R for SAS, SPSS and Stata Users (TRA53)
Monday, April 21, 2014 at 10:00 AM - Wednesday, April 23, 2014 at 2:00 PM (PDT)
This is a 2-day virtual course offered on
Monday April 21st & Wednesday April 23rd
from 13:00-17:00 Eastern DaylightTime
An Introduction to R for SAS, SPSS, and Stata Users
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 8-hour workshop introduces R in a way that takes advantage of what you already know. For many topics we will begin with add-on commands that work similarly to your current software. Then we will cover R’s built-in commands that provide simpler but more flexible output. 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 you to perplexing error messages.
We will devote most of our time to working through examples that you may run simultaneously on your computer. However, handouts will include each step and its output if you prefer instead just relax and take notes. Most examples come from the books R for SAS and SPSS Users, R for Stata Users, and http://r4stats.com. 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 learned.
After each 4-hour session you will receive a set of practice exercises for you to do on your own time, as well as solutions to the problems. The instructor will be available after the workshop via email to address these problems or any other topic in the workshop.
Prerequisites: Attendees should know how to program in SAS, SPSS or Stata and be familiar with basic statistical methods including linear regression and basic analysis of variance.
Learning Outcomes: When finished, you will be able to use R to import data, manage and transform it, create publication quality graphics, perform commonly used statistical analyses and know how to generalize that knowledge to more advanced methods. You will also have an especially thorough understanding of how R compares to SAS, SPSS and Stata.
Presenter: Robert A. Muenchen is the author of the books, R for SAS and SPSS Users, and, with Joseph Hilbe, R for Stata Users. A consulting statistician with 30 years of experience, Bob is currently the manager of Research Computing Support (formerly the Statistical Consulting Center) at the University of Tennessee. Bob has served on the advisory boards of SPSS Inc., the Statistical Graphics Corporation and PC Week Magazine. His suggested improvements and/or programming code have been incorporated into SAS, SPSS, JMP, STATGRAPHICS and several R packages.
Course Outline (Data management topics have been moved to a separate workshop)
- Introduction and statement of goals
- Overview of R
- Installing and maintaining R
- Programming Language Basics – including creating, subsetting and analyzing vectors (variables), factors (categorical variables), data frames (data sets), matrices, arrays and lists.
- Managing your files and workspace – R provides a complete environment that includes many commands for listing, printing, saving, deleting data as well as examining object structure.
- Controlling functions (procedures or commands) using arguments (options or parameters) or an object's class; how to change class
- Data Acquisition – reading comma- and tab-delimited files, Excel, SAS, SPSS & Stata
- Selecting variables and observations – R offers many more ways to do selection
- Writing functions (macros)
- Traditional graphics (similar to old SAS and SPSS graphics) including bar, scatter, strop, box plots, histograms, plotting groups, adding embellishments and regression fits.
- Lattice graphics (similar to new SAS SG* and Stata graphics) – a brief overview
- The Grammar of Graphics approach using the ggplot2 package (similar to SPSS GPL) including: qplot vs. ggplot; bar charts, histograms, scatter, strip, multi-layered plots; group plots, adding embellishments and fit lines.
- Interactive graphics – a brief overview (similar to JMP, SAS/INSIGHT, SAS/IML Studio)
- Graphics resources
- Descriptive statistics done both the SAS/SPSS/Stata way and the R way
- Crosstabulation done both the SAS/SPSS/Stata way and the R way
- “By” or “split file” processing of groups
- Correcting p-values for the effects of multiple testing
- Correlation: Pearson, Spearman
- Linear regression
- Extractor functions (like Stata’s postestimation commands)
- t-test & Wilcoxon Mann-Whitney rank sum test
- Paired t-test & Wilcoxon signed-rank test
- Analysis of variance, Kruskal-Wallis & post hoc tests
- Getting publication-quality output into Word, LibreOffice, HTML and LaTeX
- Ways to run R
- Programs that include other programs
- Running R from within SAS and SPSS
- Running R as an adjunct to Stata
- Graphical User Interfaces: R Commander, JGR, Rattle, Excel
- Summary of topics learned
If you will be taking this course behind a firewall, you will need to access, dowwload and install the course material one week prior to the start of the class.
You will also need to have:
-RStudio installed on your machine prior to the begining of the course
-be able to unzip and install some working files
Feel free to register for Managing Data with R (TRA33) as a follow up course
We have the right to cancel the event for any reason at any time. Revolution Analytics will refund all monies paid for ticket sales in full in the event of a cancellation. We are not responsible for any travel related expenses incurred by attendees for this event. This includes but not limited to transportation, hotel accommodations or any other travel related expenses secured by the attendee, due to a cancellation on our part.
- 30 or more days from the event date: Full refund less 10%
- 16-29 days from the event date: 50% refund
- 15 or less days from the event date: No refund
- all related transaction fees PayPal and Eventbrite are not refundable
- discount offers cannot be combined
- A student ID Number is not a proof of full time university enrollment to get the student’s discount. Proof of enrollment in 9 units or more on a current academic registration document will be required to receive the student's discount.