Resources
This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.
Using project scripts to keep Quarto source and output files organised
Pete Jones provides a tip for managing Quarto projects where the user wants to separate source (.qmd files) and output (.pdf, .html, etc.) files, especially when source files are in a subdirectory. The article addresses the problem that arises when Quarto's default settings place rendered output next to source files, which can create organizational issues in projects. The article intends to show how to overcome this by leveraging Quarto's features to keep files neatly organized within project subdirectories.
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Using project scripts to keep Quarto source and output files organised
Pete Jones shares a tip for organizing Quarto project files, focusing on separating source (.qmd) and output (.pdf, .html) files when source files are stored in subdirectories. This is a particular challenge due to Quarto's default behavior of placing output files next to their source. The article discusses project-based workflows, the issues with Quarto subdirectories, and leverages Quarto features to solve the problem. A scripted solution is presented to ensure outputs are organized in a specified directory, maintaining a clean project structure even with source files in subdirectories.
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Using renv in R
The content is a blog post by Erik Gahner Larsen discussing the use of the 'renv' package in R for managing package dependencies and ensuring reproducibility in R projects. It highlights issues faced when R scripts fail due to package updates or system changes and presents 'renv' as a solution for creating isolated project environments with specific package versions. This ensures that R scripts remain functional over time by snapshotting and restoring package states, thus allowing others to run the code with the intended results, even if the R landscape changes.
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Using the tidyverse with Databases
Using the tidyverse with Databases - Part I is a tutorial that provides an introduction to using databases in R with Tidyverse tools. The tutorial covers topics such as motivation, connecting to a database, using DBI and dplyr functions, executing queries with dbplyr, and more.
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Visualizing {dplyr}’s mutate(), summarize(), group_by(), and ungroup() with animations
Andrew Heiss explores {dplyr} functions like mutate(), summarize(), group_by(), and ungroup() through handmade animations. Heiss illustrates the complexities of data manipulation workflow steps that are not immediately obvious when performing operations like adding new columns or summarizing data. This tutorial helps conceptualize the impact of these functions on data frames by visualizing their behind-the-scenes mechanics. The animations serve as an effective teaching tool to demystify what happens to data during different stages of the tidyverse's split/apply/combine paradigm, using handcrafted illustrations to make the learning process more intuitive.
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vroom
vroom is a package in R that provides the fastest delimited reader. It uses lazy loading and multiple threads for improved performance. It supports various parsing features, such as delimiter guessing, custom delimiters, column types specification, and more.
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W. Joel Schneider
This text demonstrates the usage of arrow geometries in ggplot2 for creating custom arrowheads.
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Welcome to ModernDive (v2) | Statistical Inference via Data Science
ModernDive (v2) is the website for 'Statistical Inference via Data Science: A ModernDive into R and the Tidyverse (Second Edition)'. It showcases updates from the first edition, which is available online and for purchase. The book, authored by Chester Ismay, Albert Y. Kim, and Arturo Valdivia, teaches R and data science concepts. It's scheduled for print by CRC Press in 2025 and is licensed under Creative Commons. Readers can contribute on GitHub and anticipate a resource-rich approach to stats with a focus on tidyverse tools for data analysis.
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What does deprecated mean? Package lifecycle and the process of deprecation
This content describes the lifecycle stages of the tidyverse ecosystem, including stable, deprecated, superseded, and experimental stages, mainly as they apply to functions. It outlines how the stages affect the usability and changes in functions, with a focus on preventing and managing breaking changes. Emphasis is placed on ensuring code robustness by careful use of functions according to their intended effects. The content also addresses the gradual deprecation process, which provides warnings and guidance for replacing outdated functions, and introduces the 'lifecycle' package for managing these transitions.
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What does deprecated mean? Package lifecycle and the process of deprecation. - YouTube
This YouTube video provides an explanation of the meaning of 'deprecated' and discusses the package lifecycle and the process of deprecation.
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What is Takes to Tidy Census Data
This article explains the process of tidying Census data using R and tidyverse packages.
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