In a previous post, I showed you how to scrape playlist data from Columbus, OH alternative rock station CD102.5. Since it's the end of the year and best-of lists are all the fad, I thought I would share the most popular songs and artists of the year, according to this data. In addition to this, I am going to make an interactive graph using Shiny, where the user can select an artist and it will graph the most popular songs from that artist.

CD1025’s Playlist and Summerfest Last time, I showed you how to download CD1025’s playlist back to last year and did some exploratory analysis to find that there were some gaps in the data. Using this data, I would like to look at the artists that are playing in this week’s Summerfest. Summerfest is one of the biggest shows that CD1025 puts on every year and is hyped quite a bit on the station.

CD1025 is an “alternative” radio station here in Columbus. They are one of the few remaining radio stations that are independently owned and they take great pride in it. For data nerds like me, they also put a real time list of recently played songs on their website. The page has the most recent 50 songs played, but you can also click on “Older Tracks” to go back in time.

The famous probabilist and statistician Persi Diaconis wrote an article not too long ago about the "Markov chain Monte Carlo (MCMC) Revolution." The paper describes how we are able to solve a diverse set of problems with MCMC. The first example he gives is a text decryption problem solved with a simple Metropolis Hastings sampler.
I was always stumped by those cryptograms in the newspaper and thought it would be pretty cool if I could crack them with statistics.

Restricted Boltzmann Machines (RBMs) are an unsupervised learning method (like principal components). An RBM is a probabilistic and undirected graphical model. They are becoming more popular in machine learning due to recent success in training them with contrastive divergence. They have been proven useful in collaborative filtering, being one of the most successful methods in the Netflix challenge (paper). Furthermore, they have been tantamount to training deep learning models, which appear to be the best current models for image and digit recognition.

Factor Analysis of Baseball's Hall of Fame Voters body, td { font-family: sans-serif; background-color: white; font-size: 12px; margin: 8px; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace; } h1 { font-size:2.2em; } h2 { font-size:1.8em; } h3 { font-size:1.4em; } h4 { font-size:1.0em; } h5 { font-size:0.9em; } h6 { font-size:0.8em; } a:visited { color: rgb(50%, 0%, 50%); } pre { margin-top: 0; max-width: 95%; border: 1px solid #ccc; white-space: pre-wrap; } pre code { display: block; padding: 0.

With the election nearly upon us, I wanted to share an easy way I just found to download polling data and graph a few with ggplot2. dlinzer at github created a function to download poll data from the Huffington Post's Pollster API.
The default is to download national tracking polls from the presidential election. After sourcing the function, I load the required packages, download the data, and make the plot.

Finding the best subset of variables for a regression is a very common task in statistics and machine learning. There are statistical methods based on asymptotic normal theory that can help you decide whether to add or remove a variable at a time. The problem with this is that it is a greedy approach and you can easily get stuck in a local mode. Another approach is to look at all possible subsets of the variables and see which one maximizes an objective function (accuracy on a test set, for example).

I was having some fun with PITCHf/x data and generalize additive models. PITCHf/x keeps track of the trajectory, path, location of every pitch in the MLB. It is pretty accurate and opens up baseball to more analyses than ever before. Generalized additive models (GAMs) are statistical models that put minimal assumptions on the type of model you are fitting. Traditional statistical models are linear, in that they assume that the response variable you are modelling is a linear function of the explanatory variables.

Recently, Chris Perez, the closer for the Indians, displayed some frustration with the fans for not supporting the team. Currently, they have the lowest attendance in the majors -- by a decent margin. The Indians are averaging about 15,000 fans per home game, while the next closest team, the Oakland A's, is averaging 19,000. It seemed like an odd time for Perez to bring this up because they have had attendance in the 29,000s each of the last two home games.

Correlation matrices are a common way to look at the dependence of a set of variables. When the variables have spatial relationships, the correlation matrix loses some information.
Lets say you have repeated observations, each one being a matrix. For example, you could have yearly observations of health statistics for a spatial grid. Lets say the grid is n by p (n*p variables) and there are m observations of the grid.

I am a big fan of SAS's JMP software. It is the first statistical program I learned and I really like how the emphasize visualization. In their most recent update, JMP 9 now has the ability to create maps. I have been itching to test this out for a little while and I came across a map on the internet that I thought would be a good test. It is the percentage of the population of each state that has a passport.

Powered by the Academic theme for Hugo.