The following links lead to a series of lessons designed to help you get started with R and RStudio. They are designed to be studied sequentially.
RStudio Cloud is a hosted version of RStudio in the cloud that allows you to run R code without installing R and RStudio on your local computer. It is in beta and is free, so it’s worth trying out if you aren’t sure that you want to commit to installing R and RStudio on your own computer. You can set up team access to a project and work collaboratively with others.
Go to the lessons home page for information, schedule, and registration.
The Carpentries is an educational program that provides in person workshops and online resources to teach fundamental coding and data science skills. There are several Carpentries lessons for getting started with R:
Data Carpentry: Data Analysis and Visualization in R. This lesson follows a sequence very similar to the DiSC R lessons, discussing basics, data structures, manipulating tables, and visualization. The Introduction to R sesson includes some useful information about getting help.
Data Carpentry: Data Analysis and Visualization in R for Ecologists. This lesson follows a similar sequence as the DiSC R lessons, although it includes additional information about using R with SQL.
Software Carpentry: R for Reproducible Scientific Analysis. This lesson begins with data structures and subsetting data. It then discusses conditional statements and visualization buty saves maniuplation of data tables until later.
Software Carpentry: Programming with R. This lesson is focused on teaching R as a programming language and starts with traditional programming features like functions, conditional statements, and loops. Tables and files are introduced later.
Vanderbilt has a submscription to O’Reilly for Higher Education which provides access to many books and video tutorials. You can get there directly via this link. It is also possible to navigate there by going to https://www.library.vanderbilt.edu/, select
DATABASES A-Z, click on
O, then select
O'Reilly for Higher Education. Important note: it is important to sign out and close your browser tab when finished. Otherwise you may not be able to log in again withoug clearing your browser cookies.
R for Data Science This link is to the online version of Grolemund, G and H. Wickham, R for Data Science. It is also available in print version from O’Reilly via a link on the website (accessible online via Vanderbilt’s O’Reilly for Higher Education subscription).
R Graphics Cookbook by Winston Chang provides many recipes for generating high-quality graphs quickly using R.
“Introducing R” website by Germán Rodríguez provides a basic introduction to RStudio, loading data, and linear models.
Online R resources for Beginners is a page of links to many other R online resources, including online books, video tutorials, and other websites.
Getting Started in Data Analysis using Stata and R by Princeton Data and Statistical Services has links to many presentations on using R to carry out statistical tests.
UCLA R resources by the UCLA Institute for Digital Research and Education Statistical Consulting service. There are links here to lessons and examples. The data analysis examples has examples for many common statistical tests.
An R Companion for the Handbook of Biological Statistics is the code supplement for the online Handbook of Biological Statistics by John H. McDonald. The examples include many of the most common parametric and non-parametric statistical tests, including some of the simpler multivariate tests.
R examples for The Analysis of Biological Data is a companion website for the very accessible Whitlock and Schluter print text The Analysis of Biological Data.
If you are already familiar with basic statistical tests and want a jump start to doing those tests using R, Basic Statistical Analysis Using the R Statistical Package by Heeren and Milton of the Boston University School of Public Health is a good place to start.
A great resource for developing models in R is Tidy Modeling with R by Max Kuhn and Julia Silge. It presupposes background statistical understanding as well as a general understanding of how R works – particularly packages that are part of the tidyverse.
For summaries of how to perform a variety of tasks using the RStudio IDE, see the RStudio cheat sheets.
R Workflow for Reproducible Data Analysis and Reporting by Frank E. Harrel, Jr., Vanderbilt Department of Biostatistics
To try out some simple R scripts for t-test of means, paired t-test, chi-squared goodness of fit, chi-squared contingency, regression, and ANOVA, visit this page.
A few more beginner exercises for practice are on this page.
Questions? Contact us