Before you can analyze data, it must be in the right form. Getting it into that form is often where we spend most of our time. This workshop shows how to perform the most commonly used data management tasks in R. We will cover how to use R’s most popular add-on packages (e.g. dplyr, stringr, lubridate, tidyr) and compare them to R’s older built-in functions.
Most of our time will be spent working through examples that you may run simultaneously on your computer. You will see both the instructor’s screen and yours, side-by-side, as we run the examples and discuss the output. However, the handouts include each step and its output, so feel free to skip the computing; it’s easy to just relax and take notes. The slides and programming steps are numbered so you can easily switch from computing to slides and back again.
Many of the examples come from the extensive data management examples R for SAS and SPSS Users and R for Stata Users. That makes it easy to review what we did later with full explanations, or to learn more about a particular subject by extending an example which you have already seen.
The workshop is presented live-via-Internet or during a site visit to your organization. The live-via-Internet version is one 4-hour session, with exercises and their solutions provided for use after the workshop. When presented on-site, the exercises are covered between topics. Either way, the topics are presented in sections of around 20 minutes each and regular breaks are included. There is ample time to ask questions verbally or, on the Internet, by typing them into a Q&A window.
Live-via-Internet sessions are recorded and available for study for two weeks afterwards. The instructor is available to handle workshop-related questions both during the workshop and at any time in the future.
For further details or to arrange an on-site workshop, contact the instructor, Bob Muenchen, at firstname.lastname@example.org.
Attendees should know basic R programming, including how to read data files and call functions.
When finished, you will be able to prepare most data sets for analysis.
Robert A. Muenchen is the author of R for SAS and SPSS Users and, with Joseph M. Hilbe, R for Stata Users. He is also the creator of r4stats.com, a popular web site devoted to analyzing trends in analytics software and helping people learn the R language. Bob is an ASA Accredited Professional Statistician™ with 32 years of experience and is currently the manager of OIT Research Computing Support (formerly the Statistical Consulting Center) at the University of Tennessee. He has taught workshops on research computing topics for more than 500 organizations. Bob has written or coauthored over 70 articles published in scientific journals and conference proceedings, and has provided guidance on more than 1,000 graduate theses and dissertations.
Bob has served on the advisory boards of SAS Institute, SPSS Inc., StatAce OOD, the Statistical Graphics Corporation and PC Week Magazine. His suggested improvements have been incorporated into SAS, SPSS, StatAce, JMP, STATGRAPHICS and several R packages. His research interests include statistical computing, data graphics and visualization, text analytics, and data mining.
The workshop is delivered to your computer using Cisco WebEx. You can join a test meeting and see computer system requirements here. It’s important to test your computer since many organizations require special privileges to modify your computer to accept the browser plug-in that makes it work.
Course programs, data, and exercises will be sent to you a week before the workshop. The instructions include installing R, which you can download R for free here: http://www.r-project.org/
We will also use RStudio, which you can download for free here: http://RStudio.com. If you already know a different R editor, that’s fine too.
1. Object Basics: Copying, removing, keeping, dropping, renaming
2. Transforming Variables (simple, multiple, conditional, recodes)
3. Summarizing columns
4. Summarizing rows
5. “By Group” transformation, summarization & analysis
6. Sorting data
7. Selecting first or last observation per group
8. Piping data
9. Stacking / concatenating data frames
10. Finding and removing duplicate observations
11. Merging / joining data frames
12. Reshaping data frames
13. Comparing objects
14. Character string manipulations
15. Date and time manipulations
16. Using SQL within R
Here is a slide show of previous workshops.