Visualization is a useful tool for data exploration and statistical analysis, and it’s an important method for communicating your discoveries to others. While those two uses of visualization are related, they aren’t identical.
One of the reasons that I like ggplot so much is that it excels at layering together multiple views and summaries of data in ways that improve both data exploration and communication. Of course, getting at the right graph can be a bit of work, and often I will stop when I get to a visualization that tells me what I need to know — even if no one can read that graph but me. In this post I’ll look at a couple of ggplot graphs that take the extra step: communicating effectively to others.
For my examples I’ll use a pre-treated sample from the 2011 U.S. Census American Community Survey. The dataset is available as an R object in the file
phsample.RData; the data dictionary and additional information can be found here. Information about getting the original source data from the U.S. Census site is at the bottom of this post.
phsample.RData contains two data frames:
dhus (household information), and
dpus (information about individuals; they are joined to households using the column
SERIALNO). We will only use the
dhus data frame.
# Restrict to non-institutional households
# (No jails, schools, convalescent homes, vacant residences)
hhonly = subset(dhus, (dhus$TYPE==1) &(dhus$NP > 0))
Categories: Coding, math programming, Statistics, Tutorials bubble plot, dotplot, ggplot, ggplot2, R, US Census data, US housing expenses, visualization
I was watching my cousins play Unspeakable Words over Christmas break and got interested in the end game. The game starts out as a spell a word from cards and then bet some points game, but in the end (when you are down to one marker) it becomes a pure betting game. In this article we analyze an idealized form of the pure betting end game. Read more…
From time to time we work on projects that would benefit from a free lightweight pure Java linear programming library. That is a library unencumbered by a bad license, available cheaply, without an infinite amount of file format and interop cruft and available in Java (without binary blobs and JNI linkages). There are a few such libraries, but none have repeatably, efficiently and reliably met our needs. So we have re-packaged an older one of our own for release under the Apache 2.0 license. This code will have its own rough edges (not having been used widely in production), but I still feel fills an important gap. This article is brief introduction to our WVLPSolver Java library. Read more…
We describe briefly the powerful simulation technique known as “importance sampling.” Importance sampling is a technique that allows you to use numerical simulation to explore events that, at first look, appear too rare to be reliably approximated numerically. The correctness of importance sampling follows almost immediately from the definition of a change of density. Like most mathematical techniques, importance sampling brings in its own concerns and controls that were not obvious in the original problem. To deal with these concerns (like picking the re-weighting to use) we will largely appeal to the ideas from “A Tutorial on the Cross-Entropy Method” Pieter-Tjerk de Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein, Annals of Operations Research, 2005 vol. 134 (1) pp. 19-67. Read more…
We share our admiration for a set of results called “locality sensitive hashing” by demonstrating a greatly simplified example that exhibits the spirit of the techniques. Read more…
Living in the age of big data we ask what to do when we have the good fortune to be presented with a huge amount of supervised training data? Most often at large scale we are presented with the un-supervised problems of characterization and information extraction; but some problem domains offer an almost limitless supply of supervised training data (such as using older data to build models that predict the near future). Having too much training data is a good problem to have and there are ways to use traditional methods (like logistic regression) at this scale. We present an “out of core” logistic regression implementation and a quick example in Apache Hadoop running on Amazon Elastic MapReduce. This presentation assumes familiarity with Unix style command lines, Java and Hadoop. Read more…
Categories: math programming, Programming, Statistics Amazon Elastic MapReduce, Big Data, EC2, Hadoop, Logistic Regression, Machine Learning, Mahout, Map Reduce, S3
We extend the ideas of from Automatic Differentiation with Scala to include the reverse accumulation. Reverse accumulation is a non-obvious improvement to automatic differentiation that can in many cases vastly speed up calculations of gradients. Read more…
Categories: Applications, Coding, Exciting Techniques, math programming, Mathematics, Programming, Tutorials Automatic Differentiation, Conjugate Gradient, Gradient, Mathematical Bedside Reading, Optimization, Reverse Accumulation, Scala