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 What is R?                                                                                         R’s Advantages      ...
5                Source: r4stats.com/popularity                                                                           ...
 Vast selection of analytics & graphics New methods are available sooner Many packages can run R (SAS, SPSS, Excel…) I...
 Base R plus Recommended Packages like:                        Email support is free, quick, 24-hours:      Base SAS, S...
17   1819   20
 Run code interactively      Submit code from Excel, SAS, SPSS,…      Point-n-click using       Graphical User Interfac...
Copyright © 2010, 2011, Robert A Muenchen. All rights reserved.        26                               25run ExportDataSe...
29   30          3231
34                                              33 A company focused on R development & support Run by SPSS founder Norm...
43   44
mydata <- read.csv("mydata.csv")                                      > mydata <- read.csv("mydata.csv") print(mydata)    ...
> myModel <- lm(q4 ~ q1+q2+q3, data=mydata)> summary(myModel)Call:lm(formula = q4 ~ q1 + q2 + q3, data = mydata)Residuals:...
 R for SAS and SPSS Users, Muenchen                                                          R for Stata Users, Muenchen...
Intro to R for SAS and SPSS User Webinar
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Intro to R for SAS and SPSS User Webinar

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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.

Publié dans : Formation, Technologie
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Intro to R for SAS and SPSS User Webinar

  1. 1.  What is R?  R’s Advantages  R’s Disadvantages  Installing and Maintaining R  Ways of Running RBob 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 3 4
  2. 2. 5 Source: r4stats.com/popularity 6 http://r4stats.com/popularity1. 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) ) 7 8
  3. 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 9 10* Using SAS;  Language is somewhat harder to learnPROC TTEST DATA=classroom;  Help files are sparse & complexCLASS gender;  Must find R and its add-ons yourselfVAR score;  Graphical user interfaces not as polished  Most R functions hold data in main memory# In R  Rule-of-thumb: 10 million values per gigabytet.test(score ~ gender, data=classroom)  SAS/SPSS: billions of records  Several efforts underway to break R’s memory limitt.test(posttest, pretest , paired=TRUE, data=classroom) including Revolution Analytics’ distribution 11 12
  4. 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 13 141. Go to cran.r-project.org,  Comprehensive R Archive Network the Comprehensive R Archive Network  Crantastic.com2. Download binaries for Base & run  Inside-R.org3. Add-ons:  R4Stats.com install.packages(“myPackage”)4. To update: update.packages() 15 16
  5. 5. 17 1819 20
  6. 6.  Run code interactively  Submit code from Excel, SAS, SPSS,…  Point-n-click using Graphical User Interfaces (GUIs)  Batch mode21 2223 24
  7. 7. Copyright © 2010, 2011, Robert A Muenchen. All rights reserved. 26 25run 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. 27 28
  8. 8. 29 30 3231
  9. 9. 34 33 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 35
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  11. 11. 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 45 46> mydata$workshop <-factor(mydata$workshop)> summary(mydata) workshop gender 1:4 f :3 2:4 m :4 NAs:1q1 q2 q3 q4Min. :1.00 Min. :1.00 Min. :2.000 Min. :1.001st Qu.:2.00 1st Qu.:1.00 1st Qu.:4.000 1st Qu.:2.50Median :3.50 Median :2.50 Median :4.000 Median :3.50Mean :3.25 Mean :2.75 Mean :4.143 Mean :3.253rd Qu.:4.25 3rd Qu.:4.25 3rd Qu.:5.000 3rd Qu.:4.25Max. :5.00 Max. :5.00 Max. :5.000 Max. :5.00 NAs :1.000 47 48
  12. 12. > 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 -0.1246Coefficients: Estimate Std. Error t value Pr(>|t|)(Intercept) -1.3243 1.2877 -1.028 0.379q1 0.4297 0.2623 1.638 0.200q2 0.6310 0.2503 2.521 0.086q3 0.3150 0.2557 1.232 0.306Multiple R-squared: 0.9299, Adjusted R-squared: 0.8598F-statistic: 13.27 on 3 and 3 DF, p-value: 0.03084 49 Copyright © 2010, 2011, Robert A Muenchen. All rights reserved. 50 51 52
  13. 13.  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 53 54 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 Rs programming language is the way Slides: r4stats.com/misc/webinar Presentation: bit.ly/R-sas-spss to access its full power 55

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