Resources

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.

artyfarty

Introduction to artyfarty

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.

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.

bbplot

R package that helps create and export ggplot2 charts in the style used by the BBC News data team

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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.

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.

biscale

Book announcement R 4 Social Network Analysis

The blog post 'R 4 Social Network Analysis' announces an in-progress book aimed at introducing social network analysis (SNA) in R to practitioners. Authored by schochastics and Termeh Shafie, both of whom have extensive experience in SNA and R package development, the book will cover key SNA topics and demonstrate how to manage network analytical tasks in R. It addresses the scarcity and dispersal of current SNA learning materials and seeks to provide a central, up-to-date source. The book's practical focus is on applying R tools rather than delving into theory, making it suitable for those ready to apply SNA techniques. It is openly written on GitHub using quarto, inviting community feedback through issues.

brand.yml

The _brand.yml file allows for unified branding across various outputs by setting company brand guidelines in a YAML file. This file can be integrated with tools like Quarto and Python's Shiny to automatically apply brand themes to reports, dashboards, and presentations, ensuring a consistent brand identity in data science products. Support for _brand.yml is available in several formats and libraries, enabling easy theming with company logos, colors, and typography across different platforms. Examples and user stories illustrate its practical applications in data science workflows.

Branding and automating your work with R Markdown

This video is about branding and automating work with R Markdown. It discusses how a team of data scientists uses advanced features in RStudio to brand reports and presentations for clients. The speaker highlights lessons learned in areas like version control and automation, including how a few lines of code allowed them to create a specialized report on crime for every county in Utah.