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R is an elegant and comprehensive statistical and graphical programming language. Unfortunately, it can also have a steep learning curve. I created this website for both current R users, and experienced users of other statistical packages (e.g., SAS, SPSS, Stata) who would like to transition to R. My goal is to help you quickly access this language in your work. I assume that you are already familiar with the statistical methods covered and instead provide you with a roadmap and the code necessary to get started quickly, and orient yourself for future learning. I designed this web site to be an easily accessible reference.

http://www.statmethods.net/index.html

Related:  Data visualisation toolsR ResourcesCoursera Data Science SpecializationProgramming Languages

Case study: A brief review of online visualisation tools that can help There is a growing range of online tools to help users their data. This brief review highlights four online visualisation tools that can help. The links page also links to lots more useful resources. Online tools that can help visualise data (these tools are free to use, but any data uploaded is typically then available on the system for other users) highlighted below include: On the resources and links page, we also link to free software applications and libraries for visualising data, and development languages for more sophisticated data visualisation. Many Eyes

R Programming Welcome to the R programming Wikibook This book is designed to be a practical guide to the R programming language[1]. R is free software designed for statistical computing. There is already great documentation for the standard R packages on the Comprehensive R Archive Network (CRAN)[2] and many resources in specialized books, forums such as Stackoverflow[3] and personal blogs[4], but all of these resources are scattered and therefore difficult to find and to compare. Shiny - Tutorial You can teach yourself to use Shiny in two ways. You can watch the “How to Start Shiny” webinar series, or you can work through the self-paced Shiny tutorial below. Who should take the tutorial? You will get the most out of the webinar or tutorial if you already know how to program in R, but not Shiny. If R is new to you, you may want to check out the learning resources at www.rstudio.com/training before taking this tutorial. If you are not sure whether you are ready for Shiny, try our quiz.

AboutHydrology: R resources for Hydrologists R is my statistical software of election. I had hard time to convince my Ph.D. students to adopt it, but finally they did, and, as usually happens, many of them became more proficient than me in the field. Now it seems natural to use it for everything, but this was not always the case. A list of introductory material is here. how to - MathWiki From MathWiki With a bit of experience, it's easy to find one's way around the menus in Rcmdr to reach a desired analysis. It's harder for a beginner and the following recipes should help students get started. Gallery "Spike" map Interactive United States population density map. Average rating: 7.5 (23 votes)

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. Getting familiar with R Class notes: There is no point in waiting to take an introductory class on how to use R.

Leo Breiman Leo Breiman passed away on July 5, 2005. Professor Breiman was a member of the National Academy of Sciences. His research in later years focussed on computationally intensive multivariate analysis, especially the use of nonlinear methods for pattern recognition and prediction in high dimensional spaces. He was a co-author of Classification and Regression Trees and he developed decision trees as computationally efficient alternatives to neural nets. Using R for Time Series Analysis — Time Series 0.2 documentation Reading Time Series Data The first thing that you will want to do to analyse your time series data will be to read it into R, and to plot the time series. You can read data into R using the scan() function, which assumes that your data for successive time points is in a simple text file with one column. For example, the file contains data on the age of death of successive kings of England, starting with William the Conqueror (original source: Hipel and Mcleod, 1994). The data set looks like this: Age of Death of Successive Kings of England #starting with William the Conqueror#Source: McNeill, "Interactive Data Analysis"604367505642506568436534...

Related:  r-projectR-language