Data Wrangling

Munging in R with SQL and MongoDB for Financial Applications
1. Auflage 2017
ISBN: 978-1-4842-0612-6
Verlag: APress

Buch, Englisch, 280 Seiten, Book, Format (B × H): 178 mm x 254 mm

Use R to gather, clean, and manage financial data in structured and unstructured databases. Learn how to read and write the increasing volume and complexity of data from and between SQL and MongoDB databases.Data Wrangling teaches practitioners and students of financial data analysis the SQL and MongoDB database management skills they need to succeed in their analytic work. The authors, who have deep experience in the financial industry as well as in teaching quantitative finance, take most of the operational and programming examples that enrich their book from the financial arena, including both market data and text-based data. The concepts presented through these examples are nonetheless applicable to a wide range of fields, so data analysts from all industries will profit from this book.What You'll LearnUse a rich feature set of R for financial data analyticsEmploy an integrated comparison-based learning approach to SQL and NoSQL database management, including query and insert constructs Understand data wrangling best practices and solutionsBe exposured to cutting-edge database technologies such as text-based analytics and their financial applicationsStudy an abundance of practical examples from the real world of financeWho This Book Is ForData analysts in the financial industry, data analysts in nonfinancial fields, and those who deal with data in their professional or academic work

Zielgruppe

Popular/general

Autoren/Hrsg.

Houlihan, Patrick

Moreno, Alexander

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Datenbankprogrammierung
• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines
• Mathematik | Informatik Mathematik Mathematik Interdisziplinär Finanz- und Versicherungsmathematik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse

Inhaltsverzeichnis

Chapter 1. Data Wrangling on the New Frontiers of Financial Data

Structured, Semi-Structured, Unstructured, and Polymorphic Data

NoSQL Databases

Document-Based, Key-Value, Columnar, and Graph-Based Types

MongoDB

When to Use SQL and When to Use MongoDB: Efficiency and Performance Criteria

Chapter 2. Data Structures in R

Vectors

Matrices

Lists

Data Frames

Chapter 3. Time Series Financial Data in R

Although most financial data are in the form of time series, coverage of this topic in R is frustratingly scanty. Compounding the challenge for financial data analysts, different R packages implement time series differently. This chapter presents the single best package for financial time series applications: {xts}.
Packages: {xts}

Sub-Setting

Applying Daily, Weekly, Monthly, Custom Ranges

Case Study: Retrieving Online Financial Data with the {Quantmod} Package And Performing Various Manipulations

Chapter 4. The Terminologies of SQL and MongoDB

SQL and Mongo typically employ different terms for similar concepts and v.v. This chapter compares the two environments and their terminologies side by side.

Database Structure

Table vs Collection

Static vs Dynamic Schemas

Data Types

Chapter 5. Setting up the Environment

Installing MongoDB and MySQL on the Windows and Linux Oss; avoiding pitfalls.

Chapter 6. Importing and Exporting Data from Files in R
Databases are not the only mechanism for importing and exporting financial data in R; files are also an extremely common medium for transmitting data because they are stable, reliable, and easy to use. This chapter explains file I/O in the R environment, describes ways improve file I/O performance, and discusses the variety of file formats.

CSV, xlsx, txt, dat, json

Read/Write Functions

Export to jpeg, pdf, images

Local and Web-Based Files

Chapter 7. Commands in SQL and MongoDB

This chapter provides an operational knowledge of Mongo and SQL in the context of financial data wrangling in R.

Command Line Basics

Supported Packages

Accessing

Locally

Remotely

Querying

Importing

Exporting
Advanced

Indexes

Analysis Of Data Using Aggregation

Case Studies

Managing Financial Data in SQL

Managing Twitter Data in MongoDB

Advanced Commands

Chapter 8. Recommended Packages

Naming names, this chapter helps financial data wranglers extract the signal from the noise in the sprawling ecosystem of contributed packages in order to choose the best one for the job at hand.

Chapter 9. Date/Time Formats

Although there is no universal standard for date/time formats, certain formats are generally accepted within particular disciplines and sectors of the financial industry. This technical topic is scarcely sexy, but it can be a source of exasperation for data wranglers if not properly addressed.

R Dates, POSIX, {chron} Package

Mongo Datetime
Converting between Formats

Chapter 10. Text-Based Data

Sentiment analysis and news analytics of text-based data in R are topics of rapidly growing importance in the finance and financial services industry thanks to the numerous financial applications that have arisen to harness the text data explosion fueled by social media and online publication.

Regular Expressions

Different Encodings (ASCII, UTF-8, and so on)

Cleaning Text Data

Case Study: Twitter

Chapter 11. Handling Escape Characters

This chapter teaches the skills, vital to programming syntax, for distinguishing text and commands through the use of escape characters.

Chapter 12. Advanced R Data Topics

This chapter teaches techniques for handling larger data sets in R.

{mmap}

Indexing Options

Über Autor(innen)

Patrick Houlihan is a Lecturer in Quantitative Finance at the Stevens Institute of Technology, with 15 years of professional industry experience. He was a quantitative analyst for Jefferies LLC; senior field applications engineer for Nvidia supporting GPU and compute products for Dell Consumer (Dimension); senior field applications engineer for Altera, covering Hewlett Packard's workstation and server lines and field application engineering roles at Altium and Arrow Electronics. Patrick received an MSFE from Stevens Institute of Technology and an MBA in Investment Management and BSEE in Electrical Engineering from Drexel University. He is pursuing his doctorate in Financial Engineering at Stevens.