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R for Data Science Cookbook (n)

You're reading from   R for Data Science Cookbook (n) Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques

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Product type Paperback
Published in Jul 2016
Publisher
ISBN-13 9781784390815
Length 452 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Toc

Table of Contents (14) Chapters Close

Preface 1. Functions in R FREE CHAPTER 2. Data Extracting, Transforming, and Loading 3. Data Preprocessing and Preparation 4. Data Manipulation 5. Visualizing Data with ggplot2 6. Making Interactive Reports 7. Simulation from Probability Distributions 8. Statistical Inference in R 9. Rule and Pattern Mining with R 10. Time Series Mining with R 11. Supervised Machine Learning 12. Unsupervised Machine Learning Index

Reading data from databases

As R reads data into memory, it is perfect for processing and analyzing small datasets. However, as an enterprise accumulates much more data than individuals in their daily lives, database documents are becoming more common for the purpose of storing and analyzing bigger data. To access databases with R, one can use RJDBC, RODBC, or RMySQL as the communications bridge. In this section, we will demonstrate how to use RJDBC to connect data stored in the database.

Getting ready

In this section, we need to prepare a MySQL environment first. If you have a MySQL environment installed on your machine (Windows), you can inspect server status from MySQL Notifier. If the local server is running, the server status should prompt localhost (Online), as shown in the following screenshot:

Getting ready

Figure 8: MySQL Notifier

Once we have our database server online, we need to validate whether we are authorized to access the database with a given username and password by using any database...

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