Resources
This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.
Stat545: Chapters 18-20: Write your own functions
Chapter 18 Write your own R functions, part 1
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statebins
statebins is an R package that provides an alternative to choropleth maps for the United States. It generates cartogram heatmaps based on the work by the Washington Post graphics department. The package includes functions for creating binned or continuous scales, legends, and different visualizations using state data.
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Steve’s Data Tips and Tricks - Creating Population Pyramid Plots in R with ggplot2
Learn how to create population pyramid plots in R using ggplot2. This tutorial provides step-by-step instructions and sample code.
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Stop making messy line charts and create meaningful plots instead
Stop making messy line charts and create meaningful plots instead
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STOP Wasting Space on HUGE LEGENDS | A ggplot2 step-by-step guide - YouTube
A step-by-step guide on how to create more compact legends in ggplot2.
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stringr.plus
stringr.plus provides additional functions for working with strings, especially for extracting specific text from URLs and file paths. It is a package for the R programming language.
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tablerDash
Tabler API for Shiny is a package that provides a tablerDash template for creating dashboards in Shiny applications. It is based on Bootstrap 4 and offers a responsive UI design. The package can be installed from CRAN or from the GitHub repository. It works with all RStudio products and has a demo available on shinyapps.io. The package is developed by David Granjon and is licensed under GPL (>= 2).
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Take A Sad Plot & Make It Better: A Case Study with R and ggplot2
A case study with R and ggplot2 on improving a sad plot
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Telling Stories with Data
Telling Stories with Data by Rohan Alexander is a comprehensive guide on communicating insights effectively using data in R and Python. Published by Chapman and Hall/CRC, the book is endorsed by experts for its unique approach in emphasizing statistical communication, programming, and modeling. It covers the entire data science workflow, including data acquisition, analysis, and reproducibility, making it an excellent resource for statistics courses or self-learning. It focuses on developing the computational and philosophical skills necessary for sense-making and telling stories with data, making it a valuable tool for data scientists and analysts.
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Ten great R functions #3
Erik Gahner Larsen's blog post shares another ten essential R functions to aid users with different tasks in 2024, some new and some old. The post includes functions like reprex::reprex() for creating reproducible examples, data.table::let() for easier data manipulation, renv::init() for reproducible environments, and directlabels::geom_dl() for enhanced data visualizations. These functions cater to a variety of needs from efficient data frame manipulation to ensuring reproducibility, and from enhancing visual outputs to managing project environments effectively.
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The best R packages for data visualization
The best R packages for data visualization provide a comprehensive suite of tools for creating all types of charts and graphs. Core to R's visualization capabilities is the package ggplot2, which offers a versatile grammar of graphics. Extensions of ggplot2 and other packages expand these functionalities, allowing for interactive charts, improved aesthetics, specialized geospatial analysis, and managing complex data structures like networks. Packages like plotly, rmarkdown, patchwork, and hrbrthemes enhance the user experience and presentation. Additionally, there are packages dedicated to managing colors, creating tables, and supporting specific chart types like word clouds and streamgraphs.
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