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Clojure for Data Science

You're reading from   Clojure for Data Science Statistics, big data, and machine learning for Clojure programmers

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781784397180
Length 608 pages
Edition 1st Edition
Languages
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Author (1):
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Henry Garner Henry Garner
Author Profile Icon Henry Garner
Henry Garner
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Toc

Table of Contents (12) Chapters Close

Preface 1. Statistics FREE CHAPTER 2. Inference 3. Correlation 4. Classification 5. Big Data 6. Clustering 7. Recommender Systems 8. Network Analysis 9. Time Series 10. Visualization Index

Visualizing the data

First, we'll consider the spread of the heights of the London 2012 athletes. Let's plot our height values as a histogram to see how the data is distributed, remembering to filter the nil values first:

(defn ex-3-2 []
  (-> (remove nil? (i/$ "Height, cm" (athlete-data)))
      (c/histogram :nbins 20
                   :x-label "Height, cm"
                   :y-label "Frequency")
      (i/view)))

This code generates the following histogram:

Visualizing the data

The data is approximately normally distributed, as we have come to expect. The mean height of our athletes is around 177 cm. Let's take a look at the distribution of weights of swimmers from the 2012 Olympics:

(defn ex-3-3 []
  (-> (remove nil? (i/$ "Weight" (athlete-data)))
      (c/histogram :nbins 20
                   :x-label "Weight"
                   :y-label "Frequency")
      (i/view)))

This code generates the following histogram:

Visualizing the data

This data shows...

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