Category Archives: Data Science

Hadoop: Installing on macOS

Hadoop is traditionally run on a linux-based system. For learning and development purposes, you may want to install hadoop on macOS.

This is the first in a series of posts that will walkthrough working with Hadoop and cloud-based storage.

First, you’ll want to use homebrew to install hadoop and any related tools you would like.
brew install hadoop apache-spark pig hbase

Next, you’ll want to setup some environment variables. This can be in your shell rc file (.bashrc, .zshrc), or other places if you use a shell config tool like oh-my-zsh.

Make sure you have set JAVA_HOME, which may differ from my setup below.

export HADOOP_INSTALL=/usr/local/opt
export HADOOP_HOME=$HADOOP_INSTALL/hadoop/libexec
export HADOOP_CONF=$HADOOP_HOME/etc/hadoop

Then test your install with the following:

hdfs dfs -ls ~

You should see the contents of your home directory.

You can also run a hadoop example with:
yarn jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-*.jar pi 10 100

You should see a (poor) estimate of pi.

You should now be set to use hadoop. In future posts we will look at using the S3 filesystem from AWS and the Google Cloud Storage as well.

Files and Pipes in R Video Demo

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:

If you are having a hard time reading the text, click here to view the video directly on youtube.

Comment here or on the video with any feedback or questions.

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: