About Vistat. Project MOSAIC. The mosaic Package Project MOSAIC is sponsoring work on an R package to facilitate teaching modeling, statistics, and calculus using R The mosaic package is available on CRAN (the comprehensive R archive network) and via github using require(devtools) install_github("mosaic", "rpruim") Feedback Want to offer feedback or make suggestions for new features?
Rpkgs@mosaic-web.org. Additional Resources We have assembled a number of vignettes highlighting various aspects of using R and the mosaic package. Minimal R How much R does it take to teach an Intro Stats course? Minimal R: a list of commands and a sampler of their use based on his Intro Stats courses. R for instructors If you are considering teaching statistics with R but aren’t sure you know enough (either about R itself or about how to teach with R), you might like one of these.
Project MOSAIC. PCA in R. StatsRUs. Paul E.
Johnson <pauljohn @ ku.edu> The original Rtips started in 1999. It became difficult to update because of limitations in the software with which it was created. Now I know more about R, and have decided to wade in again. In January, 2012, I took the FaqManager HTML output and converted it to LaTeX with the excellent open source program pandoc, and from there I’ve been editing and updating it in LyX. You are reading the New Thing! The first chore is to cut out the old useless stuff that was no good to start with, correct mistakes in translation (the quotation mark translations are particularly dangerous, but also there is trouble with ~, $, and -. (I thought it was cute to call this “StatsRus” but the Toystore’s lawyer called and, well, you know…) If you need a tip sheet for R, here it is.
This is not a substitute for R documentation, just a list of things I had trouble remembering when switching from SAS to R. Heed the words of Brian D. 1.1 Bring raw numbers into R (05/22/2012) Step 1. FrontPage - R Cookbook. Start [R Wiki] * R is a free software environment for statistical computing and graphics. It runs on a wide variety of UNIX platforms, Windows and MacOS. This R Wiki is dedicated to the collaborative writing of R documentation. For information on browsers, RSS syndication, copyright, ... read usage. R comes with several official manuals and FAQs. These should be your primary source of information. Personal note: I have some difficulties with the statement that the official manuals should be your primary source of information. Books I found ordinary books to be the most helpful source of information for novice programmers and I can particularly recommend: Adler, J. (2010) R in a nutshell.
Help forum for programmers. Software Resources for R. Software Resources for R Below is a list of resource pages for using R to do statistics.
On each page a set of data are explored with the software. Some commentary is given on interpretation but the main focus is on getting the software to do the work. What the numbers mean should come from your prior statistical training or the statistics.com course you are currently taking. Each page listed below has a title you can click on to read the page. Getting Started Entering data Simple summary statistics Stem and leaf plots Histograms Boxplots Dotplots Logarithmic transformations Saving your data Summarizing Quantitative Data. 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.
R: Index - MathWiki. 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. Graphs with Rcmdr Statistics with Rcmdr Regression with two variables with Rcmdr Using Rcmdr for basic analyses (with pictures) The following is a pictorial how-to guide for basic analyses with Rcmdr. The drop-down menu selections to perform the analysis the input menu the ouput Each is annotated as it seems appropriate. The following analyses and transformations are shown: Recoding a numeric variable into a categorical variable To transform a numerical variable into a categorical variable, use 'recode'. Home Page.
R manipulating data. Courtney Brown, Ph.D. RforSAS&SPSSusers.doc - Tecnologia Google Docs. R for SAS and SPSS Users Book Home Page. Relative Frequency Distribution of Qualitative Data. The relative frequency distribution of a data variable is a summary of the frequency proportion in a collection of non-overlapping categories.
The relationship of frequency and relative frequency is: Example In the data set painters, the relative frequency distribution of the School variable is a summary of the proportion of painters in each school. Problem Find the relative frequency distribution of the painter schools in the data set painters. Solution We first apply the table function to compute the frequency distribution of the School variable. > library(MASS) # load the MASS package > school = painters$School # the painter schools > school.freq = table(school) # apply the table function. R-statistics blog. The power of R. Romain Francois, Professional R Enthusiast. Playwith - Project Hosting on Google Code. Learning R. Beginner to advanced resources for the R programming language.