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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 A Scientist's Guide to R: Step 2.4 - forcats for factors

A Scientist's Guide to R: Step 2.4 - forcats for factors

This post is part of a series called A Scientist's Guide to R and focuses on how to work with factors in R using the forcats package.

Screenshot of A Scientist's Guide to R: Step 2.5 - dates & times

A Scientist's Guide to R: Step 2.5 - dates & times

A Scientist's Guide to R: Step 2.5 - dates & times is a blog post that provides a guide on how to work with dates and times in R using the lubridate package. It covers topics such as date/time basics, reading dates, time zones, month names, extracting datetime components, custom date formats, date calculations, and planning a behavioural neuroscience experiment. The post aims to help readers handle dates and times effectively in R.

A SIMPLE guide to create BUMP CHARTS with ggplot2 - YouTube

A YouTube tutorial on creating bump charts with ggplot2

Screenshot of A Twitter bot with {rtweet} and GitHub Actions

A Twitter bot with {rtweet} and GitHub Actions

A blog post about creating a Twitter bot using the R package rtweet and GitHub Actions

Screenshot of Adding a logo to images with {magick} and {purrr}

Adding a logo to images with {magick} and {purrr}

Jadey Ryan shared her experience with automating the process of adding logos to images using the R packages {magick} and {purrr}. She also highlighted new merchandise such as tote bags and embroidered hats on her Etsy shop. Additionally, she announced a free webinar series on soil health where she will demo the {soils} R package. Alongside, she informed about the Parameterized Quarto workshops she is giving, focusing on efficient report generation using R and Quarto, with the next one scheduled with R-Ladies Abuja.

Screenshot of Adding social media icons to charts with {ggplot2}

Adding social media icons to charts with {ggplot2}

Adding social media icons to charts with {ggplot2}

Screenshot of Advanced Reproducible Research in R

Advanced Reproducible Research in R

This content covers an advanced workshop titled 'Advanced Reproducible Research in R,' designed to teach collaborative and automated analysis pipelines in scientific research. It emphasizes the importance of reproducibility and open scientific practices, presenting solutions to challenges such as coding standards, software dependency documentation, and data analysis automation. The course uses a code-along format with real-world datasets, created with Quarto, GitHub, and GitHub Actions. The material is available on a website and the r-cubed-advanced GitHub repository, licensed under Creative Commons for open use and modification.

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

Creating interactive visualizations with ggiraph (with or without Shiny)

Screenshot of Albert Rapp - Four reasons to learn HTML + CSS as an R programmer

Albert Rapp - Four reasons to learn HTML + CSS as an R programmer

Four reasons to learn HTML + CSS as an R programmer

Screenshot of An Exploration of Simple Features for R

An Exploration of Simple Features for R

Introduction to GIS with R through the sp and sf packages.

Screenshot of An Exploration of Simple Features for R

An Exploration of Simple Features for R

An exploration of the implementation of simple features standard by the sf package for R

Screenshot of Animated active fire maps using NASA FIRMS data in R

Animated active fire maps using NASA FIRMS data in R

This video tutorial demonstrates how to create animated active fire maps using NASA's Fire Information for Resource Management System (FIRMS) data within R. Viewers will learn the techniques for importing and handling satellite-derived fire data, processing it, and then visualizing the active fire spots over time using animation tools in R. The tutorial is designed for data analysts, environmental researchers, and GIS specialists interested in mapping and geospatial analysis to monitor wildfires and understand spatial patterns using NASA's publicly available data sets in R programming environment.