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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

Working with Excel files

Excel is another popular tool used to store and analyze data. Of course, one can convert Excel files to CSV files or other text formats by using Excel. Alternatively, to simplify the process, you can use install and load the xlsx package to read and process Excel data in R.

Getting ready

In this recipe, you need to prepare your environment with R installed and a computer that can access the Internet.

How to do it…

Please perform the following steps to read Excel documents:

  1. First, install and load the xlsx package:
    > install.packages("xlsx")
    > library(xlsx)
    
  2. Access www.data.worldbank.org/topic/economy-and-growth to find world economy indicator data in Excel:
    How to do it…

    Figure 6: World economy indicator

  3. Download world economy indicator data from the following URL using download.file:
    > download.file("http://api.worldbank.org/v2/en/topic/3?downloadformat=excel", "worldbank.xls", mode="wb")
    
  4. Examine the downloaded file with Excel (or...
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