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

Loading Incanter's sample datasets


Incanter comes with a set of default datasets that are useful for exploring Incanter's functions. I haven't made use of them in this book, since there is so much data available at other places, but they're a great way to get a feel for what we can do with Incanter. Some of these datasets, for instance, the Iris dataset, are widely used for teaching. That's the dataset we'll access today.

In this recipe, we'll load a dataset and see what it contains.

Getting ready

We'll need to include Incanter in our Leiningen project.clj file.

:dependencies [[org.clojure/clojure "1.4.0"]
               [incanter "1.4.1"]]

We'll also need to include the right Incanter namespaces into our script or REPL.

(use '(incanter core datasets))

How to do it…

Once the namespaces are available, we can access the datasets easily.

user=> (def iris (get-dataset :iris))
#'user/iris
user=> (col-names iris)
[:Sepal.Length :Sepal.Width :Petal.Length :Petal.Width :Species]
user=> (nrow iris...