Simulating the Monty Hall Problem in R.

The Monty Hall Problem is famous in the world of statistics and probability. For those struggling with the intuition, simulating the problem is a great way to get at the answer. Randomly choose a door for the prize, randomly choose a door for the user to pick first, play out Monty’s role as host, and then show the results of both strategies.

Simulating Monty Hall in R
Simulating the strategies of Monty Hall

The numeric output will vary, but look something like:

> print(summary(games$strategy) / nrow(games))
stay switch
0.342 0.658

The following code does this in a rather short R example:

Clustering in R

Clustering is a useful technique for exploring your data. It groups records into clusters based on similar features. It’s also a key technique of unsupervised learning. The following is a simple example in R where I plotted the clusters and centroids.

kmeans() car clusters with centroids

The example uses the mtcars dataset built into R, which contains auto data extracted from Motor Trend Magazine in 1973-1974.

Clustering is done with the kmeans() function. Note that the graph is 2-dimensional, and I cluster by 2 features, but you could cluster by more features and project down to a 2-dimensional plane.

Feel free to make suggestions: