Having mostly taught myself R over the past few years, I wanted to gather some of the resources I’ve found most helpful in one place in order to help others who are interested in learning R as well.

R

A good place to get started with R and learn more is R studio cloud (https://rstudio.cloud/). You just make an account and access R Studio from the browser, no software to install and nothing to configure on your computer. And there’s lots of learning materials: interactive tutorials covering the basics of data science, cheatsheets for working with popular R packages and a guide to using RStudio Cloud.

Tidyverse

The tidyverse is a collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.

See R Packages for more detail on the component packages.

The list below contains some general R and tidyverse learning resources:

• R Cheatsheets

  • RStudio IDE

  • Data Import

  • Data Transformation with dplyr

  • Data Visualization with ggplot2

• R for Data Science – The gold standard on tidyverse and using R in data science in general, written by the godfather of the tidyverse Hadley Wickham.

• Statistical Inference via Data Science: A moderndive into R and the tidyverse – A gentle introduction to the practice of analyzing data and answering questions using data the way data scientists, statisticians, data journalists, and other researchers would.

• Data Visualization: A practical introduction – “A data visualization guide that is simultaneously practical and elegant. Healy combines the beauty and insight of Tufte with the concrete helpfulness of Stack Exchange.” — Elizabeth Bruch, University of Michigan

• Datacamp Introduction to R

• [Intermediate] UBC Stat 545 – Awesome, awesome online materials for a course covering tidyverse from top to bottom (see topics).

R Studio

• Download RStudio Desktop

• RStudio Desktop Overview video

R Packages

• Datacamp’s introduction to R packages

• Tidyverse – a collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.

  • ggplot2 – powerful and popular data visualization library (see also ggplot2 extensions)

  • dplyr – provides a grammar of data manipulation, providing a consistent set of verbs that solve the most common data manipulation challenges

  • tidyr – provides functions to pivot, combine, and separate data into the format you need, especially following the concept of “tidy data”. Tidy data is data with a consistent form: in brief, every variable goes in a column, and every column is a variable.

  • readr – a fast and friendly way to read rectangular data (like csv, tsv, and fwf). It is designed to flexibly parse many types of data found in the wild, while still cleanly failing when data unexpectedly changes.

• Data cleaning

  • Janitor – simple functions for examining and cleaning dirty data

  • Clean – cleaning and checking data columns in a fast and easy way

• Data pipeline / reproducibility: Drake (manual)

• Dependency management: packrat

• Geography

  • sf - reading in and wrangling contextual data

  • raster - reading in and wrangling landsat data

  • Leaflet – an R interface to one of the most popular open-source JavaScript libraries for interactive maps

• Census data packages

  • ipumsr – The ipumsr package allows you to read data from your IPUMS extract into R along with the associated metadata like variable labels, value labels and more.

  • tidycensus – an R package that allows users to interface with the US Census Bureau’s itdecennial Census and five-year American Community APIs

  • censusapi– R package to retrieve Census data and metadata via API with more than 300 Census API endpoints available, including Decennial Census, American Community Survey, Poverty Statistics, and Population Estimates APIs.

• Survey data analysis

  • Srvyr – srvyr brings parts of dplyr’s syntax to survey analysis, using the survey package. srvyr focuses on calculating summary statistics from survey data, such as the mean, total or quantile. It allows for the use of many dplyr verbs, such as summarize, group_by, and mutate, the convenience of pipe-able functions.

  • Survey – provides facilities in R for analyzing data from complex surveys

• Other lists

  • My computer setup guide post
  • Garrett Grolemund’s list
  • Metacran’s list of most-downloaded packages
  • Quora thread

Git

• Try Git by Github and Codeschool – simple intros, cheatsheets, and interactive learn-by-doing coding sessions

• Happy Git for the R User – this is a great introduction to both git and R Studio setup. Jenny uses a familiar and funny tone throughout to make this tedious topic as bearable as possible.

• Think like (a) Git – a funny website that is structured like a git graph that goes from the highest level intro to the basics of graph theory

• The simple guide

Github

• Github guide to Git and Github
• Karl Broman’s git/github guide
• Intro to GitHub
• Understanding the Github Flow
• Github Desktop
• First Day on GitHub
• First Week on GitHub

Fun stuff

Slackmojis

Slack Article – walkthrough of how to add emojis to Slack

Slackmojis – collection of popular emojis

Step by step guide:

1. Check out Slackmojis

2. Click the emoji you want to view it as a standalone image

3. Right click the image and “Save Image As…” down to your computer

4. Follow the instructions in the Slack how-to to add into Slack

See also

• What I'm learning from "Using Big Data to Solve Social and Economic Problems"
• Computer setup
• How Learning Happens