R is free software for data analysis and graphics that is similar to SAS and SPSS. Two million people are part of the R Open Source Community. Its use is growing very rapidly and Revolution Analytics distributes a commercial version of R that adds capabilities that are not available in the Open Source version. This 60-minute webinar is for people who are familiar with SAS or SPSS who want to know how R can strengthen their analytics strategy.

1.  What is R?
 R’s Advantages
 R’s Disadvantages
 Installing and Maintaining R
 Ways of Running R
Bob Muenchen, Author R for SAS and SPSS Users,
 An Example Program
Co-Author R for Stata Users
 Where to Learn More
muenchen.bob@gmail.com, http://r4stats.com
Copyright © 2010, 2011, Robert A Muenchen. All rights reserved. 2
 “The most powerful statistical computing language
on the planet.” -Norman Nie, Developer of SPSS
 Language + package + environment for
graphics and data analysis
 Free and open source
 Created by Ross Ihaka & Robert Gentleman 1996
& extended by many more
 An implementation of the S language by
John Chambers and others
 R has 4,950 add-ons, or nearly 100,000 procs
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2. 5 Source: r4stats.com/popularity
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http://r4stats.com/popularity
1. Data input & management (data step) * SAS Approach;
2. Analytics & graphics procedures (proc step) DATA A; SET A;
3. Macro language logX = log(X);
4. Matrix language PROC REG;
5. Output management systems (ODS/OMS) MODEL Y = logX;
R integrates these all seamlessly. # R Approach
lm( Y ~ log(X) )
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3.  Vast selection of analytics & graphics
 New methods are available sooner
 Many packages can run R (SAS, SPSS, Excel…)
 Its object orientation “does the right thing”
 Its language is powerful & fully integrated
 Procedures you write are on an equal footing
 It is the universal language of data analysis
 It runs on any computer
 Being open source, you can study and modify it
 It is free
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* Using SAS;  Language is somewhat harder to learn
PROC TTEST DATA=classroom;  Help files are sparse & complex
CLASS gender;  Must find R and its add-ons yourself
VAR score;
 Graphical user interfaces not as polished
 Most R functions hold data in main memory
# In R
 Rule-of-thumb: 10 million values per gigabyte
t.test(score ~ gender, data=classroom)
 SAS/SPSS: billions of records
 Several efforts underway to break R’s memory limit
t.test(posttest, pretest , paired=TRUE, data=classroom) including Revolution Analytics’ distribution
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4.  Base R plus Recommended Packages like:  Email support is free, quick, 24-hours:
 Base SAS, SAS/STAT, SAS/GRAPH, SAS/IML Studio  www.r-project.org/mail.html
 SPSS Stat. Base, SPSS Stat. Advanced, Regression  Stackoverflow.com
 Tested via extensive validation programs  Quora.com
 But add-on packages written by…  Crossvalidated stats.stackexchange.com
 Professor who invented the method? /questions/tagged/r
 A student interpreting the method?  Phone support available commercially
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1. Go to cran.r-project.org,  Comprehensive R Archive Network
the Comprehensive R Archive Network
 Crantastic.com
2. Download binaries for Base & run  Inside-R.org
3. Add-ons:  R4Stats.com
install.packages(“myPackage”)
4. To update: update.packages()
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6.  Run code interactively
 Submit code from Excel, SAS, SPSS,…
 Point-n-click using
Graphical User Interfaces (GUIs)
 Batch mode
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7. Copyright © 2010, 2011, Robert A Muenchen. All rights reserved. 26
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run ExportDataSetToR("mydata"); GET FILE=‘mydata.sav’.
BEGIN PROGRAM R.
submit/r;
mydata <- spssdata.GetDataFromSPSS(
mydata$workshop <-
variables = c("workshop gender
factor(mydata$workshop)
q1 to q4"),
summary(mydata) missingValueToNA = TRUE,
endsubmit; row.label = "id" )
summary(mydata)
END PROGRAM.
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 A company focused on R development & support
 Run by SPSS founder Norman Nie
 Their enhanced distribution of R:
Revolution R Enterprise
 Free for colleges and universities, including for
outside consulting
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12. mydata <- read.csv("mydata.csv") > mydata <- read.csv("mydata.csv")
print(mydata) > print(mydata)
workshop gender q1 q2 q3 q4
mydata$workshop <- factor(mydata$workshop)
1 1 f 1 1 5 1
summary(mydata)
2 2 f 2 1 4 1
plot( mydata$q1, mydata$q4 ) 3 1 f 2 2 4 3
4 2 <NA> 3 1 NA 3
myModel <- lm( q4~q1+q2+q3, data=mydata ) 5 1 m 4 5 2 4
summary( myModel ) 6 2 m 5 4 5 5
anova( myModel ) 7 1 m 5 3 4 4
plot( myModel )
8 2 m 4 5 5 5
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> mydata$workshop <-factor(mydata$workshop)
> summary(mydata)
workshop gender
1:4 f :3
2:4 m :4
NA's:1
q1 q2 q3 q4
Min. :1.00 Min. :1.00 Min. :2.000 Min. :1.00
1st Qu.:2.00 1st Qu.:1.00 1st Qu.:4.000 1st Qu.:2.50
Median :3.50 Median :2.50 Median :4.000 Median :3.50
Mean :3.25 Mean :2.75 Mean :4.143 Mean :3.25
3rd Qu.:4.25 3rd Qu.:4.25 3rd Qu.:5.000 3rd Qu.:4.25
Max. :5.00 Max. :5.00 Max. :5.000 Max. :5.00
NA's :1.000
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13. > myModel <- lm(q4 ~ q1+q2+q3, data=mydata)
> summary(myModel)
Call:
lm(formula = q4 ~ q1 + q2 + q3, data = mydata)
Residuals:
1 2 3 5 6 7 8
-0.3113 -0.4261 0.9428 -0.1797 0.0765 0.0225 -0.1246
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.3243 1.2877 -1.028 0.379
q1 0.4297 0.2623 1.638 0.200
q2 0.6310 0.2503 2.521 0.086
q3 0.3150 0.2557 1.232 0.306
Multiple R-squared: 0.9299, Adjusted R-squared: 0.8598
F-statistic: 13.27 on 3 and 3 DF, p-value: 0.03084
49 Copyright © 2010, 2011, Robert A Muenchen. All rights reserved. 50
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14.  R for SAS and SPSS Users, Muenchen
 R for Stata Users, Muenchen & Hilbe
 R Through Excel: A Spreadsheet Interface for Statistics,
Data Analysis, and Graphics, Heiberger & Neuwirth
 Data Mining with Rattle and R: The Art of Excavating
Data for Knowledge Discovery, Williams
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 R is powerful, extensible, free
 Download it from CRAN
 Academics download Revolution R Enterprise
for free at www.revolutionanalytics.com
 You run it many ways & from many packages
muenchen@utk.edu
 Several graphical user interfaces are available
 R's programming language is the way Slides: r4stats.com/misc/webinar
Presentation: bit.ly/R-sas-spss
to access its full power
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