Rlogo R-PROJECT The R Project for Statistical Computing

  • The R Project for Statistical Computing – R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. To download R, please choose your preferred CRAN mirror.
  • R – Beginner TipsIf you’re just getting started with the R language, R user Pairach Piboonrungroj has published a handly list of 20 free R tutorials published by university departments.
  • R twotorials – how to do stuff in r. two minutes or less. for those of us who prefer to learn by watching and listening
  • R-SEEK – Search functions, lists, and more
  • Writing R Extensions – The Comprehensive R Archive Network
  • Inside-R – A Community Site for R – Sponsored by Revolution Analytics; How to Learn R, By RevoJoe
  • RStudio™ is a new integrated development environment (IDE) for R. RStudio combines an intuitive user interface with powerful coding tools to help you get the most out of R.
  • ggplot2 is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and none of the bad parts. It takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce complex multi-layered graphics.
  • R-bloggers – R news and tutorials contributed by R bloggers; R-Bloggers.com is a central hub (e.g: A blog aggregator) of content collected from bloggers who write about R (in English). The site will help R bloggers and users to connect and follow the “R blogosphere”.
  • Red-R: open source visual programming interface for R designed to bring the power of the R statistical environment to a broader audience. The goal of this project is to provide access to the massive library of packages in R (and even non-R packages) without any programming expertise. The Red-R framework uses concepts of data-flow programming to make data the center of attention while hiding all the programming complexity.
  • RTOOLS – Building R for Windows – This document is a collection of resources for building packages for R under Microsoft Windows, or for building R itself (version 1.9.0 or later). The original collection was put together by Prof. Brian Ripley; it is currently being maintained by Duncan Murdoch.
  • Tutorial de instalação do R e o RCommander – roteiro para quem não tem nada do R e do RCommander instalados em seu computador. Passo-a-passo com figuras. Núcleo de Apoio à Pesquisa Sobre Democratização e Desenvolvimento – USP
  • Introdução ao RCommander – O Tutorial de utilização do R e o RCommander é um tutorial de utilização básica tanto do R quanto do RCommander, através de texto e figuras. Demonstra-se como gerar gráficos, análises etc.
  • One R Tip a Day – @RLangTip – One tip per day on the R programming language M-F. Created by @JohnDCook — now brought to you by @inside_R #RStats (twitter)
  • R-related materialProf. John W. Emerson (Jay), Department of Statistics, Yale University – Workshop material: Towards High-Performance Computing with R; examples, database and more.
  • Using R for psychological research – A simple guide to an elegant language. 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.
  • Using R For Statistical Analysis – Two Useful Videos. Posted on March 29, 2013 by astrocompute. Many people use R, an extensible, community-maintained language and environment for statistical computing and graphics. It is a powerful system, but one of the complaints I have heard is that getting started can be a little difficult. Many people use R, an extensible, community-maintained language and environment for statistical computing and graphics. It is a powerful system, but one of the complaints I have heard is that getting started can be a little difficult.
  • Introdução ao uso do programa R – Instituto Nacional de Pesquisas da Amazônia / Programa de Pós Graduação em Ecologia. Autor: Victor Lemes Landeiro, Instituto Nacional de Pesquisas da Amazônia – Coordenação de Pesquisas em Ecologia – 24 de fevereiro de 2010.
  • Statistical Learning (using R) – This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).
  • R Programming Language Introduction and Resources – R is a programming language and development environment used for statistical analysis and the creation of publication-quality data visulalizations. R is completely free, open-source, part of the GNU Project, and is supported by the R Foundation for Statistical Computing.
  • Curso-r – O grupo surgiu em 2015 para ministrar o curso “Programação em R: do casual ao avançado” no programa de verão do Instituto de Matemática e Estatística da Universidade de São Paulo (IME-USP). Desde o começo, abraçou-se a filosofia Open Source – todo material desenvolvido fica disponível na conta do Github de forma aberta, para quem quiser usar. Acreditam que o conhecimento deve ser compartilhado para quem tiver interesse.
  • Grupo no facebook – R Brasil – Programadores – Baseado no Python Brasil – Programadores, o R Brasil foi criado para trocar ideias sobre a linguagem R e sua aplicação na Estatística, Aprendizado de Máquina, Datamining e áreas afins.






SAS_TPTK_logo SASInformações: 0800 704 4703 – Ouvidoria SAS: 11 4501 5366 ou sasbrasilouvidoria@sas.com

  • GUSAS – comunidade do Grupo de Usuários SAS. Interaja, troque experiências e aprenda mais sobre a utilização da Inteligência Analítica.
DMSS Software – Data Mining and Statistical Solutions (antigo SPSS) – Central de atendimento (11) 5505-3644
stat DELL STATISTICA – StatSoft South America – 55 (11) 3777-8190 / 4221-2491 / 4221-3366 – statsoft@statsoft.com.br 


DELL STATISTICA – DBA Solutions – 55 (11) 3588-4708 contato@dbasolutions.com.br




MINITAB – Representante no Brasil: Lider Software – (31) 3317-8945, lider@lidersoftwares.com.br
STATA – Data Analysis and Statistical Software; no Brasil – Distributor: Timberlake Consultores Brasil, Tel and Fax: (11) 3170-3123; info@timberlake.com.br; Reseller: Katalogo Software; (11) 3405-4507; felipea@katalogo.com.br
Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the R statistical programming language, and is open source and open development. It has two releases each year, more than 460 packages, and an active user community.
PSPP is a program for statistical analysis of sampled data. It is a Free replacement for the proprietary program SPSS, and appears very similar to it with a few exceptions 

  • Blog do PSPP – software para análises estatísticas sobre matrizes de dados. Seu uso permite gerar relatórios tabulados, normalmente utilizados na realização de análises descritivas e inferências a respeito de correlações entre variáveis. 


Free statistical software list organized by FreeStatistics.info – Free Statistical Software, Data and Resources: General Purpose Packages; Time Series and Econometrics; Structural Equation Modeling; Basic Statistics (Univariate Analysis, Prob Calculation); Sampling; Data Mining; Excel Add-Ins 

  • Free Statistical Software Comparison – In this table the main free statistical software are compared, bringing out which type of statistical analysis they can perform. See the comparisson here 


Free statistical software is a practical alternative to commercial packages. In general, free statistical software gives results that are the same as the results from commercial programs, and many of the packages are fairly easy to learn, using menu systems, although a few are command-driven. These packages come from a variety of sources, including governments, nongovernmental organizations (NGOs) like UNESCO, and universities, and are also developed by individuals.
Some packages are developed for specific purposes (e.g., time series analysis, factor analysis, calculators for probability distributions, etc.), while others are general packages, with a variety of statistical procedures. Others are meta-packages or statistical computing environments, which allow the user to code completely new statistical procedures. This article is a review of the general statistical packages.
Free Statistics Programs by Bill Miller – Free Statistics Programs, Sample Data, Tutorials, Statistics Book, User’s Manual, etc. — FOR WINDOWS
Weka 3: Data Mining Software in Java Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this.
Weka is open source software issued under the GNU General Public License.Tutorial em PDF: Data Mining com a Ferramenta – Weka. Eduardo Corrêa Gonçalves; Escola Nacional de Ciências Estatísticas (IBGE/ENCE)
Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. The library, largely written in Julia itself, also integrates mature, best-of-breed C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing. In addition, the Julia developer community is contributing a number of external packages through Julia’s built-in package manager at a rapid pace. Julia programs are organized around multiple dispatch; by defining functions and overloading them for different combinations of argument types, which can also be user-defined. For a more in-depth discussion of the rationale and advantages of Julia over other systems, see the following highlights or read the introduction in the online manual

Julia Studio – An integrated developmento environment for the Julia Language.