S-PLUS & R Class Links

Posted by Armando Brito Mendes | Filed under estatística, materiais ensino, materiais para profissionais, software

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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

SourceForge Download Site – Download Tinn-R Setup Files; Tinn-R can serve as a script editor and “pager” for R Console. See Tinn-R Convenient Script Editor for R on Win32 Platform

R

Download Site for the Current Windows Install Binary and R Packages

Main CRAN Website
CRAN Windows Binary – Installer for Win32 (also available for MacOS X and Linux
CRAN R Package Descriptions – Pdf manuals available for packages
R Packages Organized by Topic – Organized by discipline and methodology

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

Introduction to R – ppt
Using R with Excel – A (D)COM Server for the Windows Platform – Benchmarks article (needs updating badly – Rich)
R(D)COM Server Homepage – Thomas Baier
R(D)COM Listserve Archive
Download for single install file – R-2.4.1, R(D)COM and RExcel only
Download for single install file – R-2.4.1 – use this file for a comprehensive install of selectable components: R, Rcmdr, R(D)COM, RExcel, rcom, gobbi, Rgobbi, and Tinn-R. R Version 2.5.0 combined install found here.
Installing The R(D)COM server – Help Pages
Using The R(D)COM server – Help Pages
Using R Within Excel – Help Pages
Duncan Temple Lang’s R (D)COM Homepage
Duncan Temple Lang’s R (D)COM Notes
Duncan Temple Lang’s R (D)COM Client examples– directory listing
Duncan Temple Lang’s R (D)COM Server – examples listing
General Computing Considerations

Tags: Excel, R-software, software estatístico

Read more | Comments off | May 30th, 2015

Rtips. Revival 2014!

Posted by Armando Brito Mendes | Filed under estatística, matemática, software

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

Subsection 1.1: Bring raw numbers into R (05/22/2012)

Subsection 1.2: Basic notation on data access (12/02/2012)

Subsection 1.3: Checkout the new Data Import/Export manual (13/08/2001)

Subsection 1.4: Exchange data between R and other programs (Excel, etc) (01/21/2009)

Subsection 1.5: Merge data frames (04/23/2004)

Subsection 1.6: Add one row at a time (14/08/2000)

Subsection 1.7: Need yet another different kind of merge for data frames (11/08/2000)

Subsection 1.8: Check if an object is NULL (06/04/2001)

Subsection 1.9: Generate random numbers (12/02/2012)

Subsection 1.10: Generate random numbers with a fixed mean/variance (06/09/2000)

Subsection 1.11: Use rep to manufacture a weighted data set (30/01/2001)

Subsection 1.12: Convert contingency table to data frame (06/09/2000)

Subsection 1.13: Write: data in text file (31/12/2001)

Section 2: Working with data frames: Recoding, selecting, aggregating

Subsection 2.1: Add variables to a data frame (or list) (02/06/2003)

Subsection 2.2: Create variable names on the fly (10/04/2001)

Subsection 2.3: Recode one column, output values into another column (12/05/2003)

Subsection 2.4: Create indicator (dummy) variables (20/06/2001)

Subsection 2.5: Create lagged values of variables for time series regression (05/22/2012)

Subsection 2.6: How to drop factor levels for datasets that don’t have observations with those values? (08/01/2002)

Subsection 2.7: Select/subset observations out of a dataframe (08/02/2012)

Subsection 2.8: Delete first observation for each element in a cluster of observations (11/08/2000)

Subsection 2.9: Select a random sample of data (11/08/2000)

Subsection 2.10: Selecting Variables for Models: Don’t forget the subset function (15/08/2000)

Subsection 2.11: Process all numeric variables, ignore character variables? (11/02/2012)

Subsection 2.12: Sorting by more than one variable (06/09/2000)

Subsection 2.13: Rank within subgroups defined by a factor (06/09/2000)

Subsection 2.14: Work with missing values (na.omit, is.na, etc) (15/01/2012)

Subsection 2.15: Aggregate values, one for each line (16/08/2000)

Subsection 2.16: Create new data frame to hold aggregate values for each factor ()

Subsection 2.17: Selectively sum columns in a data frame (15/01/2012)

Subsection 2.18: Rip digits out of real numbers one at a time (11/08/2000)

Subsection 2.19: Grab an item from each of several matrices in a List (14/08/2000)

Subsection 2.20: Get vector showing values in a dataset (10/04/2001)

Subsection 2.21: Calculate the value of a string representing an R command (13/08/2000)

Subsection 2.22: Which can grab the index values of cases satisfying a test ()

Subsection 2.23: Find unique lines in a matrix/data frame (31/12/2001)

Section 3: Matrices and vector operations

Subsection 3.1: Create a vector, append values (01/02/2012)

Subsection 3.2: How to create an identity matrix? (16/08/2000)

Subsection 3.3: Convert matrix m to one long vector (11/08/2000)

Subsection 3.4: Creating a peculiar sequence (1 2 3 4 1 2 3 1 2 1) (11/08/2000)

Subsection 3.5: Select every n’th item (14/08/2000)

Subsection 3.6: Find index of a value nearest to 1.5 in a vector (11/08/2000)

Subsection 3.7: Find index of nonzero items in vector (18/06/2001)

Subsection 3.8: Find index of missing values (15/08/2000)

Subsection 3.9: Find index of largest item in vector (16/08/2000)

Subsection 3.10: Replace values in a matrix (22/11/2000)

Subsection 3.11: Delete particular rows from matrix (06/04/2001)

Subsection 3.12: Count number of items meeting a criterion (01/05/2005)

Subsection 3.13: Compute partial correlation coefficients from correlation matrix ()

Subsection 3.14: Create a multidimensional matrix (R array) (20/06/2001)

Subsection 3.15: Combine a lot of matrices (20/06/2001)

Subsection 3.16: Create neighbor matrices according to specific logics (20/06/2001)

Subsection 3.17: Matching two columns of numbers by a key variable (20/06/2001)

Subsection 3.18: Create Upper or Lower Triangular matrix (06/08/2012)

Subsection 3.19: Calculate inverse of X (12/02/2012)

Subsection 3.20: Interesting use of Matrix Indices (20/06/2001)

Subsection 3.21: Eigenvalues example (20/06/2001)

Section 4: Applying functions, tapply, etc

Subsection 4.1: Return multiple values from a function (12/02/2012)

Subsection 4.2: Grab ‘‘p’’ values out of a list of significance tests (22/08/2000)

Subsection 4.3: ifelse usage (12/02/2012)

Subsection 4.4: Apply to create matrix of probabilities, one for each cell (14/08/2000)

Subsection 4.5: Outer. (15/08/2000)

Subsection 4.6: Check if something is a formula/function (11/08/2000)

Subsection 4.7: Optimize with a vector of variables (11/08/2000)

Subsection 4.8: slice.index, like in S+ (14/08/2000)

Section 5: Graphing

Subsection 5.1: Adjust features with par before graphing (18/06/2001)

Subsection 5.2: Save graph output (03/21/2014)

Subsection 5.3: How to automatically name plot output into separate files (10/04/2001)

Subsection 5.4: Control papersize (15/08/2000)

Subsection 5.5: Integrating R graphs into documents: LaTeX and EPS or PDF (20/06/2001)

Subsection 5.6: ‘‘Snapshot’’ graphs and scroll through them (31/12/2001)

Subsection 5.7: Plot a density function (eg. Normal) (22/11/2000)

Subsection 5.8: Plot with error bars (11/08/2000)

Subsection 5.9: Histogram with density estimates (14/08/2000)

Subsection 5.10: How can I ‘‘overlay’’ several line plots on top of one another? (09/29/2005)

Subsection 5.11: Create ‘‘matrix’’ of graphs (18/06/2001)

Subsection 5.12: Combine lines and bar plot? (07/12/2000)

Subsection 5.13: Regression scatterplot: add fitted line to graph (03/20/2014)

Subsection 5.14: Control the plotting character in scatterplots? (11/08/2000)

Subsection 5.15: Scatterplot: Control Plotting Characters (men vs women, etc)} (11/11/2002)

Subsection 5.16: Scatterplot with size/color adjustment (12/11/2002)

Subsection 5.17: Scatterplot: adjust size according to 3rd variable (06/04/2001)

Subsection 5.18: Scatterplot: smooth a line connecting points (02/06/2003)

Subsection 5.19: Regression Scatterplot: add estimate to plot (18/06/2001)

Subsection 5.20: Axes: controls: ticks, no ticks, numbers, etc (22/11/2000)

Subsection 5.21: Axes: rotate labels (06/04/2001)

Subsection 5.22: Axes: Show formatted dates in axes (06/04/2001)

Subsection 5.23: Axes: Reverse axis in plot (12/02/2012)

Subsection 5.24: Axes: Label axes with dates (11/08/2000)

Subsection 5.25: Axes: Superscript in axis labels (11/08/2000)

Subsection 5.26: Axes: adjust positioning (31/12/2001)

Subsection 5.27: Add ‘‘error arrows’’ to a scatterplot (30/01/2001)

Subsection 5.28: Time Series: how to plot several ‘‘lines’’ in one graph? (06/09/2000)

Subsection 5.29: Time series: plot fitted and actual data (11/08/2000)

Subsection 5.30: Insert text into a plot (22/11/2000)

Subsection 5.31: Plotting unbounded variables (07/12/2000)

Subsection 5.32: Labels with dynamically generated content/math markup (16/08/2000)

Subsection 5.33: Use math/sophisticated stuff in title of plot (11/11/2002)

Subsection 5.34: How to color-code points in scatter to reveal missing values of 3rd variable? (15/08/2000)

Subsection 5.35: lattice: misc examples (12/11/2002)

Subsection 5.36: Make 3d scatterplots (11/08/2000)

Subsection 5.37: 3d contour with line style to reflect value (06/04/2001)

Subsection 5.38: Animate a Graph! (13/08/2000)

Subsection 5.39: Color user-portion of graph background differently from margin (06/09/2000)

Subsection 5.40: Examples of graphing code that seem to work (misc) (11/16/2005)}

Section 6: Common Statistical Chores

Subsection 6.1: Crosstabulation Tables (01/05/2005)

Subsection 6.2: t-test (18/07/2001)

Subsection 6.3: Test for Normality (31/12/2001)

Subsection 6.4: Estimate parameters of distributions (12/02/2012)

Subsection 6.5: Bootstrapping routines (14/08/2000)

Subsection 6.6: BY subgroup analysis of data (summary or model for subgroups)(06/04/2001)

Section 7: Model Fitting (Regression-type things)

Subsection 7.1: Tips for specifying regression models (12/02/2002)

Subsection 7.2: Summary Methods, grabbing results inside an output object

Subsection 7.3: Calculate separate coefficients for each level of a factor (22/11/2000)

Subsection 7.4: Compare fits of regression models (F test subset B’s =0) (14/08/2000)

Subsection 7.5: Get Predicted Values from a model with predict() (11/13/2005)

Subsection 7.6: Polynomial regression (15/08/2000)

Subsection 7.7: Calculate p value for an F stat from regression (13/08/2000)

Subsection 7.8: Compare fits (F test) in stepwise regression/anova (11/08/2000)

Subsection 7.9: Test significance of slope and intercept shifts (Chow test?)

Subsection 7.10: Want to estimate a nonlinear model? (11/08/2000)

Subsection 7.11: Quasi family and passing arguments to it. (12/11/2002)

Subsection 7.12: Estimate a covariance matrix (22/11/2000)

Subsection 7.13: Control number of significant digits in output (22/11/2000)

Subsection 7.14: Multiple analysis of variance (06/09/2000)

Subsection 7.15: Test for homogeneity of variance (heteroskedasticity) (12/02/2012)

Subsection 7.16: Use nls to estimate a nonlinear model (14/08/2000)

Subsection 7.17: Using nls and graphing things with it (22/11/2000)

Subsection 7.18: and hypo tests (22/11/2000)

Subsection 7.19: logistic regression with repeated measurements (02/06/2003)

Subsection 7.20: Logit (06/04/2001)

Subsection 7.21: Random parameter (Mixed Model) tips (01/05/2005)

Subsection 7.22: Time Series: basics (31/12/2001)

Subsection 7.23: Time Series: misc examples (10/04/2001)

Subsection 7.24: Categorical Data and Multivariate Models (04/25/2004)

Subsection 7.25: Lowess. Plot a smooth curve (04/25/2004)

Subsection 7.26: Hierarchical/Mixed linear models. (06/03/2003)

Subsection 7.27: Robust Regression tools (07/12/2000)

Subsection 7.28: Durbin-Watson test (10/04/2001)

Subsection 7.29: Censored regression (04/25/2004)

Section 8: Packages

Subsection 8.1: What packages are installed on Paul’s computer?

Subsection 8.2: Install and load a package

Subsection 8.3: List Loaded Packages

Subsection 8.4: Where is the default R library folder? Where does R look for packages in a computer?

Subsection 8.5: Detach libraries when no longer needed (10/04/2001)

Section 9: Misc. web resources

Subsection 9.1: Navigating R Documentation (12/02/2012)

Subsection 9.2: R Task View Pages (12/02/2012)

Subsection 9.3: Using help inside R(13/08/2001)

Subsection 9.4: Run examples in R (10/04/2001)

Section 10: R workspace

Subsection 10.1: Writing, saving, running R code (31/12/2001)

Subsection 10.2: .RData, .RHistory. Help or hassle? (31/12/2001)

Subsection 10.3: Save & Load R objects (31/12/2001)

Subsection 10.4: Reminders for object analysis/usage (11/08/2000)

Subsection 10.5: Remove objects by pattern in name (31/12/2001)

Subsection 10.6: Save work/create a Diary of activity (31/12/2001)

Subsection 10.7: Customized Rprofile (31/12/2001)

Section 11: Interface with the operating system

Subsection 11.1: Commands to system like change working directory (22/11/2000)

Subsection 11.2: Get system time. (30/01/2001)

Subsection 11.3: Check if a file exists (11/08/2000)

Subsection 11.4: Find files by name or part of a name (regular expression matching) (14/08/2001)

Section 12: Stupid R tricks: basics you can’t live without

Subsection 12.1: If you are asking for help (12/02/2012)

Subsection 12.2: Commenting out things in R files (15/08/2000)

Section 13: Misc R usages I find interesting

Subsection 13.1: Character encoding (01/27/2009)

Subsection 13.2: list names of variables used inside an expression (10/04/2001)

Subsection 13.3: R environment in side scripts (10/04/2001)

Subsection 13.4: Derivatives (10/04/2001)

Tags: análise de dados, Estat Descritiva, inferência, R-software, software estatístico

Read more | Comments off | May 30th, 2015

CRAN Task Views

Posted by Armando Brito Mendes | Filed under estatística, visualização

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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")

Tags: data mining, R-software, software estatístico

Read more | Comments off | May 29th, 2015

Montes de recursos sobre R

Posted by Armando Brito Mendes | Filed under estatística, materiais ensino, software

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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.

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.

Tags: análise de dados, data mining, Estat Descritiva, R-software, software estatístico

Read more | Comments off | May 29th, 2015

Using Open Source in Higher Education: R Tutorials

Posted by Armando Brito Mendes | Filed under estatística, software

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Alguns bons tutoriais para aprender mais.

An Example of R Versatility

By Dave Collingridge

In my last blog post I mentioned a few advantages to learning R. One of those advantages is that R opens up a world of new data analyses. There are novel techniques available in R that are not found in the ANALYZE drop down menus of SPSS, Stata, and Statistica. Novel techniques in R can be a big help in situations where data are not well-suited for traditional analyses like t-tests, ANOVA, and regression.

Tags: data mining, R-software

Read more | Comments off | February 6th, 2015

Base R Version

Posted by Armando Brito Mendes | Filed under estatística, materiais ensino, software, visualização

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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

Tags: Estat Descritiva, R-software, software estatístico

Read more | Comments off | January 21st, 2015

The beautiful R charts in London: The Information Capital

Posted by Armando Brito Mendes | Filed under visualização

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Um bom exemplo de utilização de gráficos do R em ilustração.

If you’ve lived in or simply love London, a wonderful new book for your coffee-table is London: The Information Capital. In 100 beautifully-rendered charts, the book explores the data that underlies the city and its residents. To create most of these charts, geographer James Cheshire and designer Oliver Uberti relied on programs written in R. Using the R programming language not only created beautiful results, it saved time: “a couple of lines of code in R saved a day of manually drawing lines”.

Tags: belo, mapas, R-software

Read more | Comments off | December 11th, 2014