Playing RISK recently, I was annoyed when I lost a battle despite overwhelming numbers. “What are the odds of that?!?” Which led me to write this simulation to figure it out. My first approach was to simulate each and every die roll, but that turned out to be slow. Given that there are a pretty limited number of die roll options, I decided to exhaustively enumerate all possible scenarios and the resultant probabilities of outcomes.

I attended a Greater Boston useR Group (R Programming Language) Meetup yesterday, where the topic was to work on a Tidy Tuesday dataset. This week was a wine-enthusiast ratings dataset from Kaggle. I adapted some code I had previously written for a clinical dataset to analyze the text of the wine descriptions to predict wine scores. There was some interest in seeing the code I used, so I’m posting it here.

This was originally posted in the reddit r/TheSilphRoad feed back in 2016, but I wanted to move it someplace more easily accessible for archival purposes. The entire analysis was done in R. Analysis of Pokemon Go Spawn Frequencies to Identify Possible Biomes This analysis is based on spawns from the migration epoch starting 2016-08-23, many migrations ago. While the details of the biome regions and/or species assignment to biomes have likely changed since that epoch, I still think it’s interesting to see how ‘biomes’ may be represented in Pokemon Go.

I share here the full code and output for an exploratory data visualization in R Markdown submitted for a contest in January 2017. The code includes data exploration, feature generation / simplification, summary statistics, plotting distributions of predictors, assessment of correlations, and initial exploration of predictive modeling using random forests. In January 2017, I had the pleasure of attending the 2017 ComputeFest, hosted by the Institute for Applied Computational Science at Harvard University.

I am primarily a MacOS user who does most of my analytics and presentation work in R / RStudio / RMarkdown, so it was natural to take advantage of using the R blogdown package.

Many others have already documented nice tutorials on how to get a blog started with blogdown, Hugo, GitHub, and Netlify, so I’ll just reference a number of resources that I found particularly helpful. Also, I chose to use the tranquilpeak Hugo theme, as I liked the look of it, and it generally seems to play well in this environment.

This blog will serve as a place to share data science analyses, professional and personal projects, snippets of useful code, and other various geekery. The blog’s initial focus will likely be on R as applied to data science and machine learning and technological approaches to improving clinical care.