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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. Thank You! I have received a great deal of feedback from a number of people for various errors, typos, and dumb things. I have also written a book about programming R. Related:  Data ScienceR

Statistical Computing with R: A tutorial 0.1. What is R?R is a software package especially suitable for data analysis and graphical representation. Functions and results of analysis are all stored as objects, allowing easy function modification and model building. It is very flexible and highly customizable. On the other hand, R has a few weaknesses. 0.2 Where do I get R? and follow the download instructions. 0.3 Invoking RIf properly installed, usually R has a shortcut icon on the desktop screen and/or you can find it under Start|Programs|R menu. To quit R, type q() at the R prompt (>) and press Enter key. Commands you entered can be easily recalled and modified. Interactive graphics can serve as a great learning tool. Effect of kernel choice, sample size and bandwidth can be conveniently illustrated by the following demonstration: 2.1 ComputationFirst of all, R can be used as an ordinary calculator. 2.2 VectorR handles vector objects quite easily and intuitively. When finished, click

Quick-R: Home Page R Introduction We offer here a couple of introductory tutorials on basic R concepts. It serves as background material for our main tutorial series Elementary Statistics withR. The only hardware requirement for most of the R tutorials is a PC with the latest free open source R software installed. R has extensive documentation and active online community support. It is the perfect environment to get started in statistical computing. Installation R can be downloaded from one of the mirror sites in Using External Data R offers plenty of options for loading external data, including Excel, Minitab and SPSS files. R Session After R is started, there is a console awaiting for input. Variable Assignment We assign values to variables with the assignment operator "=". Functions R functions are invoked by its name, then followed by the parenthesis, and zero or more arguments. Comments All text after the pound sign "#" within the same line is considered a comment. Extension Package

Visualizing data using a 3D printer In a break from my usual obsessions and interests here is a guest blog post by Ian Walker. I'm posting it because I think it is rather cool and hope it will be of interest to some of my regular readers. Ian is perhaps best known (in the blogosphere) for his work on transport psychology - particularly cycling - but is also an expert on psychological statistics. Some time ago, I had some data that lent themselves to a three-dimensional surface plot. Of course, displaying fundamentally three-dimensional items in two dimensions is an ancient problem, as any cartographer will tell you. I managed to meet up with Adrian back in May 2012, and he explained to me the structure of the STL (stereolithography) files commonly used for three-dimensional printing. I'm normally a terrible hacker when it comes to programming; I usually storm in and try to make things work as quickly as possible then fix all the mistakes later. Demo source('r2stl.r') x <- seq(-10, 10, length= 100) y <- x z <- outer(x, y, f)

R for Psych Research This is one page of a series of tutorials for using R in psychological research. Much of material has also covered been covered in number of short courses or in a set of tutorials for specific problems. This particular page is an update of a previous guide to R which is being converted to HTML5 to be more readable. (For a very abbreviated form of this guide meant to help students do basic data analysis in a personality research course, see a very short guide. There are many possible statistical programs that can be used in psychological research. It has been claimed that "The statistical programming language and computing environment S has become the de-facto standard among statisticians. The R project, based upon the S and S+ stats packages, has developed an extremely powerful set of "packages" that operate within one program. Although many run R as a language and programming environment, there are Graphical User Interfaces (GUIs) available for PCs, Linux and Macs. Back to Top entering ?

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.

A Complete Tutorial to learn Data Science in R from Scratch Introduction R is a powerful language used widely for data analysis and statistical computing. It was developed in early 90s. Since then, endless efforts have been made to improve R’s user interface. This was possible only because of generous contributions by R users globally. But, what about Machine Learning ? My first impression of R was that it’s just a software for statistical computing. This is a complete tutorial to learn data science and machine learning using R. Note: No prior knowledge of data science / analytics is required. Table of Contents Basics of R Programming for Data ScienceWhy learn R ? Let’s get started ! Note: The data set used in this article is from Big Mart Sales Prediction. 1. Why learn R ? I don’t know if I have a solid reason to convince you, but let me share what got me started. The style of coding is quite easy.It’s open source. There are many more benefits. How to install R / R Studio ? You could download and install the old version of R. Basic Computations in R

The MakeR way: Using R to reify social media data via 3d printing If you’ve read any of my previous posts you know that I am constantly experimenting with different ways to represent and explore social network data with R. For example, in previous posts I’ve written about sonification of tweet data, animation of dynamic twitter networks, and various ways to plot social networks (here and here). In each case the underlying idea is finding different ways to explore data under the assumption that sometimes just looking at something from a different point of view reveals something novel. First, why would anyone want to take network data, model it in 3D and then use a 3D printer to make it real? So how did I use R in this project? edge.list.df <- data.frame(from=retweeters, to=retweeted) g <- graph.data.frame(edge.list.df, directed=TRUE) Now that I have the network, I can use the features of the igraph package to set the size and colors of the nodes and edges (code not shown, but you can see an example here). writeSTL("~myNetworkPrintFile.stl")

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 GentlemanAdvanced R, by Hadley WickhamS 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'. Loops Syntax

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

R programming language · FTSRG/cheat-sheets Wiki Tutorials Data Camp Introduction to R class: -- theoretical explanation, simple codes, ideal for R beginners who would like to have a look at basics without installing Rswirl online interactive courses, covering topics like basic R programming, regression models, data collection and cleaning : Code School – Try R: tutorial: R cheat-sheets: -- recommended rather if you know exactly what you are looking for, otherwise it is waste of time Visualization Advanced topics R readability rules (at least, one version from the Berkeley): How to install packages on Linux Well nothing could be easier.

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 I have written software professionally in perhaps a dozen programming languages, and the hardest language for me to learn has been R. R is more than a programming language. This document is a work in progress. Assignment and underscore The assignment operator in R is <- as in e <- m*c^2. It is also possible, though uncommon, to reverse the arrow and put the receiving variable on the right, as in m*c^2 -> e. It is sometimes possible to use = for assignment, though I don't understand when this is and is not allowed. However, when supplying default function arguments or calling functions with named arguments, you must use the = operator and cannot use the arrow. At some time in the past R, or its ancestor S, used underscore as assignment. Vectors Sequences

Statistics, R, Graphics and Fun | Yihui Xie Beginner's guide to R: Introduction Computerworld - R is hot. Whether measured by more than 4,400 add-on packages, the 18,000+ members of LinkedIn's R group or the close to 80 R Meetup groups currently in existence, there can be little doubt that interest in the R statistics language, especially for data analysis, is soaring. Why R? Because it's a programmable environment that uses command-line scripting, you can store a series of complex data-analysis steps in R. That also makes it easier for others to validate research results and check your work for errors -- an issue that cropped up in the news recently after an Excel coding error was among several flaws found in an influential economics analysis report known as Reinhart/Rogoff. The error itself wasn't a surprise, blogs Christopher Gandrud, who earned a doctorate in quantitative research methodology from the London School of Economics. Sure, you can easily examine complex formulas on a spreadsheet. Indeed, the mantra of "Make sure your work is reproducible!"

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