April 2016

New nasadata R package

April 30, 2016 | En El Margen - R-English

This package intends to provide a hassle-free way to access some of NASA’s open-source API’s to build applications or models. Because the documentation seems inconsistent and there are tons of API’s, I have concentrated my efforts on three which I believe provide the best “bang for my ... [Read more...]

Introduction to R for Data Science

April 30, 2016 | The Exactness of Mind

Branko Kovač Data Analyst at CUBE, Data Science Mentor at Springboard, Institut savremenih nauka, Data Science Serbia, and Goran S. Milovanović, DataScientist@DiploFoundation, Data Science Serbia, are giving a free introductory course on R for Data Science in Belgrade, Serbia. All course materials - slides, R scripts, data sets, summaries ... [Read more...]

Why I fart? (or how small data changes life)

April 30, 2016 | manio

I have had gas problem for quite a while. Usually, right after I have lunch, gas starts to accumulate in my belly. Then comes the fart. It was really annoying, especially when you sat in the front row of a class. Sometimes it was even painful as there are too ... [Read more...]

Introduction to R for Data Science :: Session 1

April 30, 2016 | The Exactness of Mind

Welcome to Introduction to R for Data Science Session 1! The course is co-organized by Data Science Serbia and Startit. You will find all course material (R scripts, data sets, SlideShare presentations, readings) on these pages. [in Serbian] Lecturers dipl. ing Branko Kovač, Data Analyst at CUBE, Data Science Mentor at ... [Read more...]

Lattice exercises – part 1

April 30, 2016 | Matteo Renzi

In the exercises below we will use the lattice package. First, we have to install this package with install.packages("lattice") and then we will call it library(lattice) . The Lattice package permits us to create univariate, bivariate and trivariate plots. For this set of exercises we will see univariate ... [Read more...]

Base R Nostalgia — by, tapply, ave, …

April 30, 2016 | data_steve

photo credit: Paul Yoakum This evening I was feeling nostalgic for base R group-bys. Before there was dplyr, there was apply and its cousins. I thought it’d be nice to get out the ol’ photo-album. To start off, the base R proto-ancestor of magr... [Read more...]

Base R Nostalgia — by, tapply, ave, …

April 30, 2016 | Steve Simpson

photo credit: Paul Yoakum This evening I was feeling nostalgic for base R group-bys. Before there was dplyr, there was apply and its cousins. I thought it’d be nice to get out the ol’ photo-album. To start off, the base R proto-ancestor of magrittr piping for me was the ... [Read more...]

Data science with Docker

April 29, 2016 | StatOfMind

Using docker to facilitate your data science pipelines Until recently, and like many other fellow data scientists I have talked to, I built data science pipelines on my local machine or a remote host while relying on virtual environments. In doing so, I ensured some degree of replicability by keeping ... [Read more...]

Bad Neighbours (no, not the movie)

April 29, 2016 | Jonathan Carroll

Another day, another compulsion to see if I can do any better than someone’s solution. This one also comes from the FiveThiryEight Puzzler challenge courtesy of Xi’an The original challenge this time was The misanthropes are coming. Suppose there is...Continue Reading →
[Read more...]

Bad Neighbours (no, not the movie)

April 29, 2016 | Jonathan Carroll

Another day, another compulsion to see if I can do any better than someone's solution. This one also comes from the FiveThiryEight Puzzler challenge courtesy of Xi'an The original challenge this time was The misanthropes are coming. Suppose there is...Continue Reading →
[Read more...]

Tufte-style graphics in R

April 29, 2016 | David Smith

It's not an overstatement to say that, at least for me personally, Edward Tufte's book The Visual Display of Quantitative Information was transformative. Reading this book got me and, I feel confident saying, many many other data scientists passionate about visualizing data. This is the book that popularized Minard's chart ... [Read more...]

Reasons to Move your Surveys Online

April 29, 2016 | James Bartlett

When I was collecting data for my last project, I printed off reams upon reams of paper for my questionnaires, information sheets etc. I did not particularly like it at the time but I could not see a different way of doing it. However, when it was completed and I ...
[Read more...]

Cross-Validation: Estimating Prediction Error

April 29, 2016 | Beau Lucas

What is cross-validation? Cross-Validation is a technique used in model selection to better estimate the test error of a predictive model. The idea behind cross-validation is to create a number of partitions of sample observations, known as the validation sets, from the training data set. After fitting a model on ... [Read more...]

Talk on regtools and P-Values

April 28, 2016 | matloff

I’m deeply greatful to Hui Lin and the inimitable Yihui Xie for arranging for me to give a “virtual seminar talk” to the Central Iowa R Users Group. You can view my talk, including an interesting Q&A session, online. (The actual start is at 0:34.) There are two separate ... [Read more...]

Playing with Twitter Data

April 28, 2016 | Michael Levy - Rstats

Last Friday, the Institute for Social Sciences hosted a great one-day conference on various aspects of the reproducability crisis, Making Social Science Transparent. It was the first time I’ve done much tweeting during an event like this, and while it felt a little silly, it was also fun, it ... [Read more...]

The Life-Changing Magic of Tidying Text

April 28, 2016 | Julia Silge

When I went to the rOpenSci unconference about a month ago, I started work with Dave Robinson on a package for text mining using tidy data principles. What is this tidy data you keep hearing so much about? As described by Hadley Wickham, tidy data has a specific structure: each ... [Read more...]

gap frequencies [& e]

April 28, 2016 | xi'an

A riddle from The Riddler where brute-force simulation does not pay: For a given integer N, pick at random without replacement integers between 1 and N by prohibiting consecutive integers until all possible entries are exhausted. What is the frequency of selected integers as N grows to infinity? A simple implementation ...
[Read more...]
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