## Who Came to Vote in Utah’s Caucuses?

April 8, 2016
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Late last month, I analyzed results from Utah’s Republican and Democratic caucuses to show how the different presidential candidates fared across Utah. That was fun work to do, but I realized there was one more map I wanted to make; I want to compare the Republican and Democratic voter turnout across the counties in Utah. Utah is a...

## In case you missed it: March 2016 roundup

April 8, 2016
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In case you missed them, here are some articles from February of particular interest to R users. Reviews of new CRAN packages RtutoR, lavaan.shiny, dCovTS, glmmsr, GLMMRR, MultivariateRandomForest, genie, kmlShape, deepboost and rEDM. You can now create and host Jupyter notebooks based on R, for free, in Azure ML Studio. Calculating learning curves for predictive models with doParallel. An...

## Election analysis contest entry part 3 – interactive exploration of voting locations with leaflet and Shiny

April 8, 2016
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Motivation This post is the third in a series that make up my entry in Ari Lamstein’s R Election Analysis Contest. First I introduced the nzelect R package from a user perspective. Second was a piece on how the build of that package works. Today, the third in the series introduces an interactive map of...

## Shiny module design patterns: Pass a single input to multiple modules

April 8, 2016
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For the awesome Shiny Developers Conference back in January, I endeavoured to learn about shiny modules and overhaul an application using them in the space of two days. I succeeded and almost immediately switched onto other projects, thereby losing most of the hard-won knowledge! As I rediscover shiny modules and start putting them into more The post

## d3/R Chord Diagram of White House Petition Data

April 7, 2016
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The OpenData movement has the White House producing and releasing some novel datasets. One of them is the We The People petition site. I learned about this from Proofreader.com’s interesting python work using that data. From the petition site, you can see an interesting gallery of work done in different language and for different...

## One-sided F-tests and halving p-values

April 7, 2016
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After my previous post about one-sided tests, some people wondered about two-sided F-tests. And then Dr R recently tweeted: No, there is no such thing as a one-tailed p-value for an F-test. reported F(1,40)=3.72, p=.03; correct p=.06 use t-test for one-tailed.— R-Index (@R__INDEX) April 5, 2016I thought it would be useful...

April 7, 2016
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A new Armadillo release 6.700.3 is out, and we uploaded RcppArmadillo 0.6.700.3.0 to CRAN and Debian. This followed the usual thorough reverse-dependecy checking of by now 216 packages using. Armadillo is a powerful and expressive C++ template libra...

## Book Review: Graphical Data Analysis with R

April 7, 2016
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by Joseph Rickert Basically, there are two kinds of graphics or plots you can make from a data set: (1) those that allow you to see what is going on with the data, and (2) those you make to communicate what you have found to someone else. When making the first kind, you want to select plots that will...

## The representative reviewers project for the SJDM conference

April 7, 2016
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We have a plan to make the conference's review committee representative. The post The representative reviewers project for the SJDM conference appeared first on Decision Science News.

## Introducing cricket package yorkr: Part 3-Foxed by flight!

April 7, 2016
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Introduction He will win, who knows when to fight and when not to fight. He will win, who knows how to handle both superior and inferior forces If you know neither the enemy nor yourself, you will succumb in every battle. Hence the skilful fighter puts himself in a position which makes defeat impossible, and

## 52Vis Week 2 (2016 Week #14) – Honing in on the Homeless

April 6, 2016
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Why 52Vis? In case folks are wondering why I’m doing this, it’s pretty simple. We need a society that has high data literacy and we need folks who are capable of making awesome, truthful data visualizations. The only way to do that is by working with data over, and over, and over, and over again.

## A workflow for publishing RStudio notebooks on Blogger

April 6, 2016
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The past few years, I have been searching regularly for ways of formatting R code on +Blogger. Although different possibilities were available, almost always I ended up using the online Pretty R syntax highlighter by copying parts of a script...

## Visualising F1 Stint Strategies

April 6, 2016
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With the new F1 season upon us, I’ve started tinkering with bits of code from the Wrangling F1 Data With R book and looking at the data in some new ways. For example, I started wondering whether we might be able to learn something interesting about the race strategies by looking at laptimes on a

## How long could it take to run a regression

April 6, 2016
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$n$

This afternoon, while I was discussing with Montserrat (aka @mguillen_estany) we were wondering how long it might take to run a regression model. More specifically, how long it might take if we use a Bayesian approach. My guess was that the time should probably be linear in , the number of observations. But I thought I would be good to check. Let...

## Learn How to Clean Your Data Using R

April 6, 2016
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Data scientists often remark that 80% of their time is spent on cleaning data and only 20% on the actual analysis. Data cleaning is a critical part of the data science process, yet is often overlooked in traditional statistics and data science courses. For these reasons, we’re excited to announce our newest DataCamp course: Cleaning Data in R!

## A quick introduction to machine learning in R with caret

April 6, 2016
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If you’ve been using R for a while, and you’ve been working with basic data visualization and data exploration techniques, the next logical step is to start learning some machine learning. To help you begin learning about machine learning in R, I’m going to introduce you to an R package: the caret package. We’ll build The post

## An Analysis of Traffic Violation Data with SQL Server and R

April 6, 2016
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By Srini Kumar, Director of Data Science at Microsoft Who does not hate being stopped and given a traffic ticket? Invariably, we think that something is not fair that we got it and everyone else did not. I am no different, and living in the SF Bay Area, I have often wondered if I could get the data about...

## Perform co-operations with the coop package

April 6, 2016
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About The coop package does co-operations: covariance, correlation, and cosine, and it does them quickly. The package is available on CRAN and GitHub, and has two vignettes: Introducing coop: Fast Covariance, Correlation, and Cosine Operations Algorithms and Benchmarks for the coop Package Incidentally, the vignettes don't render correctly on CRAN's end for some reason; if any of you rmarkdown...

## 3D plotting exercises

April 6, 2016
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In this set of exercises we will introduce the concept of 3D plotting. Specifically, we will use these commands:image(), contour() and persp(). For these exercises, you need to have a basic understanding of R objects and functions, in particular some knowledge about matrix . This set is the fourth set of exercises is a series

## RcppAPT 0.0.2

April 5, 2016
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A new version of RcppAPT -- our interface from R to the C++ library behind the awesome apt, apt-get, apt-cache, ... commands and their cache -- is now on CRAN. It adds three new commands to the package. Two are relatively simple: showSrc() and dumpPa...

## I Went to ROpenSci Unconference and All I Got Were These Lousy Hex Stickers

April 5, 2016
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Just kidding; it was amazing. Last week, I traveled to San Francisco to participate in an unconference/hackathon organized and hosted by ROpenSci. This was my first R conference or meeting, and it was a such a great experience. I am still feeling a bit at a loss for words about what a tremendous time I had, actually, but...

## R pkg Easter Eggs — Revenge of Pacman!

April 5, 2016
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In my last post, I praised the virtues of pacman. I also wanted to indulge of one of the main benefits of working with one of pacman’s devs: Tyler Rinker. Tyler is a geek’s geek, in the best possible way. And one of the best officemates one could ask for to boot! As evidence of that,...

## Computational Actuarial Science, with R, in Barcelona

April 5, 2016
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This Wednesday, I will give a graduate crash course on computational actuarial science, with R, which will be the second part of the lecture of Tuesday. Slides are now available,

## Statistical rethinking [book review]

April 5, 2016
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Statistical Rethinking: A Bayesian Course with Examples in R and Stan is a new book by Richard McElreath that CRC Press sent me for review in CHANCE. While the book was already discussed on Andrew’s blog three months ago, and enthusiastically recommended by Rasmus Bååth on Amazon, here are the reasons why I

## Blow Out Tide in the Delaware Estuary

April 5, 2016
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High winds in the Delaware Estuary region caused a "blowout" tide in early April 2016, where observed water surface elevations were much lower than those predicted via harmonic constituents.  Extreme low blowout tides can hamper navigation due ins...

## The Pirate Plot (2.0) – The RDI plotting choice of R pirates

April 5, 2016
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Plain vanilla barplots are as uninformative (and ugly) as they are popular. And boy, are they popular. From the floors of congress, to our latest scientific articles, barplots surround us. The reason why barplots are so popular is because they are so simple and easy to understand. However, that simplicity also carries costs — namely, ...

## Plotting App for ggplot2

April 5, 2016
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Through this post, I would like to share an update to my RTutoR package. The first version of this package included an R Basics Tutorial App which I published earlier at DataScience+ The updated version of this package, which is now on CRAN, includes a plotting app. This app provides an automated interface for generating Plotting App for...

## AirbnB uses R to scale data science

April 5, 2016
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Airbnb, the property-rental marketplace that helps you find a place to stay when you're travelling, uses R to scale data science. Airbnb is a famously data-driven company, and has recently gone through a period of rapid growth. To accommodate the influx of data scientists (80% of whom are proficient in R, and 64% use R as their primary data...

## Travis CI: “You Have Too Many Tests LOLZ!”

April 5, 2016
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No output has been received in the last 10m0s, this potentially indicates a stalled build or something wrong with the build itself. As part of getting RSiteCatalyst 1.4.8 ready for CRAN, I’ve managed to accumulate hundreds of testthat tests across 63 test files. Each of these tests runs on Travis CI against an authenticated API, and the