John Cook recently wrote an interesting blog post on random vectors and random projections. In the post, he states two surprising facts of high-dimensional geometry and gives some intuition for the second fact. In this post, I will provide R … Continue reading →

This month’s issue of Significance magazine has a very nice summary article of the sinh-arcsinh normal distribution. (Unfortunately, the article seems to be behind a paywall.) This distribution was first introduced by Chris Jones and Arthur Pewsey in 2009 as … Continue reading →

Once in a while, you might find yourself wanting to embed one plot within another plot. ggplot2 makes this really easy with the annotation_custom function. The following example illustrates how you can achieve this. (For all the code in one … Continue reading →

Quantile regression: what is it? Let be some response variable of interest, and let be a vector of features or predictors that we want to use to model the response. In linear regression, we are trying to estimate the conditional … Continue reading →

I’m excited to announce that my first package has been accepted to CRAN! The package pcLasso implements principal components lasso, a new method for sparse regression which I’ve developed with Rob Tibshirani and Jerry Friedman. In this post, I will … Continue reading →

I’m writing a series of posts on various function options of the glmnet function (from the package of the same name), hoping to give more detail and insight beyond R’s documentation. In this post, we will look at the offset … Continue reading →

Recently I wanted to learn how to use emojis as points in a scatterplot points. It seems like the emojifont package is a popular way to do it. However, I couldn’t seem to get it to work on my machine … Continue reading →

In this previous post, I showed how one can scrape top-level NBA game data from BasketballReference.com. In the post after that, I demonstrated how to scrape play-by-play data for one game. After writing those posts, I thought to myself: why … Continue reading →

For each NBA game, nba.com has a really nice graphic which tracks the point differential between the two teams throughout the game. Here is the lead tracker graphic for the game between the LA Clippers and the Phoenix Suns on … Continue reading →

I’m a casual NBA fan: I don’t have time to watch the games but enjoy viewing the highlights on Instagram/Youtube (especially Shaqtin’ A Fool!); I sometimes read game articles and analyses (e.g. Blogtable). Apart from the game being an amazing … Continue reading →

I’m writing a series of posts on various function options of the glmnet function (from the package of the same name), hoping to give more detail and insight beyond R’s documentation. In this post, we will focus on the standardize … Continue reading →

The glmnet function (from the package of the same name) is probably the most used function for fitting the elastic net model in R. (It also fits the lasso and ridge regression, since they are special cases of elastic net.) … Continue reading →

Spatial visualizations really come to life when you have a real map as a background. In R, ggmap is the package that you’ll want to use to get these maps. In what follows, we’ll demonstrate how to use ggmap with … Continue reading →

Principal components (PC) regression is a common dimensionality reduction technique in supervised learning. The R lab for PC regression in James et al.’s Introduction to Statistical Learning is a popular intro for how to perform PC regression in R: it is … Continue reading →

A few posts ago (see here), I noted that there was a group of 7 teams in the English Premier League (EPL) that seem to be a cut above the rest: Arsenal Chelsea Everton Liverpool Manchester City Manchester United Tottenham … Continue reading →

I recently got a hold of table standings for the English Premier League (EPL) for the past 10 years. In this post, I want to explore the question: How similar are the point distributions across seasons? Code for the figures … Continue reading →

When you are plotting many points on one plot, changing the transparency, or alpha, of the points is often a good idea. For example, the plot below plots price vs. carat for each of 53,940 diamonds: We get a mess/mass … Continue reading →