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Getting Started with Haskell Data Analysis
Getting Started with Haskell Data Analysis

Getting Started with Haskell Data Analysis: Put your data analysis techniques to work and generate publication-ready visualizations

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Paperback Oct 2018 160 pages 1st Edition
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Paperback Oct 2018 160 pages 1st Edition
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Getting Started with Haskell Data Analysis

SQLite3

In this chapter, we are going to learn about SQLite3. SQLite3 is a file format for storing data in the same mindset of a relational database such as Oracle, MySQL, MariaDB, and Postgres. Well, I suppose that you could use SQLite3 as a traditional database engine, but it's really not made for that. SQLite3 allows us to open up a file, work with the data in that file using SQL statements, and then close that file. One file can contain one database, and a database can store multiple tables of information. Contrast this with the CSV file format that we used in the last chapter which, at best, stores a single table of information. The advantages of SQLite3 are that it doesn't require any server-side programs running in the background, there are no configurations to discuss, and the executable for working with SQLite3 is a single file. So, in my opinion, working with...

SQLite3 command line

This section is going to be a primer on SQLite3 and it won't have any Haskell code. We're going to take a moment, and translate a CSV file into SQLite3. In this section, we're going to take a look at introducing SQLite3; we will be creating a table in an SQLite3 database, and also adding a CSV file to that table that we created in our SQLite3 database.

So, let's go to our Haskell environment and open our browser. Using Google, search for usgs earthquake feed csv. USG is the United States Geological Survey, and they keep a database of every single earthquake that takes place on planet earth, and they offer this data in a CSV file. So, we're going to click that very first link, https://earthquake.usgs.gov/earthquakes/feed/v1.0/csv.php. You should see Spreadsheet Format at the top; scroll down to the heading where it says Past 7 Days...

Working with SQLite3 and Haskell

In this section, we will talk about getting data from SQLite3 into Haskell. We're going to understand the basic types within SQLite3 and their Haskell counterparts. We're also going to be installing the necessary software in order to get Haskell and SQLite3 to communicate with each other, and we're going to be writing a few SELECT queries within Haskell.

There are a few different data types in SQLite3 with their Haskell counterparts:

The four primary types are INTEGER, REAL, TEXT, and BLOB; TEXT and BLOB are almost the same types. One is for raw data and the other is for string data, but we can interpret both of those in Haskell as String. INTEGER corresponds to Integer; REAL corresponds to Decimal. There is a fifth type in SQLite3 called NUMERIC, which is adaptive and is treated as an integer with INTEGER data and a real with...

Slices of data

This section will be an overview of the versatility of the SELECT query in SQLite3. Most of the content of this section will pertain to the inner workings of the SELECT query, and not the Haskell language itself. We're going to have an understanding of the following SELECT clauses: WHERE, ORDER BY, and LIMIT. Each of these have their own utility, but when these clauses work together you can quickly see how data can be sliced into workable chunks that can be studied later. This is my preferred way of working with data where we let SQL do the slicing and Haskell do the dicing. Once we have a data slice that we're happy with, we'll spend some time at the end looking at how to parse that chunk of data into something usable by Haskell.

Let's go back to our Jupyter Notebook; we will flop over to our primary Earthquakes notebook. We will begin with...

Working with SQLite3 and descriptive statistics

So far, we've seen how to generate an SQLite3 database using the sqlite3 command-line utility, and how to interact with that database within Haskell. This section combines the knowledge of descriptive statistics from Chapter 1, Descriptive Statistics, with our database work in Chapter 2, SQLite3. We will be using descriptive statistics with our SQLite3 database in this section. First, we will create our descriptive statistics module from functions found in our Baseball notebook. Second, we will slice up some data using SELECT queries. Third, we will pass data to our descriptive statistics functions, and discuss the results. We'll be looking at earthquakes, specifically in the region of Oklahoma. So, let's glide over to our virtual machine running our IHaskell Notebook, and what I would like to do is to discuss how...

Summary

In this chapter, we installed the sqlite3 command-line utility and installed the necessary SQLite3 libraries for working with data in the IHaskell environment. Most data doesn't come in SQLite3 format, but in CSV format. So, we covered how to convert a CSV file into an SQLite3 table. We explored the versatility of SELECT queries in SQLite3 by means of the WHERE clause, the ORDER BY clause, and the LIMIT clause. We also explored how to create our own custom module of descriptive statistics, and then we used that module in order to study earthquake data in the IHaskell environment. In our next chapter, we're going to take a look at regular expressions in Haskell.

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Key benefits

  • Take your data analysis skills to the next level using the power of Haskell
  • Understand regression analysis, perform multivariate regression, and untangle different cluster varieties
  • Create publication-ready visualizations of data

Description

Every business and organization that collects data is capable of tapping into its own data to gain insights how to improve. Haskell is a purely functional and lazy programming language, well-suited to handling large data analysis problems. This book will take you through the more difficult problems of data analysis in a hands-on manner. This book will help you get up-to-speed with the basics of data analysis and approaches in the Haskell language. You'll learn about statistical computing, file formats (CSV and SQLite3), descriptive statistics, charts, and progress to more advanced concepts such as understanding the importance of normal distribution. While mathematics is a big part of data analysis, we've tried to keep this course simple and approachable so that you can apply what you learn to the real world. By the end of this book, you will have a thorough understanding of data analysis, and the different ways of analyzing data. You will have a mastery of all the tools and techniques in Haskell for effective data analysis.

Who is this book for?

This book is intended for people who wish to expand their knowledge of statistics and data analysis via real-world examples. A basic understanding of the Haskell language is expected. If you are feeling brave, you can jump right into the functional programming style.

What you will learn

  • Learn to parse a CSV file and read data into the Haskell environment
  • Create Haskell functions for common descriptive statistics functions
  • Create an SQLite3 database using an existing CSV file
  • Learn the versatility of SELECT queries for slicing data into smaller chunks
  • Apply regular expressions in large-scale datasets using both CSV and SQLite3 files
  • Create a Kernel Density Estimator visualization using normal distribution

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Publication date : Oct 31, 2018
Length: 160 pages
Edition : 1st
Language : English
ISBN-13 : 9781789802863
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Length: 160 pages
Edition : 1st
Language : English
ISBN-13 : 9781789802863
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Concepts :

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Table of Contents

7 Chapters
Descriptive Statistics Chevron down icon Chevron up icon
SQLite3 Chevron down icon Chevron up icon
Regular Expressions Chevron down icon Chevron up icon
Visualizations Chevron down icon Chevron up icon
Kernel Density Estimation Chevron down icon Chevron up icon
Course Review Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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