Resources
This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.
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.
Go to Resource
Apple Music Wrapped with R
Andrew Heiss demonstrates how to create an Apple Music Wrapped equivalent using R, providing a data-driven alternative for Apple Music users who lack the Spotify Wrapped feature. Heiss shares his personal preference for owning music files over streaming, utilizing iTunes/Apple Music and iTunes Match for cross-device access. The article details extracting song metadata from an XML file, which stores play counts and other information, to perform data analysis and visualization, mimicking the Spotify Wrapped experience. He criticizes the single-entry play count system but optimistically approaches creating personalized music data statistics.
Go to Resource
Applied Data Skills
The 'Applied Data Skills' book by Emily Nordmann and Lisa DeBruine is designed to teach the fundamentals of data processing and presentation using R. It guides learners through data import, cleaning, summarization, visualization, and report generation, aiming to provide skills for professional reporting and presenting. The book is part of a 10-week course with each chapter introducing new concepts and practical exercises. It emphasizes learning through practice, error resolution, and the efficient use of help resources rather than memorization. The goal is to enable learners to create automated, updateable reports and visualizations with R.
Go to Resource
argonDash
Argon Shiny Dashboard Template is a Bootstrap4 dashboard template for creating Shiny applications. It requires the installation of the 'argonR' package. The template includes vertical and horizontal layouts and is based on the original argon dashboard HTML template designed by Creative Tim. The project has a Contributor Code of Conduct and is licensed under GPL-2. It is developed by David Granjon.
Go to Resource
Automate subset plots with ggplot2 and purrr
Cedric Scherer's blog post, 'Efficiency and Consistency: Automate Subset Graphics with ggplot2 and purrr,' guides readers through the process of automating the creation of subset graphics in R using the ggplot2 and purrr packages. It explains how to eliminate redundant work when generating explorative or explanatory charts for various data subsets by iterating over a vector of groups with a custom function. The post provides a practical tutorial on improving efficiency and consistency when visualizing relationships for different numeric variables, with a focus on polished charts and including examples and shortcuts for data exploration.
Go to Resource
Baby got backreferences
This content is a written confession of a data practitioner about their struggle with and reluctant embrace of regular expressions. The author describes emotions and thoughts they undergo when considering the use of regular expressions for text manipulation tasks. The narrative is coupled with humorous references to fantasy elements such as Mordor and Sauron from 'The Lord of the Rings'. Despite self-admittedly not being an expert, the author offers a guide for regular expressions in R, beginning with loading the stringr package and reading song lyrics which serve as examples for pattern matching exercises.
Go to Resource
BBC Visual and Data Journalism cookbook for R graphics
The BBC Visual and Data Journalism team has crafted an R package, complemented by a cookbook, designed to assist in generating graphics in the BBC's signature style using the ggplot2 library in R. This resource streamlines the creation of professional-looking visuals and eases the learning curve for newcomers to R. The cookbook outlines the procedures for installation and usage of the necessary R packages, including 'bbplot', which is available directly from GitHub. It includes detailed guidance on customizing plots with BBC style elements, such as text size, font, and axis formatting, through practical examples with gapminder data.
Go to Resource
bbplot
R package that helps create and export ggplot2 charts in the style used by the BBC News data team
Go to Resource
Best Practices for Data Visualisation
This content outlines best practices for creating effective and accessible data visualisations. It provides insights, advice, and code examples to enhance the readability and impact of data presentations. Emphasizing both the art and science of data visualisation, it guides readers through core principles and elements of designing charts and tables. The guide also addresses the need for authorial choices in storytelling through data and the importance of customizing default settings to convey information effectively. Aimed at Royal Statistical Society contributors, the advice is broadly applicable and includes resources for chart selection and accessibility.
Go to Resource
![Screenshot of Big Book of R at 400 [New milestone!]](/img/containers/screenshots/oscarbaruffa-com-bbor_400.jpg/74e022268aa288d6c1711920b3365082.jpg)
Big Book of R at 400 [New milestone!]
Oscar Baruffa's 'Big Book of R' has reached a new milestone with over 400 entries of mostly free R books, witnessing the growth of an invaluable resource for the R community. Acknowledging contributors and the support of visitors, Baruffa emphasizes the quality and impact of the collection. The announcement highlights the costs associated with hosting the 'Big Book of R' and encourages contributions. New additions cover topics like big data analytics, hierarchical compartmental reserving models, R package design, epidemiology, causal data science, and psychometrics, showcasing the diversity and depth of the resources available.
Go to Resource