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Mastering Clojure Data Analysis

You're reading from  Mastering Clojure Data Analysis

Product type Book
Published in May 2014
Publisher
ISBN-13 9781783284139
Pages 340 pages
Edition 1st Edition
Languages
Author (1):
Eric Richard Rochester Eric Richard Rochester
Profile icon Eric Richard Rochester

Table of Contents (17) Chapters

Mastering Clojure Data Analysis
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Network Analysis – The Six Degrees of Kevin Bacon 2. GIS Analysis – Mapping Climate Change 3. Topic Modeling – Changing Concerns in the State of the Union Addresses 4. Classifying UFO Sightings 5. Benford's Law – Detecting Natural Progressions of Numbers 6. Sentiment Analysis – Categorizing Hotel Reviews 7. Null Hypothesis Tests – Analyzing Crime Data 8. A/B Testing – Statistical Experiments for the Web 9. Analyzing Social Data Participation 10. Modeling Stock Data Index

Exploring the data


Let's explore a little and try to get a feel for the data. First, let's try to get some summary statistics for the various datasets. Afterward, we'll generate some graphs to get a more intuitive sense for what's in the data and how they're related.

Generating summary statistics

Incanter makes generating summary statistics easy. You can pass a dataset to the incanter.stats/summary function. It returns a sequence of maps. Each map represents the summary data for each column in the original dataset. This includes whether the data is numeric or not. For nominal data, it returns some sample items and their counts. For numeric data, it returns the mean, median, minimum, and maximum.

Summarizing UNODC crime data

If we load the data and filter it for the crime of "burglary", we can get the summary statistics for those fields as follows:

(s/summary
  (i/$where {:crime {:$eq "CTS 2012 Burglary"}} by-ag-lnd))

And if we pick apart the data structures that it outputs, the following are the...

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