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Home > Data > Data Analysis > Haskell Data Analysis cookbook
Haskell Data Analysis cookbook
Haskell Data Analysis cookbook

Haskell Data Analysis cookbook: Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes

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Arrow left icon
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Paperback Jun 2014 334 pages 1st Edition
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Haskell Data Analysis cookbook

Chapter 2. Integrity and Inspection

This chapter will cover the following recipes:

  • Trimming excess whitespace
  • Ignoring punctuation and specific characters
  • Coping with unexpected or missing input
  • Validating records by matching regular expressions
  • Lexing and parsing an e-mail address
  • Deduplication of nonconflicting data items
  • Deduplication of conflicting data items
  • Implementing a frequency table using Data.List
  • Implementing a frequency table using Data.MultiSet
  • Computing the Manhattan distance
  • Computing the Euclidean distance
  • Comparing scaled data using the Pearson correlation coefficient
  • Comparing sparse data using cosine similarity

Introduction

Introduction

The conclusions drawn from data analysis are only as robust as the quality of the data itself. After obtaining raw text, the next natural step is to validate and clean it carefully. Even the slightest bias may risk the integrity of the results. Therefore, we must take great precautionary measures, which involve thorough inspection, to ensure sanity checks are performed on our data before we begin to understand it. This section should be the starting point for cleaning data in Haskell.

Real-world data often has an impurity that needs to be addressed before it can be processed. For example, extraneous whitespaces or punctuation could clutter data, making it difficult to parse. Duplication and data conflicts are another area of unintended consequences of reading real-world data. Sometimes it's just reassuring to know that data makes sense by conducting sanity checks. Some examples of sanity checks include matching regular expressions as well as detecting outliers by establishing...

Trimming excess whitespace

The text obtained from sources may unintentionally include beginning or trailing whitespace characters. When parsing such an input, it is often wise to trim the text. For example, when Haskell source code contains trailing whitespace, the GHC compiler ignores it through a process called lexing. The lexer produces a sequence of tokens, effectively ignoring meaningless characters such as excess whitespace.

In this recipe, we will use built-in libraries to make our own trim function.

How to do it...

Create a new file, which we will call Main.hs, and perform the following steps:

  1. Import the isSpace :: Char -> Bool function from the built-in Data.Char package:
    import Data.Char (isSpace)
  2. Write a trim function that removes the beginning and trailing whitespace:
    trim :: String -> String
trim = f . f
  where f = reverse . dropWhile isSpace
  3. Test it out within main:
    main :: IO ()
main = putStrLn $ trim " wahoowa! "
  4. Running the code will result in the following trimmed...

Ignoring punctuation and specific characters

Usually in natural language processing, some uninformative words or characters, called stop words, can be filtered out for easier handling. When computing word frequencies or extracting sentiment data from a corpus, punctuation or special characters might need to be ignored. This recipe demonstrates how to remove these specific characters from the body of a text.

How to do it...

There are no imports necessary. Create a new file, which we will call Main.hs, and perform the following steps:

  1. Implement main and define a string called quote. The back slashes (\) represent multiline strings:
    main :: IO ()
main = do
  let quote = "Deep Blue plays very good chess-so what?\ 
    \Does that tell you something about how we play chess?\
    \No. Does it tell you about how Kasparov envisions,\ 
    \understands a chessboard? (Douglas Hofstadter)"
  putStrLn $ (removePunctuation.replaceSpecialSymbols) quote
  2. Replace all punctuation marks with an empty...

Coping with unexpected or missing input

Data sources often contain incomplete and unexpected data. One common approach to parsing such data in Haskell is using the Maybe data type.

Imagine designing a function to find the nth element in a list of characters. A naïve implementation may have the type Int -> [Char] -> Char. However, if the function is trying to access an index out of bounds, we should try to indicate that an error has occurred.

A common way to deal with these errors is by encapsulating the output Char into a Maybe context. Having the type Int -> [Char] -> Maybe Char allows for some better error handling. The constructors for Maybe are Just a or Nothing, which will become apparent by running GHCi and testing out the following commands:

$ ghci

Prelude> :type Just 'c'
Just 'c' :: Maybe Char

Prelude> :type Nothing
Nothing :: Maybe a

We will set each field as a Maybe data type so that whenever a field cannot be parsed, it will simply be...

Validating records by matching regular expressions

A regular expression is a language for matching patterns in a string. Our Haskell code can process a regular expression to examine a text and tell us whether or not it matches the rules described by the expression. Regular expression matching can be used to validate or identify a pattern in the text.

In this recipe, we will read a corpus of English text to find possible candidates of full names in a sea of words. Full names usually consist of two words that start with a capital letter. We use this heuristic to extract all the names from an article.

Getting ready

Create an input.txt file with some text. In this example, we use a snippet from a New York Times article on dinosaurs (http://www.nytimes.com/2013/12/17/science/earth/outsider-challenges-papers-on-growth-of-dinosaurs.html)

Other co-authors of Dr. Erickson's include Mark Norell, chairman of paleontology at the American Museum of Natural History; Philip Currie, a professor of dinosaur...

Introduction

The conclusions drawn from data analysis are only as robust as the quality of the data itself. After obtaining raw text, the next natural step is to validate and clean it carefully. Even the slightest bias may risk the integrity of the results. Therefore, we must take great precautionary measures, which involve thorough inspection, to ensure sanity checks are performed on our data before we begin to understand it. This section should be the starting point for cleaning data in Haskell.

Real-world data often has an impurity that needs to be addressed before it can be processed. For example, extraneous whitespaces or punctuation could clutter data, making it difficult to parse. Duplication and data conflicts are another area of unintended consequences of reading real-world data. Sometimes it's just reassuring to know that data makes sense by conducting sanity checks. Some examples of sanity checks include matching regular expressions as well as detecting outliers by establishing...

Trimming excess whitespace

The text obtained from sources may unintentionally include beginning or trailing whitespace characters. When parsing such an input, it is often wise to trim the text. For example, when Haskell source code contains trailing whitespace, the GHC compiler ignores it through a process called lexing. The lexer produces a sequence of tokens, effectively ignoring meaningless characters such as excess whitespace.

In this recipe, we will use built-in libraries to make our own trim function.

How to do it...

Create a new file, which we will call Main.hs, and perform the following steps:

  1. Import the isSpace :: Char -> Bool function from the built-in Data.Char package:

    import Data.Char (isSpace)

  2. Write a trim function that removes the beginning and trailing whitespace:

    trim :: String -> String
trim = f . f
  where f = reverse . dropWhile isSpace

  3. Test it out within main:

    main :: IO ()
main = putStrLn $ trim " wahoowa! "

  4. Running the code will result in the following trimmed string:

    ...

Ignoring punctuation and specific characters

Usually in natural language processing, some uninformative words or characters, called stop words, can be filtered out for easier handling. When computing word frequencies or extracting sentiment data from a corpus, punctuation or special characters might need to be ignored. This recipe demonstrates how to remove these specific characters from the body of a text.

How to do it...

There are no imports necessary. Create a new file, which we will call Main.hs, and perform the following steps:

  1. Implement main and define a string called quote. The back slashes (\) represent multiline strings:

    main :: IO ()
main = do
  let quote = "Deep Blue plays very good chess-so what?\ 
    \Does that tell you something about how we play chess?\
    \No. Does it tell you about how Kasparov envisions,\ 
    \understands a chessboard? (Douglas Hofstadter)"
  putStrLn $ (removePunctuation.replaceSpecialSymbols) quote

  2. Replace all punctuation marks with an empty string, and...

Coping with unexpected or missing input

Data sources often contain incomplete and unexpected data. One common approach to parsing such data in Haskell is using the Maybe data type.

Imagine designing a function to find the nth element in a list of characters. A naïve implementation may have the type Int -> [Char] -> Char. However, if the function is trying to access an index out of bounds, we should try to indicate that an error has occurred.

A common way to deal with these errors is by encapsulating the output Char into a Maybe context. Having the type Int -> [Char] -> Maybe Char allows for some better error handling. The constructors for Maybe are Just a or Nothing, which will become apparent by running GHCi and testing out the following commands:

$ ghci

Prelude> :type Just 'c'
Just 'c' :: Maybe Char

Prelude> :type Nothing
Nothing :: Maybe a

We will set each field as a Maybe data type so that whenever a field cannot be parsed, it will simply be represented as Nothing....

Validating records by matching regular expressions

A regular expression is a language for matching patterns in a string. Our Haskell code can process a regular expression to examine a text and tell us whether or not it matches the rules described by the expression. Regular expression matching can be used to validate or identify a pattern in the text.

In this recipe, we will read a corpus of English text to find possible candidates of full names in a sea of words. Full names usually consist of two words that start with a capital letter. We use this heuristic to extract all the names from an article.

Getting ready

Create an input.txt file with some text. In this example, we use a snippet from a New York Times article on dinosaurs (http://www.nytimes.com/2013/12/17/science/earth/outsider-challenges-papers-on-growth-of-dinosaurs.html)

Other co-authors of Dr. Erickson's include Mark Norell, chairman of paleontology at the American Museum of Natural History; Philip Currie, a professor of dinosaur...

Lexing and parsing an e-mail address

An elegant way to clean data is by defining a lexer to split up a string into tokens. In this recipe, we will parse an e-mail address using the attoparsec library. This will naturally allow us to ignore the surrounding whitespace.

Getting ready

Import the attoparsec parser combinator library:

$ cabal install attoparsec

How to do it…

Create a new file, which we will call Main.hs, and perform the following steps:

  1. Use the GHC OverloadedStrings language extension to more legibly use the Text data type throughout the code. Also, import the other relevant libraries:

    {-# LANGUAGE OverloadedStrings #-}
import Data.Attoparsec.Text
import Data.Char (isSpace, isAlphaNum)

  2. Declare a data type for an e-mail address:

    data E-mail = E-mail 
  { user :: String
  , host :: String
  } deriving Show

  3. Define how to parse an e-mail address. This function can be as simple or as complicated as required:

    e-mail :: Parser E-mail
e-mail = do
  skipSpace
  user <- many' $ satisfy isAlphaNum...

Deduplication of nonconflicting data items

Duplication is a common problem when collecting large amounts of data. In this recipe, we will combine similar records in a way that ensures no information is lost.

Getting ready

Create an input.csv file with repeated data:

How to do it...

Create a new file, which we will call Main.hs, and perform the following steps:

  1. We will be using the CSV, Map, and Maybe packages:

    import Text.CSV (parseCSV, Record)
import Data.Map (fromListWith)
import Control.Applicative ((<|>))

  2. Define the Item data type corresponding to the CSV input:

    data Item = Item   { name :: String
                   , color :: Maybe String
                   , cost :: Maybe Float
                   } deriving Show

  3. Get each record from CSV and put them in a map by calling our doWork function:

    main :: IO ()
main = do
  let fileName = "input.csv"
  input <- readFile fileName
  let csv = parseCSV fileName input
  either handleError doWork csv

  4. If we're unable to parse CSV, print an error message...

Deduplication of conflicting data items

Unfortunately, information about an item may be inconsistent throughout the corpus. Collision strategies are often domain-dependent, but one common way to manage this conflict is by simply storing all variations of the data. In this recipe, we will read a CSV file that contains information about musical artists and store all of the information about their songs and genres in a set.

Getting ready

Create a CSV input file with the following musical artists. The first column is for the name of the artist or band. The second column is the song name, and the third is the genre. Notice how some musicians have multiple songs or genres.

How to do it...

Create a new file, which we will call Main.hs, and perform the following steps:

  1. We will be using the CSV, Map, and Set packages:

    import Text.CSV (parseCSV, Record)
import Data.Map (fromListWith)
import qualified Data.Set as S

  2. Define the Artist data type corresponding to the CSV input. For fields that may contain conflicting...

Implementing a frequency table using Data.List

A frequency map of values is often useful to detect outliers. We can use it to identify frequencies that seem out of the ordinary. In this recipe, we will be counting the number of different colors in a list.

How to do it...

Create a new file, which we will call Main.hs, and perform the following steps:

  1. We will use the group and sort functions from Data.List:

    import Data.List (group, sort)

  2. Define a simple data type for colors:

    data Color = Red | Green | Blue deriving (Show, Ord, Eq)

  3. Create a list of these colors:

    main :: IO ()
main = do
  let items = [Red, Green, Green, Blue, Red, Green, Green]

  4. Implement the frequency map and print it out:

      let freq = 
     map (\x -> (head x, length x)) . group . sort $ items
  print freq

How it works...

Grouping identical items after sorting the list is the central idea.

See the following step-by-step evaluation in ghci:

Prelude> sort items

[Red,Red,Green,Green,Green,Green,Blue]
Prelude> group it

[[Red,Red...
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Description

Step-by-step recipes filled with practical code samples and engaging examples demonstrate Haskell in practice, and then the concepts behind the code. This book shows functional developers and analysts how to leverage their existing knowledge of Haskell specifically for high-quality data analysis. A good understanding of data sets and functional programming is assumed.

What you will learn

  • Obtain and analyze raw data from various sources including text files, CSV files, databases, and websites
  • Implement practical tree and graph algorithms on various datasets
  • Apply statistical methods such as moving average and linear regression to understand patterns
  • Fiddle with parallel and concurrent code to speed up and simplify timeconsuming algorithms
  • Find clusters in data using some of the most popular machine learning algorithms
  • Manage results by visualizing or exporting data

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 25, 2014
Length: 334 pages
Edition : 1st
Language : English
ISBN-13 : 9781783286331
Category :
Data
Languages :
Haskell
Concepts :
Data Analysis

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Product Details

Publication date : Jun 25, 2014
Length: 334 pages
Edition : 1st
Language : English
ISBN-13 : 9781783286331
Category :
Data
Languages :
Haskell
Concepts :
Data Analysis

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Table of Contents

13 Chapters
1. The Hunt for Data Chevron down icon Chevron up icon
1. The Hunt for Data
Introduction
Harnessing data from various sources
Accumulating text data from a file path
Catching I/O code faults
Keeping and representing data from a CSV file
Examining a JSON file with the aeson package
Reading an XML file using the HXT package
Capturing table rows from an HTML page
Understanding how to perform HTTP GET requests
Learning how to perform HTTP POST requests
Traversing online directories for data
Using MongoDB queries in Haskell
Reading from a remote MongoDB server
Exploring data from a SQLite database
2. Integrity and Inspection Chevron down icon Chevron up icon
2. Integrity and Inspection
Introduction
Trimming excess whitespace
Ignoring punctuation and specific characters
Coping with unexpected or missing input
Validating records by matching regular expressions
Lexing and parsing an e-mail address
Deduplication of nonconflicting data items
Deduplication of conflicting data items
Implementing a frequency table using Data.List
Implementing a frequency table using Data.MultiSet
Computing the Manhattan distance
Computing the Euclidean distance
Comparing scaled data using the Pearson correlation coefficient
Comparing sparse data using cosine similarity
3. The Science of Words Chevron down icon Chevron up icon
3. The Science of Words
Introduction
Displaying a number in another base
Reading a number from another base
Searching for a substring using Data.ByteString
Searching a string using the Boyer-Moore-Horspool algorithm
Searching a string using the Rabin-Karp algorithm
Splitting a string on lines, words, or arbitrary tokens
Finding the longest common subsequence
Computing a phonetic code
Computing the edit distance
Computing the Jaro-Winkler distance between two strings
Finding strings within one-edit distance
Fixing spelling mistakes
4. Data Hashing Chevron down icon Chevron up icon
4. Data Hashing
Introduction
Hashing a primitive data type
Hashing a custom data type
Running popular cryptographic hash functions
Running a cryptographic checksum on a file
Performing fast comparisons between data types
Using a high-performance hash table
Using Google's CityHash hash functions for strings
Computing a Geohash for location coordinates
Using a bloom filter to remove unique items
Running MurmurHash, a simple but speedy hashing algorithm
Measuring image similarity with perceptual hashes
5. The Dance with Trees Chevron down icon Chevron up icon
5. The Dance with Trees
Introduction
Defining a binary tree data type
Defining a rose tree (multiway tree) data type
Traversing a tree depth-first
Traversing a tree breadth-first
Implementing a Foldable instance for a tree
Calculating the height of a tree
Implementing a binary search tree data structure
Verifying the order property of a binary search tree
Using a self-balancing tree
Implementing a min-heap data structure
Encoding a string using a Huffman tree
Decoding a Huffman code
6. Graph Fundamentals Chevron down icon Chevron up icon
6. Graph Fundamentals
Introduction
Representing a graph from a list of edges
Representing a graph from an adjacency list
Conducting a topological sort on a graph
Traversing a graph depth-first
Traversing a graph breadth-first
Visualizing a graph using Graphviz
Using Directed Acyclic Word Graphs
Working with hexagonal and square grid networks
Finding maximal cliques in a graph
Determining whether any two graphs are isomorphic
7. Statistics and Analysis Chevron down icon Chevron up icon
7. Statistics and Analysis
Introduction
Calculating a moving average
Calculating a moving median
Approximating a linear regression
Approximating a quadratic regression
Obtaining the covariance matrix from samples
Finding all unique pairings in a list
Using the Pearson correlation coefficient
Evaluating a Bayesian network
Creating a data structure for playing cards
Using a Markov chain to generate text
Creating n-grams from a list
Creating a neural network perceptron
8. Clustering and Classification Chevron down icon Chevron up icon
8. Clustering and Classification
Introduction
Implementing the k-means clustering algorithm
Implementing hierarchical clustering
Using a hierarchical clustering library
Finding the number of clusters
Clustering words by their lexemes
Classifying the parts of speech of words
Identifying key words in a corpus of text
Training a parts-of-speech tagger
Implementing a decision tree classifier
Implementing a k-Nearest Neighbors classifier
Visualizing points using Graphics.EasyPlot
9. Parallel and Concurrent Design Chevron down icon Chevron up icon
9. Parallel and Concurrent Design
Introduction
Using the Haskell Runtime System options
Evaluating a procedure in parallel
Controlling parallel algorithms in sequence
Forking I/O actions for concurrency
Communicating with a forked I/O action
Killing forked threads
Parallelizing pure functions using the Par monad
Mapping over a list in parallel
Accessing tuple elements in parallel
Implementing MapReduce to count word frequencies
Manipulating images in parallel using Repa
Benchmarking runtime performance in Haskell
Using the criterion package to measure performance
Benchmarking runtime performance in the terminal
10. Real-time Data Chevron down icon Chevron up icon
10. Real-time Data
Introduction
Streaming Twitter for real-time sentiment analysis
Reading IRC chat room messages
Responding to IRC messages
Polling a web server for latest updates
Detecting real-time file directory changes
Communicating in real time through sockets
Detecting faces and eyes through a camera stream
Streaming camera frames for template matching
11. Visualizing Data Chevron down icon Chevron up icon
11. Visualizing Data
Introduction
Plotting a line chart using Google's Chart API
Plotting a pie chart using Google's Chart API
Plotting bar graphs using Google's Chart API
Displaying a line graph using gnuplot
Displaying a scatter plot of two-dimensional points
Interacting with points in a three-dimensional space
Visualizing a graph network
Customizing the looks of a graph network diagram
Rendering a bar graph in JavaScript using D3.js
Rendering a scatter plot in JavaScript using D3.js
Diagramming a path from a list of vectors
12. Exporting and Presenting Chevron down icon Chevron up icon
12. Exporting and Presenting
Introduction
Exporting data to a CSV file
Exporting data as JSON
Using SQLite to store data
Saving data to a MongoDB database
Presenting results in an HTML web page
Creating a LaTeX table to display results
Personalizing messages using a text template
Exporting matrix values to a file
Index Chevron down icon Chevron up icon
Index

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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(6 Ratings)
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4 star 0%
3 star 33.3%
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1 star 16.7%
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Nelson Solano Nov 09, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Was intimidated by all the content within this book, but turns out it's very approachable! Lots of examples and different ways of explaining concepts. I'm already beginning to feel like I have a stronger grasp with Haskell, especially in the context to data science and statistics. I recommend this book to anyone who wants an intro to data analysis techniques for real-world use.
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Student May 12, 2015
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This book enumerates through dozens of important algorithms used in typical data analysis tasks. It’s one of the most practical and hands-on books on this subject for the Haskell programming language. The examples tie together nicely. I can easily copy and paste the code to test each algorithm. The author also provides the code for each recipe on GitHub.I would recommend this to anyone who has touched Haskell and is willing to explore more interesting applications.
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David Jameson Jul 05, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great idea, I have been looking for a cookbook like this for some time and I have been slowly working through the examples. The Haskell world needs books like this really badly as most documentation that you find focuses more on defining the functions rather than helping you use them.There are some typos here and there such that the compiler produces errors that are hard to understand if you're not already pretty good with Haskell. That had spoiled it a bit for me at first.However, the great news is that up to date source code is available on github and so as long as you get code from there rather than just copying from the book directly, you should be fine.
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garrison jensen Apr 03, 2015
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
I thought this book would explain algorithms. It doesn't. It simply points to numerous libraries that already implement them.I like it, I will use it as a reference for libraries. But if you are expecting to find advice on implementing algorithms yourself, this is not the book for you.
Amazon Verified review Amazon
Jake McCrary Sep 01, 2014
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Packt Publishing recently asked me to write a review of the book Haskell Data Analysis Cookbook by Nishant Shukla. The book is broken into small sections that show you how to do a particular task related to data analysis. These tasks vary from reading a csv file or parsing json to listening to a stream of tweets.I’m not a Haskell programmer. My Haskell experience is limited to reading some books (Learn You a Haskell for Great Good and most of Real World Haskell) and solving some toy problems. All of reading and programming happened years ago though so I’m out of practice.This book is not for a programmer that is unfamiliar with Haskell. If you’ve never studied it before you’ll find yourself turning towards documentation. If you enter this book with a solid understanding of functional programming you can get by with a smaller understanding of Haskell but you will not get much from the book.I’ve only read a few cookbook style books and this one followed the usual format. It will be more useful as a quick reference than as something you would read through. It doesn’t dive deep into any topic but does point you toward libraries for various tasks and shows a short example of using them.A common critic I have of most code examples applies to this book. Most examples do not do qualified imports of namespaces or selective imports of functions from namespaces. This is especially useful when your examples might be read by people who are not be familiar with the languages standard libraries. Reading code and immediately knowing where a function comes from is incredibly useful to understanding.The code for this book is available on GitHub. It is useful to look at the full example for a section. The examples in the book are broken into parts with English explanations and I found that made it hard to fully understand how the code fit together. Looking at the examples in the GitHub repo helped.RecommendationI’d recommend this book for Haskell programmers who find the table of contents interesting. If you read the table of contents and think it would be useful to have a shallow introduction to the topics listed then you’ll find this book useful. It doesn’t give a detailed dive into anything but at least gives you a starting point.If you either learning Haskell or using Haskell then this book doesn’t have much to offer you.
Amazon Verified review Amazon

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About the author

Profile icon Nishant Shukla
Nishant Shukla
Nishant Shukla is a computer scientist with a passion for mathematics. Throughout the years, he has worked for a handful of start-ups and large corporations including WillowTree Apps, Microsoft, Facebook, and Foursquare. Stepping into the world of Haskell was his excuse for better understanding Category Theory at first, but eventually, he found himself immersed in the language. His semester-long introductory Haskell course in the engineering school at the University of Virginia (http://shuklan.com/haskell) has been accessed by individuals from over 154 countries around the world, gathering over 45,000 unique visitors. Besides Haskell, he is a proponent of decentralized Internet and open source software. His academic research in the fields of Machine Learning, Neural Networks, and Computer Vision aim to supply a fundamental contribution to the world of computing.
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