Does this skill support the latest ggplot2 4.0 updates?

Yes, this skill is specifically optimized for ggplot2 4.0 and later, including S7 property access using the @ symbol and new theme-based layer defaults.

How does it handle axis and legend labels?

The skill supports multiple labeling methods, including dictionary-based labels, functional labels, and auto-populating labels from data frame attributes.

Can I use this skill to create custom themes?

Absolutely. It covers advanced theming features like element_geom(), from_theme(), and the new theme_sub_* shortcuts to make theme customization more efficient.

What new statistical transformations are included?

It provides guidance on new stats like stat_manual() for custom transformations and stat_connect() for creating specialized connection shapes between data points.

R ggplot2 Data Visualization

Name: R ggplot2 Data Visualization
Author: jsperger

byjsperger

•

Data Science & ML

Streamlines R data visualization workflows with expert guidance on ggplot2 4.0 features and grammar of graphics implementation.

The ggplot2 skill empowers Claude to act as a specialist in R data visualization, specifically optimized for the major advancements in ggplot2 4.0. It provides comprehensive support for the S7 class migration, advanced theme customization using ink and paper properties, and streamlined theme shortcuts. By integrating deep knowledge of the grammar of graphics, this skill assists developers in creating publication-quality plots, managing complex faceting layouts, and utilizing new statistical transformations like stat_manual and stat_connect.

Key Features

01Automated labeling via data frame attributes and dictionary mapping

021 GitHub stars

03Advanced theme styling with ink, paper, and accent property defaults

04Comprehensive support for ggplot2 4.0 S7 class property access

05Streamlined theme_sub_* shortcuts for concise axis and legend control

06Advanced facet control including directional wrapping and free space layouts

Use Cases

01Migrating legacy R plotting scripts to the modern ggplot2 4.0 S7 standard

02Designing complex, branded visualization themes for professional reports

03Implementing custom statistical layers and connections for specialized data analysis

Key Features

01Automated labeling via data frame attributes and dictionary mapping

021 GitHub stars

03Advanced theme styling with ink, paper, and accent property defaults

04Comprehensive support for ggplot2 4.0 S7 class property access

05Streamlined theme_sub_* shortcuts for concise axis and legend control

06Advanced facet control including directional wrapping and free space layouts

Use Cases

01Migrating legacy R plotting scripts to the modern ggplot2 4.0 S7 standard

02Designing complex, branded visualization themes for professional reports

03Implementing custom statistical layers and connections for specialized data analysis