Resources
This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.
R Primers
R Primers offer updated RStudio/Posit educational content, now utilizing Quarto and webR. Originally developed by RStudio/Posit Education Team, these open-source tutorials help users learn R programming, deriving content from the book 'R for Data Science'. They are licensed under the CC BY-SA 4.0, ensuring wide accessibility for learners to improve their data science skills with R.
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Recreate Some SAS® Procedures in R Using {procs}
The R package 'procs' replicates commonly used SAS procedures, targeting functions like PROC FREQ, PROC MEANS, PROC TTEST, and PROC REG. It simplifies the transition for SAS users to R by providing familiar functionality and outputs. This includes rich reporting outputs similar to SAS, pre-validated results to ensure fidelity with SAS outputs, ease of adoption for existing SAS users, and stability to maintain backward compatibility. The package includes data manipulation functions and aims to help save time in statistical results comparison and reporting.
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Reproducible Data Science in R: Iterate, don't duplicate
This blog post on the Water Data For The Nation Blog guides novice to intermediate R users on how to achieve reproducible data science by replacing code duplication with iteration techniques. It introduces the 'map()' function from the purrr package, explaining its advantages over copy/paste approaches and for loops. The post covers mapping techniques, the usage of lists, various map_*() function variants, and working with multiple inputs or no outputs. It is part of a series aimed at building functional programming skills and creating efficient data workflows with the targets R package.
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resouRces
This content encompasses a comprehensive list of R-related educational materials, packages, tutorials, and datasets with projected dates ranging up to the year 2025. It includes various titles that focus on learning R programming, data analysis, data visualization, geospatial mapping, and statistical methods. Significant emphasis is placed on resources for learning R, such as introductions to R, books, courses, and video tutorials. Additionally, specific packages for data wrangling, statistical modeling, and visualization are mentioned, indicating the evolution and specialization of R's ecosystem to cater to diverse data science needs.
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shinymcp
shinymcp is a package that adapts Shiny applications for integration with AI chat interfaces such as Claude Desktop. It enables the creation of MCP Apps, which feature interactive UIs that run directly within chat conversations. To accomplish this, it restructures the traditional reactive programming model of Shiny into discrete tool functions that respond to user input changes. The package provides utilities for both manual and automatic conversion of existing Shiny applications. Developers can leverage familiar Shiny components, with the shinymcp bridge automatically detecting the connection between UI components and R code tools.
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Some good practices for research with R
The article discusses best practices for conducting research with R, covering data validation with the {validate} package, ensuring reproducibility with {renv}, and using {here} for reproducible paths. It explains how to validate data by defining rules and confronting datasets with expectations, providing examples with code snippets. Moreover, it emphasizes the importance of package management for reproducibility, cautioning against the risks of evolving or unsupported packages and offering solutions like package snapshots with {renv}. It is aimed at R users conducting research who seek to avoid errors and ensure consistent results over time.
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Use SAS, R, and quarto Together • sasquatch
sasquatch is an R package that enables the integration of SAS, R, and Quarto for creating reproducible multilingual reports. It utilizes SASPy and reticulate to run SAS code blocks within R, transfer data between SAS and R, perform SAS client file management, and render SAS output in quarto documents. The package includes installation instructions for development version, Python, and SASPy. It offers functionality such as interactive execution of SAS code, data conversion between R and SAS, and rendering quarto documents with SAS output, distinguishing it from similar packages like sasr, configSAS, and SASmarkdown.
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Use SAS, R, and quarto together with sasquatch
sasquatch is a package that allows the integration of SAS, R, and Quarto to create reproducible multilingual reports. The package facilitates running SAS code blocks, managing data and files across SAS and R, and rendering outputs within Quarto or R Markdown documents. It also provides functionalities for installing dependencies like Python's SASPy, configuring SAS, especially for SAS On Demand for Academics, and managing Quarto document templates for seamless integration with SAS output. Users can pass data between R and SAS, execute code blocks interactively, and render polished documents with familiar SAS styles.
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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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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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