Monthly Archives: October 2012

Javascript and D3 for R users, part 2: running off the R server instead of Python

October 26, 2012
By

Thank you all for the positive responses to Basics of JavaScript and D3 for R Users! Quick update: last time we had to dabble in a tiny bit of Python to start a local server, in order to actually run JavaScript … Continue reading →

Read more »

Plotting correlation ellipses

October 26, 2012
By
Plotting correlation ellipses

This is an oldie but a goodie. There are a lot of ways to plot multiple bivariate relationships, but this is one of my favorites, courtesy of the R Graph Gallery. https://gist.github.com/819111

Read more »

NSCB Sexy Stats Version 2

October 25, 2012
By
NSCB Sexy Stats Version 2

This was a revised version of my previous post about the NSCB article. With the suggestion from Tal Galili, below were the new pie charts and the R codes to produce these plots by directly scrapping the data from the webpage using XML and RColorBrewer ...

Read more »

Using FAFSA Data to study Competitors – Part 2

October 25, 2012
By
Using FAFSA Data to study Competitors – Part 2

I wanted to build upon my previous post and dive a little deeper into the sorts of questions we can answer using the FAFSA data supplied to us by applicants. As a quick overview, students completing the FAFSA for student aid can list up to ten institutions on the form. I consider this the student’s

Read more »

Modeling Couch Potato strategy

October 25, 2012
By
Modeling Couch Potato strategy

I first read about the Couch Potato strategy in the MoneySense magazine. I liked this simple strategy because it was easy to understand and easy to manage. The Couch Potato strategy is similar to the Permanent Portfolio strategy that I have analyzed previously. The Couch Potato strategy invests money in the given proportions among different

Read more »

Accelerating R code: Computing Implied Volatilities Orders of Magnitude Faster

October 25, 2012
By

This blog, together with Romain's, is one of the main homes of stories about how Rcpp can help with getting code to run faster in the context of the R system for statistical programming and analysis. By making it easier to get already existing C or C++ code to R, or equally to extend R with new C++...

Read more »

My Goodness. What a Fat Dataset!

October 25, 2012
By
My Goodness.  What a Fat Dataset!

Recently at work we got sent a data file containing information on donations to a specific charitable organization, ranging all the way back to the 80′s.  Usually, when we receive a dataset with a donation history in it, each row … Continue reading →

Read more »

Allstate compares SAS, Hadoop and R for Big-Data Insurance Models

October 25, 2012
By
Allstate compares SAS, Hadoop and R for Big-Data Insurance Models

At the Strata conference in New York today, Steve Yun (Principal Predictive Modeler at Allstate's Research and Planning Center) described the various ways he tackled the problem of fitting a generalized linear model to 150M records of insurance data. He evaluated several approaches: Proc GENMOD in SAS Installing a Hadoop cluster Using open-source R (both on the full data...

Read more »

Notes on a Scandal – When Jimmy beat Katy

October 25, 2012
By
Notes on a Scandal  – When Jimmy beat Katy

No the title doesn’t refer to how Katy Perry suffered at another of Jimmy Savile’s sexual predelictions, although these are two of  the participants. I’ll get to the details later Just over a year ago, I reflected on the relative wiki searches of leading female singing celebrities, including Ms Perry. In the light of the

Read more »

Palettes in R

October 25, 2012
By
Palettes in R

In its simplest form, a palette in R is simply a vector of colors. This vector can be include the hex triplet or R color names.The default palette can be seen through palette(): > palette("default") # you'll only need this line if you've previ...

Read more »