Book Image

Clojure Data Analysis Cookbook

By : Eric Rochester
Book Image

Clojure Data Analysis Cookbook

By: Eric Rochester

Overview of this book

<p>Data is everywhere and it's increasingly important to be able to gain insights that we can act on. Using Clojure for data analysis and collection, this book will show you how to gain fresh insights and perspectives from your data with an essential collection of practical, structured recipes.<br /><br />"The Clojure Data Analysis Cookbook" presents recipes for every stage of the data analysis process. Whether scraping data off a web page, performing data mining, or creating graphs for the web, this book has something for the task at hand.<br /><br />You'll learn how to acquire data, clean it up, and transform it into useful graphs which can then be analyzed and published to the Internet. Coverage includes advanced topics like processing data concurrently, applying powerful statistical techniques like Bayesian modelling, and even data mining algorithms such as K-means clustering, neural networks, and association rules.</p>
Table of Contents (18 chapters)
Clojure Data Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Aggregating data with Cascalog


So far, the Cascalog queries we've seen have all returned tables of results. However, sometimes we'll want to aggregate the tables, to boil them down to a single value, or into a table where groups from the original data are aggregated.

Cascalog makes this easy to do also, and it includes a number of aggregate functions. For this recipe, we'll only use one—cascalog.ops/count—but you can find more easily in the API documentation on the Cascalog website (http://nathanmarz.github.com/cascalog/cascalog.ops.html).

Getting ready

We'll use the same dependencies and imports as we did in the Distributed processing with Cascalog and Hadoop recipe. We'll also use the same data that we defined in that recipe.

How to do it…

We'll look at a couple of examples of aggregating with the count function.

  1. First, we'll query how many companions the doctor has had (If you're using just the data listed earlier in this chapter, you'll only have 10 companions).

    user=> (?<- (stdout)...