S-PLUS & R Class Links

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montes de materiais para R e S-PLUS.

S-PLUS & R Class Links

Instructor: Richard Herrington

Why Do We Care To Use the “S” Language?  Does anyone care besides us? The Association for Computing Machinery (ACM) cares

S-Plus

S-PLUS Student Edition Download (Free)

  • Student Edition 6.2 – This version of S-Plus has a 20,000 cell or 1,000 row limitation; is only for educational use; is good for only one year; and is a rather large download (100+ meg).

S-PLUS Free Experimental Libraries and User Contributed Libraries

  • Research Libraries – Includes: S+CorrelatedData (mixed effects generalized linear models), S+Best (B-Spline methods), S+Resample (bootstrap library), S+Bayes (bayesian analysis), S+FDA (functional data analysis).
  • User Contributed Libraries

Tinn-R Script Editor

R

Download Site for the Current Windows Install Binary and R Packages

Web Interfaces to R Web Servers and Example R Scripts

  • R Web Interfaces – Web/browser based interfaces to R script processing on a server
  • Example R Scripts – Some of these scripts run on a server and results are communicated thru a web browser
  • RSS Rweb Server – Link to http:/rss.acs.unt.edu R server

R, R(D)COM and Excel

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Rtips. Revival 2014!

Uma animação com todos os lugares referidos numa canção de johnny cash

Uma animação com todos os lugares referidos numa canção de johnny cash

Montes de exemplos de R numa única longa página.

Table of Contents
Section: Original Preface
Section 1: Data Input/Output
Section 2: Working with data frames: Recoding, selecting, aggregating
Section 3: Matrices and vector operations
Section 4: Applying functions, tapply, etc
Section 5: Graphing
Section 6: Common Statistical Chores
Section 7: Model Fitting (Regression-type things)
Section 8: Packages
Section 9: Misc. web resources
Section 10: R workspace
Section 11: Interface with the operating system
Section 12: Stupid R tricks: basics you can’t live without
Section 13: Misc R usages I find interesting

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CRAN Task Views

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Uma lista de temas com uma descrição dos principais pacotes R ligados ao tema

CRAN Task Views

Bayesian Bayesian Inference
ChemPhys Chemometrics and Computational Physics
ClinicalTrials Clinical Trial Design, Monitoring, and Analysis
Cluster Cluster Analysis & Finite Mixture Models
DifferentialEquations Differential Equations
Distributions Probability Distributions
Econometrics Econometrics
Environmetrics Analysis of Ecological and Environmental Data
ExperimentalDesign Design of Experiments (DoE) & Analysis of Experimental Data
Finance Empirical Finance
Genetics Statistical Genetics
Graphics Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization
HighPerformanceComputing High-Performance and Parallel Computing with R
MachineLearning Machine Learning & Statistical Learning
MedicalImaging Medical Image Analysis
MetaAnalysis Meta-Analysis
Multivariate Multivariate Statistics
NaturalLanguageProcessing Natural Language Processing
NumericalMathematics Numerical Mathematics
OfficialStatistics Official Statistics & Survey Methodology
Optimization Optimization and Mathematical Programming
Pharmacokinetics Analysis of Pharmacokinetic Data
Phylogenetics Phylogenetics, Especially Comparative Methods
Psychometrics Psychometric Models and Methods
ReproducibleResearch Reproducible Research
Robust Robust Statistical Methods
SocialSciences Statistics for the Social Sciences
Spatial Analysis of Spatial Data
SpatioTemporal Handling and Analyzing Spatio-Temporal Data
Survival Survival Analysis
TimeSeries Time Series Analysis
WebTechnologies Web Technologies and Services
gR gRaphical Models in R

To automatically install these views, the ctv package needs to be installed, e.g., via
install.packages("ctv")
library("ctv")
and then the views can be installed via install.views or update.views (which first assesses which of the packages are already installed and up-to-date), e.g.,
install.views("Econometrics")
or
update.views("Econometrics")

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Montes de recursos sobre R

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Muitos recursos para o R que vão de exemplos introdutórios até ao multivariado.

Do it yourself Introduction to R

R is a free statistical programming language environment. It is completely free to anyone — like the air you breath is free.

For more information on why everyone should be using R, see here.

The goal of this site is to allow someone to overcome the intimidation associated with learning the very basics of R and showing them the tools for continued usage. Let’s get started.

Some assumptions: This site assumes you are using a Windows operating system and have a basic understanding of file structures and paths. You will also need to have administrator privileges in order to install R. Some of the notes linked on this page are standard HTML pages; most of the links on this page are in R script file format (they have the file extension.R). Beyond that; the site and any instructions or links on it should be self-explanatory. It is STRONGLY recommended that one progress through the modules in order.

A brief explanation of this page is here.

UPDATE NOTE: April 23, 2015: current R version is 3.2.0

These pages have been tested for use with Firefox, other browsers may display the pages incorrectly.

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Using Open Source in Higher Education: R Tutorials

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Alguns bons tutoriais para aprender mais.

Recent Posts

Recent Comments

R Tutorial: A Script… on R Tutorial: A Script to Create…
Rees Morrison on Using R for Basic Cross Tabula…

Archives

Categories

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Base R Version

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Excelentes exemplos de gráficos que podem usar nos trabalhos.

One Variable: Numeric Variable

One Variable: Factor Variable

Two Variables: Two Numeric Variables

Two Variables: Two Factor Variables

Two Variables: One Factor and One Numeric

Three Variables: Three Factor Variables

Three Variables: One Numeric and Two Factor Variables

Three Variables: Two Numeric and One Factor Variables

Three Variables: Three Numeric Variables

Scatterplot Matrix of all Numeric Vars, colored by a Factor variable

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Moving Past Default Charts

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Um excelente tutorial para aprender a trabalhar com os parâmetros dos gráficos em R.

Posted to Tutorials |  Tags: , ,
By Nathan Yau
Customizing your charts doesn’t have to be a time-intensive process. With just a teeny bit more effort, you can get something that fits your needs.

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Using Open Source Technology in Higher Education

Um blog com muitos posts sobre a utilização do R

Um blogue com muitos posts sobre a utilização do R

Using R for Basic Cross Tabulation Analysis: Part Three, Using the xtabs Function

Using R to Work with GSS Survey Data: Cross Tabulation Tables

R Tutorial: Using R to Work With Datasets From the NORC General Social Science Survey

How to Set Up SSH to Remotely Control Your Raspberry Pi

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Resources for Getting Started with R

Recursos para começar a usar o R

Recursos para começar a usar o R

Resources for Getting Started with R

June 4, 2012  |  Software

R, the open source statistical software environment, is powerful but can be a challenge to approach for beginners. For me, the best way to learn R, especially on the visualization side of things, is to dive right in. Grab some data and make some charts, or better yet, find a graph you like and try to replicate it.

R core functionality and the many available packages let you do a lot without having to know what’s going on underneath. I use this approach in Visualize This and the tutorials around here. I like the satisfaction of immediate results. Then I learn the nitty gritty later.

That said, it doesn’t hurt to familiarize yourself with the environment. Also, visualization is a small part of what you can do with R, so it can help to know what else you can do analysis-wise.

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An intro to R for new programmers

Introdução às estruturas de dados no R

Introdução às estruturas de dados no R

Following the lead of JavaScript for Cats by Maxwell Ogden, Scott Chamberlain and Carson Sievert wrote R for Cats. It’s a playful introduction to R intended for those who have little to no programming experience.

The bulk of it so far is a primer on data structures, and there’s a little bit on functions and some dos and don’ts. It’s stuff you should know before you get into more advanced tutorials.

Mainly though: ooo look, kitty.

Once you’re done with that (It only takes about 30 minutes.), there are lots of other resources for getting started with R.

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