Resources
This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.
RStudio Cloud Primer: Tidy Your Data
Posit Cloud is a cloud-based platform that provides data storage and analysis tools for the R programming language.
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RStudio Cloud Primer: Work with Data
Posit Cloud is a cloud-based platform for managing and analyzing data using the R language.
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sjlabelled
This package contains utility functions that are useful when working with labelled data (especially intended for people coming from ‘SPSS’, ‘SAS’ or ‘Stata’ and/or who are new to R). Basically, this package covers reading and writing data between other statistical packages (like ‘SPSS’) and R, based on the haven and foreign packages; hence, this package also includes functions to make working with labelled data easier. This includes easy ways to get, set or change value and variable label attributes, to convert labelled vectors into factors or numeric (and vice versa), or to deal with multiple declared missing values.
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Spreadsheet Munging Strategies
This is a work-in-progress book about getting data out of spreadsheets, no matter how peculiar. It is designed for R users who have to extract data from spreadsheets and who are already familiar with the tidyverse. It has a cookbook structure, and can be used as a reference.
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Spreadsheet workflows in R
A resource that focuses on the intersection of spreadsheets and R, providing tips and tricks on best practices for working with both. It covers the R versions of common spreadsheet workflows, such as data wrangling and visualization.
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Stat545
This is the table of contents for the STAT 545 resource, which covers various topics related to R programming.
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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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Tidy Data Vignette
Tidy data is a concept in data analysis that involves structuring datasets to facilitate analysis. The tidy data standard provides a standardized way to organize data values within a dataset. This resource is a vignette that explains the principles and importance of tidy data and provides examples in R using the tidyr package.
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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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TidyTuesday: Racial disparities in reproductive research
This TidyTuesday project investigates racial and ethnic disparities in reproductive medicine in the United States, as presented in a narrative review from a January 2025 issue of the American Journal of Obstetrics and Gynecology. The dataset consists of studies from top peer-reviewed Ob/Gyn journals between 2010 and 2023, aiming to explore differences in racial group representations across study types and examine changes in representation over time. The project involves data cleaning, analysis, and visualization using R packages like tidytuesdayR, tidyverse, and ggplot2, with an intent to identify trends and recommend informed strategies for future research in obstetrics and gynecology.
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Trying out dplyr 1.2.0
Crystal Lewis experiments with the new dplyr 1.2.0 features in her article, demonstrating the improved filtering and recoding functions. The Posit team's latest update to this essential R package simplifies data cleaning processes with functions like filter_out(), recode_values(), replace_values(), and replace_when(). With practical examples, Lewis showcases the enhancements and provides a smooth introduction to these changes, making data wrangling more intuitive in R.
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