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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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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

Comparisons with relative risk and odds


The preceding Incanter dataset is an easily comprehensible representation of our data, but to extract the numbers for each of the groups individually we'll want to store the data in a more readily accessible data structure. Let's write a function to convert the dataset to a series of nested maps:

(defn frequency-map [sum-column group-cols dataset]
  (let [f (fn [freq-map row]
            (let [groups (map row group-cols)]
              (->> (get row sum-column)
                   (assoc-in freq-map groups))))]
    (->> (frequency-table sum-column group-cols dataset)
         (:rows)
         (reduce f {}))))

For example, we can use the frequency-map function as follows to calculate a nested map of :sex and :survived:

(defn ex-4-3 []
  (->> (load-data "titanic.tsv")
       (frequency-map :count [:sex :survived])))

;; => {"female" {"y" 339, "n" 127}, "male" {"y" 161, "n" 682}}

More generally, given any dataset and sequence of columns...

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