Johnny-Five R Programming Welcome to the R programming Wikibook This book is designed to be a practical guide to the R programming language. 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) and many resources in specialized books, forums such as Stackoverflow and personal blogs, 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. It is supposed to be organized by task but not by discipline. How can you share your R experience ? Explain the syntax of a commandCompare the different ways of performing each task using R.Try to make unique examples based on fake data (ie simulated data sets).As with any Wikibook please feel free to make corrections, expand explanations, and make additions where necessary. Some rules : Prerequisites See also
Multiple Regression R provides comprehensive support for multiple linear regression. The topics below are provided in order of increasing complexity. Fitting the Model # Multiple Linear Regression Example fit <- lm(y ~ x1 + x2 + x3, data=mydata) summary(fit) # show results # Other useful functions coefficients(fit) # model coefficients confint(fit, level=0.95) # CIs for model parameters fitted(fit) # predicted values residuals(fit) # residuals anova(fit) # anova table vcov(fit) # covariance matrix for model parameters influence(fit) # regression diagnostics Diagnostic Plots Diagnostic plots provide checks for heteroscedasticity, normality, and influential observerations. # diagnostic plots layout(matrix(c(1,2,3,4),2,2)) # optional 4 graphs/page plot(fit) click to view For a more comprehensive evaluation of model fit see regression diagnostics. Comparing Models You can compare nested models with the anova( ) function. Cross Validation You can assess R2 shrinkage via K-fold cross-validation. Variable Selection
R-bloggers | R news & tutorials from the web HTTP+JSON Services in Modern Java At Airbnb, we build most of our user facing apps in Ruby on Rails, or more recently Node.js and our own Rendr framework. We also have a number of internal services, and those are mainly written in Java for stability and performance. Coming from a Ruby world, building anything in Java can feel pretty painful and boring. But thankfully there are modern Java libraries that make it easy and even fun. We build our Java services with Twitter Commons, a collection of libraries for building HTTP (and other) services. The Stack Twitter Commons uses Jetty, and provides a lot of the glue and miscellaneous parts of a web service, like logging, statistics, registration, and lifecycle management. On top of Jetty we use Jersey and Jackson, which is a tried and true combination that is also used by other stacks like Yammer’s Dropwizard. Jetty is an incredibly fast embeddable web server and servlet container. Jackson is the de facto standard for fast JSON processing on the JVM. Example App Using the Service
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. Recommended Books Introducing R Getting familiar with the statistical procedures Textbook examples: We have examples from popular textbooks and worked them out using R. Going further Frequently Asked Questions: We have a list of frequently asked questions (FAQs) regarding R. The content of this web site should not be construed as an endorsement of any particular web site, book, or software product by the University of California.
Graphical Parameters You can customize many features of your graphs (fonts, colors, axes, titles) through graphic options. One way is to specify these options in through the par( ) function. If you set parameter values here, the changes will be in effect for the rest of the session or until you change them again. The format is par(optionname=value, optionname=value, ...) # Set a graphical parameter using par() par() # view current settings opar <- par() # make a copy of current settings par(col.lab="red") # red x and y labels hist(mtcars$mpg) # create a plot with these new settings par(opar) # restore original settings A second way to specify graphical parameters is by providing the optionname=value pairs directly to a high level plotting function. # Set a graphical parameter within the plotting function hist(mtcars$mpg, col.lab="red") See the help for a specific high level plotting function (e.g. plot, hist, boxplot) to determine which graphical parameters can be set this way. Text and Symbol Size Lines Colors
ProjectTemplate jxcore·io Home Page The Workspace The workspace is your current R working environment and includes any user-defined objects (vectors, matrices, data frames, lists, functions). At the end of an R session, the user can save an image of the current workspace that is automatically reloaded the next time R is started. Commands are entered interactively at the R user prompt. Up and down arrow keys scroll through your command history. You will probably want to keep different projects in different physical directories. Here are some standard commands for managing your workspace. IMPORTANT NOTE FOR WINDOWS USERS: R gets confused if you use a path in your code like c:\mydocuments\myfile.txt This is because R sees "\" as an escape character. getwd() # print the current working directory - cwd ls() # list the objects in the current workspace setwd(mydirectory) # change to mydirectory setwd("c:/docs/mydir") # note / instead of \ in windows setwd("/usr/rob/mydir") # on linux q() # quit R.
Google's R Style Guide R is a high-level programming language used primarily for statistical computing and graphics. The goal of the R Programming Style Guide is to make our R code easier to read, share, and verify. The rules below were designed in collaboration with the entire R user community at Google. Summary: R Style Rules File Names: end in .R Identifiers: variable.name (or variableName), FunctionName, kConstantName Line Length: maximum 80 characters Indentation: two spaces, no tabs Spacing Curly Braces: first on same line, last on own line else: Surround else with braces Assignment: use <-, not = Semicolons: don't use them General Layout and Ordering Commenting Guidelines: all comments begin with # followed by a space; inline comments need two spaces before the # Function Definitions and Calls Function Documentation Example Function TODO Style: TODO(username) Summary: R Language Rules Notation and Naming File Names File names should end in .R and, of course, be meaningful. Identifiers Syntax Spacing
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