Data Analysis: R

Why R?

Why R?

R is an open-source programming language and software designed for statistical computing and graphics creation. R is free to download and use. There is a large and active R community online, with many resources for learning R.

Where to Find R?

Download R for use on your own computer. R can also be installed by users on all computers in the Data & GIS Lab in Geisel Library and in some ETS labs.

RStudio is an open source, integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management.

Learn R

R for Reproducible Scientific Analysis (Software Carpentry)
This set of lessons from Software Carpentry is an introduction to R for people with no programming background. It introduces R, the RStudio interface, working with data structures, organizing/subsetting data, making plots, and creating reports. This is a great "get up to speed quickly" set of lessons that use the same data throughout.
RStudio's online learning guide
RStudio provides an extensive set of links to learning resources. From getting started with R, to making interactive plots with Shiny, to R code best practices, this page is definitely worth a look.

Analysis Tutorials

R Tutorial by UCLA
The tutorial is prepared by Advanced Research Computing at UCLA. It has a suite of data analysis examples for beginners, and also topics for a range of data analysis models.
R Tutorial by Clarkson
R tutorial is complied by Clarkson University. The first sections introduce the R basics, and then looks into basic statistics, linear least squares regression, confidence intervals, p-values.
Using R for psychological research
This "simple guide to an elegant package" is made by Psychology Department of Northwestern University. Getting the data, Basic descriptive statistics, Analysis of variance

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Further topics: Structural Equation Modeling, Multi-level models, Rasch models for Item Response, Wilcox robust statistics

Useful R packages

User can develop their own functions in R to do specific tasks (i.e., adding text to graphics, reformatting data, etc.). Before writing code from scratch, however, keep in mind that one of the main benefits of R is the vast array of pre-existing packages, written by other R users and available for download. You can find officially vetted R packages on CRAN (Comprehensive R Archive Network).

Here are some packages that may be useful:

Useful R packages
Overview of packages for: loading data, manipulating data, visualizing data, creating models, reporting results, working with spatial or time series data (including financial data), and writing high performance R code.
tidyverse
Collection of packages used for common data analysis tasks, such as: data wrangling, plotting, working with dates and times, and more. Learn more about how to use these packages for analysis with Statistical Inference via Data Science: A ModernDive into R and the Tidyverse (Second Edition).
tidymodels
Collection of packages for modeling and machine learning. Includes packages for data splitting, pre- and post-processing, optimizing hyperparameters, and performance metrics.
CRAN task views
CRAN task views aim to provide guidance which packages on CRAN are relevant for tasks related to a certain topic. Topics include: Bayesian inference, causal inference, probability distributions, machine learning, missing data, survival analysis, spatial data, time series, and many more.
Bioconductor (R)
Packages for bioinformatics
gtsummary
The {gtsummary} package provides an elegant and flexible way to create publication-ready analytical and summary tables using the R programming language. The {gtsummary} package summarizes data sets, regression models, and more.

Featured Books

R for Data Science (2nd edition) by Hadley Wickham; Garrett Grolemund
ISBN: 9781491910344

Publication Date: 2023

This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science guides you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results.
An Introduction to Statistical and Data Sciences via R by Chester Ismay and Albert Y. Kim
Call Number: Open Access ebook

This book assumes no prerequisites: no algebra, no calculus, and no prior programming/coding experience. This is intended to be 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.
Modern Statistics with R by Måns Thulin
ISBN: 9781032512440

Publication Date: 2024-08-20

No prior programming experience is necessary. In addition to plenty of examples, the book includes more than 200 exercises, with fully worked solutions available at: www.modernstatisticswithr.com.

UC Library Search: subject = R (Computer Programming Language)