I’ve worked with various alternate file handlers in python before and wanted to explore the options in R. I was pleasantly surprised to find handlers prebuilt for tasks like compressing data. In addition, a pipe function is available to allow you to use less common commands on your file, like gpg for encryption.
I put together a quick video demo of how to use these functions, and it’s available on youtube:
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 the strategies of Monty Hall
The numeric output will vary, but look something like:
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.
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.
Here is a recent interview I did for CLK Tech. CLK Tech is a newsletter based out of Northeast Ohio, run by a couple of tech recruiters in the area. Topics span general career questions and data science in particular.
In addition, I’m busy with a project that I look forward to announcing soon. It’s shaping up to be a a busy year…