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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 Create stylish tables in R using formattable

Create stylish tables in R using formattable

Create stylish tables in R using formattable

Screenshot of Create, scan, and correct exams with R | by Edgar J. Treischl | Medium

Create, scan, and correct exams with R | by Edgar J. Treischl | Medium

This blog introduces the R exams package and shows how to create, scan, and correct student exams using R. It demonstrates how R scans exam images, extracts answers from single or multiple choice questions, and corrects them automatically. It also highlights the next steps and how they are implemented in R, as well as how to create your own exam questions. The package helps automate the entire process of generating, scanning, and assessing exams.

Screenshot of Creating a cracked egg plot using {ggplot2} in R | Nicola Rennie

Creating a cracked egg plot using {ggplot2} in R | Nicola Rennie

Creating a cracked egg plot using {ggplot2} in R

Screenshot of Creating a data pipeline with Github Actions & the {googledrive} package for the Canadian Premier League soccer data initiative!

Creating a data pipeline with Github Actions & the {googledrive} package for the Canadian Premier League soccer data initiative!

Creating a data pipeline with Github Actions & the {googledrive} package for the Canadian Premier League soccer data initiative!

Screenshot of Creating interactive visualizations with {ggiraph} (with or without Shiny)

Creating interactive visualizations with {ggiraph} (with or without Shiny)

Albert Rapp's blog post explains how to create interactive visualizations using the {ggiraph} package with or without Shiny in the R programming environment. It guides readers through the process of turning a ggplot into an interactive plot where users can focus on details that interest them. The tutorial includes data preparation with 'dplyr' and 'ggplot2', and demonstrates how to add interactivity to both lines and points in a chart. The post covers the use of 'geom_point_interactive', 'geom_line_interactive', and 'girafe()' function for rendering, and customization options for hover effects and plot sizing.

Screenshot of Creating template files with R

Creating template files with R

Nicola Rennie's blog post teaches readers how to save time when dealing with repetitive tasks by creating template files with R. The post explains fine-tuning R scripts for tasks like #TidyTuesday, where similar sections are involved each week. Instead of copying and pasting scripts and GitHub README files weekly and updating parts manually, Rennie introduces a method for generating template files and folders based on a date argument. This process includes creating organized directories and template files, replete with content placeholders, which can then be customized for the specific week's work.

Creating template files with R | Nicola Rennie

Learn how to create template files in R to automate repetitive tasks.

Creating typewriter-styled maps in {ggplot2} | Nicola Rennie

Creating typewriter-styled maps in ggplot2. This blog post explains the process of gathering elevation data, selecting a suitable typewriter font, and coding up a map.

Screenshot of Data Cleaning Flipbook

Data Cleaning Flipbook

A flipbook with examples of data cleaning using R and the tidyverse package

Screenshot of Data cleaning for data sharing | Crystal Lewis

Data cleaning for data sharing | Crystal Lewis

Data cleaning for data sharing by Crystal Lewis in tutorials February 14, 2023.

Screenshot of Data Humans Podcast

Data Humans Podcast

Libby Heeren is a self-professed Data Human on a mission to speak candidly about the day-to-day work of data professionals and tear down the veil of mystery that hangs over the world of data jobs. Find her at datahumans.club

Screenshot of Data Science for the Biomedical Sciences

Data Science for the Biomedical Sciences

Data Science for the Biomedical Sciences is a book that provides an introduction to data science concepts and tools specifically tailored for the biomedical sciences. It covers topics such as spreadsheets, R and RStudio, data loading, descriptive calculations, data cleaning, visualization, analysis, working with multiple datasets, APIs, functions, survival analysis, machine learning, and more.