1. Introduction to R Dr. Satish Nargundkar

2. What is R? R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. https://www.r-project.org/ 2

3. Advantages of R Free and open source No license restrictions 4800 packages for specialized topics Runs on many operating systems Active user groups 3

4. Limitations of R R can consume all the available memory  Use 64 bit systems than 32 bit to solve problem String comparisons can be slow, numeric normal speed versus other software R slower for some matrix operations such as 4

5. Downloading R-3.2.3 Windows(32/64 bits) https://cran.r-project.org/bin/windows/base/ R for Mac OS X https://cran.r-project.org/bin/macosx/ Downloading R studio https://www.rstudio.com/products/rstudio/downlo ad/ 5

6. Why get R studio? Ease of use Datasets readily accessible for viewing Commands at your finger tips Variable names at your fingertips Multiple windows for comprehensive view:  Script  Global Environment  Console  Home Screen 6

7. Screenshot for the script R script : Upper left hand corner of R studio screen Store all your commands in script 7

8. Screenshot for Global environment window Upper right hand corner of R-studio screen Shows al the datasets that have been imported and created through dataset transformations 8

9. Screenshot for Console R console: Lower left hand corner Runs your commands(Press enter) 9

10. Screenshot for Home Home: Lower right hand corner List of files you will have in your working directory 10

11. Reading data into R Set working directory through setwd(:/C…) Use forward slash while setting working directory Ensure dataset is in your working directory Save dataset as CSV file in your working directory Use import dataset option in R studio 11

12. Common Commands1 Getting list of variables Names(datasetname) Getting individual variable data column datasetname$variable Getting the metadata Summary(datasetname) Merging datasets: Totaldata<-merge(dataset1, dataset2, by = “common variable") Sorting Data attach(trainwhite) sorteddata<-data[order(variable),] Testing for missing values is.na(trainwhite) 12

13. Common Commands2 Finding variables that have missing values dataset[!complete.cases(dataset),] Regression regression1<-lm(y~ x1+ x2+ x3, data=dataset) Summary of regression Summary(regression1) Coefficients of regression Coefficients(regression1) Predicted values and residuals Fitted(regression1) Residuals(regression1) 2 way frequency table attach(trainwhite) freqtable<-table(pH,fixed_acidity) 13

14. Common commands 3 Making histograms hist(dataset$variable) Creating a graph Attach(datasetname) Plot(variable1, varaible2) abline(lm(v1~v2)) Title(“Regression of v1 on v2”) 14

15. Best Practices in R Use R studio to import dataset Convert dataset into CSV format before importing Use the online R communities for quick help, better than R’s help page Save scripts for common commands Make notes using ## for future reference Close all other work before using R, makes other things slower 15

16. Type the variable names as is (R is case sensitive) Common Errors: Watch out 16

17. References http://www.statmethods.net/graphs/density.html 17