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This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.

Screenshot of Telling Stories with Data

Telling Stories with Data

Telling Stories with Data by Rohan Alexander is a comprehensive guide on communicating insights effectively using data in R and Python. Published by Chapman and Hall/CRC, the book is endorsed by experts for its unique approach in emphasizing statistical communication, programming, and modeling. It covers the entire data science workflow, including data acquisition, analysis, and reproducibility, making it an excellent resource for statistics courses or self-learning. It focuses on developing the computational and philosophical skills necessary for sense-making and telling stories with data, making it a valuable tool for data scientists and analysts.

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Screenshot of Tidy Flowchart Generator

Tidy Flowchart Generator

The Tidy Flowchart Generator, or the 'flowchart' package, is an R package designed for drawing participant flow diagrams directly from a dataframe, employing the tidyverse syntax. It offers a suite of functions that utilize the pipe operator to generate flowcharts conveniently and flexibly from dataframes. The package is accessible through CRAN and can be installed traditionally or via the development version on GitHub. The process of creating a flowchart with this tool is demonstrated through a GIF example on its homepage, showcasing its usefulness in drafting flow diagrams for clinical trials or similar studies.

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Screenshot of Transform Google Docs into Quarto Books with {quartificate}

Transform Google Docs into Quarto Books with {quartificate}

The 'quartificate' package is designed to convert Google Documents into Quarto books, facilitating the transition from a simple document to a structured and maintainable book format. It streamlines the process by exporting the document into a Docx file, converting it to Markdown via Pandoc, and then sectioning it into HTML chapters based on header levels. This enables users to easily manage and render their content as a Quarto book. The package also provides seamless integration with Googledrive for authentication and document retrieval, and offers a quick start to render and view the book using the 'servr' package.

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Screenshot of Use meta shortcode variables with Quarto Includes to insert precise content - Posit

Use meta shortcode variables with Quarto Includes to insert precise content - Posit

This blog post by Ashley Henry at Posit discusses how to use meta shortcode variables with Quarto Includes to ensure precise content insertion in your documentation. It delves into the benefits of using Quarto, a system for technical writing that enhances the insertion process by allowing for better customization and modularity. The post is targeted at users who are involved in the documentation process and are looking for methods to optimize content consistency while maintaining flexibility. Ashley Henry also shares her expertise from her time in the Navy and at Posit, where she focuses on clear and effective documentation.

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Screenshot of Use SAS, R, and quarto Together • sasquatch

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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Screenshot of Using project scripts to keep Quarto source and output files organised

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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Screenshot of Using project scripts to keep Quarto source and output files organised

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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Screenshot of Welcome to ModernDive (v2) | Statistical Inference via Data Science

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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Screenshot of Which names that are also names of countries are most common?

Which names that are also names of countries are most common?

Simon P. Couch's blog post investigates which names that are also names of countries are most popular in the United States from 1880 to 2017. The post details the process of using R and packages like 'babynames' and 'countrycode' to filter and analyze baby name data. It offers a glimpse into the results, revealing the top country-names and their trends over time. Readers are encouraged to guess the most common names before seeing the data-driven answer. The post concludes with interesting visualizations showing the historical trends for the top country-names.

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Screenshot of xaringan

xaringan

Presentation Ninja with xaringan - a tutorial and package for creating presentations in R using the xaringan package

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Screenshot of xaringanBuilder

xaringanBuilder

renderthis is an R package for generating HTML slideshows using the xaringan package.

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Screenshot of xaringancolor

xaringancolor

Uniform Colors in Xaringan Presentations

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