CMU LibGuides: Economics: 73-265 Economics and Data Science: Resources for Using R and RStudio

Books for Learning R

R Cookbook by J. D. Long; Paul Teetor
Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this edition. The R language provides everything you need to do statistical work, but its structure can be difficult to master. These task-oriented recipes make you productive with R immediately. Solutions range from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem and includes a discussion that explains the solution and provides insight into how it works.
R for Data Science (2e) by Hadley Wickham; Mine Çetinkaya-Rundel; Garrett Grolemund
Use R to turn data into insight, knowledge, and understanding. In this SECOND EDITION, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. This edition includes more thorough coverage in how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details.
ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham
This second edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. This book will be useful to everyone who has struggled with displaying data in an informative and attractive way.

Online Courses and Learning

Sage Campus This link opens in a new window
Sage Campus provides access to a growing collection of online courses on introductory skills and research methods including critical thinking, data literacy, research design, R and Python, statistical methods and more. NOTE: You must create an account using your CMU email address. If you are an instructor, please contact Sarah Young to upgrade your account in order to manage student cohorts.

Data Science Box
A data science course for learners who have little to no background in data science, statistics, or programming, or have been using R for a while but wants to modernize their skills. It contains lecture slides, source codes, exercises, and projects to experiment and learn with R.
swirl
swirl teaches you R programming and data science interactively, at your own pace, and right in the R console.
LinkedIn Learning
Includes courses related to R including: Leaning R, R Essential Training, R for Data Science and Data Visualization in R with ggplot2. Consist of training videos and tutorials.

CMU Libraries R Workshop materials

The CMU Libraries Open Science and Data Collaborations (OSDC) program offers a series of workshops related to data wrangling and analysis using open source tools like R and OpenRefine.

CMU Libraries: Introduction to R Lesson
This 2-part introductory workshop aims to teach basic concepts, skills, and tools for working with data in R so that you can get more done in less time, and apply concepts of reproducibility to your research. This is an introduction to R designed for participants with no programming experience.
CMU Libraries Data Visualization with R Lesson
This links to a PDF of the full lesson for the CMU Libraries Data Visualization with R workshop. The workshop focuses on using the ggplot2 package.
CMU Libraries Open Science Workshop Mini-Series
Access workshop materials from CMU libraries that help build computational skills for Open Research and Data Science.

In addition to the lesson content, you can view a series of short videos that walk through the content covered in these workshops.

Introduction to R Video Playlist
This series of short videos walks you through the full content of the Introduction to R lesson.
Data Visualization with R Video Playlist
This series of short videos walks you through the full content of the Data Visualization with R lesson.

Finding help with coding

Comic panels of an alligator trying to debug some code. First panel: A confident looking alligator gets an error message. Second panel: a few minutes later, the error remains and the alligator is looking carefully at their code. Third panel: 10 minutes after that, the error remains and the alligator is giving a frustrated

Artwork by @allison_horst.

Coding in R, whether you're an expert, novice or beginner, almost always involves building on other's code and learning from the broader coding community. Googling for answers to your coding questions, like troubleshooting error messages or figuring out how to get a plot to look a certain way, is part of the normal process of R programming. Here are a few useful websites and resources that can help you through your coding journey.

Getting help in R
Sam Tyner offers 10 tips on getting help in R that can be helpful when you're feeling a little stuck on what to do.
Troubleshooting Error Messages
Wendy Huynh shares her troubleshooting tips and processes in this chapter, including when and how to use the wider Internet to look for solutions.
Stack Overflow
This is one of the most common places to find answers to your coding questions. Anyone is welcome to post questions to Stack Overflow and guidance for how to ask a good question is available.
Rubber Duck Debugging
Debug by explaining your error to your rubber duck!

Many R packages come with useful resources that can help you navigate the various functions and options available. For example, many packages include extensive documentation with a full description of package functions and arguments. Some also offer a vignette, which is a more narrative style example of how the package can be used. Reference manuals and vignettes for two popular packages that you'll be using in this course, dplyr and ggplot2, are linked to below.

Additional resources

The sources below can support your learning in R by offering quick lookups through cheatsheets, alternative forms of learning data science, and expand your skills when you are ready to move to the next stage of computational methods and data analyses.

R Markdown Cheatsheet
Quick guide to look up options for formatting in R markdown.
R Cheatsheets
There are numerous 'cheat sheets' developed for commonly used packages that can serve as useful references while coding. Cheatsheets for tidyr, dplyr , and ggplot2 packages would be most useful for data cleaning, transformation, and visualization.
R Color Cheatsheet
Finding the best colors for your plots and visualizations in R can be challenging. This handy cheat sheet gives you the names of the many color options available.
The R Graph Gallery
The R Graph Gallery is a useful resource for exploring the many different ways of visualizing data in R. It points you to useful R packages for creating different types of plots and graphs.
Posit Recipes: Some tasty R code snippets
Provides code snippets to do specific tasks with R, from importing and exporting data to fitting and working with basic models.
Big Book of R
Big Book of R is an extensive collection of R-related programming books and courses that ranges from resources for getting started to specific computational methods.