Here we aim to list resources that at least one of us has tried or has a specific reason for recommending
- Installing R and setting up your environment
- Introductions to R
- General resources and tips
- Online Textbooks
- Text Analysis
- Predictive Analytics
- Web Scraping
- Git and GitHub
- Dashboarding
- installing R, the RStudio IDE, getting data into RStudio
- this is the intro I used (Stef)
- free series of online learning resources for using R, oriented towards beginners. Aims to provide a solid foundation of R skills using the "tidyverse" approach
- very practical and easy to follow (Stef)
An Irresponsibly Brief Introduction to the Tidyverse, by Sean Kross
- This is short, easy to understand, and Sean Kross is awesome
Learn R on Codecademy is a great beginner's introduction course to the syntax of R and a few basic statistical methods using R. (recommended by Monika)
- The Twitter hashtag #rstats is a friendly place to find stuff
- R for Data Science online learning community inspired by the R for Data Science text
- includes answer keys to the text
- The Tidy Tuesday Project: "Every week we post a raw dataset, an original chart associated with that dataset, and ask you to apply your take on the chart. The goal of Tidy Tuesday is to apply your R skills, get feedback, explore other’s work, and connect with the greater RStats community! As such we encourage everyone of all skills to participate!" (Monika did the Roman Emperors dataset)
- rstudio.cloud primers - they basically follow R 4 Data Science text with interactive code exercises, using the RStudio IDE in your web browser (free) (h/t Alison Hill)
- R for Data Science by Garrett Grolemund and Hadley Wickham "introduces the key tools for doing data science with R"
- Advanced R by Hadley Wickham "helps you master R as a programming language, teaching you what makes R tick"
- R packages "teaches good software engineering practices for R, using packages for bundling, documenting, and testing your code"
- Text Mining with R, by Julia Silge and David Robinson
- Text Mining Tutorial - Using R libraries to implement common text mining techniques
- Sentiment Analysis Tutorial 1 - Analyzes the sentiment of the state of the union address from 1989 to 2017
- Sentiment Analysis Tutorial 2 - Analyzes the sentiment of the Harry Potter series
- Regression Modeling Tutorial - Predictive analytics using 4 different linear regression models
- Predictive Analytic Models Tutorial - Predictive modeling using Linear Regression, Decision Tree, and Random Forest Plots
- Forecasting Tutorial - Time series forecasting (predicting sales patterns, customer churn, new product launch effectiveness, etc)
- Timeseries Modelling Tutorial - A complete tutorial in analyzing time series data
- Web Scraping 1 - Web scraping IMDB.com movie reviews using the rvest package
- Web Scrapting 2 - Web scraping Trust Pilot reviews using the rvest package
- Manage your webscraping with the {polite} #rstats pkg (TIL via twitter)
- Getting started with GitHub, by GitHub
- Happy Git and GitHub for the useR, by Jenny Bryan, STAT 545 TAs, Jim Hester
- git exercises: navigate a repository, by Julia Evans
- learn by navigating the repository for the Ruby programming language; it’s just about getting comfortable with looking at how files in a repository change over time (Stef tried this and gave up - felt pretty advanced)
- git branching, in The Unix Workbench, by the awesome Sean Kross (Stef will try this one)
- Learn Git Branching, recommended by Garrick Aden-Buie: "by far my favorite resource for learning and practicing git"
- Shiny Tutorial - Beginner tutorial for people looking to build a dashboarding tool using shiny. (Kasey and Joel used this tutorial)
- RStudio Cloud playlist of short videos (1-3mins) that helps instructors & educators get up and running as quickly as possible. RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online.