Resources
This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.
Project-oriented workflow
This blog post discusses the importance of a project-oriented workflow in R and provides recommendations for organizing data analysis into self-contained projects.
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purrr
Purrr is a package in R that enhances functional programming (FP) toolkit by providing a complete and consistent set of tools for working with functions and vectors.
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purrr tutorial
A tutorial on using the purrr package in R, including examples and lessons on various topics such as vectors, lists, mapping, list columns, and more.
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qualtRics
The qualtRics R package implements the retrieval of survey data using the Qualtrics API and aims to reduce the pre-processing steps needed in analyzing such surveys.
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Quarto Extensions
This content presents a catalog of various Quarto extensions, complete with metadata such as the release date, author, version, and the number of stars on GitHub. The extensions cover a wide array of functionalities to enhance HTML documents, websites, blogs, books, and academic publications. Some specific features include embedding webR, minimalist themes for presentations, APA7 document formatting, countdown timers, inclusion of vector icons, and integration of interactive elements like Shinylive and code editors. The Quarto extensions support a diversity of outputs, such as PDF, HTML, and slides, and cater to needs in publishing, theming, and interactivity.
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Quarto for Scientists
Quarto for Scientists is an educational material designed to teach scientists how to create reproducible reports using Quarto with R Markdown. It covers installation, workflow, and various features such as figure and table management, equations, bibliographies, and debugging. Initially a 3-hour workshop, it has evolved into a living book, providing a structured learning experience. With Quarto, scientists can integrate code, text, and figures into one file, enabling anyone to reproduce their research with the provided datasets and Quarto files. Nicholas Tierney authored this resource to fill a niche in R Markdown education for scientists.
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R as GIS, Part 1
This post is part of a series that explores the GIS capabilities of R, focusing on working with vector spatial data using the `sf` package. It covers topics such as loading spatial data, manipulating sf objects using dplyr functions, and working with coordinate reference systems.
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R Best Practices
This post provides a discussion of best practices for developing code-based projects and for writing R code in a research setting with an eye toward proactively avoiding common pitfalls.
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R for Data Science (2e)
R for Data Science (2e) is a comprehensive guide to performing data science tasks with R. It covers how to import, structure, transform, and visualize data while teaching best practices in data cleaning, plotting, and more. The book promotes literate programming and reproducible research to streamline work. It supports cognitive resource management for data wrangling and exploration. The content is freely available under the CC BY-NC-ND 3.0 License, with an option to support kākāpō conservation. Physical copies can be ordered on Amazon, and solutions to exercises are provided online.
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R for Data Science, Chapter 16: Dates and Times
This chapter in the R for Data Science book provides an introduction to working with dates and times in R. It covers topics such as creating date/times, parsing strings into date/time objects, and working with date/time components. The chapter also introduces the 'lubridate' package, which makes it easier to work with dates and times in R.
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R for Data Science: Chapter 21: Iteration
This text is a part of the book 'R for Data Science' and provides an introduction to iteration in R. It covers the benefits of reducing code duplication, the use of functions and iteration to achieve this, and introduces the concepts of imperative programming and functional programming. It also provides examples of using for loops to compute the median of each column in a dataframe.
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