Revolutions. The R Project for Statistical Computing. Untitled. Untitled. Visualization. The followings introductory post is intended for new users of R. It deals with interactive visualization using R through the iplots package. This is a guest article by Dr. Robert I. Kabacoff, the founder of (one of) the first online R tutorials websites: Quick-R. For readers of this blog, there is a 38% discount off the “R in Action” book (as well as all other eBooks, pBooks and MEAPs at Manning publishing house), simply by using the code rblogg38 when reaching checkout. Let us now talk about Interactive Graphics with the iplots Package: Interactive Graphics with the iplots Package The base installation of R provides limited interactivity with graphs.
While playwith and latticist allow you to interact with a single graph, the iplots package takes interaction in a different direction. The iplots package is implemented through Java and the primary functions are listed in table 1. Table 1 iplot functions To understand how iplots works, execute the code provided in listing 1. Summary. An Introduction to R. Table of Contents This is an introduction to R (“GNU S”), a language and environment for statistical computing and graphics. R is similar to the award-winning1 S system, which was developed at Bell Laboratories by John Chambers et al. It provides a wide variety of statistical and graphical techniques (linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, ...). This manual provides information on data types, programming elements, statistical modelling and graphics.
This manual is for R, version 3.1.0 (2014-04-10). Copyright © 1990 W. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Preface This introduction to R is derived from an original set of notes describing the S and S-PLUS environments written in 1990–2 by Bill Venables and David M. Comments and corrections are always welcome. Suggestions to the reader 1.1 The R environment Try ? Refine - Google Refine, a power tool for working with messy data (formerly Freebase Gridworks)