Introduction to R for Data Mining For a quick start: Find a way of orienting yourself in the open source R worldHave a definite application area in mindSet an initial goal of doing something useful and then build on it In this webinar, we focus on data mining as the application area and show how anyone with just a basic knowledge of elementary data mining techniques can become immediately productive in R. We will:
R Tutorial — R Tutorial 321a Boyd Graduate Studies University of Georgia Athens, Georgia 30602 Introductory Materials¶ These materials are designed to offer an introduction to the use of R. Exploratory Data Analysis and Regression in R Exploratory Data Analysis (EDA) and Regression This tutorial demonstrates some of the capabilities of R for exploring relationships among two (or more) quantitative variables. Bivariate exploratory data analysis We begin by loading the Hipparcos dataset used in the descriptive statistics tutorial, found at Type hip <- read.table(" header=T,fill=T) names(hip) attach(hip) In the descriptive statistics tutorial, we considered boxplots, a one-dimensional plotting technique. Reshape R provides a variety of methods for reshaping data prior to analysis. Transpose Use the t() function to transpose a matrix or a data frame.
Programming in R The R languageData structuresDebuggingObject Oriented Programming: S3 ClassesObject Oriented Programming: S3 ClassesData storage, Data import, Data exportPackagesOther languages(Graphical) User InterfaceWeb interface: RpadWeb programming: RZopeWeb servicesClusters, parallel programmingMiscellaneousNumerical optimizationMiscellaneousDirty Tricks In this part, after quickly listing the main characteristics of the language, we present the basic data types, how to create them, how to explore them, how to extract pieces of them, how to modify them. We then jump to more advanced subjects (most of which can -- should?
Statistics with R Warning Here are the notes I took while discovering and using the statistical environment R. However, I do not claim any competence in the domains I tackle: I hope you will find those notes useful, but keep you eyes open -- errors and bad advice are still lurking in those pages... Should you want it, I have prepared a quick-and-dirty PDF version of this document.
R Starter Kit R Starter Kit This page is intended for people who: These materials have been collected from various places on our website and have been ordered so that you can, in step-by-step fashion, develop the skills needed to conduct common analyses in R.
R: The R Datasets Package Documentation for package ‘datasets’ version 3.3.0 DESCRIPTION file. Help Pages heather.cs.ucdavis.edu/~matloff/r.html Professor Norm Matloff Dept. of Computer Science University of California at Davis Davis, CA 95616 R is a wonderful programming language for statistics and data management, used widely in industry, business, government, medicine and so on. And it's free, an open source product. The S language, of which R is essentially an open source version, won the ACM Software System Award in 1998.