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R Tutorial — R Tutorial

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

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

Time series data in R Time series data in R There is no short­age of time series data avail­able on the web for use in stu­dent projects, or self-​​learning, or to test out new fore­cast­ing algo­rithms. It is now rel­a­tively easy to access these data sets directly in R. M Com­pe­ti­tion data The 1001 series from the M-​​competition and the 3003 series from the M3-​​competition are avail­able as part of the Mcomp pack­age in R. Data­Mar­ket and Quandl Both Data­Mar­ket and Quandl con­tain many thou­sands of time series that can be down­loaded directly into R. The two series should be iden­ti­cal. The dmseries func­tion from the rdata­mar­ket pack­age is sim­pler to use. For many years, I main­tained the Time Series Data Library con­sist­ing of about 800 time series includ­ing many from well-​​known text­books. R pack­ages A num­ber of other R pack­ages con­tain time series data. Related Posts:

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

R: Compute distance metrics between strings Description stringdist computes pairwise string distances between elements of a and b, where the argument with less elements is recycled. stringdistmatrix computes the string distance matrix with rows according to a and columns according to b. Usage stringdist(a, b, method = c("osa", "lv", "dl", "hamming", "lcs", "qgram", "cosine", "jaccard", "jw", "soundex"), useBytes = FALSE, weight = c(d = 1, i = 1, s = 1, t = 1), maxDist = Inf, q = 1, p = 0, nthread = getOption("sd_num_thread")) stringdistmatrix(a, b, method = c("osa", "lv", "dl", "hamming", "lcs", "qgram", "cosine", "jaccard", "jw", "soundex"), useBytes = FALSE, weight = c(d = 1, i = 1, s = 1, t = 1), maxDist = Inf, q = 1, p = 0, useNames = c("none", "strings", "names"), ncores = 1, cluster = NULL, nthread = getOption("sd_num_thread")) Arguments Value For stringdist, a vector with string distances of size max(length(a),length(b)). For stringdistmatrix: if both a and b are passed, a length(a)xlength(b) matrix. See Also Examples

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.

R: String metrics in 'stringdist' Description This page gives an overview of the string dissimilarity measures offered by stringdist. String Metrics String metrics are ways of quantifying the dissimilarity between two finite sequences, usually text strings. Over the years, many such measures have been developed. The terms 'string metrics' and 'string distance' are used more or less interchangibly in literature. The metric you need to choose for an application strongly depends on both the nature of the string (what does the string represent?) Currently, the following distance metrics are supported by stringdist. A short description of string metrics supported by stringdist See Van der Loo (2014) for an extensive description and references. The Hamming distance (method='hamming') counts the number of character substitutions that turns b into a. The Levenshtein distance (method='lv') counts the number of deletions, insertions and substitutions necessary to turn b into a. References See Also

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!"

dgrtwo/fuzzyjoin: Join tables together on inexact matching The R language, for programmers IntroductionAssignment and underscoreVariable name gotchasVectorsSequencesTypesBoolean operatorsListsMatricesMissing values and NaNsCommentsFunctionsScopeMisc.Other resources 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. Variable name gotchas Vectors The primary data type in R is the vector.

R 101 (by DataCamp) - Big Data University About This Course Master the basics of the R open source language, such as factors, lists and data frames. With the knowledge gained, you will be ready to undertake your first very own data analysis. Get access to the Introduction to R course on DataCamp. By clicking on 'Start Chapter', a new tab will open that takes you directly to the first exercise of that chapter on DataCamp.When you complete exercises on DataCamp, your score is sent back to Big Data University for grading purposes.Special offer by Datacamp: Complete this course through Big Data University, and gain free access to the entire DataCamp catalog of courses for two weeks! Course Syllabus Module 1 - Intro to BasicsModule 2 - VectorsModule 3 - MatricesModule 4 - FactorsModule 5 - Data FramesModule 6 - Lists General Information This course is free.It is self-paced.It can be taken at any time.It can be audited as many times as you wish. Recommended skills for this course Requirements None Course Staff Jonathan Cornelissen