Tools for Business AnalyticsMSBA 320Dr. Siamak ZadehAdjunct ProfessorAgeno School of BusinessGolden Gate University
Introduction to R: R Basics
Applied Statistical Computing and Graphics3Outline•Why R, and R Paradigm•References, Tutorials and links•R Overview•R Interface•R Workspace•Help•R Packages•Input/Output•Reusing Results
Applied Statistical Computing and Graphics4Why R?It's free! It runs on a variety of platforms including Windows, Unix and MacOS. It provides an unparalleled platform for programming new statistical methods in an easy and straightforward manner. It contains advanced statistical routines not yet available in other packages. It has state-of-the-art graphics capabilities.
Applied Statistical Computing and Graphics5R has a Steep Learning Curve(steeper for those that knew SAS or other software before)First, while there are many introductory tutorials (covering data types, basic commands, the interface), none alone are comprehensive. In part, this is because much of the advanced functionality of Rcomes from hundreds of user contributed packages. Hunting for what you want can be time consuming, and it can be hard to get a clear overview of what procedures are available.
Applied Statistical Computing and Graphics6R has a Learning Curve(steeper for those that knew SAS or other software before)The second reason is more transient. As users of statistical packages, we tend to run one controlled procedure for each type of analysis. Think of PROC GLM in SAS. We can carefully set up the run with all the parameters and options that we need. When we run the procedure, the resulting output may be a hundred pages long. We then sift through this output pulling out what we need and discarding the rest.
Applied Statistical Computing and Graphics7R paradigm is differentRather than setting up a complete analysis at once, the process is highly interactive. You run a command (say fit a model), take the results and process it through another command (say a set of diagnostic plots), take those results and process it through another command (say cross-validation), etc. The cycle may include transforming the data, and looping back through the whole process again. You stop when you feel that you have fully analyzed the data.
Applied Statistical Computing and Graphics8How to download?•Google it using R or CRAN (Comprehensive R Archive Network)•
Applied Statistical Computing and Graphics9TutorialsEach of the following tutorials are in PDF format.•P. Kuhnert & B. Venables, An Introduction to R: Software for Statistical Modeling & Computing•J.H. Maindonald, Using R for Data Analysis and Graphics•B. Muenchen, R for SAS and SPSS Users•W.J. Owen, The R Guide•D. Rossiter, Introduction to the R Project for Statistical Computing for Use at the ITC•W.N. Venebles & D. M. Smith, An Introduction to R