Munging in R with SQL and MongoDB for Financial Applications
Buch, Englisch, 280 Seiten, Book, Format (B × H): 178 mm x 254 mm
ISBN: 978-1-4842-0612-6
Verlag: APress
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 Learn
- Use a rich feature set of R for financial data analytics
- Employ an integrated comparison-based learning approach to SQL and NoSQL database management, including query and insert constructs
- Understand data wrangling best practices and solutions
- Be exposured to cutting-edge database technologies such as text-based analytics and their financial applications
- Study an abundance of practical examples from the real world of finance
Who This Book Is For
Data 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.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Datenbankprogrammierung
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
- Mathematik | Informatik Mathematik Mathematik Interdisziplinär Finanz- und Versicherungsmathematik
- Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines
Weitere Infos & Material
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
