I am planning to finish school soon and I would like to shed some weight before moving on. I have collected a fair number of books that I will likely never use again and it would be nice to get some money for them. Sites like Amazon and eBay let you sell your book to other customers, but Amazon will also buy some of your books directly (Trade-Ins), saving you the hassle of waiting for a buyer.

Time lapses are a fun way to quickly show a long period of time. They typically involve setting up your camera on a tripod and taking photos at a regular interval, like every 5 seconds. After all the photos have been taken, they are combined into a movie at a much faster rate, for example 30 frames per second.
Time stacking is a way to combine all the photos into a single photo, instead of a movie.

Fangraphs recently published an interesting dataset that measures defensive efficiency of fielders. For each player, the Inside Edge dataset breaks their opportunities to make plays into five categories, ranging from almost impossible to routine. It also records the proportion of times that the player successfully made the play. With this data, we can see how successful each player is for each type of play. I wanted to think of a way to combine these five proportions into one fielding metric.

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.

Note: I started this post way back when the NCAA men's basketball tournament was going on, but didn't finish it until now.
Since the NCAA Men's Basketball Tournament has moved to 64 teams, a 16 seed as never upset a 1 seed. You might be tempted to say that the probability of such an event must be 0 then. But we know better than that.
In this post, I am interested in looking at different ways of estimating how the odds of winning a game change as the difference between seeds increases.

A lot of times we are given a data set in Excel format and we want to run a quick analysis using R's functionality to look at advanced statistics or make better visualizations. There are packages for importing/exporting data from/to Excel, but I have found them to be hard to work with or only work with old versions of Excel (*.xls, not *.xlsx). So for a one time analysis, I usually save the file as a csv and import it into R.

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.

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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).

Random forests ™ are great. They are one of the best "black-box" supervised learning methods. If you have lots of data and lots of predictor variables, you can do worse than random forests. They can deal with messy, real data. If there are lots of extraneous predictors, it has no problem. It automatically does a good job of finding interactions as well. There are no assumptions that the response has a linear (or even smooth) relationship with the predictors.

Forgive me if you are already aware of this, but I found it quite alarming. I know that most code is interpreted by the computer in binary and we input in decimal, so problems can arise in conversion and with floating point. But the example I have below is so simple that it really surprised me.
I was converting a function from R into MATLAB so that a colleague could use it.

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.

Don't you hate it when you are running a long piece of code and you keep checking the results every 15 minutes, hoping it will finish? There is a better way.
I got the idea from here. He uses a Python script and the text interface is not free. I thought someone must have already thought of this for R. There is an easy solution. You can send an email fairly easily in R.

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.

After signing a huge deal with the Angels, Pujols has been having a really bad year. He hasn't hit a home run this year, breaking a career long streak. So I thought it would be a good idea to use some statistics to tell how good or bad we think Pujols will actually be this year.
Coming into the year, he had a career .328/.420/.617 career AVG/OBP/SLG. Through one month, he has a .

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.

That title is quite a mouthful. This quarter, I have been reading papers on Spectral Clustering for a reading group. The basic goal of clustering is to find groups of data points that are similar to each other. Also, data points in one group should be dissimilar to data in other clusters. This way you can summarize your data by saying there are a few groups to consider instead of all the points.

I guess you could call this On Bayes Percentage. *cough*
Fresh off learning Bayesian techniques in one of my classes last quarter, I thought it would be fun to try to apply the method. I was able to find some examples of Hierarchical Bayes being used to analyze baseball data at Wharton. Setting up the problem
On base percentage (OBP) is probably the most important basic offensive statistic in baseball.

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