SurfStat

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

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Um bom livro on-line com apps e muito mais…

SURFSTAT australia

This site has already benefited from the contributions of many people. Please do your bit and let us know of errors, missing topics or things you think could be better explained.

Detailed contents

An introduction to Statistics

Textbooks
Hotlist for Java applets
Exercises

Other Statistics sites
S.P.I.M

Sales and support
Licence
Funding acknowledgements

Control Charts

Tags: análise de dados, Estat Descritiva

Read more | Comments off | January 2nd, 2016

R and the Nobel Prize API

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

Blog com vários tutoriais para usar estatísticas não paramétricas simples em R

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Um bom exemplo do que se pode fazer com o R e uma API q serviços de dados.

he Nobel Prizes. Love them? Hate them? Are they still relevant, meaningful? Go on admit it, you always imagined you would win one day.

Whatever you think of them, the 2015 results are in. What’s more, the good people of the Nobel Foundation offer us free access to data via an API. I’ve published a document over at RPubs, showing some of the ways to access and analyse their data using R. Just to get you started:

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

Read more | Comments off | October 29th, 2015

The Statistics Portal

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

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Um portal cheio de facts & figures

One of the world’s largest statistics portals

We pride ourselves in being one of the world’s first and only companies to bring together data from over 18,000 sources.
Our research analysts source the most up-to-date statistical data from around the globe; our analytical approach and efficient research processes enable us to supply our clients with the data that is relevant to them.

Tags: análise de dados, Estat Descritiva, software estatístico

Read more | Comments off | August 24th, 2015

Ternary Diagrams Using R

Posted by Armando Brito Mendes | Filed under visualização

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Ensina a construir um diagrama ternário no R

Ternary Diagrams Using R: The ggtern Package

A tutorial by Douglas M. Wiig

There are a number of very useful and popular graphics packages available for R such as lattice, ggplot, ggplot2 and others. Some of these offer general purpose graphics capabilities and others are more specialized. A recently developed extension to the ggplot2 package is ggtern. This package is essentially a wrapper for a number of functions that can be used to create a variety of ternary diagrams. Ternary diagrams are useful when analyzing the relationship among three factors or elements. A ternary diagram essentially represents the proportions of three related factors in two-dimensional space.

Tags: análise de dados, R-software

Read more | Comments off | August 10th, 2015

Comprehensive Guide to Data Visualization in R

Posted by Armando Brito Mendes | Filed under estatística, visualização

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Bom resumo de alguns tipos de gráficos que podem ser obtidos no R, do mais simples a alguns mais complexos.

This visualization (originally created using Tableau) is a great example of how data visualization can help decision makers. Imagine telling this information to an investor through a table. How long do you think you will take to explain it to him?

With ever increasing volume of data in today’s world, it is impossible to tell stories without these visualizations. While there are dedicated tools like Tableau, QlikView and d3.js, nothing can replace a modeling / statistics tools with good visualization capability. It helps tremendously in doing any exploratory data analysis as well as feature engineering. This is where R offers incredible help.

R Programming offers a satisfactory set of inbuilt function and libraries (such as ggplot2, leaflet, lattice) to build visualizations and present data. In this article, I have covered the steps to create the common as well as advanced visualizations in R Programming.

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

Read more | Comments off | August 10th, 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

Electronic Statistics Textbook: StatSoft

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

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Uma referência muito completa sobre métodos estatísticos e de data mining.

Proper citation:

(Electronic Version): StatSoft, Inc. (2013). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/.
(Printed Version): Hill, T. & Lewicki, P. (2007). STATISTICS: Methods and Applications. StatSoft, Tulsa, OK.

Overview of Elementary Concepts in Statistics. In this introduction, we will briefly discuss those elementary statistical concepts that provide the necessary foundations for more specialized expertise in any area of statistical data analysis. The selected topics illustrate the basic assumptions of most statistical methods and/or have been demonstrated in research to be necessary components of one’s general understanding of the “quantitative nature” of reality (Nisbet, et al., 1987). Because of space limitations, we will focus mostly on the functional aspects of the concepts discussed and the presentation will be very short.

Further information on each of those concepts can be found in the Introductory Overview and Examples sections of this manual and in statistical textbooks. Recommended introductory textbooks are: Kachigan (1986), and Runyon and Haber (1976); for a more advanced discussion of elementary theory and assumptions of statistics, see the classic books by Hays (1988), and Kendall and Stuart (1979).

Tags: análise de dados, data mining, Estat Descritiva, qualidade

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.

Armando B. Mendes

SurfStat

SURFSTAT australia

This site has already benefited from the contributions of many people. Please do your bit and let us know of errors, missing topics or things you think could be better explained.

Summarising & Presenting Data

Producing Data

Variation and Probability

Statistical Inference

Control Charts

R and the Nobel Prize API

The Statistics Portal

Ternary Diagrams Using R

Comprehensive Guide to Data Visualization in R

Rtips. Revival 2014!

Electronic Statistics Textbook: StatSoft

Montes de recursos sobre R

Using Open Source in Higher Education: R Tutorials

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SURFSTAT australia This site has already benefited from the contributions of many people. Please do your bit and let us know of errors, missing topics or things you think could be better explained.

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This site has already benefited from the contributions of many people. Please do your bit and let us know of errors, missing topics or things you think could be better explained.