How many statisticians does it take to split a bill?

Posted by Armando Brito Mendes | Filed under estatística, software

stas

Bom blogue, bem disposto aborda diferenças entre estatística e ML

Some thoughts on the Fall term, now that Spring is well under way [edit: added a few more points]:

RMarkdown and knitr are amazing. When I next teach a course using R, my students will be turning in homeworks using these tools: The output immediately shows whether the code runs and what its results are. This is much better than students copying and pasting possibly-broken code and unconnected output into a text file or (gasp) Word document.
I’m glad my cohort socializes outside the office, taking each other out for birthday lunches or going to see a Pirates game. Some of the older PhD students are so focused on their thesis work that they don’t take time for a social break, and I’d like to avoid getting stuck in that rut.
However! Our lunches always lead us back to the age old question: How many statisticians does it take to split a bill? Answer: too long. I threw together a Shiny app, DinneR, to help us answer this question.

Tags: big data, data mining, R-software, software estatístico

Read more | Comments off | February 14th, 2014

Using Dates and Times in R

Posted by Armando Brito Mendes | Filed under estatística, software

Excelente sobre a utilização de tempos e datas no R

Using Dates and Times in R

by Bonnie Dixon, 10 February 2014

Today at the Davis R Users’ Group, Bonnie Dixon gave a tutorial on the various ways to handle dates and times in R. Bonnie provided this great script which walks through essential classes, functions, and packages. Here it is piped throughknitr::spin. The original R script can be found as a gist here.

Tags: data mining, R-software, software estatístico

Read more | Comments off | February 14th, 2014

Interactive maps with R

Posted by Armando Brito Mendes | Filed under software, visualização

Bibliotecas para construir mapas com alguma interação no R

You can make static maps in R relatively well, if you know what packages to use and what to look for, but there isn’t much direct interaction with your graphics. rMaps is a package that helps you create maps that you can mouse over and zoom in to.

Don’t get too excited though. A scan of the docs shows that it’s basically a wrapper around JavaScript libraries Leaflet, DataMaps and Crosslet, so you could learn those directly instead, and you’d be better for it in the long run if you plan to make more maps. But if you’re just working on a one-off or must stay in R because your life depends on, rMaps might be an option.

Tags: data mining, desnvolvimento de software, mapas, R-software

Read more | Comments off | February 13th, 2014

Learn R interactively with the swirl package

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

Um pacote R para construir lições interativas

swirl is a software package for the R statistical programming language. Its purpose is to teach users statistics and R simultaneously and interactively.

Tags: data mining, desnvolvimento de software, R-software, software estatístico

Read more | Comments off | February 3rd, 2014

How R came to be

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

Uma entrevista sobre como surgiu o R

How R came to be

Statistician John Chambers, the creator of S and a core member of R, talks about how R came to be in the short video below. Warning: Super nerdy waters ahead.

Tags: desnvolvimento de software, R-software, software estatístico

Read more | Comments off | February 3rd, 2014

introducing R to a non-programmer in one hour

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

Uma introdução muito rápida

Biostatistics PhD candidate Alyssa Frazee was tasked with teaching her sister, an undergraduate in sociology, how to use R. She had only one hour.

Once you load in a dataset, things start to get fun. We learned a whole bunch of stuff from this data frame, like how to do basic tabulations and calculate summary statistics, how to figure out if you have missing data, and how to fit a simple linear model. This part was pretty fun because my sister started leading the session: instead of me saying “I’m going to show you how to do this,” it was her asking “Hey, could we make a scatterplot?” or “Do you think we could put the best-fit line on that plot?” I was really glad this happened — I hope it meant she was engaged and enjoying herself!

This is the nice thing about R. There are so many built-in functions and packages that you can get something useful with a few lines of code, and you don’t really even have to know what a function is to get started (although you should eventually). Then you can go as far down the rabbit hole as you want.

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

Read more | Comments off | January 8th, 2014

Probability and Monte Carlo methods

Posted by Armando Brito Mendes | Filed under estatística, Habilitações Académicas, matemática, materiais ensino

Um bom texto de introdução à probabilidade e simulação de Monte-Carlo

This is a lecture post for my students in the CUNY MS Data Analytics program. In this series of lectures I discuss mathematical concepts from different perspectives. The goal is to ask questions and challenge standard ways of thinking about what are generally considered basic concepts. I also emphasize using programming to help gain insight into mathematics. Consequently these lectures will not always be as rigorous as they could be.

Tags

monte carlo, numerical integration, probability, simulation

Tags: Estat Descritiva, R-software, software estatístico

Read more | Comments off | January 2nd, 2014

Rattle: A Graphical User Interface for Data Mining using R

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

Rattle (the R Analytical Tool To Learn Easily) presents statistical and visual summaries of data, transforms data into forms that can be readily modelled, builds both unsupervised and supervised models from the data, presents the performance of models graphically, and scores new datasets.