Clojure for Data Science

Table of Contents

Clojure for Data Science

Credits

About the Author

Acknowledgments

About the Reviewer

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Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Statistics

Downloading the sample code

Running the examples

Downloading the data

Inspecting the data

Data scrubbing

Descriptive statistics

The mean

Interpreting mathematical notation

The median

Variance

Quantiles

Binning data

Histograms

The normal distribution

The central limit theorem

Poincaré's baker

Generating distributions

Skewness

Quantile-quantile plots

Comparative visualizations

Box plots

Cumulative distribution functions

The importance of visualizations

Visualizing electorate data

Adding columns

Adding derived columns

Comparative visualizations of electorate data

Visualizing the Russian election data

Comparative visualizations

Probability mass functions

Scatter plots

Scatter transparency

Summary

2. Inference

Introducing AcmeContent

Download the sample code

Load and inspect the data

Visualizing the dwell times

The exponential distribution

The distribution of daily means

The central limit theorem

Standard error

Samples and populations

Confidence intervals

Sample comparisons

Bias

Visualizing different populations

Hypothesis testing

Significance

Testing a new site design

Performing a z-test

Student's t-distribution

Degrees of freedom

The t-statistic

Performing the t-test

Two-tailed tests

One-sample t-test

Resampling

Testing multiple designs

Calculating sample means

Multiple comparisons

Introducing the simulation

Compile the simulation

The browser simulation

jStat

B1

Scalable Vector Graphics

Plotting probability densities

State and Reagent

Updating state

Binding the interface

Simulating multiple tests

The Bonferroni correction

Analysis of variance

The F-distribution

The F-statistic

The F-test

Effect size

Cohen's d

Summary

3. Correlation

About the data

Inspecting the data

Visualizing the data

The log-normal distribution

Visualizing correlation

Jittering

Covariance

Pearson's correlation

Sample r and population rho

Hypothesis testing

Confidence intervals

Regression

Linear equations

Residuals

Ordinary least squares

Slope and intercept

Interpretation

Visualization

Assumptions

Goodness-of-fit and R-square

Multiple linear regression

Matrices

Dimensions

Vectors

Construction

Addition and scalar multiplication

Matrix-vector multiplication

Matrix-matrix multiplication

Transposition

The identity matrix

Inversion

The normal equation

More features

Multiple R-squared

Adjusted R-squared

Incanter's linear model

The F-test of model significance

Categorical and dummy variables

Relative power

Collinearity

Multicollinearity

Prediction

The confidence interval of a prediction

Model scope

The final model

Summary

4. Classification

About the data

Inspecting the data

Comparisons with relative risk and odds

The standard error of a proportion

Estimation using bootstrapping

The binomial distribution

The standard error of a proportion formula

Significance testing proportions

Adjusting standard errors for large samples

Chi-squared multiple significance testing

Visualizing the categories

The chi-squared test

The chi-squared statistic

The chi-squared test

Classification with logistic regression

The sigmoid function

The logistic regression cost function

Parameter optimization with gradient descent

Gradient descent with Incanter

Convexity

Implementing logistic regression with Incanter

Creating a feature matrix

Evaluating the logistic regression classifier

The confusion matrix

The kappa statistic

Probability

Bayes theorem

Bayes theorem with multiple predictors

Naive Bayes classification

Implementing a naive Bayes classifier

Evaluating the naive Bayes classifier

Comparing the logistic regression and naive Bayes approaches

Decision trees

Information

Entropy

Information gain

Using information gain to identify the best predictor

Recursively building a decision tree

Using the decision tree for classification

Evaluating the decision tree classifier

Classification with clj-ml

Loading data with clj-ml

Building a decision tree in clj-ml

Bias and variance

Overfitting

Cross-validation

Addressing high bias

Ensemble learning and random forests

Bagging and boosting

Saving the classifier to a file

Summary

5. Big Data

Downloading the code and data

Inspecting the data

Counting the records

The reducers library

Parallel folds with reducers

Loading large files with iota

Creating a reducers processing pipeline

Curried reductions with reducers

Statistical folds with reducers

Associativity

Calculating the mean using fold

Calculating the variance using fold

Mathematical folds with Tesser

Calculating covariance with Tesser

Commutativity

Simple linear regression with Tesser

Calculating a correlation matrix

Multiple regression with gradient descent

The gradient descent update rule

The gradient descent learning rate

Feature scaling

Feature extraction

Creating a custom Tesser fold

Creating a matrix-sum fold

Calculating the total model error

Creating a matrix-mean fold

Applying a single step of gradient descent

Running iterative gradient descent

Scaling gradient descent with Hadoop

Gradient descent on Hadoop with Tesser and Parkour

Parkour distributed sources and sinks

Running a feature scale fold with Hadoop

Running gradient descent with Hadoop

Preparing our code for a Hadoop cluster

Building an uberjar

Submitting the uberjar to Hadoop

Stochastic gradient descent

Stochastic gradient descent with Parkour

Defining a mapper

Parkour shaping functions

Defining a reducer

Specifying Hadoop jobs with Parkour graph

Chaining mappers and reducers with Parkour graph

Summary

6. Clustering

Downloading the data

Extracting the data

Inspecting the data

Clustering text

Set-of-words and the Jaccard index

Tokenizing the Reuters files

Applying the Jaccard index to documents

The bag-of-words and Euclidean distance

Representing text as vectors

Creating a dictionary

Creating term frequency vectors

The vector space model and cosine distance

Removing stop words

Stemming

Clustering with k-means and Incanter

Clustering the Reuters documents

Better clustering with TF-IDF

Zipf's law

Calculating the TF-IDF weight

k-means clustering with TF-IDF

Better clustering with n-grams

Large-scale clustering with Mahout

Converting text documents to a sequence file

Using Parkour to create Mahout vectors

Creating distributed unique IDs

Distributed unique IDs with Hadoop

Sharing data with the distributed cache

Building Mahout vectors from input documents

Running k-means clustering with Mahout

Viewing k-means clustering results

Interpreting the clustered output

Cluster evaluation measures

Inter-cluster density

Intra-cluster density

Calculating the root mean square error with Parkour

Loading clustered points and centroids

Calculating the cluster RMSE

Determining optimal k with the elbow method

Determining optimal k with the Dunn index

Determining optimal k with the Davies-Bouldin index

The drawbacks of k-means

The Mahalanobis distance measure

The curse of dimensionality

Summary

7. Recommender Systems

Download the code and data

Inspect the data

Parse the data

Types of recommender systems

Collaborative filtering

Item-based and user-based recommenders

Slope One recommenders

Calculating the item differences

Making recommendations

Practical considerations for user and item recommenders

Building a user-based recommender with Mahout

k-nearest neighbors

Recommender evaluation with Mahout

Evaluating distance measures

The Pearson correlation similarity

Spearman's rank similarity

Determining optimum neighborhood size

Information retrieval statistics

Precision

Recall

Mahout's information retrieval evaluator

F-measure and the harmonic mean

Fall-out

Normalized discounted cumulative gain

Plotting the information retrieval results

Recommendation with Boolean preferences

Implicit versus explicit feedback

Probabilistic methods for large sets

Testing set membership with Bloom filters

Jaccard similarity for large sets with MinHash

Reducing pair comparisons with locality-sensitive hashing

Bucketing signatures

Dimensionality reduction

Plotting the Iris dataset

Principle component analysis

Singular value decomposition

Large-scale machine learning with Apache Spark and MLlib

Loading data with Sparkling

Mapping data

Distributed datasets and tuples

Filtering data

Persistence and caching

Machine learning on Spark with MLlib

Movie recommendations with alternating least squares

ALS with Spark and MLlib

Making predictions with ALS

Evaluating ALS

Calculating the sum of squared errors

Summary

8. Network Analysis

Download the data

Inspecting the data

Visualizing graphs with Loom

Graph traversal with Loom

The seven bridges of Königsberg

Breadth-first and depth-first search

Finding the shortest path

Minimum spanning trees

Subgraphs and connected components

SCC and the bow-tie structure of the web

Whole-graph analysis

Scale-free networks

Distributed graph computation with GraphX

Creating RDGs with Glittering

Measuring graph density with triangle counting

GraphX partitioning strategies

Running the built-in triangle counting algorithm

Implement triangle counting with Glittering

Step one – collecting neighbor IDs

Steps two, three, and four – aggregate messages

Step five – dividing the counts

Running the custom triangle counting algorithm

The Pregel API

Connected components with the Pregel API

Step one – map vertices

Steps two and three – the message function

Step four – update the attributes

Step five – iterate to convergence

Running connected components

Calculating the size of the largest connected component

Detecting communities with label propagation

Step one – map vertices

Step two – send the vertex attribute

Step three – aggregate value

Step four – vertex function

Step five – set the maximum iterations count

Running label propagation

Measuring community influence using PageRank

The flow formulation

Implementing PageRank with Glittering

Sort by highest influence

Running PageRank to determine community influencers

Summary

9. Time Series

About the data

Loading the Longley data

Fitting curves with a linear model

Time series decomposition

Inspecting the airline data

Visualizing the airline data

Stationarity

De-trending and differencing

Discrete time models

Random walks

Autoregressive models

Determining autocorrelation in AR models

Moving-average models

Determining autocorrelation in MA models

Combining the AR and MA models

Calculating partial autocorrelation

Autocovariance

PACF with Durbin-Levinson recursion

Plotting partial autocorrelation

Determining ARMA model order with ACF and PACF

ACF and PACF of airline data

Removing seasonality with differencing

Maximum likelihood estimation

Calculating the likelihood

Estimating the maximum likelihood

Nelder-Mead optimization with Apache Commons Math

Identifying better models with Akaike Information Criterion

Time series forecasting

Forecasting with Monte Carlo simulation

Summary

10. Visualization

Download the code and data

Exploratory data visualization

Representing a two-dimensional histogram

Using Quil for visualization

Drawing to the sketch window

Quil's coordinate system

Plotting the grid

Specifying the fill color

Color and fill

Outputting an image file

Visualization for communication

Visualizing wealth distribution

Bringing data to life with Quil

Drawing bars of differing widths

Adding a title and axis labels

Improving the clarity with illustrations

Adding text to the bars

Incorporating additional data

Drawing complex shapes

Drawing curves

Plotting compound charts

Output to PDF

Summary

Index

Clojure for Data Science

Clojure for Data Science

Copyright © 2015 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: September 2015

Production reference: 1280815

Published by Packt Publishing Ltd.

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ISBN 978-1-78439-718-0

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Credits

Author

Henry Garner

Reviewer

Dan Hammer

Commissioning Editor

Ashwin Nair

Acquisition Editor

Meeta Rajani

Content Development Editor

Shubhangi Dhamgaye

Technical Editor

Shivani Kiran Mistry

Copy Editor

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Project Coordinator

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Proofreader

Safis Editing

Indexer

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Graphics

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Production Coordinator

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Cover Work

Arvindkumar Gupta

About the Author

Henry Garner is a graduate from the University of Oxford and an experienced developer, CTO, and coach.

He started his technical career at Britain's largest telecoms provider, BT, working with a traditional data warehouse infrastructure. As a part of a small team for 3 years, he built sophisticated data models to derive insight from raw data and use web applications to present the results. These applications were used internally by senior executives and operatives to track both business and systems performance.

He then went on to co-found Likely, a social media analytics start-up. As the CTO, he set the technical direction, leading to the introduction of an event-based append-only data pipeline modeled after the Lambda architecture. He adopted Clojure in 2011 and led a hybrid team of programmers and data scientists, building content recommendation engines based on collaborative filtering and clustering techniques. He developed a syllabus and copresented a series of evening classes from Likely's offices for professional developers who wanted to learn Clojure.

Henry now works with growing businesses, consulting in both a development and technical leadership capacity. He presents regularly at seminars and Clojure meetups in and around London.

Acknowledgments

Thank you Shubhangi Dhamgaye, Meeta Rajani, Shivani Mistry, and the entire team at Packt for their help in bringing this project to fruition. Without you, this book would never have come to pass.

I'm grateful to Dan Hammer, my Packt reviewer, for his valuable perspective as a practicing data scientist, and to those other brave souls who patiently read through the very rough early (and not-so-early) drafts. Foremost among these are Éléonore Mayola, Paul Butcher, and Jeremy Hoyland. Your feedback was not always easy to hear, but it made the book so much better than it would otherwise have been.

Thank you to the wonderful team at MastodonC who tackled a pre-release version of this book in their company book club, especially Éléonore Mayola, Jase Bell, and Elise Huard. I'm grateful to Francine Bennett for her advice early on—which helped to shape the structure of the book—and also to Bruce Durling, Neale Swinnerton, and Chris Adams for their company during the otherwise lonely weekends spent writing in the office.

Thank you to my friends from the machine learning study group: Sam Joseph, Geoff Hogg, and Ben Taylor for reading the early drafts and providing feedback suitable for Clojure newcomers; and also to Luke Snape and Tom Coupland of the Bristol Clojurians for providing the opportunity to test the material out on its intended audience.

A heartfelt thanks to my dad, Nicholas, for interpreting my vague scribbles into the fantastic figures you see in this book, and to my mum, Jacqueline, and sister, Mary, for being such patient listeners in the times I felt like thinking aloud. Last, but by no means least, thank you to the Nuggets of Wynford Road, Russell and Wendy, for the tea and sympathy whenever it occasionally became a bit too much. I look forward to seeing much more of you both from now on.

About the Reviewer

Dan Hammer is a presidential innovation fellow working on Data Innovation initiatives at the NASA headquarters in the CTO's office. Dan is an economist and data scientist. He was the chief data scientist at the World Resources Institute, where he launched Global Forest Watch in partnership with Google, USAID, and many others. Dan is on leave from a PhD program at UC Berkeley, as advised by Max Auffhammer and George Judge. He teaches mathematics at the San Quentin State Prison as a lead instructor with the Prison University Project. Dan graduated with high honors in economics and mathematics from Swarthmore College, where he was a language scholar. He spent a full year building and racing Polynesian outrigger canoes in the South Pacific as a Watson Fellow. He has also reviewed Learning R for Geospatial Analysis by Packt Publishing.

Thanks to my wonderful wife Emily for suffering through my terrible jokes.

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For Helen.

You provided support, encouragement, and welcome distraction in roughly equal measure.

Preface

A web search for data science Venn diagram returns numerous interpretations of the skills required to be an effective data scientist (it appears that data science commentators love Venn diagrams). Author and data scientist Drew Conway produced the prototypical diagram back in 2010, putting data science at the intersection of hacking skills, substantive expertise (that is, subject domain understanding), and mathematics and statistics knowledge. Between hacking skills and substantive expertise—those practicing without strong mathematics and statistics knowledge—lies the danger zone.

Five years on, as a growing number of developers seek to plug the data science skills' shortage, there's more need than ever for statistical and mathematical education to help developers out of this danger zone. So, when Packt Publishing invited me to write a book on data science suitable for Clojure programmers, I gladly agreed. In addition to appreciating the need for such a book, I saw it as an opportunity to consolidate much of what I had learned as CTO of my own Clojure-based data analytics company. The result is the book I wish I had been able to read before starting out.

Clojure for Data Science aims to be much more than just a book of statistics for Clojure programmers. A large reason for the spread of data science into so many diverse areas is the enormous power of machine learning. Throughout the book, I'll show how to use pure Clojure functions and third-party libraries to construct machine learning models for the primary tasks of regression, classification, clustering, and recommendation.

Approaches that scale to very large datasets, so-called big data, are of particular interest to data scientists, because they can reveal subtleties that are lost in smaller samples. This book shows how Clojure can be used to concisely express jobs to run on the Hadoop and Spark distributed computation frameworks, and how to incorporate machine learning through the use of both dedicated external libraries and general optimization techniques.

Above all, this book aims to foster an understanding not just on how to perform particular types of analysis, but why such techniques work. In addition to providing practical knowledge (almost every concept in this book is expressed as a runnable example), I aim to explain the theory that will allow you to take a principle and apply it to related problems. I hope that this approach will enable you to effectively apply statistical thinking in diverse situations well into the future, whether or not you decide to pursue a career in data science.

What this book covers

Chapter 1, Statistics, introduces Incanter, Clojure's primary statistical computing library used throughout the book. With reference to the data from the elections in the United Kingdom and Russia, we demonstrate the use of summary statistics and the value of statistical distributions while showing a variety of comparative visualizations.

Chapter 2, Inference, covers the difference between samples and populations, and statistics and parameters. We introduce hypothesis testing as a formal means of determining whether the differences are significant in the context of A / B testing website designs. We also cover sample bias, effect size, and solutions to the problem of multiple testing.

Chapter 3, Correlation, shows how we can discover linear relationships between variables and use the relationship to make predictions about some variables given others. We implement linear regression—a machine learning algorithm—to predict the weights of Olympic swimmers given their heights, using only core Clojure functions. We then make our model more sophisticated using matrices and more data to improve its accuracy.

Chapter 4, Classification, describes how to implement several different types of machine learning algorithm (logistic regression, naive Bayes, C4.5, and random forests) to make predictions about the survival rates of passengers on the Titanic. We learn about another test for statistical significance that works for categories instead of continuous values, explain various issues you're likely to encounter while training machine learning models such as bias and overfitting, and demonstrate how to use the clj-ml machine learning library.

Chapter 5, Big Data, shows how Clojure can leverage the parallel capabilities in computers of all sizes using the reducers library, and how to scale up these techniques to clusters of machines on Hadoop with Tesser and Parkour. Using ZIP code level tax data from the IRS, we demonstrate how to perform statistical analysis and machine learning in a scalable way.

Chapter 6, Clustering, shows how to identify text documents that share similar subject matter using Hadoop and the Java machine learning library, Mahout. We describe a variety of techniques particular to text processing as well as more general concepts related to clustering. We also introduce some more advanced features of Parkour that can help get the best performance from your Hadoop jobs.

Chapter 7, Recommender Systems, covers a variety of different approaches to the challenge of recommendation. In addition to implementing a recommender with core Clojure functions, we tackle the ancillary challenge of dimensionality reduction by using principle component analysis and singular value decomposition, as well as probabilistic set compression using Bloom filters and the MinHash algorithm. Finally, we introduce the Sparkling and MLlib libraries for machine learning on the Spark distributed computation framework and use them to produce movie recommendations with alternating least squares.

Chapter 8, Network Analysis, shows a variety of ways of analyzing graph-structured data. We demonstrate the methods of traversal using the Loom library and then show how to use the Glittering and GraphX libraries with Spark to discover communities and influencers in social networks.

Chapter 9, Time Series, demonstrates how to fit curves to simple time series data. Using data on the monthly airline passenger counts, we show how to forecast future values for more complex series by training an autoregressive moving-average model. We do this by implementing a method of parameter optimization called maximum likelihood estimation with help from the Apache Commons Math library.

Chapter 10, Visualization, shows how the Clojure library Quil can be used to create custom visualizations for charts not provided by Incanter, and attractive graphics that can communicate findings clearly to your audience, whatever their background.

What you need for this book

The code for each chapter has been made available as a project on GitHub at https://github.com/clojuredatascience. The example code can be downloaded as a zip file from there, or cloned with the Git command-line tool. All of the book's examples can be compiled and run with the Leiningen build tool as described in Chapter 1, Statistics.

This book assumes that you're already able to compile and run Clojure code using Leiningen (http://leiningen.org/). Refer to Leiningen's website if you're not yet set up to do this.

In addition, the code for many of the sample chapters makes use of external datasets. Where possible, these have been included together with the sample code. Where this has not been possible, instructions for downloading the data have been provided in the sample code's README file. Bash scripts have also been provided with the relevant sample code to automate this process. These can be run directly by Linux and OS X users, as described in the relevant chapter, provided the curl, wget, tar, gzip, and unzip utilities are installed. Windows users may have to install a Linux emulator such as Cygwin (https://www.cygwin.com/) to run the scripts.

Who this book is for

This book is intended for intermediate and advanced Clojure programmers who want to build their statistical knowledge, apply machine learning algorithms, or process large amounts of data with Hadoop and Spark. Many aspiring data scientists will benefit from learning all of these skills, and Clojure for Data Science is intended to be read in order from the beginning to the end. Readers who approach the book in this way will find that each chapter builds on concepts introduced in the prior chapters.

If you're not already comfortable reading Clojure code, you're likely to find this book particularly challenging. Fortunately, there are now many excellent resources for learning Clojure and I do not attempt to replicate their work here. At the time of writing, Clojure for the Brave and True (http://www.braveclojure.com/) is a fantastic free resource for learning the language. Consult http://clojure.org/getting_started for links to many other books and online tutorials suitable for newcomers.

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: Each example is a function in the cljds.ch1.examples namespace that can be run.

A block of code is set as follows:

(defmulti load-data identity)

(defmethod load-data :uk [_]

  (-> (io/resource UK2010.xls)

      (str)

      (xls/read-xls)))

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

    (q/fill (fill-fn x y))

    (q/rect x-pos y-pos x-scale y-scale))

    (q/save heatmap.png))]

    (q/sketch :setup setup :size size))

Any command-line input or output is written as follows:

lein run –e 1.1

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: Each time the New Sample button is pressed, a pair of new samples from an exponential distribution with population means taken from the sliders are generated.

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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Feedback from our readers is always welcome. Let us know what you think about this book—what you liked or disliked. Reader feedback is important for us as it helps us develop titles that you will really get the most out of.

To send us general feedback, simply e-mail <[email protected]>, and mention the book's title in the subject of your message.

If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide at www.packtpub.com/authors.

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Errata

Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you find a mistake in one of our books—maybe a mistake in the text or the code—we would be grateful if you could report this to us. By doing so, you can save other readers from frustration and help us improve subsequent versions of this book. If you find any errata, please report them by visiting http://www.packtpub.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details of your errata. Once your errata are verified, your submission will be accepted and the errata will be uploaded to our website or added to any list of existing errata under the Errata section of that title.

To view the previously submitted errata, go to https://www.packtpub.com/books/content/support and enter the name of the book in the search field. The required information will appear under the Errata section.

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We appreciate your help in protecting our authors and our ability to bring you valuable content.

Questions

If you have a problem with any aspect of this book, you can contact us at <[email protected]>, and we will do our best to address the problem.

Chapter 1. Statistics

Over the course of the following ten chapters of Clojure for Data Science, we'll attempt to discover a broadly linear path through the field of data science. In fact, we'll find as we go that the path is not quite so linear, and the attentive reader ought to notice many recurring themes along the way.

Descriptive statistics concern themselves with summarizing sequences of numbers and they'll appear, to some extent, in every chapter in this book. In this chapter, we'll build foundations for what's to come by implementing functions to calculate the mean, median, variance, and standard deviation of numerical sequences in Clojure. While doing so, we'll attempt to take the fear out of interpreting mathematical formulae.

As soon as we have more than one number to analyze it becomes meaningful to ask how those numbers are distributed. You've probably already heard expressions such as long tail and the 80/20 rule. They concern the spread of numbers throughout a range. We demonstrate the value of distributions in this chapter and introduce the most useful of them all: the normal distribution.

The study of distributions is aided immensely by visualization, and for this we'll use the Clojure library Incanter. We'll show how Incanter can be used to load, transform, and visualize real data. We'll compare the results of two national elections—the 2010 United Kingdom general election and the 2011 Russian presidential election—and see how even basic analysis can provide evidence of potentially fraudulent activity.

Downloading the sample code

All of the book's sample code is available on Packt Publishing's website at http://www.packtpub.com/support or from GitHub at http://github.com/clojuredatascience. Each chapter's sample code is available in its own repository.

Note