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Clojure Data Analysis Cookbook - Second Edition

You're reading from   Clojure Data Analysis Cookbook - Second Edition Dive into data analysis with Clojure through over 100 practical recipes for every stage of the analysis and collection process

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781784390297
Length 372 pages
Edition 2nd Edition
Languages
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Author (1):
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Eric Richard Rochester Eric Richard Rochester
Author Profile Icon Eric Richard Rochester
Eric Richard Rochester
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Toc

Table of Contents (14) Chapters Close

Preface 1. Importing Data for Analysis FREE CHAPTER 2. Cleaning and Validating Data 3. Managing Complexity with Concurrent Programming 4. Improving Performance with Parallel Programming 5. Distributed Data Processing with Cascalog 6. Working with Incanter Datasets 7. Statistical Data Analysis with Incanter 8. Working with Mathematica and R 9. Clustering, Classifying, and Working with Weka 10. Working with Unstructured and Textual Data 11. Graphing in Incanter 12. Creating Charts for the Web Index

Maintaining consistency with synonym maps

One common problem with data is inconsistency. Sometimes, a value is capitalized, while sometimes it is not. Sometimes it is abbreviated, and sometimes it is full. At times, there is a misspelling.

When it's an open domain, such as words in a free-text field, the problem can be quite difficult. However, when the data represents a limited vocabulary (such as US state names, for our example here) there's a simple trick that can help. While it's common to use full state names, standard postal codes are also often used. A mapping from common forms or mistakes to a normalized form is an easy way to fix variants in a field.

Getting ready

For the project.clj file, we'll use a very simple configuration:

(defproject cleaning-data "0.1.0-SNAPSHOT"
  :dependencies [[org.clojure/clojure "1.6.0"]])

We just need to make sure that the clojure.string/upper-case function is available to us:

(use '[clojure.string :only (upper...
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