Resources

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.

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

The tidyverse style guide

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 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.

A live screencast of a Tidy Tuesday session where global crop yields are analyzed using R.

Load US Census Boundary and Attribute Data as tidyverse and sf-Ready Data Frames

Animations of tidyverse verbs using R, the tidyverse, and gganimate.

Tidygeocoder is an R package that makes getting data from geocoding services easy. It provides a unified high-level interface for a selection of supported geocoding services and returns results in tibble (dataframe) format.

State-by-State Spending on Kids Dataset from the Urban Institute in a Tidy Format

Tidylog provides feedback about dplyr and tidyr operations. It provides wrapper functions for the most common functions, such as filter, mutate, select, and group_by, and provides detailed output for joins.