill = category)) +
  geom_density(alpha = 0.7) +
  scale_fill_manual(values = c("#1A1A2E", "#533A71", "#0F3460")) +
  scale_x_continuous(labels = scales::dollar_format()) +
  facet_wrap(~category, scales = "free_y") +
  labs(
    title = "Income Distribution by Customer Category",
    subtitle = "Density plots revealing different spending patterns",
    x = "Annual Income",
    y = "Density",
    fill = "Category"
  ) +
  theme(legend.position = "none")

print(p7)

Show Code 
print(p8)

5. Box Plots and Violin Plots

These plots show distributions, quartiles, and outliers effectively.

Show Code 
# Enhanced box plot
p9 <- ggplot(customer_data, aes(x = category, y = satisfaction, fill = category)) +
  geom_violin(alpha = 0.5, width = 0.8) +
  geom_boxplot(width = 0.3, alpha = 0.8, outlier.shape = 21, outlier.size = 2) +
  geom_jitter(alpha = 0.3, width = 0.2, size = 1) +
  scale_fill_manual(values = c("#1A1A2E", "#533A71", "#0F3460")) +
  scale_y_continuous(breaks = 1:10) +
  labs(
    title = "Customer Satisfaction Distribution",
    subtitle = "Violin plots with box plots and individual data points",
    x = "Customer Category",
    y = "Satisfaction Score (1-10)",
    fill = "Category"
  ) +
  theme(legend.position = "none")

# Grouped box plot
p10 <- ggplot(customer_data, aes(x = category, y = income, fill = gender)) +
  geom_boxplot(alpha = 0.8, outlier.shape = 21) +
  scale_fill_manual(values = c("#1A1A2E", "#533A71", "#6A0572")) +
  scale_y_continuous(labels = scales::dollar_format()) +
  labs(
    title = "Income Distribution by Category and Gender",
    subtitle = "Grouped box plots revealing demographic patterns",
    x = "Customer Category",
    y = "Annual Income",
    fill = "Gender"
  )

print(p9)

Show Code 
print(p10)

6. Heatmaps and Correlation Matrices

Heatmaps are excellent for showing relationships and patterns in matrix data.

Show Code 
# Correlation heatmap
cor_matrix <- cor(correlation_data)
cor_df <- expand.grid(Var1 = rownames(cor_matrix), Var2 = colnames(cor_matrix))
cor_df$value <- as.vector(cor_matrix)

p11 <- ggplot(cor_df, aes(x = Var1, y = Var2, fill = value)) +
  geom_tile(color = "white", size = 0.5) +
  geom_text(aes(label = round(value, 2)), color = "white", size = 5, family = "fira") +
  scale_fill_gradient2(
    low = "#1A1A2E", 
    mid = "white", 
    high = "#533A71", 
    midpoint = 0,
    name = "Correlation"
  ) +
  labs(
    title = "Correlation Matrix Heatmap",
    subtitle = "Relationships between variables",
    x = "", y = ""
  ) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.border = element_rect(color = "#2C3E50", fill = NA, size = 1)
  )

# Monthly sales heatmap
monthly_matrix <- sales_data %>%
  select(month, revenue, profit) %>%
  tidyr::pivot_longer(cols = c(revenue, profit), names_to = "metric") %>%
  mutate(value_scaled = scale(value)[,1])

p12 <- ggplot(monthly_matrix, aes(x = month, y = metric, fill = value_scaled)) +
  geom_tile(color = "white", size = 0.5) +
  scale_fill_gradient2(
    low = "#1A1A2E", 
    mid = "white", 
    high = "#533A71",
    name = "Scaled\nValue"
  ) +
  labs(
    title = "Monthly Performance Heatmap",
    subtitle = "Standardized revenue and profit metrics",
    x = "Month", y = "Metric"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

print(p11)

Show Code 
print(p12)

7. Pie Charts and Donut Charts

While sometimes criticized, pie charts can be effective for showing parts of a whole.

Show Code 
# Enhanced pie chart
category_counts <- customer_data %>%
  count(category) %>%
  mutate(
    percentage = n / sum(n) * 100,
    label = paste0(category, "\n", round(percentage, 1), "%")
  )

p13 <- ggplot(category_counts, aes(x = "", y = n, fill = category)) +
  geom_col(width = 1, alpha = 0.8) +
  coord_polar(theta = "y") +
  scale_fill_manual(values = c("#1A1A2E", "#533A71", "#0F3460")) +
  geom_text(aes(label = label), 
            position = position_stack(vjust = 0.5),
            color = "white", 
            size = 4, 
            family = "source",
            fontface = "bold") +
  labs(
    title = "Customer Category Distribution",
    subtitle = "Market segmentation overview"
  ) +
  theme_void() +
  theme(
    plot.title = element_text(family = "playfair", size = rel(1.6), face = "bold", color = "#2C3E50"),
    plot.subtitle = element_text(family = "source", size = rel(1.1), color = "#7F8C8D"),
    legend.position = "none"
  )

# Donut chart
p14 <- ggplot(category_counts, aes(x = 2, y = n, fill = category)) +
  geom_col(alpha = 0.8) +
  coord_polar(theta = "y") +
  xlim(0.5, 2.5) +
  scale_fill_manual(values = c("#1A1A2E", "#533A71", "#0F3460")) +
  labs(
    title = "Customer Segments - Donut View",
    subtitle = "Clean modern representation",
    fill = "Category"
  ) +
  theme_void() +
  theme(
    plot.title = element_text(family = "playfair", size = rel(1.6), face = "bold", color = "#2C3E50"),
    plot.subtitle = element_text(family = "source", size = rel(1.1), color = "#7F8C8D")
  )

print(p13)

Show Code 
print(p14)

8. Advanced Plots: Ridgeline and Waterfall

Let’s explore some advanced visualization techniques.

Show Code 
# Ridgeline plot (requires ggridges)
library(ggridges)

p15 <- ggplot(customer_data, aes(x = income, y = category, fill = category)) +
  geom_density_ridges(alpha = 0.8, scale = 0.9) +
  scale_fill_manual(values = c("#1A1A2E", "#533A71", "#0F3460")) +
  scale_x_continuous(labels = scales::dollar_format()) +
  labs(
    title = "Income Distribution Ridgeline Plot",
    subtitle = "Elegant way to compare distributions across categories",
    x = "Annual Income",
    y = "Customer Category"
  ) +
  theme(legend.position = "none")

# Waterfall chart simulation
waterfall_data <- data.frame(
  category = c("Starting", "Q1 Growth", "Q2 Growth", "Q3 Decline", "Q4 Growth", "Ending"),
  value = c(100, 25, 30, -15, 20, 160),
  type = c("start", "increase", "increase", "decrease", "increase", "end")
)

waterfall_data$cumulative <- cumsum(waterfall_data$value)
waterfall_data$xmin <- 1:nrow(waterfall_data) - 0.4
waterfall_data$xmax <- 1:nrow(waterfall_data) + 0.4
waterfall_data$ymin <- c(0, head(waterfall_data$cumulative, -1))
waterfall_data$ymax <- waterfall_data$cumulative

p16 <- ggplot(waterfall_data) +
  geom_rect(aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax, fill = type), 
            alpha = 0.8, color = "white", size = 0.5) +
  geom_text(aes(x = 1:nrow(waterfall_data), y = ymax + 5, label = paste0("+", value)), 
            family = "source", fontface = "bold", color = "#2C3E50") +
  scale_fill_manual(values = c("start" = "#455A64", "increase" = "#533A71", 
                               "decrease" = "#6A0572", "end" = "#1A1A2E")) +
  scale_x_continuous(breaks = 1:6, labels = waterfall_data$category) +
  labs(
    title = "Business Performance Waterfall Chart",
    subtitle = "Quarterly progression breakdown",
    x = "", y = "Performance Value",
    fill = "Type"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

print(p15)

Show Code 
print(p16)

9. Faceting and Small Multiples

Faceting allows you to create multiple plots based on grouping variables.

Show Code 
# Facet wrap example
p17 <- ggplot(customer_data, aes(x = age, y = income, color = satisfaction)) +
  geom_point(size = 2, alpha = 0.7) +
  geom_smooth(method = "lm", se = FALSE, color = "#2C3E50") +
  scale_color_viridis_c(option = "plasma") +
  scale_y_continuous(labels = scales::dollar_format()) +
  facet_wrap(~category, scales = "free") +
  labs(
    title = "Age-Income Relationship Across Customer Categories",
    subtitle = "Satisfaction levels shown by color intensity",
    x = "Age", y = "Income", color = "Satisfaction"
  )

# Facet grid example
time_analysis <- time_series_data %>%
  mutate(
    year = lubridate::year(date),
    quarter = paste0("Q", lubridate::quarter(date))
  ) %>%
  filter(year %in% 2022:2024)

p18 <- ggplot(time_analysis, aes(x = quarter, y = value, fill = category)) +
  geom_col(position = "dodge", alpha = 0.8) +
  scale_fill_manual(values = c("#1A1A2E", "#533A71", "#0F3460")) +
  facet_grid(year ~ ., scales = "free_y") +
  labs(
    title = "Quarterly Performance by Category and Year",
    subtitle = "Faceted analysis showing temporal patterns",
    x = "Quarter", y = "Performance Value", fill = "Category"
  )

print(p17)

Show Code 
print(p18)

10. Interactive Elements and Annotations

Adding annotations and highlights to make your plots more informative.

Show Code 
# Plot with annotations
best_month <- sales_data[which.max(sales_data$revenue), ]

p19 <- ggplot(sales_data, aes(x = month, y = revenue)) +
  geom_col(aes(fill = month == best_month$month), width = 0.7, alpha = 0.8) +
  scale_fill_manual(values = c("FALSE" = "#455A64", "TRUE" = "#1A1A2E"), guide = "none") +
  scale_y_continuous(labels = scales::dollar_format(scale = 1e-3, suffix = "K")) +
  annotate("text", 
           x = which(sales_data$month == best_month$month), 
           y = best_month$revenue + 2000,
           label = paste("Peak Month\n$", scales::comma(best_month$revenue)),
           family = "source", fontface = "bold", color = "#2C3E50",
           hjust = 0.5) +
  annotate("curve", 
           x = which(sales_data$month == best_month$month) + 0.5, 
           y = best_month$revenue + 1000,
           xend = which(sales_data$month == best_month$month) + 0.1, 
           yend = best_month$revenue + 500,
           arrow = arrow(length = unit(0.2, "cm")), 
           color = "#533A71", size = 1) +
  labs(
    title = "Revenue Performance with Peak Highlight",
    subtitle = "Annotations draw attention to key insights",
    x = "Month", y = "Revenue (in thousands)"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Multi-layer plot with trend analysis
p20 <- ggplot(time_series_data, aes(x = date)) +
  # Background ribbon for trend
  geom_ribbon(aes(ymin = trend - 50, ymax = trend + 50), 
              fill = "#533A71", alpha = 0.2) +
  # Actual values
  geom_line(aes(y = value), color = "#1A1A2E", size = 1.2) +
  # Trend line
  geom_line(aes(y = trend), color = "#2C3E50", size = 1, linetype = "dashed") +
  # Highlight recent period
  geom_rect(aes(xmin = as.Date("2024-01-01"), xmax = as.Date("2024-12-31"),
                ymin = -Inf, ymax = Inf), 
            fill = "#6A0572", alpha = 0.1) +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year") +
  labs(
    title = "Performance Analysis with Trend Confidence Interval",
    subtitle = "Recent period highlighted in yellow, trend shown with confidence band",
    x = "Date", y = "Performance Value",
    caption = "Dashed line shows underlying trend, ribbon shows ±50 confidence band"
  )

print(p19)

Show Code 
print(p20)

11. Area Charts and Stacked Plots

Area charts are excellent for showing cumulative values and trends over time.

Show Code 
# Simple area chart
p21 <- ggplot(time_series_data, aes(x = date, y = value)) +
  geom_area(fill = "#1A1A2E", alpha = 0.7) +
  geom_line(color = "#533A71", size = 1.2) +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year") +
  scale_y_continuous(labels = scales::comma_format()) +
  labs(
    title = "Performance Area Chart",
    subtitle = "Cumulative performance visualization over time",
    x = "Year",
    y = "Performance Value"
  )

# Stacked area chart
area_data <- time_series_data %>%
  group_by(date) %>%
  mutate(
    category_value = case_when(
      category == "A" ~ value * 0.4,
      category == "B" ~ value * 0.35,
      category == "C" ~ value * 0.25
    )
  ) %>%
  ungroup()

p22 <- ggplot(area_data, aes(x = date, y = category_value, fill = category)) +
  geom_area(alpha = 0.8, position = "stack") +
  scale_fill_manual(values = c("#1A1A2E", "#533A71", "#0F3460")) +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year") +
  scale_y_continuous(labels = scales::comma_format()) +
  labs(
    title = "Stacked Area Chart by Category",
    subtitle = "Component contribution to total performance",
    x = "Year",
    y = "Performance Value",
    fill = "Category"
  )

print(p21)

Show Code 
print(p22)

12. Polar and Radar Charts

Polar coordinates can create unique and effective visualizations.

Show Code 
# Polar bar chart (rose chart)
monthly_summary <- sales_data %>%
  mutate(month_num = as.numeric(month))

p23 <- ggplot(monthly_summary, aes(x = month, y = revenue, fill = month)) +
  geom_col(width = 0.8, alpha = 0.8) +
  scale_fill_manual(values = custom_colors) +
  coord_polar(start = 0) +
  scale_y_continuous(labels = scales::dollar_format(scale = 1e-3, suffix = "K")) +
  labs(
    title = "Circular Revenue Chart",
    subtitle = "Monthly performance in polar coordinates",
    y = "Revenue (thousands)"
  ) +
  theme(
    legend.position = "none",
    axis.text.x = element_text(size = rel(0.8))
  )

# Radar chart simulation
radar_data <- customer_data %>%
  group_by(category) %>%
  summarise(
    avg_age = mean(age, na.rm = TRUE),
    avg_income = mean(income, na.rm = TRUE) / 1000,  # Scale down
    avg_satisfaction = mean(satisfaction, na.rm = TRUE),
    count = n() / 10,  # Scale down
    .groups = "drop"
  ) %>%
  tidyr::pivot_longer(cols = -category, names_to = "metric", values_to = "value") %>%
  mutate(
    # Normalize values to 0-10 scale
    value_norm = scales::rescale(value, to = c(1, 10))
  )

p24 <- ggplot(radar_data, aes(x = metric, y = value_norm, fill = category)) +
  geom_col(position = "dodge", alpha = 0.7, width = 0.8) +
  scale_fill_manual(values = c("#1A1A2E", "#533A71", "#0F3460")) +
  coord_polar() +
  facet_wrap(~category) +
  labs(
    title = "Customer Profile Radar Charts",
    subtitle = "Multi-dimensional comparison across categories",
    y = "Normalized Score"
  ) +
  theme(
    legend.position = "none",
    axis.text.x = element_text(size = rel(0.7))
  )

print(p23)

Show Code 
print(p24)

13. Statistical Plots

Advanced statistical visualizations for deeper analysis.

Show Code 
# Q-Q plot for normality testing
p25 <- ggplot(customer_data, aes(sample = income)) +
  stat_qq(color = "#1A1A2E", alpha = 0.7, size = 2) +
  stat_qq_line(color = "#533A71", size = 1.2) +
  facet_wrap(~category) +
  labs(
    title = "Q-Q Plots for Income Distribution",
    subtitle = "Testing normality assumption by customer category",
    x = "Theoretical Quantiles",
    y = "Sample Quantiles"
  )

# Error bars and confidence intervals
error_data <- customer_data %>%
  group_by(category, gender) %>%
  summarise(
    mean_income = mean(income, na.rm = TRUE),
    sd_income = sd(income, na.rm = TRUE),
    n = n(),
    se_income = sd_income / sqrt(n),
    ci_lower = mean_income - 1.96 * se_income,
    ci_upper = mean_income + 1.96 * se_income,
    .groups = "drop"
  )

p26 <- ggplot(error_data, aes(x = category, y = mean_income, fill = gender)) +
  geom_col(position = "dodge", alpha = 0.8) +
  geom_errorbar(
    aes(ymin = ci_lower, ymax = ci_upper),
    position = position_dodge(width = 0.9),
    width = 0.2,
    color = "#2C3E50",
    size = 0.8
  ) +
  scale_fill_manual(values = c("#1A1A2E", "#533A71", "#6A0572")) +
  scale_y_continuous(labels = scales::dollar_format()) +
  labs(
    title = "Mean Income with 95% Confidence Intervals",
    subtitle = "Statistical significance testing across groups",
    x = "Customer Category",
    y = "Mean Annual Income",
    fill = "Gender"
  )

print(p25)

Show Code 
print(p26)

14. Network and Flow Diagrams

Visualizing relationships and flows between entities.

Show Code 
# Alluvial/Sankey-style plot
flow_data <- customer_data %>%
  count(category, gender, satisfaction > 7) %>%
  rename(high_satisfaction = `satisfaction > 7`) %>%
  mutate(
    satisfaction_level = ifelse(high_satisfaction, "High", "Low"),
    flow_id = row_number()
  )

# Create flow visualization using area plots
p27 <- flow_data %>%
  mutate(
    x1 = 1, x2 = 2, x3 = 3,
    category_y = as.numeric(as.factor(category)),
    gender_y = as.numeric(as.factor(gender)) + 3,
    satisfaction_y = as.numeric(as.factor(satisfaction_level)) + 6
  ) %>%
  ggplot() +
  # Category to Gender flows
  geom_segment(aes(x = x1, y = category_y, xend = x2, yend = gender_y, size = n),
               color = "#1A1A2E", alpha = 0.6) +
  # Gender to Satisfaction flows  
  geom_segment(aes(x = x2, y = gender_y, xend = x3, yend = satisfaction_y, size = n),
               color = "#533A71", alpha = 0.6) +
  # Add points for nodes
  geom_point(aes(x = x1, y = category_y), size = 8, color = "#1A1A2E") +
  geom_point(aes(x = x2, y = gender_y), size = 8, color = "#533A71") +
  geom_point(aes(x = x3, y = satisfaction_y), size = 8, color = "#0F3460") +
  scale_size_continuous(range = c(1, 10), guide = "none") +
  scale_x_continuous(breaks = 1:3, labels = c("Category", "Gender", "Satisfaction")) +
  labs(
    title = "Customer Flow Diagram",
    subtitle = "Relationships between category, gender, and satisfaction",
    x = "", y = ""
  ) +
  theme(
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank()
  )

# Chord diagram simulation using polar coordinates
chord_data <- customer_data %>%
  count(category, gender) %>%
  mutate(
    angle = seq(0, 2*pi, length.out = n()),
    radius = scales::rescale(n, to = c(2, 5))
  )

p28 <- ggplot(chord_data, aes(x = angle, y = radius)) +
  geom_col(aes(fill = category), width = 0.3, alpha = 0.8) +
  scale_fill_manual(values = c("#1A1A2E", "#533A71", "#0F3460")) +
  coord_polar(start = 0) +
  labs(
    title = "Circular Relationship Plot",
    subtitle = "Category-Gender distribution in polar coordinates",
    fill = "Category"
  ) +
  theme_void()

print(p27)

Show Code 
print(p28)

15. Advanced Distribution Plots

Sophisticated ways to visualize and compare distributions.

Show Code 
# Strip charts with jitter
p29 <- ggplot(customer_data, aes(x = category, y = income, color = category)) +
  geom_jitter(alpha = 0.6, width = 0.3, size = 1.5) +
  stat_summary(fun = median, geom = "crossbar", width = 0.5, color = "#2C3E50", size = 1) +
  scale_color_manual(values = c("#1A1A2E", "#533A71", "#0F3460")) +
  scale_y_continuous(labels = scales::dollar_format()) +
  labs(
    title = "Income Distribution Strip Chart",
    subtitle = "Individual data points with median crossbars",
    x = "Customer Category",
    y = "Annual Income"
  ) +
  theme(legend.position = "none")

# Slope graph
slope_data <- sales_data %>%
  select(month, revenue, profit) %>%
  filter(month %in% c("Jan", "Jun", "Dec")) %>%
  tidyr::pivot_longer(cols = c(revenue, profit), names_to = "metric", values_to = "value") %>%
  mutate(month = factor(month, levels = c("Jan", "Jun", "Dec")))

p30 <- ggplot(slope_data, aes(x = month, y = value, group = metric, color = metric)) +
  geom_line(size = 2, alpha = 0.8) +
  geom_point(size = 4, alpha = 0.9) +
  geom_text(aes(label = scales::dollar(value, scale = 1e-3, suffix = "K")), 
            vjust = -0.5, family = "source", fontface = "bold", size = 3) +
  scale_color_manual(values = c("profit" = "#1A1A2E", "revenue" = "#533A71")) +
  scale_y_continuous(labels = scales::dollar_format(scale = 1e-3, suffix = "K")) +
  labs(
    title = "Revenue vs Profit Slope Graph",
    subtitle = "Performance progression across key months",
    x = "Month",
    y = "Amount (thousands)",
    color = "Metric"
  )

print(p29)

Show Code 
print(p30)

16. Specialized Business Plots

Industry-specific and business-focused visualizations.

Show Code 
# Bullet chart simulation
target_data <- data.frame(
  metric = c("Revenue", "Profit", "Customers", "Satisfaction"),
  actual = c(78000, 23400, 500, 7.2),
  target = c(80000, 25000, 600, 8.0),
  poor = c(60000, 15000, 300, 5.0),
  good = c(75000, 22000, 500, 7.5)
)

p31 <- target_data %>%
  tidyr::pivot_longer(cols = c(poor, good, target), names_to = "benchmark", values_to = "value") %>%
  ggplot(aes(x = metric)) +
  geom_col(aes(y = value, fill = benchmark), 
           position = "identity", alpha = 0.6, width = 0.5) +
  geom_point(aes(y = actual), size = 4, color = "#1A1A2E") +
  geom_text(aes(y = actual, label = scales::comma(actual)), 
            hjust = -0.2, family = "source", fontface = "bold") +
  scale_fill_manual(values = c("poor" = "#6A0572", "good" = "#533A71", "target" = "#0F3460")) +
  coord_flip() +
  labs(
    title = "Performance Bullet Chart",
    subtitle = "Actual vs target performance with benchmark ranges",
    x = "Metrics",
    y = "Value",
    fill = "Benchmark"
  )

# Funnel chart
funnel_data <- data.frame(
  stage = c("Leads", "Qualified", "Proposals", "Negotiations", "Closed"),
  count = c(1000, 750, 400, 200, 120),
  order = 1:5
) %>%
  mutate(
    percentage = count / max(count) * 100,
    stage = factor(stage, levels = stage)
  )

p32 <- ggplot(funnel_data, aes(x = order, y = count, fill = stage)) +
  geom_col(width = 0.8, alpha = 0.8) +
  geom_text(aes(label = paste0(count, "\n(", round(percentage, 1), "%)")), 
            color = "white", fontface = "bold", family = "source") +
  scale_fill_manual(values = custom_colors[1:5]) +
  scale_x_continuous(breaks = 1:5, labels = funnel_data$stage) +
  scale_y_continuous(labels = scales::comma_format()) +
  labs(
    title = "Sales Funnel Analysis",
    subtitle = "Conversion rates through sales pipeline",
    x = "Sales Stage",
    y = "Count",
    fill = "Stage"
  ) +
  theme(legend.position = "none")

print(p31)

Show Code 
print(p32)

17. Tree Maps and Hierarchical Plots

Visualizing hierarchical data and proportional relationships.

Show Code 
# Treemap simulation using rectangles
treemap_data <- customer_data %>%
  count(category, gender) %>%
  group_by(category) %>%
  mutate(
    category_total = sum(n),
    prop_in_category = n / category_total,
    category_prop = category_total / sum(customer_data %>% count(category) %>% pull(n))
  ) %>%
  ungroup() %>%
  arrange(desc(category_total), desc(n)) %>%
  mutate(
    # Calculate rectangle positions
    id = row_number(),
    xmin = case_when(
      category == "Standard" ~ 0,
      category == "Premium" ~ 0.5,
      category == "Basic" ~ 0.75
    ),
    xmax = case_when(
      category == "Standard" ~ 0.5,
      category == "Premium" ~ 0.75,
      category == "Basic" ~ 1
    ),
    ymin = case_when(
      gender == "Female" ~ 0,
      gender == "Male" ~ 0.5,
      gender == "Other" ~ 0.8
    ),
    ymax = case_when(
      gender == "Female" ~ 0.5,
      gender == "Male" ~ 0.8,
      gender == "Other" ~ 1
    )
  )

p33 <- ggplot(treemap_data) +
  geom_rect(aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax, 
                fill = category), color = "white", size = 2, alpha = 0.8) +
  geom_text(aes(x = (xmin + xmax)/2, y = (ymin + ymax)/2, 
                label = paste0(category, "\n", gender, "\n", n)), 
            color = "white", fontface = "bold", family = "source", size = 3) +
  scale_fill_manual(values = c("#1A1A2E", "#533A71", "#0F3460")) +
  labs(
    title = "Customer Segment Treemap",
    subtitle = "Hierarchical view of category and gender distribution"
  ) +
  theme_void() +
  theme(
    legend.position = "none",
    plot.title = element_text(family = "playfair", size = rel(1.6), face = "bold", color = "#2C3E50"),
    plot.subtitle = element_text(family = "source", size = rel(1.1), color = "#7F8C8D")
  )

# Sunburst chart simulation
sunburst_data <- customer_data %>%
  mutate(satisfaction_group = ifelse(satisfaction > 7, "High", "Low")) %>%
  count(category, gender, satisfaction_group) %>%
  mutate(
    angle_start = cumsum(lag(n, default = 0)) / sum(n) * 2 * pi,
    angle_end = cumsum(n) / sum(n) * 2 * pi,
    angle_mid = (angle_start + angle_end) / 2
  )

p34 <- ggplot(sunburst_data) +
  geom_rect(aes(xmin = 1, xmax = 2, 
                ymin = angle_start, ymax = angle_end, 
                fill = category), alpha = 0.8) +
  geom_rect(aes(xmin = 2, xmax = 3,
                ymin = angle_start, ymax = angle_end,
                fill = gender), alpha = 0.6) +
  scale_fill_manual(values = c("#1A1A2E", "#533A71", "#0F3460", "#6A0572", "#AB0E86", "#263238")) +
  coord_polar(theta = "y") +
  xlim(0, 3) +
  labs(
    title = "Customer Hierarchy Sunburst",
    subtitle = "Multi-level categorical breakdown"
  ) +
  theme_void() +
  theme(legend.position = "bottom")

print(p33)

Show Code 
print(p34)

18. Time Series Decomposition and Calendar Plots

Advanced time-based visualizations.

Show Code 
# Calendar heatmap simulation
calendar_data <- expand.grid(
  week = 1:52,
  weekday = 1:7
) %>%
  mutate(
    date = as.Date("2024-01-01") + (week - 1) * 7 + (weekday - 1),
    value = sin(week * 0.1) * 100 + rnorm(n(), 0, 20) + 500,
    month = format(date, "%b")
  ) %>%
  filter(date <= as.Date("2024-12-31"))

p35 <- ggplot(calendar_data, aes(x = weekday, y = week, fill = value)) +
  geom_tile(color = "white", size = 0.1) +
  scale_fill_gradient2(
    low = "#1A1A2E", 
    mid = "#533A71", 
    high = "#AB0E86",
    midpoint = 500,
    name = "Activity"
  ) +
  scale_x_continuous(breaks = 1:7, labels = c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")) +
  scale_y_reverse() +
  labs(
    title = "Annual Activity Calendar Heatmap",
    subtitle = "Daily activity levels throughout 2024",
    x = "Day of Week",
    y = "Week of Year"
  ) +
  theme(
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank()
  )

# Time series decomposition plot
decomp_data <- time_series_data %>%
  mutate(
    seasonal = 50 * sin(2 * pi * as.numeric(date - min(date)) / 365.25),
    trend_component = trend,
    noise = value - trend - seasonal,
    reconstructed = trend_component + seasonal + noise
  ) %>%
  select(date, value, trend_component, seasonal, noise) %>%
  tidyr::pivot_longer(cols = -date, names_to = "component", values_to = "val")

p36 <- ggplot(decomp_data, aes(x = date, y = val)) +
  geom_line(color = "#1A1A2E", size = 0.8) +
  facet_wrap(~component, scales = "free_y", ncol = 1, 
             labeller = labeller(component = c(
               "value" = "Original Series",
               "trend_component" = "Trend",
               "seasonal" = "Seasonal",
               "noise" = "Residuals"
             ))) +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year") +
  labs(
    title = "Time Series Decomposition",
    subtitle = "Breaking down the signal into components",
    x = "Date",
    y = "Value"
  ) +
  theme(strip.text = element_text(face = "bold"))

print(p35)

Show Code 
print(p36)

19. 3D-Style and Perspective Plots

Creating depth and dimension in 2D visualizations.

Show Code 
# 3D-style surface plot using contours and fills
surface_data <- expand.grid(
  x = seq(-3, 3, 0.2),
  y = seq(-3, 3, 0.2)
) %>%
  mutate(
    z = sin(sqrt(x^2 + y^2)) * exp(-sqrt(x^2 + y^2)/3),
    z_group = cut(z, breaks = 10)
  )

p37 <- ggplot(surface_data, aes(x = x, y = y, fill = z)) +
  geom_tile() +
  geom_contour(aes(z = z), color = "white", alpha = 0.5, size = 0.5) +
  scale_fill_gradient2(
    low = "#1A1A2E", 
    mid = "#533A71", 
    high = "#AB0E86",
    name = "Elevation"
  ) +
  labs(
    title = "3D Surface Visualization",
    subtitle = "Simulated topographic map with contour lines",
    x = "X Coordinate",
    y = "Y Coordinate"
  ) +
  coord_equal()

# Isometric-style plot
iso_data <- data.frame(
  x = rep(1:10, each = 10),
  y = rep(1:10, 10),
  height = rpois(100, lambda = 5)
) %>%
  mutate(
    # Create isometric transformation
    iso_x = x + y * 0.5,
    iso_y = y * 0.866 + height * 0.5,
    height_group = cut(height, breaks = 5)
  )

p38 <- ggplot(iso_data, aes(x = iso_x, y = iso_y)) +
  geom_point(aes(color = height, size = height), alpha = 0.8) +
  geom_segment(aes(xend = iso_x, yend = iso_y - height * 0.5), 
               color = "#2C3E50", alpha = 0.3) +
  scale_color_gradient2(
    low = "#1A1A2E", 
    mid = "#533A71", 
    high = "#AB0E86",
    name = "Height"
  ) +
  scale_size_continuous(range = c(2, 8), guide = "none") +
  labs(
    title = "Isometric Data Visualization",
    subtitle = "3D perspective of categorical data points",
    x = "Isometric X",
    y = "Isometric Y"
  ) +
  theme(
    axis.text = element_blank(),
    axis.ticks = element_blank()
  )

print(p37)

Show Code 
print(p38)

20. Animated-Style and Motion Plots

Visualizations that suggest movement and change over time.

Show Code 
# Comet plot (showing trajectory with fading trail)
trajectory_data <- data.frame(
  time = 1:50,
  x = cumsum(rnorm(50, 0, 1)),
  y = cumsum(rnorm(50, 0, 1))
) %>%
  mutate(
    alpha_trail = exp(-0.2 * (max(time) - time)),
    size_trail = pmax(1, 10 * alpha_trail)
  )

p39 <- ggplot(trajectory_data, aes(x = x, y = y)) +
  geom_path(color = "#533A71", size = 1, alpha = 0.6) +
  geom_point(aes(alpha = alpha_trail, size = size_trail), 
             color = "#1A1A2E") +
  geom_point(data = trajectory_data[nrow(trajectory_data), ], 
             color = "#AB0E86", size = 8) +
  scale_alpha_identity() +
  scale_size_identity() +
  labs(
    title = "Trajectory Comet Plot",
    subtitle = "Path visualization with fading trail effect",
    x = "X Position",
    y = "Y Position"
  ) +
  coord_equal()

# Wind rose / directional plot
wind_data <- data.frame(
  direction = seq(0, 359, by = 10),
  speed = abs(rnorm(36, 15, 5)),
  category = sample(c("Light", "Moderate", "Strong"), 36, replace = TRUE)
) %>%
  mutate(
    direction_rad = direction * pi / 180,
    x = speed * cos(direction_rad),
    y = speed * sin(direction_rad)
  )

p40 <- ggplot(wind_data, aes(x = x, y = y)) +
  geom_spoke(aes(angle = direction_rad, radius = speed, color = category), 
             size = 1.5, alpha = 0.8) +
  geom_point(aes(color = category, size = speed), alpha = 0.7) +
  scale_color_manual(values = c("#1A1A2E", "#533A71", "#AB0E86")) +
  scale_size_continuous(range = c(2, 6), guide = "none") +
  coord_equal() +
  labs(
    title = "Wind Rose Directional Plot",
    subtitle = "Direction and magnitude visualization",
    x = "East-West Component",
    y = "North-South Component",
    color = "Wind Category"
  ) +
  theme(
    axis.text = element_blank(),
    axis.ticks = element_blank()
  )

print(p39)

Show Code 
print(p40)

21. Mathematical and Scientific Plots

Specialized visualizations for mathematical and scientific data.

Show Code 
# Phase space plot
phase_data <- data.frame(
  t = seq(0, 4*pi, 0.1)
) %>%
  mutate(
    x = sin(t) + 0.1 * sin(10*t),
    y = cos(t) + 0.1 * cos(10*t),
    velocity_x = lead(x) - x,
    velocity_y = lead(y) - y,
    speed = sqrt(velocity_x^2 + velocity_y^2)
  ) %>%
  filter(!is.na(speed))

p41 <- ggplot(phase_data, aes(x = x, y = y)) +
  geom_path(aes(color = speed), size = 1.5, alpha = 0.8) +
  geom_point(data = phase_data[1, ], color = "#1A1A2E", size = 4) +
  geom_point(data = phase_data[nrow(phase_data), ], color = "#AB0E86", size = 4) +
  scale_color_gradient2(
    low = "#1A1A2E", 
    mid = "#533A71", 
    high = "#AB0E86",
    name = "Speed"
  ) +
  labs(
    title = "Phase Space Trajectory",
    subtitle = "Position vs velocity in state space",
    x = "Position X",
    y = "Position Y"
  ) +
  coord_equal()

# Mandelbrot-style fractal visualization
fractal_data <- expand.grid(
  x = seq(-2, 2, 0.05),
  y = seq(-2, 2, 0.05)
) %>%
  mutate(
    # Simplified fractal calculation
    c_real = x,
    c_imag = y,
    iterations = pmin(20, abs(x^2 + y^2) * 10),
    fractal_value = iterations + rnorm(n(), 0, 0.5)
  )

p42 <- ggplot(fractal_data, aes(x = x, y = y, fill = fractal_value)) +
  geom_tile() +
  scale_fill_gradient2(
    low = "#1A1A2E", 
    mid = "#533A71", 
    high = "#AB0E86",
    name = "Iterations"
  ) +
  labs(
    title = "Fractal-Style Visualization",
    subtitle = "Mathematical pattern visualization",
    x = "Real Component",
    y = "Imaginary Component"
  ) +
  coord_equal() +
  theme(
    axis.text = element_blank(),
    axis.ticks = element_blank()
  )

print(p41)

Show Code 
print(p42)

22. Geographic and Spatial Plots

Advanced spatial visualization techniques.

Show Code 
# Hexagonal binning for spatial data
spatial_data <- data.frame(
  longitude = rnorm(1000, -74, 0.1),
  latitude = rnorm(1000, 40.7, 0.1),
  value = rpois(1000, 5)
)

p43 <- ggplot(spatial_data, aes(x = longitude, y = latitude)) +
  geom_hex(aes(fill = after_stat(count)), bins = 20, alpha = 0.8) +
  scale_fill_gradient2(
    low = "#1A1A2E", 
    mid = "#533A71", 
    high = "#AB0E86",
    name = "Density"
  ) +
  labs(
    title = "Hexagonal Spatial Binning",
    subtitle = "Geographic density visualization",
    x = "Longitude",
    y = "Latitude"
  ) +
  coord_equal()

# Voronoi diagram simulation
voronoi_seeds <- data.frame(
  x = runif(15, 0, 10),
  y = runif(15, 0, 10),
  category = sample(c("A", "B", "C"), 15, replace = TRUE)
)

voronoi_grid <- expand.grid(
  x = seq(0, 10, 0.2),
  y = seq(0, 10, 0.2)
) %>%
  rowwise() %>%
  mutate(
    distances = list(sqrt((x - voronoi_seeds$x)^2 + (y - voronoi_seeds$y)^2)),
    nearest_seed = which.min(unlist(distances)),
    category = voronoi_seeds$category[nearest_seed]
  ) %>%
  ungroup()

p44 <- ggplot(voronoi_grid, aes(x = x, y = y, fill = category)) +
  geom_tile(alpha = 0.7) +
  geom_point(data = voronoi_seeds, aes(color = category), 
             size = 4, shape = 21, fill = "white", stroke = 2) +
  scale_fill_manual(values = c("#1A1A2E", "#533A71", "#0F3460")) +
  scale_color_manual(values = c("#1A1A2E", "#533A71", "#0F3460")) +
  labs(
    title = "Voronoi Diagram Visualization",
    subtitle = "Spatial territory and influence mapping",
    x = "X Coordinate",
    y = "Y Coordinate",
    fill = "Territory",
    color = "Seed Points"
  ) +
  coord_equal() +
  theme(legend.position = "bottom")

print(p43)

Show Code 
print(p44)

23. Advanced Statistical and ML Plots

Machine learning and advanced statistical visualizations.

Show Code 
# Decision boundary visualization
set.seed(123)
ml_data <- data.frame(
  x1 = rnorm(200),
  x2 = rnorm(200)
) %>%
  mutate(
    y = ifelse(x1^2 + x2^2 > 1.5, "Class_A", "Class_B"),
    predicted = ifelse(x1^2 + x2^2 > 1.3, "Class_A", "Class_B"),
    correct = y == predicted
  )

# Create decision boundary grid
boundary_grid <- expand.grid(
  x1 = seq(-3, 3, 0.1),
  x2 = seq(-3, 3, 0.1)
) %>%
  mutate(
    boundary_value = x1^2 + x2^2,
    decision = ifelse(boundary_value > 1.3, "Class_A", "Class_B")
  )

p45 <- ggplot() +
  geom_tile(data = boundary_grid, aes(x = x1, y = x2, fill = decision), alpha = 0.3) +
  geom_point(data = ml_data, aes(x = x1, y = x2, color = y, shape = correct), 
             size = 3, alpha = 0.8) +
  geom_contour(data = boundary_grid, aes(x = x1, y = x2, z = boundary_value), 
               breaks = 1.3, color = "#2C3E50", size = 2) +
  scale_fill_manual(values = c("Class_A" = "#1A1A2E", "Class_B" = "#533A71")) +
  scale_color_manual(values = c("Class_A" = "#1A1A2E", "Class_B" = "#533A71")) +
  scale_shape_manual(values = c("TRUE" = 16, "FALSE" = 4)) +
  labs(
    title = "Machine Learning Decision Boundary",
    subtitle = "Classification visualization with prediction accuracy",
    x = "Feature 1",
    y = "Feature 2",
    color = "True Class",
    fill = "Predicted Region",
    shape = "Correct Prediction"
  ) +
  coord_equal()

# ROC Curve simulation
roc_data <- data.frame(
  threshold = seq(0, 1, 0.01)
) %>%
  mutate(
    tpr = 1 - pnorm(qnorm(1 - threshold) - 1),  # True Positive Rate
    fpr = 1 - pnorm(qnorm(1 - threshold)),      # False Positive Rate
    model = "Model A"
  ) %>%
  bind_rows(
    data.frame(
      threshold = seq(0, 1, 0.01)
    ) %>%
    mutate(
      tpr = 1 - pnorm(qnorm(1 - threshold) - 0.5),
      fpr = 1 - pnorm(qnorm(1 - threshold) + 0.5),
      model = "Model B"
    )
  )

p46 <- ggplot(roc_data, aes(x = fpr, y = tpr, color = model)) +
  geom_line(size = 2, alpha = 0.8) +
  geom_abline(intercept = 0, slope = 1, linetype = "dashed", 
              color = "#95A5A6", size = 1) +
  geom_ribbon(aes(ymin = fpr, ymax = tpr, fill = model), alpha = 0.2) +
  scale_color_manual(values = c("#1A1A2E", "#533A71")) +
  scale_fill_manual(values = c("#1A1A2E", "#533A71")) +
  scale_x_continuous(labels = scales::percent_format()) +
  scale_y_continuous(labels = scales::percent_format()) +
  labs(
    title = "ROC Curve Comparison",
    subtitle = "Model performance visualization",
    x = "False Positive Rate",
    y = "True Positive Rate",
    color = "Model",
    fill = "AUC Area"
  ) +
  coord_equal()

print(p45)

Show Code 
print(p46)

24. Financial and Economic Plots

Specialized visualizations for financial and economic data.

Show Code 
# Candlestick chart simulation
candlestick_data <- data.frame(
  date = seq(as.Date("2024-01-01"), by = "day", length.out = 30)
) %>%
  mutate(
    open = 100 + cumsum(rnorm(30, 0, 1)),
    close = open + rnorm(30, 0.5, 2),
    high = pmax(open, close) + abs(rnorm(30, 0, 1)),
    low = pmin(open, close) - abs(rnorm(30, 0, 1)),
    direction = ifelse(close > open, "Up", "Down")
  )

p47 <- ggplot(candlestick_data, aes(x = date)) +
  geom_segment(aes(y = low, yend = high), color = "#2C3E50", size = 0.5) +
  geom_segment(aes(y = open, yend = close, color = direction), size = 4, alpha = 0.8) +
  scale_color_manual(values = c("Up" = "#533A71", "Down" = "#6A0572")) +
  scale_x_date(date_labels = "%b %d", date_breaks = "5 days") +
  labs(
    title = "Financial Candlestick Chart",
    subtitle = "OHLC price visualization",
    x = "Date",
    y = "Price",
    color = "Direction"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Economic bubble chart
economic_data <- data.frame(
  country = paste("Country", LETTERS[1:20]),
  gdp_per_capita = exp(rnorm(20, 10, 0.5)),
  life_expectancy = rnorm(20, 75, 8),
  population = exp(rnorm(20, 15, 1)),
  region = sample(c("Asia", "Europe", "Americas", "Africa"), 20, replace = TRUE)
)

p48 <- ggplot(economic_data, aes(x = gdp_per_capita, y = life_expectancy)) +
  geom_point(aes(size = population, color = region), alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE, color = "#2C3E50", fill = "#95A5A6", alpha = 0.2) +
  scale_x_log10(labels = scales::dollar_format()) +
  scale_size_continuous(range = c(3, 15), labels = scales::comma_format(scale = 1e-6, suffix = "M")) +
  scale_color_manual(values = c("#1A1A2E", "#533A71", "#0F3460", "#6A0572")) +
  labs(
    title = "Economic Development Bubble Chart",
    subtitle = "GDP per capita vs life expectancy by population",
    x = "GDP per Capita (log scale)",
    y = "Life Expectancy (years)",
    size = "Population",
    color = "Region"
  )

print(p47)

Show Code 
print(p48)

25. Artistic and Creative Visualizations

Pushing the boundaries of data art and creative expression.

Show Code 
# Spirograph-style data art
spiral_data <- data.frame(
  t = seq(0, 20*pi, 0.1)
) %>%
  mutate(
    x = (10 + 3 * cos(5*t)) * cos(t),
    y = (10 + 3 * cos(5*t)) * sin(t),
    color_val = sin(t) + cos(5*t),
    alpha_val = (sin(t/2) + 1) / 2
  )

p49 <- ggplot(spiral_data, aes(x = x, y = y)) +
  geom_path(aes(color = color_val, alpha = alpha_val), size = 1) +
  geom_point(aes(color = color_val, alpha = alpha_val), size = 0.5) +
  scale_color_gradient2(
    low = "#1A1A2E", 
    mid = "#533A71", 
    high = "#AB0E86",
    guide = "none"
  ) +
  scale_alpha_identity() +
  labs(
    title = "Data Spirograph Art",
    subtitle = "Mathematical beauty in data visualization"
  ) +
  coord_equal() +
  theme_void() +
  theme(
    plot.background = element_rect(fill = "#0A0A0A", color = NA),
    plot.title = element_text(color = "white"),
    plot.subtitle = element_text(color = "white")
  )

# Word cloud simulation using text positioning
word_data <- data.frame(
  word = c("ggplot2", "visualization", "data", "science", "R", "beautiful", 
           "insights", "analytics", "charts", "graphs", "statistical", "modern"),
  frequency = c(50, 45, 40, 35, 30, 25, 20, 18, 15, 12, 10, 8),
  x = runif(12, -5, 5),
  y = runif(12, -3, 3),
  angle = sample(c(0, 45, 90), 12, replace = TRUE)
)

p50 <- ggplot(word_data, aes(x = x, y = y)) +
  geom_text(aes(label = word, size = frequency, color = frequency, angle = angle),
            family = "playfair", fontface = "bold", alpha = 0.8) +
  scale_size_continuous(range = c(3, 12), guide = "none") +
  scale_color_gradient2(
    low = "#1A1A2E", 
    mid = "#533A71", 
    high = "#AB0E86",
    guide = "none"
  ) +
  labs(
    title = "Data Visualization Word Cloud",
    subtitle = "Key concepts in beautiful typography"
  ) +
  xlim(-6, 6) +
  ylim(-4, 4) +
  theme_void()

print(p49)

Show Code 
print(p50)

26. Interactive-Style and Dashboard Elements

Creating dashboard-like visualizations with multiple panels.

Show Code 
# KPI dashboard style
kpi_data <- data.frame(
  metric = c("Revenue", "Customers", "Conversion", "Satisfaction"),
  current = c(78000, 1250, 12.5, 8.2),
  target = c(80000, 1200, 15.0, 8.5),
  previous = c(72000, 1100, 10.2, 7.8)
) %>%
  mutate(
    vs_target = (current - target) / target * 100,
    vs_previous = (current - previous) / previous * 100,
    status = case_when(
      vs_target > 0 ~ "Above Target",
      vs_target > -5 ~ "Near Target", 
      TRUE ~ "Below Target"
    )
  )

p51 <- kpi_data %>%
  tidyr::pivot_longer(cols = c(current, target, previous), 
                      names_to = "period", values_to = "value") %>%
  ggplot(aes(x = metric, y = value, fill = period)) +
  geom_col(position = "dodge", alpha = 0.8, width = 0.7) +
  geom_text(aes(label = scales::comma(value, accuracy = 0.1)), 
            position = position_dodge(width = 0.7), vjust = -0.3,
            family = "source", fontface = "bold", size = 3) +
  scale_fill_manual(values = c("current" = "#1A1A2E", "target" = "#533A71", "previous" = "#0F3460")) +
  facet_wrap(~metric, scales = "free", nrow = 1) +
  labs(
    title = "Executive Dashboard - Key Performance Indicators",
    subtitle = "Current performance vs targets and historical comparison",
    x = "",
    y = "Value",
    fill = "Period"
  ) +
  theme(
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    strip.text = element_text(size = rel(1.1), face = "bold")
  )

# Gauge chart simulation
gauge_data <- data.frame(
  metric = "Performance Score",
  value = 75,
  min_val = 0,
  max_val = 100
) %>%
  mutate(
    # Create gauge segments
    angle_start = pi,
    angle_end = 0,
    value_angle = angle_start + (value / max_val) * (angle_end - angle_start)
  )

# Create tick marks separately
gauge_ticks <- data.frame(
  tick_angles = seq(pi, 0, length.out = 11),
  tick_values = seq(0, 100, 10)
)

gauge_segments <- data.frame(
  start_angle = seq(pi, 0, length.out = 101)[-101],
  end_angle = seq(pi, 0, length.out = 101)[-1],
  segment_value = 0:99
) %>%
  mutate(
    color_zone = case_when(
      segment_value < 30 ~ "Low",
      segment_value < 70 ~ "Medium",
      TRUE ~ "High"
    ),
    x1 = 0.8 * cos(start_angle),
    y1 = 0.8 * sin(start_angle),
    x2 = cos(start_angle),
    y2 = sin(start_angle)
  )

p52 <- ggplot() +
  geom_segment(data = gauge_segments, 
               aes(x = x1, y = y1, xend = x2, yend = y2, color = color_zone),
               size = 3, alpha = 0.8) +
  geom_segment(x = 0, y = 0, 
               xend = 0.7 * cos(gauge_data$value_angle), 
               yend = 0.7 * sin(gauge_data$value_angle),
               color = "#2C3E50", size = 3, 
               arrow = arrow(length = unit(0.3, "cm"))) +
  geom_point(x = 0, y = 0, size = 5, color = "#2C3E50") +
  scale_color_manual(values = c("Low" = "#6A0572", "Medium" = "#533A71", "High" = "#1A1A2E")) +
  annotate("text", x = 0, y = -0.3, label = paste(gauge_data$value, "%"), 
           size = 8, family = if (.Platform$OS.type == "windows") "Times New Roman" else "playfair", 
           fontface = "bold", color = "#2C3E50") +
  labs(
    title = "Performance Gauge Visualization",
    subtitle = "Real-time metric monitoring",
    color = "Performance Zone"
  ) +
  coord_equal() +
  xlim(-1.2, 1.2) +
  ylim(-0.5, 1.2) +
  theme_void()

print(p51)

Show Code 
print(p52)

Conclusion: Best Practices for Beautiful ggplot2 Visualizations

Key Takeaways:

  1. Custom Themes: Creating a consistent, branded look across all your visualizations
  2. Color Psychology: Using colors that enhance readability and convey the right message
  3. Typography: Selecting appropriate fonts that match your visualization’s purpose
  4. White Space: Embracing clean, uncluttered designs with strategic use of white space
  5. Annotations: Adding context and highlighting key insights directly on the plot
  6. Layering: Combining multiple geoms to create rich, informative visualizations

Advanced Tips:

  • Use scales package for professional formatting of axes
  • Leverage viridis and RColorBrewer for scientifically-backed color palettes
  • Apply patchwork for combining multiple plots elegantly
  • Implement consistent spacing and alignment across plot elements
  • Consider your audience and the story you want to tell

This comprehensive guide covers the essential plot types in ggplot2, each enhanced with our custom theme that prioritizes clean aesthetics, readability, and visual appeal. The combination of thoughtful color choices, beautiful typography, and strategic use of white space creates visualizations that not only inform but also inspire.

Remember: Great data visualization is not just about the data—it’s about creating a visual narrative that guides your audience to insights in an elegant and memorable way.