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R Programming - Manuals

R Programming - Manuals
R Basics The R & BioConductor manual provides a general introduction to the usage of the R environment and its basic command syntax. Code Editors for R Several excellent code editors are available that provide functionalities like R syntax highlighting, auto code indenting and utilities to send code/functions to the R console. Programming in R using Vim or Emacs Programming in R using RStudio Integrating R with Vim and Tmux Users interested in integrating R with vim and tmux may want to consult the Vim-R-Tmux configuration page. Finding Help Reference list on R programming (selection)R Programming for Bioinformatics, by Robert GentlemanS Programming, by W. Control Structures Conditional Executions Comparison Operators equal: ==not equal: ! Logical Operators If Statements If statements operate on length-one logical vectors. Syntax if(cond1=true) { cmd1 } else { cmd2 } Example if(1==0) { print(1) } else { print(2) } [1] 2 Avoid inserting newlines between '} else'. Ifelse Statements Loops For Loop Example

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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? -- be omitted by first-time readers): debugging, profiling, namespaces, objects, interface with other programs, with data bases, with other languages. The R language Control structures

60+ R resources to improve your data skills This list was originally published as part of the Computerworld Beginner's Guide to R but has since been expanded to also include resources for advanced beginner and intermediate users. If you're just starting out with R, I recommend first heading to the Beginner's Guide. These websites, videos, blogs, social media/communities, software and books/ebooks can help you do more with R; my favorites are listed in bold. Want to see a sortable list of resources by subject and type? Expand the chart below.

Deep Learning in a Nutshell 29 December 2014 Deep learning. Neural networks. The R programming language for programmers coming from other programming languages IntroductionAssignment and underscoreVariable name gotchasVectorsSequencesTypesBoolean operatorsListsMatricesMissing values and NaNsCommentsFunctionsScopeMisc.Other resources Ukrainian translation Other languages: Powered by Translate Introduction 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. The aim of this Wikibook is to be the place where anyone can share his or her knowledge and tricks on R.

Medley: a new R package for blending regression models Hi Sashi, Sorry for the muddled explaination. What I was trying to say is, if you give Martin's medley package a tuning grid, it will fit a model to each parameter set in the grid, and then include ALL the models in the final ensemble. The timeline of statistics ‘Study the past if you would define the future’ - Confucius. ‘The further back you can look, the further forward you are likely to see’ – Churchill. ‘If history were taught in the form of stories it would never be forgotten’ – Kipling. Significance magazine has been going for ten years. To mark its birthday we have published a fold-out timeline of more or less everything that is important in the history of statistics. And as Kipling hinted, history is a story.

R Beginner's Guide and R Bloggers Updates 1/1/2011 Update: Tal Galili wrote an article that revisits the first year of R-Bloggers and this post was listed as one of the top 14. Therefore, I decided to make a small update to each section. I start by describing the initial series of tutorials that I wrote. 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.

Onepager Now with knitR \documentclass[nohyper,justified]{tufte-handout} %\documentclass{article} %\usepackage[absolute,showboxes]{textpos} Nuts and Bolts of Quantstrat, Part I Recently, I gave a webinar on some introductory quantstrat. Here’s the link. So to follow up on it, I’m going to do a multi-week series of posts delving into trying to explain the details of parts of my demos, so as to be sure that everyone has a chance to learn and follow along with my methodologies, what I do, and so on. To keep things simple, I’ll be using the usual RSI 20/80 filtered on SMA 200 demo. This post will deal with the initial setup of any demo–code which will be largely similar from demo to demo.