April 2013

Predicting Dichotomous Outcomes I

April 14, 2013 | Kevin Davenport

We are trying to predict a dependent dichotomous variable (male/female, yes/no, like/dislike,etc) with independent “predictor” variables. Let’s say we want to determine whether or not an employee will quit based on the percentage of their tenure spent traveling. We assemble the data from HR and ... [Read more...]

Using R — Working with Geospatial Data

April 14, 2013 | Steven Brey

This entry is part 6 of 12 in the series Using RGIS, an acronym that brings joy to some and strikes fear in the heart of those not interested in buying expensive software. Luckily fight or flight can be saved for another day because you … read more ... [Read more...]

Tip: Julia vs. R – introduction videos and more

April 14, 2013 | Jeroen Ooms

Last years UseR 2012 conference in Nashville had an interesting discussion session titled “What other languages should R users know about?“. General consensus was that multilingualism is inevitable in modern computing, and panel members presented various languages that complement R in different ways. Some of the usual suspects included SQL, python, ... [Read more...]

Continuing Sync

April 14, 2013 | Wingfeet

I am continuing in Sync: How Order Emerges from Chaos in the Universe, Nature, and Daily Lifeby Steven Strogatz. To get a feeling on it, I was building a group of things which have only a minute influence on each other are able to synchronize thei... [Read more...]

Benchmarking Machine Learning Models Using Simulation

April 13, 2013 | Max Kuhn

What is the objective of most data analysis? One way I think about it is that we are trying to discover or approximate what is really going on in our data (and in general, nature). However, I occasionally run into people think that if one model fulfills our expectations (e.... [Read more...]

Using ddply to select the first record of every group

April 13, 2013 | inkhorn82

I had a very long file of monetary transactions (about 207,000 rows) with about two handfuls of columns describing each transaction (including date). The task I needed to perform on this file was to select the value from one of … Continue reading → [Read more...]

Webinar Follow-Up: First Batch of Answers to Overflow Questions

April 13, 2013 | Paradigm4 Team

We’re delighted with the response to our webinar, Big Analytics for R Users without Big Hassles. Attendees were engaged and we didn’t have time to answer all the questions submitted through the chat window. We’ll answer the overflow questions in batches. Here’s the first batch. Is ... [Read more...]

Motivating Students

April 13, 2013 | Vivek Patil

Figure shown to students in a particular class to show the effect of slacking after the mid-semester grades are received.Background information: A course has multiple components - Exams, projects, quizzes, assignments, etc. The objective of this set of... [Read more...]

knitr documents with tikzDevice graphics

April 13, 2013 | Marek Gągolewski

Setting up tikzDevice output in knitr may be a frustrating task, but gives outstandingly aesthetic, LaTeX-like figures. Here are my global knitr settings for typesetting documents in Polish (in UTF-8, make sure your R also runs in a Unicode locale…Read more › [Read more...]

Configure Kile for knitr under GNU/Linux

April 13, 2013 | Marek Gągolewski

Kile – a convenient LaTeX editor – may also be used to prepare knitr-generated reports. Here is how we may make our work much more efficient with a convenient compile-on-keypress feature. Create a bash script in your home directory, named…Read more › [Read more...]

Classification Tree Models

April 13, 2013 | Ron Pearson (aka TheNoodleDoodler)

On March 26, I attended the Connecticut R Meetup in New Haven, which featured a talk by Illya Mowerman on decision trees in R. I have gone to these Meetups before, and I have always found them to be interesting and informative. Attendees range from those who are just starting to ... [Read more...]

Spring Cleaning Data: 6 of 6- Saving the Data

April 13, 2013 | 0utlieR

With all the cleaning done, the only thing left to do is save the data to be analyzed, for future use, and I hope by others. The data I thought would be simple, but there were a few interesting twist, like the Primary Credit*, and using ifelse() to edi... [Read more...]

Calculate Fantasy Players’ Risk Levels using R

April 12, 2013 | Isaac Petersen

In prior posts, I have demonstrated how to download, calculate, and compare fantasy football projections from ESPN, CBS, and NFL.com. In this post, I will demonstrate how to calculate fantasy[...] The post Calculate Fantasy Players’ Risk Levels using R appeared first on Fantasy Football Analytics. [Read more...]

Calculate Fantasy Players’ Risk Levels using R

April 12, 2013 | Isaac

In prior posts, I have demonstrated how to download, calculate, and compare fantasy football projections from ESPN, CBS, and NFL.com. In this post, I will demonstrate how to calculate fantasy football players' risk levels. Just like when determin... [Read more...]

Mathematical abstraction and the robustness to assumptions

April 12, 2013 | Corey Chivers

I’ve been showing my new favourite toys to just about anyone foolish enough to actually engage me in conversation. I described how my shiny new set of non-transitive dice work here, complete with a map showing all the relevant probabilities. All was neat and tidy and wonderful until fellow ... [Read more...]

Stan 1.3.0 and RStan 1.3.0 Ready for Action

April 12, 2013 | Bob Carpenter

The Stan Development Team is happy to announce that Stan 1.3.0 and RStan 1.3.0 are available for download. Follow the links on: Stan home page: http://mc-stan.org/ Please let us know if you have problems updating. Here’s the full set of release notes. v1.3.0 (12 April 2013) ====================================================================== Enhancements ---------------------------------- Modeling Language * forward ...

[Read more...]

Extending RevoScaleR for Mining Big Data – Discretization

April 12, 2013 | Derek Norton

by Derek McCrae Norton, Senior Sales Engineer In this second installment of Extending RevoScaleR for Mining Big Data we look at how to use the building blocks provided by RevoScaleR to transform continuous variables into discrete. Motivation: Discretize continuous variables on big data. Discretization is a technique to convert continuous ... [Read more...]

Spring Cleaning Data: 5 of 6- 2 ifelse vs Merge

April 12, 2013 | 0utlieR

The blog in the data cleaning series looks at separating out the Federal Reserve Districts. What I wanted was two additional columns, where I had the name of the city and the number for each district. Since I was on a separation kick I thought it would... [Read more...]

Using the RcppArmadillo-based Implementation of R’s sample()

April 12, 2013 | Rcpp Gallery

Overview and Motivation All of R’s (r*, p*, q*, d*) distribution functions are available in C++ via the R API. R is written in C, and the R API has no concept of a vector (at least not in the STL sense). Consequently, R’s sample() function can’t ...

[Read more...]

Travis CI for R! (not yet)

April 12, 2013 | Yihui Xie

A few days ago I wrote about Travis CI, and was wondering if we could integrate the testing of R packages into this wonderful platform. A reader (Vincent Arel-Bundock) pointed out in the comments that Travis was running Ubuntu that allows you to install software packages at your will. I ...

[Read more...]

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R-bloggers

R news and tutorials contributed by hundreds of R bloggers

April 2013

Predicting Dichotomous Outcomes I

Using R — Working with Geospatial Data

Tip: Julia vs. R – introduction videos and more

Continuing Sync

Benchmarking Machine Learning Models Using Simulation

Using ddply to select the first record of every group

Webinar Follow-Up: First Batch of Answers to Overflow Questions

Motivating Students

knitr documents with tikzDevice graphics

Configure Kile for knitr under GNU/Linux

Classification Tree Models

Spring Cleaning Data: 6 of 6- Saving the Data

Calculate Fantasy Players’ Risk Levels using R

Calculate Fantasy Players’ Risk Levels using R

Mathematical abstraction and the robustness to assumptions

Stan 1.3.0 and RStan 1.3.0 Ready for Action

Extending RevoScaleR for Mining Big Data – Discretization

Spring Cleaning Data: 5 of 6- 2 ifelse vs Merge

Using the RcppArmadillo-based Implementation of R’s sample()

Travis CI for R! (not yet)

April 2013

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