AdvancedGgplot

A modern and flexible data visualization package for R, designed to provide an alternative to ggplot2 with a clean, composable API and object-oriented design.

Features

• Modular, object-oriented design using R6 classes
• Composable layers for building complex visualizations
• Flexible aesthetic mapping system
• Support for multiple geometry types:
  • Points, lines, bars, histograms
  • Area, density, ribbons, contours
  • Smoothing with confidence intervals
  • Statistical visualizations (boxplots, violin plots)
• Advanced features:
  • Faceting for multi-panel plots
  • Theming system for customizing plot appearance
  • Coordinate systems for different data projections
  • Annotations for adding context to visualizations
  • Interactive elements for dynamic visualization
  • Animation capabilities for temporal data

Installation

# Install from GitHub
# install.packages("devtools")
devtools::install_github("christopherodoom/AdvancedGgplot")

Usage Examples

Basic Plot

library(AdvancedGgplot)

# Create sample data
data <- data.frame(
  x = 1:10,
  y = sin(1:10),
  z = cos(1:10)
)

# Create a simple scatter plot
plot <- AGPlot$new(data)
plot$add_layer(geom_point(mapping = list(x = "x", y = "y", color = "blue")))
plot$plot()

Combined Geometries

# Combine multiple geometries
plot <- AGPlot$new(data)
plot$add_layer(geom_point(mapping = list(x = "x", y = "y", color = "blue")))
plot$add_layer(geom_line(mapping = list(x = "x", y = "y", color = "blue")))
plot$add_layer(geom_point(mapping = list(x = "x", y = "z", color = "red")))
plot$add_layer(geom_line(mapping = list(x = "x", y = "z", color = "red")))
plot$plot()

Customizing with Themes and Annotations

# Create a more advanced plot with custom theme and annotations
plot <- AGPlot$new(data)
plot$add_layer(geom_line(mapping = list(x = "x", y = "y", color = "blue")))
plot$add_layer(geom_smooth(mapping = list(x = "x", y = "y")))
plot$set_theme(theme_dark())
plot$add_annotation(annotation_text(x = 5, y = 0.5, label = "Peak"))
plot$plot()

Statistical Visualization with Boxplots

# Create data for boxplot example
categories <- rep(c("A", "B", "C", "D"), each = 30)
values <- c(
  rnorm(30, mean = 5, sd = 1),   # Category A
  rnorm(30, mean = 7, sd = 1.5), # Category B
  rnorm(30, mean = 4, sd = 0.8), # Category C
  rnorm(30, mean = 6, sd = 2)    # Category D
)
box_data <- data.frame(category = categories, value = values)

# Create a boxplot
plot <- AGPlot$new(box_data)
plot$add_layer(geom_boxplot(mapping = list(
  x = "category", 
  y = "value",
  fill = "lightblue",
  color = "navy"
)))
plot$plot()

Violin Plots for Distribution Visualization

# Using the same data as the boxplot example
plot <- AGPlot$new(box_data)
plot$add_layer(geom_violin(mapping = list(
  x = "category", 
  y = "value",
  fill = "lightgreen",
  color = "darkgreen",
  alpha = 0.7
)))
plot$plot()

# Combining boxplots and violin plots
plot <- AGPlot$new(box_data)
plot$add_layer(geom_violin(mapping = list(
  x = "category", 
  y = "value",
  fill = "lightgreen",
  alpha = 0.5
)))
plot$add_layer(geom_boxplot(mapping = list(
  x = "category", 
  y = "value",
  width = 0.3
)))
plot$plot()

Faceted Plots

# Create a faceted plot
long_data <- reshape2::melt(data, id.vars = "x", variable.name = "series", value.name = "value")
plot <- AGPlot$new(long_data)
plot$add_layer(geom_line(mapping = list(x = "x", y = "value", color = "series")))
plot$set_facet(facet_wrap(formula = ~ series, ncol = 2))
plot$plot()

Roadmap

• Additional statistical visualizations
• Geographic mapping support
• Interactive web export
• Enhanced theming capabilities
• Performance optimizations for large datasets

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Areas we're particularly interested in:

• New geometry types
• Enhanced documentation and examples
• Performance improvements
• Test coverage expansion

License

This project is licensed under the MIT License - see the LICENSE file for details.