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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 A Scientist's Guide to R: Step 2.1. Data Transformation - Part 1

A Scientist's Guide to R: Step 2.1. Data Transformation - Part 1

This post is part of the Scientist's Guide to R series and focuses on data transformation techniques for wrangling, tidying, and cleaning data. It introduces the core functions of the dplyr package, as well as other relevant functions in base R. The post covers topics such as selecting columns, filtering rows, modifying columns, obtaining descriptive summaries of data, assigning grouping structures, and arranging data frames. The post also mentions the data.table package for working with large datasets. The examples in the post demonstrate how to use the select() function from the dplyr package to subset columns from a data frame.

Screenshot of A Scientist's Guide to R: Step 2.2 - Joining Data with dplyr

A Scientist's Guide to R: Step 2.2 - Joining Data with dplyr

This post is part of the Scientist's Guide to R series and focuses on using joins to combine data frames in R with the dplyr package. It covers different types of joins, such as inner, left, right, full, semi, and anti join, as well as using bind_rows() or bind_cols() to build data frames. The post also mentions the merge() function in base R for joining data frames.

Screenshot of A Scientist's Guide to R: Step 2.3 - string manipulation and regex

A Scientist's Guide to R: Step 2.3 - string manipulation and regex

A Scientist's Guide to R: Step 2.3 - string manipulation and regex

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 Add last rendered or modified time to Quarto

Add last rendered or modified time to Quarto

Garrick Aden-Buie's blog post introduces 'now,' a Quarto extension that allows the automatic update of time information in Quarto documents. This extension saves time by eliminating the need for manual updates of dates in documentation footers. By adding the extension using 'quarto add gadenbuie/quarto-now,' Quarto users can employ shortcodes like '{{< now >}}' and '{{< modified >}}' to display the current or last modified time. The extension supports customization of time output formats and may significantly streamline Quarto project maintenance by ensuring date accuracy without manual intervention.

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)