Resources
This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.
tidygeocoder
Tidygeocoder is an R package that makes getting data from geocoding services easy. It provides a unified high-level interface for a selection of supported geocoding services and returns results in tibble (dataframe) format.
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tidykids
State-by-State Spending on Kids Dataset from the Urban Institute in a Tidy Format
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tidylog
Tidylog provides feedback about dplyr and tidyr operations. It provides wrapper functions for the most common functions, such as filter, mutate, select, and group_by, and provides detailed output for joins.
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tidyr
The `tidyr` package in R is used to create tidy data, where every column is a variable, every row is an observation, and every cell is a single value. It provides functions for pivoting, rectangling, nesting, splitting and combining character columns. The package supersedes `reshape2` and `reshape` and is designed specifically for tidying data. It has an active community and a Contributor Code of Conduct.
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tilemaps
The tilemaps package implements an algorithm for generating tile maps, which represent regions with single tiles of the same shape and size. This package allows users to generate single or multiple tile maps and provides functions for visualizing and analyzing the maps.
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Time Series Data Sets
The timeSeriesDataSets package in R offers an extensive collection of time series datasets from diverse fields such as economics, finance, energy, and healthcare. Aimed to facilitate time series analysis, the datasets include suffixes for easy identification. For instance, AirPassengers_ts represents monthly airline passenger numbers, while taylor_30_min_df_ts indicates half-hourly electricity demand. Users can install the package from CRAN and access datasets using simple commands. This package is valuable for those seeking structured time series data for research or analysis in various domains.
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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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tmap
A tutorial to get started with the tmap package in R for creating thematic maps.
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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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Transform Google Docs into Quarto Books with {quartificate}
The 'quartificate' package is designed to convert Google Documents into Quarto books, facilitating the transition from a simple document to a structured and maintainable book format. It streamlines the process by exporting the document into a Docx file, converting it to Markdown via Pandoc, and then sectioning it into HTML chapters based on header levels. This enables users to easily manage and render their content as a Quarto book. The package also provides seamless integration with Googledrive for authentication and document retrieval, and offers a quick start to render and view the book using the 'servr' package.
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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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