Book Image

Clojure for Data Science

By : Garner
Book Image

Clojure for Data Science

By: Garner

Overview of this book

The term “data science” has been widely used to define this new profession that is expected to interpret vast datasets and translate them to improved decision-making and performance. Clojure is a powerful language that combines the interactivity of a scripting language with the speed of a compiled language. Together with its rich ecosystem of native libraries and an extremely simple and consistent functional approach to data manipulation, which maps closely to mathematical formula, it is an ideal, practical, and flexible language to meet a data scientist’s diverse needs. Taking you on a journey from simple summary statistics to sophisticated machine learning algorithms, this book shows how the Clojure programming language can be used to derive insights from data. Data scientists often forge a novel path, and you’ll see how to make use of Clojure’s Java interoperability capabilities to access libraries such as Mahout and Mllib for which Clojure wrappers don’t yet exist. Even seasoned Clojure developers will develop a deeper appreciation for their language’s flexibility! You’ll learn how to apply statistical thinking to your own data and use Clojure to explore, analyze, and visualize it in a technically and statistically robust way. You can also use Incanter for local data processing and ClojureScript to present interactive visualisations and understand how distributed platforms such as Hadoop sand Spark’s MapReduce and GraphX’s BSP solve the challenges of data analysis at scale, and how to explain algorithms using those programming models. Above all, by following the explanations in this book, you’ll learn not just how to be effective using the current state-of-the-art methods in data science, but why such methods work so that you can continue to be productive as the field evolves into the future.
Table of Contents (12 chapters)
11
Index

Binning data

To develop an intuition for what these various calculations of variance are measuring, we can employ a technique called binning. Where data is continuous, using frequencies (as we did with the election data to count the nils) is not practical since no two values may be the same. However, it's possible to get a broad sense of the structure of the data by grouping the data into discrete intervals.

The process of binning is to divide the range of values into a number of consecutive, equally-sized, smaller bins. Each value in the original series falls into exactly one bin. By counting the number of points falling into each bin, we can get a sense of the spread of the data:

Binning data

The preceding illustration shows fifteen values of x split into five equally-sized bins. By counting the number of points falling into each bin we can clearly see that most points fall in the middle bin, with fewer points falling into the bins towards the edges. We can achieve the same in Clojure with the following bin function:

(defn bin [n-bins xs]
  (let [min-x    (apply min xs)
        max-x    (apply max xs)
        range-x  (- max-x min-x)
        bin-fn   (fn [x]
                   (-> x
                       (- min-x)
                       (/ range-x)
                       (* n-bins)
                       (int)
                       (min (dec n-bins))))]
    (map bin-fn xs)))

For example, we can bin range 0-14 into 5 bins like so:

(bin 5 (range 15))

;; (0 0 0 1 1 1 2 2 2 3 3 3 4 4 4)

Once we've binned the values we can then use the frequencies function once again to count the number of points in each bin. In the following code, we use the function to split the UK electorate data into five bins:

(defn ex-1-11 []
  (->> (load-data :uk-scrubbed)
       (i/$ "Electorate")
       (bin 10)
       (frequencies)))

;; {1 26, 2 450, 3 171, 4 1, 0 2}

The count of points in the extremal bins (0 and 4) is much lower than the bins in the middle—the counts seem to rise up towards the median and then down again. In the next section, we'll visualize the shape of these counts.