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Home > Data > Data Analysis > Clojure Data Analysis Cookbook - Second Edition
Clojure Data Analysis Cookbook - Second Edition
Clojure Data Analysis Cookbook - Second Edition

Clojure Data Analysis Cookbook - Second Edition: Dive into data analysis with Clojure through over 100 practical recipes for every stage of the analysis and collection process , Second Edition

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Clojure Data Analysis Cookbook - Second Edition

Chapter 2. Cleaning and Validating Data

In this chapter, we will cover the following recipes:

  • Cleaning data with regular expressions
  • Maintaining consistency with synonym maps
  • Identifying and removing duplicate data
  • Regularizing numbers
  • Calculating relative values
  • Parsing dates and times
  • Lazily processing very large data sets
  • Sampling from very large data sets
  • Fixing spelling errors
  • Parsing custom data formats
  • Validating data with Valip

Introduction

You probably won't spend as much time to get the data as you will in trying to get it into shape. Raw data is often inconsistent, duplicated, or full of holes. Addresses might be missing, years and dates might be formatted in a thousand different ways, or names might be entered into the wrong fields. You'll have to fix these issues before the data is usable.

This is often an iterative, interactive process. If it's a very large dataset, I might create a sample to work with at this stage. Generally, I start by examining the data files. Once I find a problem, I try to code a solution, which I run on the dataset. After each change, I archive the data, either using a ZIP file or, if the data files are small enough, a version control system. Using a version control system is a good option because I can track the code to transform the data along with the data itself and I can also include comments about what I'm doing. Then, I take a look at the data again, and...

Cleaning data with regular expressions

Often, cleaning data involves text transformations. Some, such as adding or removing a set and static strings, are pretty simple. Others, such as parsing a complex data format such as JSON or XML, requires a complete parser. However, many fall within a middle range of complexity. These need more processing power than simple string manipulation, but full-fledged parsing is too much. For these tasks, regular expressions are often useful.

Probably, the most basic and pervasive tool to clean data of any kind is a regular expression. Although they're overused sometimes, regular expressions truly are the best tool for the job many times. Moreover, Clojure has a built-in syntax for compiled regular expressions, so they are convenient too.

In this example, we'll write a function that normalizes U.S. phone numbers.

Getting ready

For this recipe, we will only require a very basic project.clj file. It should have these lines:

(defproject cleaning-data &quot...

Maintaining consistency with synonym maps

One common problem with data is inconsistency. Sometimes, a value is capitalized, while sometimes it is not. Sometimes it is abbreviated, and sometimes it is full. At times, there is a misspelling.

When it's an open domain, such as words in a free-text field, the problem can be quite difficult. However, when the data represents a limited vocabulary (such as US state names, for our example here) there's a simple trick that can help. While it's common to use full state names, standard postal codes are also often used. A mapping from common forms or mistakes to a normalized form is an easy way to fix variants in a field.

Getting ready

For the project.clj file, we'll use a very simple configuration:

(defproject cleaning-data "0.1.0-SNAPSHOT"
  :dependencies [[org.clojure/clojure "1.6.0"]])

We just need to make sure that the clojure.string/upper-case function is available to us:

(use '[clojure.string :only (upper...

Identifying and removing duplicate data

One problem when cleaning up data is dealing with duplicates. How do we find them? What do we do with them once we have them? While a part of this process can be automated, often merging duplicated data is a manual task, because a person has to look at potential matches and determine whether they are duplicates or not and determining what needs to be done with the overlapping data. We can code heuristics, of course, but at some point, a person needs to make the final call.

The first question that needs to be answered is what constitutes identity for the data. If you have two items of data, which fields do you have to look at in order to determine whether they are duplicates? Then, you must determine how close they need to be.

For this recipe, we'll examine some data and decide on duplicates by doing a fuzzy comparison of the name fields. We'll simply return all of the pairs that appear to be duplicates.

Getting ready

First, we need to add the...

Regularizing numbers

If we need to read in numbers as strings, we have to worry about how they're formatted. However, we'll probably want the computer to deal with them as numbers, not as strings, and this can't happen if the string contains a comma or period to separate the thousands place. This allows the numbers to be sorted and to be available for mathematical functions.

In this recipe, we'll write a short function that takes a number string and returns the number. The function will strip out all of the extra punctuation inside the number and only leave the last separator. Hopefully, this will be the one that marks the decimal place.

Of course, the version of this function, which we'll see here, only works in locales that use commas to separate thousands and periods to separate decimals. However, it would be relatively easy to write versions that will work in any particular locale.

Getting ready

For this recipe, we're back to the most simple project.clj files...

Introduction

You probably won't spend as much time to get the data as you will in trying to get it into shape. Raw data is often inconsistent, duplicated, or full of holes. Addresses might be missing, years and dates might be formatted in a thousand different ways, or names might be entered into the wrong fields. You'll have to fix these issues before the data is usable.

This is often an iterative, interactive process. If it's a very large dataset, I might create a sample to work with at this stage. Generally, I start by examining the data files. Once I find a problem, I try to code a solution, which I run on the dataset. After each change, I archive the data, either using a ZIP file or, if the data files are small enough, a version control system. Using a version control system is a good option because I can track the code to transform the data along with the data itself and I can also include comments about what I'm doing. Then, I take a look at the data again, and the entire process starts...

Cleaning data with regular expressions

Often, cleaning data involves text transformations. Some, such as adding or removing a set and static strings, are pretty simple. Others, such as parsing a complex data format such as JSON or XML, requires a complete parser. However, many fall within a middle range of complexity. These need more processing power than simple string manipulation, but full-fledged parsing is too much. For these tasks, regular expressions are often useful.

Probably, the most basic and pervasive tool to clean data of any kind is a regular expression. Although they're overused sometimes, regular expressions truly are the best tool for the job many times. Moreover, Clojure has a built-in syntax for compiled regular expressions, so they are convenient too.

In this example, we'll write a function that normalizes U.S. phone numbers.

Getting ready

For this recipe, we will only require a very basic project.clj file. It should have these lines:

(defproject cleaning-data "0.1.0-SNAPSHOT...

Maintaining consistency with synonym maps

One common problem with data is inconsistency. Sometimes, a value is capitalized, while sometimes it is not. Sometimes it is abbreviated, and sometimes it is full. At times, there is a misspelling.

When it's an open domain, such as words in a free-text field, the problem can be quite difficult. However, when the data represents a limited vocabulary (such as US state names, for our example here) there's a simple trick that can help. While it's common to use full state names, standard postal codes are also often used. A mapping from common forms or mistakes to a normalized form is an easy way to fix variants in a field.

Getting ready

For the project.clj file, we'll use a very simple configuration:

(defproject cleaning-data "0.1.0-SNAPSHOT"
  :dependencies [[org.clojure/clojure "1.6.0"]])

We just need to make sure that the clojure.string/upper-case function is available to us:

(use '[clojure.string :only (upper-case)])

How to do it…

  1. For this recipe, we'll define...

Identifying and removing duplicate data

One problem when cleaning up data is dealing with duplicates. How do we find them? What do we do with them once we have them? While a part of this process can be automated, often merging duplicated data is a manual task, because a person has to look at potential matches and determine whether they are duplicates or not and determining what needs to be done with the overlapping data. We can code heuristics, of course, but at some point, a person needs to make the final call.

The first question that needs to be answered is what constitutes identity for the data. If you have two items of data, which fields do you have to look at in order to determine whether they are duplicates? Then, you must determine how close they need to be.

For this recipe, we'll examine some data and decide on duplicates by doing a fuzzy comparison of the name fields. We'll simply return all of the pairs that appear to be duplicates.

Getting ready

First, we need to add the library to...

Regularizing numbers

If we need to read in numbers as strings, we have to worry about how they're formatted. However, we'll probably want the computer to deal with them as numbers, not as strings, and this can't happen if the string contains a comma or period to separate the thousands place. This allows the numbers to be sorted and to be available for mathematical functions.

In this recipe, we'll write a short function that takes a number string and returns the number. The function will strip out all of the extra punctuation inside the number and only leave the last separator. Hopefully, this will be the one that marks the decimal place.

Of course, the version of this function, which we'll see here, only works in locales that use commas to separate thousands and periods to separate decimals. However, it would be relatively easy to write versions that will work in any particular locale.

Getting ready

For this recipe, we're back to the most simple project.clj files:

(defproject cleaning-data "0...

Calculating relative values

One way to normalize values is to scale frequencies by the sizes of their groups. For example, say the word truth appears three times in a document. This means one thing if the document has thirty words. It means something else if the document has 300 or 3,000 words. Moreover, if the dataset has documents of all these lengths, how do you compare the frequencies for words across documents?

One way to do this is to rescale the frequency counts. In some cases, we can just scale the terms by the length of the documents. Or, if we want better results, we might use something more complicated such as term frequency-inverse document frequency (TF-IDF).

For this recipe, we'll rescale some term frequencies by the total word count for their document.

Getting ready

We don't need much for this recipe. We'll use the minimal project.clj file, which is listed here:

(defproject cleaning-data "0.1.0-SNAPSHOT"
  :dependencies [[org.clojure/clojure "1.6.0"]])

However, it will be easier...

Parsing dates and times

One difficult issue when normalizing and cleaning up data is how to deal with time. People enter dates and times in a bewildering variety of formats; some of them are ambiguous, and some of them are vague. However, we have to do our best to interpret them and normalize them into a standard format.

In this recipe, we'll define a function that attempts to parse a date into a standard string format. We'll use the clj-time Clojure library, which is a wrapper around the Joda Java library (http://joda-time.sourceforge.net/).

Getting ready

First, we need to declare our dependencies in the Leiningen project.clj file:

(defproject cleaning-data "0.1.0-SNAPSHOT"
  :dependencies [[org.clojure/clojure "1.6.0"]
                 [clj-time "0.9.0-beta1"]])

Then, we need to load these dependencies into our script or REPL. We'll exclude second from clj-time to keep it from clashing with clojure.core/second:

(use '[clj-time.core :exclude (extend second)]
     '[clj-time.format])

How to do...

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Description

This book is for those with a basic knowledge of Clojure, who are looking to push the language to excel with data analysis.

Who is this book for?

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
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 27, 2015
Length: 372 pages
Edition : 2nd
Language : English
ISBN-13 : 9781784390297
Category :
Data
Languages :
Clojure
Concepts :
Data Analysis
Tools :
Leiningen

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Publication date : Jan 27, 2015
Length: 372 pages
Edition : 2nd
Language : English
ISBN-13 : 9781784390297
Category :
Data
Languages :
Clojure
Concepts :
Data Analysis
Tools :
Leiningen

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Table of Contents

13 Chapters
1. Importing Data for Analysis Chevron down icon Chevron up icon
1. Importing Data for Analysis
Introduction
Creating a new project
Reading CSV data into Incanter datasets
Reading JSON data into Incanter datasets
Reading data from Excel with Incanter
Reading data from JDBC databases
Reading XML data into Incanter datasets
Scraping data from tables in web pages
Scraping textual data from web pages
Reading RDF data
Querying RDF data with SPARQL
Aggregating data from different formats
2. Cleaning and Validating Data Chevron down icon Chevron up icon
2. Cleaning and Validating Data
Introduction
Cleaning data with regular expressions
Maintaining consistency with synonym maps
Identifying and removing duplicate data
Regularizing numbers
Calculating relative values
Parsing dates and times
Lazily processing very large data sets
Sampling from very large data sets
Fixing spelling errors
Parsing custom data formats
Validating data with Valip
3. Managing Complexity with Concurrent Programming Chevron down icon Chevron up icon
3. Managing Complexity with Concurrent Programming
Introduction
Managing program complexity with STM
Managing program complexity with agents
Getting better performance with commute
Combining agents and STM
Maintaining consistency with ensure
Introducing safe side effects into the STM
Maintaining data consistency with validators
Monitoring processing with watchers
Debugging concurrent programs with watchers
Recovering from errors in agents
Managing large inputs with sized queues
4. Improving Performance with Parallel Programming Chevron down icon Chevron up icon
4. Improving Performance with Parallel Programming
Introduction
Parallelizing processing with pmap
Parallelizing processing with Incanter
Partitioning Monte Carlo simulations for better pmap performance
Finding the optimal partition size with simulated annealing
Combining function calls with reducers
Parallelizing with reducers
Generating online summary statistics for data streams with reducers
Using type hints
Benchmarking with Criterium
5. Distributed Data Processing with Cascalog Chevron down icon Chevron up icon
5. Distributed Data Processing with Cascalog
Introduction
Initializing Cascalog and Hadoop for distributed processing
Querying data with Cascalog
Distributing data with Apache HDFS
Parsing CSV files with Cascalog
Executing complex queries with Cascalog
Aggregating data with Cascalog
Defining new Cascalog operators
Composing Cascalog queries
Transforming data with Cascalog
6. Working with Incanter Datasets Chevron down icon Chevron up icon
6. Working with Incanter Datasets
Introduction
Loading Incanter's sample datasets
Loading Clojure data structures into datasets
Viewing datasets interactively with view
Converting datasets to matrices
Using infix formulas in Incanter
Selecting columns with $
Selecting rows with $
Filtering datasets with $where
Grouping data with $group-by
Saving datasets to CSV and JSON
Projecting from multiple datasets with $join
7. Statistical Data Analysis with Incanter Chevron down icon Chevron up icon
7. Statistical Data Analysis with Incanter
Introduction
Generating summary statistics with $rollup
Working with changes in values
Scaling variables to simplify variable relationships
Working with time series data with Incanter Zoo
Smoothing variables to decrease variation
Validating sample statistics with bootstrapping
Modeling linear relationships
Modeling non-linear relationships
Modeling multinomial Bayesian distributions
Finding data errors with Benford's law
8. Working with Mathematica and R Chevron down icon Chevron up icon
8. Working with Mathematica and R
Introduction
Setting up Mathematica to talk to Clojuratica for Mac OS X and Linux
Setting up Mathematica to talk to Clojuratica for Windows
Calling Mathematica functions from Clojuratica
Sending matrixes to Mathematica from Clojuratica
Evaluating Mathematica scripts from Clojuratica
Creating functions from Mathematica
Setting up R to talk to Clojure
Calling R functions from Clojure
Passing vectors into R
Evaluating R files from Clojure
Plotting in R from Clojure
9. Clustering, Classifying, and Working with Weka Chevron down icon Chevron up icon
9. Clustering, Classifying, and Working with Weka
Introduction
Loading CSV and ARFF files into Weka
Filtering, renaming, and deleting columns in Weka datasets
Discovering groups of data using K-Means clustering
Finding hierarchical clusters in Weka
Clustering with SOMs in Incanter
Classifying data with decision trees
Classifying data with the Naive Bayesian classifier
Classifying data with support vector machines
Finding associations in data with the Apriori algorithm
10. Working with Unstructured and Textual Data Chevron down icon Chevron up icon
10. Working with Unstructured and Textual Data
Introduction
Tokenizing text
Finding sentences
Focusing on content words with stoplists
Getting document frequencies
Scaling document frequencies by document size
Scaling document frequencies with TF-IDF
Finding people, places, and things with Named Entity Recognition
Mapping documents to a sparse vector space representation
Performing topic modeling with MALLET
Performing naïve Bayesian classification with MALLET
11. Graphing in Incanter Chevron down icon Chevron up icon
11. Graphing in Incanter
Introduction
Creating scatter plots with Incanter
Graphing non-numeric data in bar charts
Creating histograms with Incanter
Creating function plots with Incanter
Adding equations to Incanter charts
Adding lines to scatter charts
Customizing charts with JFreeChart
Customizing chart colors and styles
Saving Incanter graphs to PNG
Using PCA to graph multi-dimensional data
Creating dynamic charts with Incanter
12. Creating Charts for the Web Chevron down icon Chevron up icon
12. Creating Charts for the Web
Introduction
Serving data with Ring and Compojure
Creating HTML with Hiccup
Setting up to use ClojureScript
Creating scatter plots with NVD3
Creating bar charts with NVD3
Creating histograms with NVD3
Creating time series charts with D3
Visualizing graphs with force-directed layouts
Creating interactive visualizations with D3
Index Chevron down icon Chevron up icon
Index

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Fabio Mancinelli Jun 03, 2015
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This is a very interesting book with plenty of practical recipes about Data Analysis.The author goes through the different phases of Data Analysis, starting from the low level details about how to read data from actual sources, continuing on how to clean it up to obtain meaningful results and presenting different algorithms for actually performing data analysis.The recipes go from querying, aggregating data and displaying, to statistical analysis and machine learning (clustering and classification).Recipes are presented in a very clear way, and they give the reader a clear context where to apply them, how they work, actual working code to experiment with and some additional reference for getting more in-depth information.I really appreciated the fact the author is well versed both in the theorical aspect and in Clojure programming. He presents very important details about advanced topics like parallelism, concurrency and laziness, and warn the reader about the pitfalls to be aware of. For example when talking about lazy-data-reading he clearly explains how to correcly handle underlying resources explicitly showing the source of potential issues.I read also the first edition and I've found that almost all the common recipes have been updated, and also a new chapter about unstructured and textual data has been added.Even though I am not a data analyst this book was very clear, and gave me a lot of insights about how to deal with data. I will for sure apply some of these recipes in my daily work to make more sense of what happens in what I manage.
Amazon Verified review Amazon
armel esnault Apr 10, 2015
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Clojure Data Analysis Cookbook, Second Edition by EricRochester. Format ebook PDFTwo years after the first version Eric Rochester has published anupdated version of his book "Clojure Data Analysis Cookbook".The book gives a nice overview of data analysis in the clojureprogramming language. It provides hundreds of useful tips on varioussoftware such as Incanter: the clojure statistics platform or Weka: ajava platform for machine learning.The examples provided are easy to test assuming you have a basicknowledge of clojure (especially regarding the repl interaction). Asmost examples are independent from each other it is easy to pickrecipes without having to follow the whole chapter from the beginning.The first two chapter deal with the importation and the validation ofdata using common format such as XML,JSON,CSV or RDF. It appears to bevery useful as it is a mandatory step (usually the first) of dataanalysis. While it is not very complicated to do everything byyourself those tips may save you some time.Clojure has a very good concurrency and parallel model by default,which are usually covered in every clojure introduction book but you will stillfind information in this book that you don't find inothers. I particularly like the Monte Carlo and simulated annealing methodsthat find optimal partition size for parallel processing.Chapter 8 is perhaps less interresting becausse it covers Clojureinteraction with Mathematica and the R language. and the only reason touse them is when a big library or framework is not available inclojure and you have some constraints (e.g. time or performance) that preventyou from implementing it in clojure.As the title implies the book will not make you autonomous on dataanalysis but it would be good to have some tips and examples on how todesign and build a full scale data analysis oriented application.A good book for discovering and playing with data in Clojure.
Amazon Verified review Amazon

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About the author

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Rochester
Eric Richard Rochester Studied medieval English literature and linguistics at UGA. Dissertated on lexicography. Now he programs in Haskell and writes. He's also a husband and parent.
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