This page contains information on the R programming language
There are lots of great tutorials on the internet, so we don't want to duplicate things. Rather, we can use this page to list our favorite resources.
This document is starting off small, with just a few items added by the instructor. But because this is on github, students can contribute their own ideas using a pull (merge) request, and then the admins for this github repo (the instructor and TA) can accept the merge (or modify or reject it).
Find a great R tutorial on Youtube? Please add the link to this website!
You can edit this document using the github website itself -- look for the pencil icon.
This document is written in markdown, which is a very simple way to include links and things like that. It takes about 2 minutes to learn the most important basics (in fact, you probably don't need to "learn" it, just follow the examples).
- When you search google for "R [something]", it's hard for google to know if you mean the letter "R" or the language "R" (though it probably does a fairly good job). An alternative is to use rseek, a dedicated search engine for R that is powered by google.
- An Introduction to R is an official guide to R written by the R developers.
- R tutorial was developed by Prof. Will Kleiber when he taught this course in 2018. It's a very brief introduction to R, and I've adapted it to work with Colab
- Most of our demonstrations will be using github in conjunction with R via colab (you can use the alias
https://colab.to/r). - For more involved coding projects, we recommend use of an IDE that supports R. The standard choice is R Studio which is excellent, but you can also use multi-purpose IDEs like VS Code. When installing these IDEs, you still need to install R itself.
- To make a new R document in Colab, go to https://colab.to/r
- From colab, you can manually upload/download files, or even mount google drive (google this to find out how, e.g., https://towardsdatascience.com/how-to-use-r-in-google-colab-b6e02d736497)
- You can use R within a python Colab file, but for this class I wouldn't recommend it
- Rstudio's suggested list of top packages including ggplot2 for nice plotting, tidyverse packages like
dyplyr,tidyr,stringrandlubridate
- RStudio cheat sheet collection is quite good
- A curated list of time series topics in R.
- The Time Series Analysis and Its Applications With R Examples — 4th Edition by Shumway & Stoffer has some tutorials
- Time Series and Forecasting, by Quick-R has some basic info too