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Resources

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

Screenshot of cowplot

cowplot

The cowplot package provides various features that help with creating publication-quality figures, such as a set of themes, functions to align plots and arrange them into complex compound figures, and functions that make it easy to annotate plots and or mix plots with images.

Screenshot of CRAN - Package ggquiver

CRAN - Package ggquiver

An extension of 'ggplot2' to provide quiver plots to visualise vector fields.

Screenshot of Create spatial square/hexagon grids and count points inside in R with sf | Urban Data Palette

Create spatial square/hexagon grids and count points inside in R with sf | Urban Data Palette

Create spatial square/hexagon grids and count points inside in R with sf

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