Data Services

Spring 2024 workshop information now available at:

NBL Workshop Calendar - https://libcal.rutgers.edu/calendar/nblworkshops

This integrated calendar contains information on all open workshops offered by the New Brunswick Libraries. Topics include Python, R, Digital Humanities, GIS, NVivo, and the Data Science Basics Workshop Series.

Including upcoming R workshops here.

Starting in Fall 2023, the Data Topics series is being slowing phased in to supplement and ultimately supplant the "tidyverse approach" series.

Current offerings in this series are:

• Data Analysis 2 (statistical tests, regression, sampling, bootstrap with R + comparison with Python) (coming Spring 2024)
• Data Publication 2 (publishing to data repositories and creating R packages)

Materials are available at

https://github.com/ryandata/tidyverse_approach

for the following workshops:

• R for Data Analytics, a tidyverse approach
• R graphics with ggplot2
• R data wrangling with dplyr, tidyr, readr and more
• R for interactivity: an introduction to Shiny [last offered Spring 2024]
• R for reproducible scientific documents: knitr, rmarkdown, and beyond

See the full playlist for videos of the R "tidyverse approach" workshop series.

R for data analysis: a tidyverse approach 

• Recording of Session  (Instructor, Ryan Womack)
  • Introduction, Installation, and Setup
  • Data Analysis, part 1
  • Data Analysis, part 2

The session introduces the R statistical software environment and basic methods of data analysis, and also introduces the "tidyverse".  While R is much more than the "tidyverse", the development of the "tidyverse" set of packages, led by RStudio, has provided a powerful and connected toolkit to get started with using R.  Note that graphics and data manipulation are covered in subsequent sessions.

R graphics with ggplot2 

• Recording of Session  (Instructor, Ryan Womack)
  • ggplot2, part 1
  • ggplot2, part 2
  • ggplot2, part 3

The ggplot2 package from the tidyverse provides extensive and flexible graphical capabilities within a consistent framework.  This session introduces the main features of ggplot2. Some prior familiarity with R is assumed (packages, structure, syntax), but the presentation can be followed without this background.  

R data wrangling with dplyr, tidyr, readr and more 

• Recording of Session  (Instructor, Ryan Womack)
  • data wrangling, part 1
  • data wrangling, part 2
  • data wrangling, part 3

Some of the most powerful features of the tidyverse relate to its abilities to import, filter, and otherwise manipulate data.  This session reviews major packages within the tidyverse that relate to the essential data handling steps require before (and during) data analysis.

R for interactivity: an introduction to Shiny  

[last offered Spring 2024]

• Recording of Session  (Instructor, Ryan Womack)
  • Shiny, part 1

Shiny is an R package that enables the creation of interactive websites for data visualization.   This session provides a brief overview of the Shiny framework, and how to edit and publish Shiny sites in RStudio (with shinyapps.io).  Familiarity with R/RStudio is assumed.

R for reproducible scientific documents: knitr, rmarkdown, and beyond 

• R for reproducibility  (Instructor, Ryan Womack)

The RStudio environment enables the easy creation of documents in various formats (HTML, DOC, PDF) using Rmarkdown, while knitr allows the incorporation of executable R code to produce the tables and figures in those documents. This session introduces these concepts and other packages and practices supporting reproducibility with the R environment.

Also see the Digital Humanities workshops series for several workshops involving R.

Screencast versions of older versions of the workshops are linked below.

R scripts for Special Topics workshops

R is open source software for statistical analysis.  Being open source (Gnu GPL licensed) doesn't just mean that the software is free.  It means that you can use it for a variety of applications, and install it virtually anywhere you'd like, without any restrictions.  Open source also means that the code for all statistical procedures and analysis can be independently checked and verified.  The activity community of R users is constantly developing new add-on packages that use the latest techniques, which you are free to do as well.  And, being free, you can always have access to the latest version of the software, no matter where you are.

R is also a programming language, which makes it easy to document, reuse and reproduce all the steps of your statistical analysis. 

You can get R, and full documentation on R, at www.r-project.org or by downloading from any CRAN mirror (Comprehensive R Archive Network).

Guides and Tutorials

• R-Exercises - various exercises to help you learn R
• How to Learn R - good starting point that reviews basics and points to tutorials for many topics
• Learning Statistics with R (Flavio Azevedo) - extensive links to video tutorials that combine learning statistics with learning R (and other programming languages)
• Cookbook for R - wiki style information
• Learn R by Intensive Practice - YouTube videos thoroughly covering language basics, also a Part 2
• R Programming Wikibook - a practical guide to the R programming language.
• R Tutorial Series - short tutorials on common topics.
• Manuals, FAQs, Listservs and more are available from http://www.r-project.org/. The R Reference Card is a quite handy summary.
• Quick-R is a guide for experienced users of other stats packages like SAS, SPSS, or Stata.
• Springer Use R! series (available from Springerlink) has many useful guides to using R in different fields

Searching for R on the Internet

• Don't type "R" on Google. "R project", "R statistics", or "R stats" are OK
• Better, use www.rseek.org
• Rsitesearch("your string") is built into R
• Web version of rsitesearch
• http://addictedtor.free.fr/rsitesearch/ offers an R search plug-in for Firefox

More Information

• RDocumentation - searchable site for packages across CRAN, Bioconductor, and Github
• Task Views – excellent outlines of the packages that are relevant to different disciplines
• R Inferno common problems with R
• R-bloggers – combining posts from several R blogs

 

Enjoy R!

Help Commands within R
 • help.start() - launches interactive help system
 • help(function) or ?function launch the manual pages
   describing a function
 • example(function) provides detailed examples
 • for help on a whole package, try library(help=packagename)
 • apropos and help.search (deep vs. fuzzy search, respectively)
 • vignette("mypackage")

These are some miscellaneous useful and interesting links that may help you accomplish some specific tasks in R.

• Read SAS files with sas7bdat
• RNetCDF - working with the NetCDF format
• asdfree - analyze survey data with supplied code to access many major governmental surveys
• R Color Tables - for easy reference to see colors and their names
• A detailed guide to ggplot colors