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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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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

Managing program complexity with STM

The basis of Clojure's concurrency is its STM system. Basically, this extends the semantics of database transactions to the computer's memory.

For this recipe, we'll use the STM to calculate the families per housing unit from a piece of U.S. census data. We'll use future-call to perform the calculations in the thread pool and spread the execution over multiple cores. Afterwards, we'll go into more detail about how the STM works in general, and how it's applied in this particular recipe.

Getting ready

To prepare for this recipe, we first need to list our dependencies in the Leiningen project.clj file:

(defproject concurrent-data "0.1.0-SNAPSHOT"
  :dependencies [[org.clojure/clojure "1.6.0"]
                 [org.clojure/data.csv "0.1.2"]])

We also need to import these dependencies to our script or REPL:

(require '[clojure.java.io :as io]
         '[clojure.data.csv :as csv])

Finally, we need...

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