Quick-R: Home Page 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. The old, French version is still available, in HTML or as a single file. You may also want all the code in this document. 1. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Cookbook for R » Cookbook for R R by example Basics Reading files Graphs Probability and statistics Regression Time-series analysis All these examples in one tarfile. Outright non-working code is unlikely, though occasionally my fingers fumble or code-rot occurs. Other useful materials Suggestions for learning R The R project is at : In particular, see the `other docs' there. Over and above the strong set of functions that you get in `off the shelf' R, there is a concept like CPAN (of the perl world) or CTAN (of the tex world), where there is a large, well-organised collection of 3rd party software, written by people all over the world. The dynamism of R and of the surrounding 3rd party packages has thrown up the need for a newsletter, R News. library(help=boot) library(boot) ? But you will learn a lot more by reading the article Resampling Methods in R: The boot package by Angelo J. Ajay Shah, 2005
Model visualisation. had.co.nz This page lists my published software for model visualisation. This work forms the basis for the third chapter of my thesis. classifly: Explore classification boundaries in high dimensions. Given p-dimensional training data containing d groups (the design space), a classification algorithm (classifier) predicts which group new data belongs to. clusterfly: Explore clustering results in high dimensions. Typically, there is somewhat of a divide between statistics and visualisation software. There are also some custom methods for certain types of clustering, mostly inspired by the work of Dr Dianne Cook: Self organising maps (aka Kohonen neural networks), ? meifly: Models explored interactively. Meifly is tool that uses R and GGobi to explore ensembles of linear models, where we look at all possible main effects models for a given dataset (or a large subset of these models). Installation Please make sure you have a current version of R and rggobi installed, then use the following R code:
How to use R R is a powerful, free and open source, cross-platform, statistical and graphing software package;programming language;software environment for statistical computing. Downloading R[edit] Visit the R Project home page. Tutorials[edit] Books that are Helpful When Learning R[edit] See also[edit] External links[edit] Books[edit] Highland Statistics Ltd Jump straight to Price and Order the book Outline Keywords Table of Contents Data sets and R code used Video files Support chapters Discussion board Outline This book presents Generalized Linear Models (GLM) and Generalized Linear Mixed Models (GLMM) based on both frequency-based and Bayesian concepts. The book uses the functions glm, lmer, glmer, glmmADMB, and also JAGS from within R. R code to construct, fit, interpret, and comparatively evaluate models is provided at every stage. Readers of this book have free access to: Chapter 1 of Zero Inflated Models and Generalized Linear Mixed Models with R. (2012a) Zuur, Saveliev, Ieno. See the Preface (and the text below) how to access the pdfs of these chapters. Keywords Table of contents Click for Table of contents Price and Order the book The paperback is priced at 49 GBP. Copyright statement This book is copyright material from Highland Statistics Ltd. Data sets and R code used in the book. Video file with general comments Alain Zuur Support chapters
Big Data, Data Mining, Predictive Analytics, Statistics, StatSoft Electronic Textbook This free ebook has been provided as a public service since 1995. Statistics: Methods and Applications textbook offers training in the understanding and application of statistics and data mining. It covers a wide variety of applications, including laboratory research (biomedical, agricultural, etc.), business statistics, credit scoring, forecasting, social science statistics and survey research, data mining, engineering and quality control applications, and many others. The Textbook begins with an overview of the relevant elementary (pivotal) concepts and continues with a more in depth exploration of specific areas of statistics, organized by "modules", representing classes of analytic techniques. You have filtered out all documents.
RStudio Server Amazon Machine Image (AMI) - Louis Aslett Current AMI Quick Reference (27nd Jun 2015)Amazon instance type reference Click to launch through AWS web interface: What’s new recently? Easy Dropbox setup to make syncing files on/off server easy, including selective folder sync. Preinstalled RStudioAMI R package for server control. HVM AMIs for full current generation instance support. < Back to homepage Amazon’s EC2 platform provides a convenient environment for rapidly procuring computational resources in the cloud. To get started with the Amazon cloud, you must first signup for an AWS account if you don’t already have one. Click here for a simple video guide to using the AMIs listed here, or for more detailed information read on. What is this? If you want to run a server in the Amazon cloud, you have to select what system you are going to bootup. In particular, many common tools and dependencies are built-in. Why an RStudio AMI? AMI Release History * N/A since these data centres were not yet open when the images were built. Usage
Building an R Hadoop System - RDataMining.com: R and Data Mining This page shows how to build an R Hadoop system, and presents the steps to set up my first R Hadoop system in single-node mode on Mac OS X. After reading documents and tutorials on MapReduce and Hadoop and playing with RHadoop for about 2 weeks, finally I have built my first R Hadoop system and successfully run some R examples on it. Here I’d like to share my experience and steps to achieve that. Before going through the complex steps below, let’s have a look what you can get, to give you a motivation to continue. Now let’s start. 1. 1.1 Download Hadoop Download Hadoop (hadoop-1.1.2-bin.tar.gz) at and then unpack it. 1.2 Set JAVA_HOME In conf/hadoop_env.sh, add the line below: export JAVA_HOME=/Library/Java/Home 1.3 Set up Remote Desktop and Enabling Self-Login System Preferences > Sharing (under Internet & Network), Under the list of services, check "Remote Login". ssh-keygen -t rsa -P "" cat $HOME/.ssh/id_rsa.pub >> $HOME/.ssh/authorized_keys 2.
Cookbook for R » Cookbook for R developers:projects:gsoc2012:ropensci Summary: Dynamic access and visualization of scientific data repositories Description: rOpenSci is a collaborative effort to develop R-based tools for facilitating Open Science. Projects in rOpenSci fall into two categories: those for working with the scientific literature, and those for working directly with the databases. Visit the active development hub of each project on github, where you can see and download source-code, see updates, and follow or join the developer discussions of issues. Most of the packages work through an API provided by the resource (database, paper archive) to access data and bring it within reach of R’s powerful manipulation. See a complete list of our R packages currently in development. The student could choose to work on a package for a particular data repository of interest, or develop tools for visualization and exploration that could function across the existing packages.
10 tips for making your R graphics look their best So you've spent hours slaving over the code for a beautiful statistical graphic in R, and now you're ready to show it to the world. You might be printing it, embedding it in a document, or displaying it on the web. Don't do your graph a disservice by causing it to look anything less than perfect in its final venue. Here are 10 tips to help make sure your graphic will always look best. 1. It's tempting to just create graphics to the on-screen device (such as X11 on Linux or Quartz on MacOS) and then just use "Save As..." from the menu. The best practice is to create a script file that begins with a call to the device driver (usually pdf or png), runs the graphics commands, and then finishes with a call to dev.off(). png(file="mygraphic.png",width=400,height=350)plot(x=rnorm(10),y=rnorm(10),main="example")dev.off() 2. If you plan to print your graphic, you want to use a vector-based format. 3. The main choice you need to make when using png() is the dimensions of the graphic in pixels. 4.
www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html R Graphicsby Paul Murrell The SECOND EDITION of this book is now available, with its own web page. A book on the core graphics facilities of the R language and environment for statistical computing and graphics (Chapman & Hall/CRC, August 2005). A link to the publisher's web page for the book. A list of Errata. PDF version of the preface, table of contents, and Chapters 1, 4, and 5. R code for figures: Chapter 1: An Introduction to R Graphics Chapter 2: Simple Usage of Traditional Graphics Chapter 3: Customising Traditional Graphics Chapter 4: Trellis Graphics: The Lattice Package Chapter 5: The Grid Graphics Model Chapter 6: The Grid Graphics Object Model Chapter 7: Developing New Graphics Functions and Objects Appendix A: A Brief Introduction to R Appendix B: Combining Traditional Graphics and Grid Graphics Extras: Some extra plots not in the book An R add-on package called "RGraphics" is available from CRAN. Figure 3.12 -- uses 'hjust' argument in grid.text()