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Learning R Programming

You're reading from   Learning R Programming Language, tools, and practical techniques

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Product type Paperback
Published in Oct 2016
Publisher Packt
ISBN-13 9781785889776
Length 582 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Kun Ren Kun Ren
Author Profile Icon Kun Ren
Kun Ren
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Toc

Table of Contents (16) Chapters Close

Preface 1. Quick Start FREE CHAPTER 2. Basic Objects 3. Managing Your Workspace 4. Basic Expressions 5. Working with Basic Objects 6. Working with Strings 7. Working with Data 8. Inside R 9. Metaprogramming 10. Object-Oriented Programming 11. Working with Databases 12. Data Manipulation 13. High-Performance Computing 14. Web Scraping 15. Boosting Productivity

Using regular expressions


For research, you may need to download data from open-access websites or authentication-required databases. These data sources provide data in various formats, and most of the data supplied are very likely well-organized. For example, many economic and financial databases provide data in the CSV format, which is a widely supported text format to represent tabular data. A typical CSV format looks like this:

id,name,score 
1,A,20 
2,B,30 
3,C,25

In R, it is convenient to call read.csv() to import a CSV file as a data frame with the right header and data types because the format is a natural representation of a data frame.

However, not all data files are well organized, and dealing with poorly organized data is painstaking. Built-in functions such as read.table() and read.csv() work in many situations, but they may not help at all for such format-less data.

For example, if you need to analyze raw data (messages.txt) organized in a CSV-like format as shown...

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