Resources

The guide to gradients in R and ggplot2

This content is a comprehensive guide to using gradients in R and ggplot2, created by James Goldie. It covers everything from basic gradient applications to creating advanced mesh gradients within ggplot2 for enhanced visualization. The guide, published on February 24, 2025, includes examples and tutorials for applying gradient effects to various plot elements in R's ggplot2 package, utilizing functions from the 'grid' package, and considerations for themes and aesthetic choices. It also touches on the support for gradients in R version 4.1 and ggplot2 version 3.5.0, as well as how to work with system fonts and R graphics devices that support gradients.

The MockUp - Creating and using custom ggplot2 themes

Creating and using custom ggplot2 themes

The power of three: purrr-poseful iteration in R with map, pmap and imap

This post explores the map family of functions in the purrr package, which provide useful tools for iterating through lists and vectors in R. It focuses on map, pmap, and imap functions and their uses in manipulating multi-dimensional datasets and applying statistical models.

The R Package Workflow

This text describes the R package workflow for structuring data science projects.

The RedMonk Programming Language Rankings: January 2024

This content provides an overview of the RedMonk Programming Language Rankings for January 2024. Sponsored by AWS, the analysis examines programming language popularity based on GitHub pull requests and Stack Overflow discussions, aiming to forecast adoption trends rather than current usage. The methodology is a continuation of work from 2010 by Conway and Myles White, updated for changes in data sources and collection methods. While anomalies in the data suggest an impact from AI code assistants, the rankings still strive to reflect meaningful insights into language traction and potential future shifts in developer preference.

The Test Set

The Test Set is a podcast series by Posit featuring discussions with key figures in data science. Listeners can expect conversations with experts like Hadley Wickham and Wes McKinney, covering topics from the inception of tidyverse to open source funding. The show dives into the personal origin stories of its guests, the evolution of data science tools, community-building, and the importance of reproducible research. The aim is to provide insights into the journeys that have shaped modern data science, and to explore the narratives that drive this field forward.

The Tidy Trekker - Making Circular Maps in ggplot

Learn how to create circular maps in ggplot using R, with a step-by-step tutorial and code examples.

The tidyverse style guide

The tidyverse style guide

The World Started Tracking Severe Food Insecurity in 2016

Published on October 13, 2025, this content outlines the significant rise in global tracking of Severe Food Insecurity following the year 2016. Using TidyTuesday data visualization techniques with R Programming, a line chart depicted the participation of countries from 2005 to 2025 in tracking five food security indicators. A stark increase is observed post-2016 with approximately 70% more countries reporting on Moderate/Severe Food Insecurity. The document also provides an insightful tutorial on how to load packages, read data, tidy datasets, and create engaging visuals using R, culminating in a compelling narrative about the state of food security worldwide.

tidy data

Tidy data for efficiency, reproducibility, and collaboration

This illustrated series discusses the power of tidy data for efficiency, reproducibility, and collaboration in data science. It emphasizes the importance of organizing data in a structured and standardized format, which enables the use of existing tools, facilitates collaboration, and enhances reproducibility. The series provides examples and resources for working with tidy data and highlights its benefits in data analysis and research.

Tidy Data Vignette

Tidy data is a concept in data analysis that involves structuring datasets to facilitate analysis. The tidy data standard provides a standardized way to organize data values within a dataset. This resource is a vignette that explains the principles and importance of tidy data and provides examples in R using the tidyr package.