R is free and powerful software for data analysis and graphics. However, its flexible approach is so different from other software that it can be frustrating to learn. This workshop introduces R in a way that takes advantage of what you already know. For many topics we will begin with R’s builtin commands that offer sparse but flexible output. Then we’ll cover addon commands that work similarly to your current software. We will also discuss aspects of R that are likely to trip you up. For example, many R functions let you specify which data set to use in a way that looks identical to SAS, but which differs in a way that is likely to lead to perplexing error messages.
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, 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.
Most of the examples come from the highlyregarded books by the instructor, R for SAS and SPSS Users and R for Stata Users (no knowledge of those languages is required). 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.
This workshop is available in three ways: site visits, webinars, and interactive video.
The 0nsite version is the most engaging by far, generating much discussion and occasionally veering off briefly to cover topics specific to a particular organization. The instructor presents a topic for around twenty minutes. Then we switch to exercises, which are already open in another tabbed window. The exercises contain hints that show the general structure of the solution; you adapt those hints to get the final solution. The complete solutions are in a third tabbed window, so if you get stuck the answers are a click away. The typical schedule for training on site are located here.
A webinar version is also available. The approach is saves travel expenses and is especially useful for organizations with branch offices. It’s offered as two halfday sessions, often with a day or two skipped in between to give participants a chance to do the exercises and catch up on other work. There is time for questions on the lecture topics (live) and the exercises (via email). However, webinar participants are typically much less engaged, and far less discussion takes place.
The interactive video version is available at DataCamp.com. That uses a similar lecture / exercise combination, but you can stop and restart at any time. This lets you learn at your own pace and it minimizes the disruption to your regular work. However, there is no way to ask the instructor questions, or generate discussion among the participants.
For further details or to arrange a webinar or site visit, contact the instructor, Bob Muenchen, at muenchen.bob@gmail.com.
Prerequisites
Despite the title, this workshop requires no knowledge of other software. However, if the audience has expertise in SAS, SPSS, or Stata, the instructor will adapt his presentation to use language they’re most familiar with. Some knowledge of statistics is helpful, but not required. The instructor is well aware that knowledge of statistics fades rapidly when not used!
Learning Outcomes
When finished, participants will be able to use R to import data, transform it, create publication quality graphics, perform commonly used statistical analyses and know how to generalize that knowledge to more advanced methods. They will also have an especially thorough understanding of how R compares to SAS, SPSS and Stata.
Presenter
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 35 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 and has offered training in partnership with the American Statistical Association, DataCamp.com, New Horizons Computer Learning Centers, Revolution Analytics, RStudio and Xerox Learning Services. 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, Intuitics, the Statistical Graphics Corporation and PC Week Magazine (now eWeek). His suggested improvements have been incorporated into SAS, SPSS, JMP, STATGRAPHICS and several R packages. His research interests include statistical computing, data graphics and visualization, text analytics, and data mining.
Computer Requirements
Onsite training is best done in a computer lab with a projector and, for large rooms, a PA system. The webinar version is delivered to your computer using Zoom (or similar webinar systems if your organization has a preference.)
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.rproject.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.
Course Outline
(Indepth data management topics are covered in an optional separate workshop that usually follows immediately after this one.)

Introduction and statement of goals
 Overview of R
 Installing and maintaining R
 Getting help

Programming Language Basics – including creating, subsetting and analyzing:
 Vectors (variables)
 Factors (categorical variables)
 Data frames (data sets)
 “Tibbles" (dplyr’s tbl_df data frames)
 Matrices
 Arrays
 Lists

Managing your files and workspace
 Listing their names
 Printing
 Deleting
 Saving
 Examining structure of data sets, etc.

Controlling functions (procedures or commands) using
 Arguments (options or parameters)
 An object’s class
 How to change class
 Model formulas

Data Acquisition – Reading files (includes whichever formats your organization needs)
 Comma separated value files
 Tabdelimited files
 Excel files
 Minitab data sets
 SAS data sets
 SPSS save file
 Stata data sets

Data Transformations using
 Math formulas
 Recoding
 Conditional (logical) formulas

Selecting variables and observations using:
 Dollar format
 The “attach" function
 The “with" function
 Subscripting (a.k.a. indexing)
 dplyr’s select and filter functions
 Model formulas and the “data=" argument

Writing functions (macros)
 Why they’re more important in R than most languages
 How to create functions
 How to apply functions to data frames
 Applying functions by group (using dplyr + broom packages)

Graphics

Traditional graphics including:
 Bar charts
 Scatter plots
 Strip plots
 Box plots
 Histograms
 Repeating above plots by groups
 Adding titles, etc.
 Adding regression lines
 Lattice graphics – a brief overview

The Grammar of Graphics approach using the ggplot2 package
 qplot vs. ggplot
 Bar charts
 Histograms
 Scatter plots
 Strip plots
 Multilayered plots
 Group plots
 Adding titles, etc.
 Adding regression lines
 Applying standard style templates
 Interactive graphics – a brief overview
 Graphics resources

Traditional graphics including:

Statistics – many are done showing sparse R output and the richer output that most people prefer.
 Descriptive statistics
 Crosstabulation with chisquared test
 Repeating an analysis by groups or departments (a.k.a. “By" or “split file")
 Correcting pvalues for the effects of multiple testing
 Correlation: Pearson, Spearman
 Linear regression
 Extractor functions (a.k.a. postestimation commands)
 Ttests
 Wilcoxon MannWhitney rank sum test
 Paired ttest
 Wilcoxon signedrank test
 Analysis of variance
 Post hoc tests, leastsquares means, & interaction plots
 Type I, II, & III tests & why R deemphasizes type III
 KruskalWallis

Getting publicationquality output into
 Word
 HTML
 LaTeX (optional)

Ways to run R (includes only those of interest to your organization)
 Interactively
 Programs that include other programs
 Running R from within SAS
 Running R from within SPSS
 Running R as an adjunct to Stata

Graphical User Interfaces:
 R Commander
 Rattle data mining interface
 Excel integration
 Alteryx/KNIME/RapidMiner
 Summary of topics learned
Here is a slide show of previous workshops.
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Hi Bob,
I was wondering how the training will be held next week.
Will the training be a WebEx and will it be recorded like SAS training so I can view it later for 20 business days.
I appreciate your reply as it would help me in preparing for the training.
Regards,
Amit
Hi Amit,
It’s via WebEx and yes, it’s recorded so you can watch again any time for 60 days.
Cheers,
Bob
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Same question as Amit, where is this being held or is there a webex?
Hi Santiago,
It’s being done via WebEx. If you register, Revolution Analytics will send you the login info and where to download the handouts, practice programs, data sets and exercises.
Cheers,
Bob
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I am interested to attend the workshop. Plz inform about your next workshop