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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 The power of three: purrr-poseful iteration in R with map, pmap and imap

The power of three: purrr-poseful iteration in R with map, pmap and imap

This post explores the map family of functions in the purrr package, which provide useful tools for iterating through lists and vectors in R. It focuses on map, pmap, and imap functions and their uses in manipulating multi-dimensional datasets and applying statistical models.

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Screenshot of The R Package Workflow

The R Package Workflow

This text describes the R package workflow for structuring data science projects.

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Screenshot of The RedMonk Programming Language Rankings: January 2024

The RedMonk Programming Language Rankings: January 2024

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.

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Screenshot of The Tidy Trekker - Making Circular Maps in ggplot

The Tidy Trekker - Making Circular Maps in ggplot

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

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Screenshot of The tidyverse style guide

The tidyverse style guide

The tidyverse style guide

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Screenshot of Tidy data for efficiency, reproducibility, and collaboration

Tidy data for efficiency, reproducibility, and collaboration

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.

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Screenshot of Tidy Data Vignette

Tidy Data Vignette

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.

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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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Tidy Tuesday live screencast: Analyzing global crop yields in R

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

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Screenshot of tidycensus

tidycensus

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

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Screenshot of tidyexplain

tidyexplain

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

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