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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
Key Features
Book Description
This book is for those with a basic knowledge of Clojure, who are looking to push the language to excel with data analysis.What you will learn
- Read data from a variety of data formats
- Transform data to make it more useful and easier to analyze
- Process data concurrently and in parallel for faster performance
- Harness multiple computers to analyze big data
- Use powerful data analysis libraries such as Incanter, Hadoop, and Weka to get things done quickly
- Apply powerful clustering and data mining techniques to better understand your data
Who this book is for
This book is for those with a basic knowledge of Clojure, who are looking to push the language to excel with data analysis.
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Seitenzahl: 439
Veröffentlichungsjahr: 2015
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Table of Contents
Clojure Data Analysis Cookbook Second Edition
Clojure Data Analysis Cookbook Second Edition
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: March 2013
Second edition: January 2015
Production reference: 1220115
Published by Packt Publishing Ltd.
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ISBN 978-1-78439-029-7
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Credits
Author
Eric Rochester
Reviewers
Vitomir Kovanovic
Muktabh Mayank Srivastava
Federico Tomassetti
Commissioning Editor
Ashwin Nair
Acquisition Editor
Sam Wood
Content Development Editor
Parita Khedekar
Technical Editor
Ryan Kochery
Copy Editors
Dipti Kapadia
Puja Lalwani
Vikrant Phadke
Project Coordinator
Neha Thakur
Proofreaders
Ameesha Green
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Indexer
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Graphics
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Production Coordinator
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Cover Work
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About the Author
Eric Rochester enjoys reading, writing, and spending time with his wife and kids. When he’s not doing these things, he programs in a variety of languages and platforms, including websites and systems in Python, and libraries for linguistics and statistics in C#. Currently, he is exploring functional programming languages, including Clojure and Haskell. He works at Scholars’ Lab in the library at the University of Virginia, helping humanities professors and graduate students realize their digitally informed research agendas. He is also the author of Mastering Clojure Data Analysis, Packt Publishing.
I’d like to thank everyone. My technical reviewers proved invaluable. Also, thank you to the editorial staff at Packt Publishing. This book is much stronger because of all of their feedback, and any remaining deficiencies are mine alone.
A special thanks to Jackie, Melina, and Micah. They’ve been patient and supportive while I worked on this project. It is, in every way, for them.
About the Reviewers
Vitomir Kovanovic is a PhD student at the School of Informatics, University of Edinburgh, Edinburgh, UK. He received an MSc degree in computer science and software engineering in 2011, and BSc in information systems and business administration in 2009 from the University of Belgrade, Serbia. His research interests include learning analytics, educational data mining, and online education. He is a member of the Society for Learning Analytics Research and a member of program committees of several conferences and journals in technology-enhanced learning. In his PhD research, he focuses on the use of trace data for understanding the effects of technology use on the quality of the social learning process and learning outcomes. For more information, visit http://vitomir.kovanovic.info/
Muktabh Mayank Srivastava is a data scientist and the cofounder of ParallelDots.com. Previously, he helped in solving many complex data analysis and machine learning problems for clients from different domains such as healthcare, retail, procurement, automation, Bitcoin, social recommendation engines, geolocation fact-finding, customer profiling, and so on.
His new venture is ParallelDots. It is a tool that allows any content archive to be presented in a story using advanced techniques of NLP and machine learning. For publishers and bloggers, it automatically creates a timeline of any event using their archive and presents it in an interactive, intuitive, and easy-to-navigate interface on their webpage. You can find him on LinkedIn at http://in.linkedin.com/in/muktabh/ and on Twitter at @muktabh / @ParallelDots.
Federico Tomassetti has been programming since he was a child and has a PhD in software engineering. He works as a consultant on model-driven development and domain-specific languages, writes technical articles, teaches programming, and works as a full-stack software engineer.
He has experience working in Italy, Germany, and Ireland, and he is currently working at Groupon International.
You can read about his projects on http://federico-tomassetti.it/ or https://github.com/ftomassetti/.
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Preface
Welcome to the second edition of Clojure Data Analysis Cookbook! It seems that books become obsolete almost as quickly as software does, so here we have the opportunity to keep things up-to-date and useful.
Moreover, the state of the art of data analysis is also still evolving and changing. The techniques and technologies are being refined and improved. Hopefully, this book will capture some of that. I've also added a new chapter on how to work with unstructured textual data.
In spite of these changes, some things have stayed the same. Clojure has further proven itself to be an excellent environment to work with data. As a member of the lisp family of languages, it inherits a flexibility and power that is hard to match. The concurrency and parallelization features have further proven themselves as great tools for developing software and analyzing data.
Clojure's usefulness for data analysis is further improved by a number of strong libraries. Incanter provides a practical environment to work with data and perform statistical analysis. Cascalog is an easy-to-use wrapper over Hadoop and Cascading. Finally, when you're ready to publish your results, ClojureScript, an implementation of Clojure that generates JavaScript, can help you to visualize your data in an effective and persuasive way.
Moreover, Clojure runs on the Java Virtual Machine (JVM), so any libraries written for Java are available too. This gives Clojure an incredible amount of breadth and power.
I hope that this book will give you the tools and techniques you need to get answers from your data.
What this book covers
Chapter 1, Importing Data for Analysis, covers how to read data from a variety of sources, including CSV files, web pages, and linked semantic web data.
Chapter 2, Cleaning and Validating Data, presents strategies and implementations to normalize dates, fix spelling, and work with large datasets. Getting data into a useable shape is an important, but often overlooked, stage of data analysis.
Chapter 3, Managing Complexity with Concurrent Programming, covers Clojure's concurrency features and how you can use them to simplify your programs.
Chapter 4, Improving Performance with Parallel Programming, covers how to use Clojure's parallel processing capabilities to speed up the processing of data.
Chapter 5, Distributed Data Processing with Cascalog, covers how to use Cascalog as a wrapper over Hadoop and the Cascading library to process large amounts of data distributed over multiple computers.
Chapter 6, Working with Incanter Datasets, covers the basics of working with Incanter datasets. Datasets are the core data structures used by Incanter, and understanding them is necessary in order to use Incanter effectively.
Chapter 7, Statistical Data Analysis with Incanter, covers a variety of statistical processes and tests used in data analysis. Some of these are quite simple, such as generating summary statistics. Others are more complex, such as performing linear regressions and auditing data with Benford's Law.
Chapter 8, Working with Mathematica and R, talks about how to set up Clojure in order to talk to Mathematica or R. These are powerful data analysis systems, and we might want to use them sometimes. This chapter will show you how to get these systems to work together, as well as some tasks that you can perform once they are communicating.
Chapter 9, Clustering, Classifying, and Working with Weka, covers more advanced machine learning techniques. In this chapter, we'll primarily use the Weka machine learning library. Some recipes will discuss how to use it and the data structures its built on, while other recipes will demonstrate machine learning algorithms.
Chapter 10, Working with Unstructured and Textual Data, looks at tools and techniques used to extract information from the reams of unstructured, textual data.
Chapter 11, Graphing in Incanter, shows you how to generate graphs and other visualizations in Incanter. These can be important for exploring and learning about your data and also for publishing and presenting your results.
Chapter 12, Creating Charts for the Web, shows you how to set up a simple web application in order to present findings from data analysis. It will include a number of recipes that leverage the powerful D3 visualization library.
What you need for this book
One piece of software required for this book is the Java Development Kit (JDK), which you can obtain from http://www.oracle.com/technetwork/java/javase/downloads/index.html. JDK is necessary to run and develop on the Java platform.
The other major piece of software that you'll need is Leiningen 2, which you can download and install from http://leiningen.org/. Leiningen 2 is a tool used to manage Clojure projects and their dependencies. It has become the de facto standard project tool in the Clojure community.
Throughout this book, we'll use a number of other Clojure and Java libraries, including Clojure itself. Leiningen will take care of downloading these for us as we need them.
You'll also need a text editor or Integrated Development Environment (IDE). If you already have a text editor of your choice, you can probably use it. See http://clojure.org/getting_started for tips and plugins for using your particular favorite environment. If you don't have a preference, I'd suggest that you take a look at using Eclipse with Counterclockwise. There are instructions to this set up at https://code.google.com/p/counterclockwise/.
That is all that's required. However, at various places throughout the book, some recipes will access other software. The recipes in Chapter 8, Working with Mathematica and R, that are related to Mathematica will require Mathematica, obviously, and those that are related to R will require that. However, these programs won't be used in the rest of the book, and whether you're interested in those recipes might depend on whether you already have this software.
Who this book is for
This book is for programmers or data scientists who are familiar with Clojure and want to use it in their data analysis processes. This isn't a tutorial on Clojure—there are already a number of excellent introductory books out there—so you'll need to be familiar with the language, but you don't need to be an expert.
Likewise, you don't have to be an expert on data analysis, although you should probably be familiar with its tasks, processes, and techniques. While you might be able to glean enough from these recipes to get started with, for it to be truly effective, you'll want to get a more thorough introduction to this field.
Conventions
In this book, you will find a number of styles of text 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: "Now, there will be a new subdirectory named getting-data.
A block of code is set as follows:
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
Any command-line input or output is written as follows:
New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "Take a look at the Hadoop website for the Getting Started documentation of your version. Get a single node setup working".
Note
Warnings or important notes appear in a box like this.
Tip
Tips and tricks appear like this.
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Chapter 1. Importing Data for Analysis
In this chapter, we will cover the following recipes:
Introduction
There's not much data analysis that can be done without data, so the first step in any project is to evaluate the data we have and the data that we need. Once we have some idea of what we'll need, we have to figure out how to get it.
Many of the recipes in this chapter and in this book use Incanter (http://incanter.org/) to import the data and target Incanter datasets. Incanter is a library that is used for statistical analysis and graphics in Clojure (similar to R) an open source language for statistical computing (http://www.r-project.org/). Incanter might not be suitable for every task (for example, we'll use the Weka library for machine learning later) but it is still an important part of our toolkit for doing data analysis in Clojure. This chapter has a collection of recipes that can be used to gather data and make it accessible to Clojure.
For the very first recipe, we'll take a look at how to start a new project. We'll start with very simple formats such ascomma-separated values (CSV) and move into reading data from relational databases using JDBC. We'll examine more complicated data sources, such as web scraping and linked data (RDF).
Creating a new project
Over the course of this book, we're going to use a number of third-party libraries and external dependencies. We will need a tool to download them and track them. We also need a tool to set up the environment and start a REPL (read-eval-print-loop or interactive interpreter) that can access our code or to execute our program. REPLs allow you to program interactively. It's a great environment for exploratory programming, irrespective of whether that means exploring library APIs or exploring data.
We'll use Leiningen for this (http://leiningen.org/). This has become a standard package automation and management system.
Getting ready
Visit the Leiningen site and download the lein script. This will download the Leiningen JAR file when it's needed. The instructions are clear, and it's a simple process.
How to do it...
To generate a new project, use the lein new command, passing the name of the project to it:
There will be a new subdirectory named getting-data. It will contain files with stubs for the getting-data.core namespace and for tests.
How it works...
The new project directory also contains a file named project.clj. This file contains metadata about the project, such as its name, version, license, and more. It also contains a list of the dependencies that our code will use, as shown in the following snippet. The specifications that this file uses allow it to search Maven repositories and directories of Clojure libraries (Clojars, https://clojars.org/) in order to download the project's dependencies. Thus, it integrates well with Java's own packaging system as developed with Maven (http://maven.apache.org/).
In the Getting ready section of each recipe, we'll see the libraries that we need to list in the :dependencies section of this file. Then, when you run any lein command, it will download the dependencies first.
Reading CSV data into Incanter datasets
One of the simplest data formats is comma-separated values (CSV), and you'll find that it's everywhere. Excel reads and writes CSV directly, as do most databases. Also, because it's really just plain text, it's easy to generate CSV files or to access them from any programming language.
Getting ready
First, let's make sure that we have the correct libraries loaded. Here's how the project Leiningen (https://github.com/technomancy/leiningen) project.clj file should look (although you might be able to use more up-to-date versions of the dependencies):
Tip
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Also, in your REPL or your file, include these lines:
Finally, downloaded a list of rest area locations from POI Factory at http://www.poi-factory.com/node/6643. The data is in a file named data/RestAreasCombined(Ver.BN).csv. The version designation might be different though, as the file is updated. You'll also need to register on the site in order to download the data. The file contains this data, which is the location and description of the rest stops along the highway:
In the project directory, we have to create a subdirectory named data and place the file in this subdirectory.
I also created a copy of this file with a row listing the names of the columns and named it RestAreasCombined(Ver.BN)-headers.csv.
How to do it…
How it works…
Together, Clojure and Incanter make a lot of common tasks easy, which is shown in the How to do it section of this recipe.
We've taken some external data, in this case from a CSV file, and loaded it into an Incanter dataset. In Incanter, a dataset is a table, similar to a sheet in a spreadsheet or a database table. Each column has one field of data, and each row has an observation of data. Some columns will contain string data (all of the columns in this example did), some will contain dates, and some will contain numeric data. Incanter tries to automatically detect when a column contains numeric data and coverts it to a Java int or double. Incanter takes away a lot of the effort involved with importing data.
There's more…
For more information about Incanter datasets, see Chapter 6, Working with Incanter Datasets.
Reading JSON data into Incanter datasets
Another data format that's becoming increasingly popular is JavaScript Object Notation (JSON, http://json.org/). Like CSV, this is a plain text format, so it's easy for programs to work with. It provides more information about the data than CSV does, but at the cost of being more verbose. It also allows the data to be structured in more complicated ways, such as hierarchies or sequences of hierarchies.
Because JSON is a much richer data model than CSV, we might need to transform the data. In that case, we can just pull out the information we're interested in and flatten the nested maps before we pass it to Incanter. In this recipe, however, we'll just work with fairly simple data structures.
Getting ready
First, here are the contents of the Leiningen project.clj file:
Use these libraries in your REPL or program (inside an ns form):
Moreover, you need some data. For this, I have a file named delicious-rss-214k.json and placed it in the folder named data. It contains a number of top-level JSON objects. For example, the first one starts like this:
You can download this data file from Infochimps at http://www.ericrochester.com/clj-data-analysis/data/delicious-rss-214k.json.xz. You'll need to decompress it into the data directory.
How to do it…
Once everything's in place, we'll need a couple of functions to make it easier to handle the multiple JSON objects at the top level of the file:
This binds d to a new dataset that contains the information read in from the JSON documents.
How it works…
Similar to all Lisp's (List Processing), Clojure is usually read from the inside out and from right to left. Let's break it down. clojure.java.io/reader opens the file for reading. read-all-json parses all of the JSON documents in the file into a sequence. In this case, it returns a vector of the maps. incanter.core/to-dataset takes a sequence of maps and returns an Incanter dataset. This dataset will use the keys in the maps as column names, and it will convert the data values into a matrix. Actually, to-dataset can accept many different data structures. Try doc to-dataset in the REPL (doc shows the documentation string attached to the function), or see the Incanter documentation at http://data-sorcery.org/contents/ for more information.
Reading data from Excel with Incanter
We've seen how Incanter makes a lot of common data-processing tasks very simple, and reading an Excel spreadsheet is another example of this.
Getting ready
First, make sure that your Leiningen project.clj file contains the right dependencies:
Also, make sure that you've loaded those packages into the REPL or script:
Find the Excel spreadsheet you want to work on. The file name of my spreadsheet is data/small-sample-header.xls, as shown in the following screenshot. You can download this from http://www.ericrochester.com/clj-data-analysis/data/small-sample-header.xls.
How to do it…
Now, all you need to do is call incanter.excel/read-xls:
How it works…
This can read standard Excel files (.xls) and the XML-based file format introduced in Excel 2003 (.xlsx).
Reading data from JDBC databases
Reading data from a relational database is only slightly more complicated than reading from Excel, and much of the extra complication involves connecting to the database.
Fortunately, there's a Clojure-contributed package that sits on top of JDBC (the Java database connector API, http://www.oracle.com/technetwork/java/javase/jdbc/index.html) and makes working with databases much easier. In this example, we'll load a table from an SQLite database (http://www.sqlite.org/), which stores the database in a single file.
Getting ready
First, list the dependencies in your Leiningen project.clj file. We will also need to include the database driver library. For this example, it is org.xerial/sqlite-jdbc:
Then, load the modules into your REPL or script file:
Finally, get the database connection information. I have my data in an SQLite database file named data/small-sample.sqlite, as shown in the following screenshot. You can download this from http://www.ericrochester.com/clj-data-analysis/data/small-sample.sqlite.
How to do it…
Loading the data is not complicated, but we'll make it easier with a wrapper function:
How it works…
The load-table-data function passes the database connection information directly through to clojure.java.jdbc/query.query. It creates an SQL query that returns all of the fields in the table that is passed in. Each row of the result is a sequence of hashes mapping column names to data values. This sequence is wrapped in a dataset by incanter.core/to-dataset.
See also
Connecting to different database systems using JDBC isn't necessarily a difficult task, but it's dependent on which database you wish to connect to. Oracle has a tutorial for how to work with JDBC at http://docs.oracle.com/javase/tutorial/jdbc/basics, and the documentation for the clojure.java.jdbc library has some good information too (http://clojure.github.com/java.jdbc/). If you're trying to find out what the connection string looks like for a database system, there are lists available online. The list at http://www.java2s.com/Tutorial/Java/0340__Database/AListofJDBCDriversconnectionstringdrivername.htm includes the major drivers.
Reading XML data into Incanter datasets
One of the most popular formats for data is XML. Some people love it, while some hate it. However, almost everyone has to deal with it at some point. While Clojure can use Java's XML libraries, it also has its own package which provides a more natural way to work with XML in Clojure.
Getting ready
First, include these dependencies in your Leiningen project.clj file:
Use these libraries in your REPL or program:
Then, find a data file. I visited the website for the Open Data Catalog for Washington, D.C. (http://data.octo.dc.gov/), and downloaded the data for the 2013 crime incidents. I moved this file to data/crime_incidents_2013_plain.xml. This is how the contents of the file look:
How to do it…
Now, let's see how to load this file into an Incanter dataset:
This looks good. This gives you the number of crimes reported in the dataset.
How it works…
This recipe follows a typical pipeline for working with XML:
load-xml-data implements this process. This takes three parameters:
First, the function parses the XML file and wraps it in a zipper (we'll talk more about zippers in the next section). Then, it uses the two functions that are passed in to extract all of the data nodes as a sequence. For each data node, the function retrieves that node's child nodes and converts them into a series of tag name / content pairs. The pairs for each data node are converted into a map, and the sequence of maps is converted into an Incanter dataset.
There's more…
We used a couple of interesting data structures or constructs in this recipe. Both are common in functional programming or Lisp, but neither have made their way into more mainstream programming. We should spend a minute with them.
Navigating structures with zippers
The first thing that happens to the parsed XML is that it gets passed to clojure.zip/xml-zip. Zippers are standard data structures that encapsulate the data at a position in a tree structure, as well as the information necessary to navigate back out. This takes Clojure's native XML data structure and turns it into something that can be navigated quickly using commands such as clojure.zip/down and clojure.zip/right. Being a functional programming language, Clojure encourages you to use immutable data structures, and zippers provide an efficient, natural way to navigate and modify a tree-like structure, such as an XML document.
Zippers are very useful and interesting, and understanding them can help you understand and work better with immutable data structures. For more information on zippers, the Clojure-doc page is helpful (http://clojure-doc.org/articles/tutorials/parsing_xml_with_zippers.html). However, if you would rather dive into the deep end, see Gerard Huet's paper, The Zipper (http://www.st.cs.uni-saarland.de/edu/seminare/2005/advanced-fp/docs/huet-zipper.pdf).
Processing in a pipeline
We used the ->> macro to express our process as a pipeline. For deeply nested function calls, this macro lets you read it from the left-hand side to the right-hand side, and this makes the process's data flow and series of transformations much more clear.
We can do this in Clojure because of its macro system. ->> simply rewrites the calls into Clojure's native, nested format as the form is read. The first parameter of the macro is inserted into the next expression as the last parameter. This structure is inserted into the third expression as the last parameter, and so on, until the end of the form. Let's trace this through a few steps. Say, we start off with the expression (->> x first (map length) (apply +)). As Clojure builds the final expression, here's each intermediate step (the elements to be combined are highlighted at each stage):
Comparing XML and JSON
XML and JSON (from the Reading JSON data into Incanter datasets recipe) are very similar. Arguably, much of the popularity of JSON is driven by disillusionment with XML's verboseness.
When we're dealing with these formats in Clojure, the biggest difference is that JSON is converted directly to native Clojure data structures that mirror the data, such as maps and vectors Meanwhile, XML is read into record types that reflect the structure of XML, not the structure of the data.
In other words, the keys of the maps for JSON will come from the domains, first_name or age, for instance. However, the keys of the maps for XML will come from the data format, such as tag, attribute, or children, and the tag and attribute names will come from the domain. This extra level of abstraction makes XML more unwieldy.
Scraping data from tables in web pages
There's data everywhere on the Internet. Unfortunately, a lot of it is difficult to reach. It's buried in tables, articles, or deeply nested div tags. Web scraping (writing a program that walks over a web page and extracts data from it) is brittle and laborious, but it's often the only way to free this data so it can be used in our analyses. This recipe describes how to load a web page and dig down into its contents so that you can pull the data out.
To do this, we're going to use the Enlive (https://github.com/cgrand/enlive/wiki) library. This uses a domain specific language (DSL, a set of commands that make a small set of tasks very easy and natural) based on CSS selectors to locate elements within a web page. This library can also be used for templating. In this case, we'll just use it to get data back out of a web page.
Getting ready
First, you have to add Enlive to the dependencies in the project.clj file:
Next, use these packages in your REPL or script:
Finally, identify the file to scrape the data from. I've put up a file at http://www.ericrochester.com/clj-data-analysis/data/small-sample-table.html, which looks like this:
It's intentionally stripped down, and it makes use of tables for layout (hence the comment about 1999).
How to do it…
How it works…
The let bindings in load-data tell the story here. Let's talk about them one by one.
The first binding has Enlive download the resource and parse it into Enlive's internal representation:
The next binding selects the table with the data ID:
Now, select of all the header cells from the table, extract the text from them, convert each to a keyword, and then convert the entire sequence into a vector. This gives headers for the dataset:
First, select each row individually. The next two steps are wrapped in map
