Resources
This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.
Tidy Flowchart Generator
The Tidy Flowchart Generator, or the 'flowchart' package, is an R package designed for drawing participant flow diagrams directly from a dataframe, employing the tidyverse syntax. It offers a suite of functions that utilize the pipe operator to generate flowcharts conveniently and flexibly from dataframes. The package is accessible through CRAN and can be installed traditionally or via the development version on GitHub. The process of creating a flowchart with this tool is demonstrated through a GIF example on its homepage, showcasing its usefulness in drafting flow diagrams for clinical trials or similar studies.
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Time-aware isochrones for accessibility mapping with R and Mapbox tools
This article discusses creating time-aware isochrones to analyze accessibility at different times of the day using R and Mapbox tools. It demonstrates how to work with traffic data and visualize the impact of traffic using Mapbox's predicted traffic data. The tutorial includes step-by-step instructions and code snippets for generating isochrones for specific addresses at different times, and visualizing them interactively on a map with a comparison slider to highlight the accessibility differences at noon versus rush hour. The tools discussed are the R packages mapboxapi and mapgl.
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Tips for debugging and cleaning broken code
This guide provides strategies for debugging and cleaning broken R code, specifically in a data visualization context using 'dplyr' and 'ggplot2'. It helps identify common mistakes in function chaining and plot layering, offering tips on how to spot and fix errors such as misspelled words or misplaced punctuation. The article illustrates the debugging process using an example with incorrect R code, followed by the corrected version. The guide emphasizes the importance of code formatting and reindenting for troubleshooting, making the debugging process less daunting.
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Transform data for easier multi-column tables
Andrew Weatherman's tutorial provides a step by step guide on transforming data into a wide format to facilitate the creation of intuitive multi-column tables in R using the gt package. It shows how to apply a "538-style" caption to add visual clarity to average team performance statistics against top 100 opponents in men's college basketball over the past five seasons. The tutorial includes detailed explanation of the data manipulation process and the R code needed to produce a visually appealing and informative table.
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TSA Screening Volume and Epiweeks
This content showcases a visualization of TSA screening volumes compared to Subway ridership, particularly amidst the pandemic's impact. It involves data aggregation challenges, like determining weekly counts from daily data, which can be skewed at year's end. The author addresses these issues using R code and TSA data available since January 2019, presenting a more detailed view of travel patterns over time and emphasizing the importance of careful handling of time-series data when aggregating by weeks, using ISO-8601 date format for this purpose.
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tvthemes
The tvthemes package is a collection of various ggplot2 themes and color/fill palettes based on popular TV shows.
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Understanding the Rise in Domestic Terrorism: Context Matters
Steven Ponce's analytical dashboard examines the increase in US domestic terrorism incidents from 1994-2024, adjusting for population growth. Utilizing raw attack counts and US Census Bureau data, the dashboard presents a four-panel visualization showcasing the adjusted per capita rates and trends by ideology. While the raw attack counts increased by 53%, after adjustment, a 26% rise in per capita rates is observed, highlighting the significant but less dramatic increase. Right-wing attacks have been dominant throughout, with an emphasis on the more modest rise when considering population growth. Released on November 19, 2025, this analysis emphasizes context in understanding domestic terrorism trends.
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UNHCR Dataviz Platform - Aim of better data storytelling
The UNHCR Data Visualization Platform provides insights, guidelines, and tools designed to improve data storytelling. With a vast chart gallery, users can select charts to effectively showcase data and highlight specific attributes and relationships. The UNHCR Data Visualization Guidelines offer clear, brand-compliant advice for professional graphics. The platform supplies various tools, templates, and scripts compatible with Excel, Power BI, Adobe Illustrator, R, Python, D3, and GIS, aiding in the creation of high-quality visualizations. Additionally, a collection of storymaps, dashboards, and infographics serves as inspiration for crafting compelling data narratives.
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Use dumbbell plots instead of paired bar charts in 130 seconds - YouTube
This YouTube video discusses the use of dumbbell plots as an alternative to paired bar charts.
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Use of flextable – Notes from a data witch
This content intertwines a tutorial on using the flextable package in R with personal reflections on the book 'Use of Weapons' by Iain Banks. It starts with a nostalgic recollection from the author's past, and leads into an exploration of crafting tables in R. The post integrates a fondness for the Culture novels by Banks, touching upon aspects of storytelling within a technical guide. The aim is to show how the flextable package enhances data representation, paralleling the way stories weave narratives, thereby making data more approachable and engaging, just like a good science fiction universe.
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Use SAS, R, and quarto Together • sasquatch
sasquatch is an R package that enables the integration of SAS, R, and Quarto for creating reproducible multilingual reports. It utilizes SASPy and reticulate to run SAS code blocks within R, transfer data between SAS and R, perform SAS client file management, and render SAS output in quarto documents. The package includes installation instructions for development version, Python, and SASPy. It offers functionality such as interactive execution of SAS code, data conversion between R and SAS, and rendering quarto documents with SAS output, distinguishing it from similar packages like sasr, configSAS, and SASmarkdown.
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