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R Data Analysis Cookbook, Second Edition

You're reading from   R Data Analysis Cookbook, Second Edition Customizable R Recipes for data mining, data visualization and time series analysis

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787124479
Length 560 pages
Edition 2nd Edition
Languages
Tools
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Authors (3):
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Kuntal Ganguly Kuntal Ganguly
Author Profile Icon Kuntal Ganguly
Kuntal Ganguly
Shanthi Viswanathan Shanthi Viswanathan
Author Profile Icon Shanthi Viswanathan
Shanthi Viswanathan
Viswa Viswanathan Viswa Viswanathan
Author Profile Icon Viswa Viswanathan
Viswa Viswanathan
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Toc

Table of Contents (14) Chapters Close

Preface 1. Acquire and Prepare the Ingredients - Your Data 2. What's in There - Exploratory Data Analysis FREE CHAPTER 3. Where Does It Belong? Classification 4. Give Me a Number - Regression 5. Can you Simplify That? Data Reduction Techniques 6. Lessons from History - Time Series Analysis 7. How does it look? - Advanced data visualization 8. This may also interest you - Building Recommendations 9. It's All About Your Connections - Social Network Analysis 10. Put Your Best Foot Forward - Document and Present Your Analysis 11. Work Smarter, Not Harder - Efficient and Elegant R Code 12. Where in the World? Geospatial Analysis 13. Playing Nice - Connecting to Other Systems

Reading XML data

You may sometimes need to extract data from websites. Many providers also supply data in XML and JSON formats. In this recipe, we learn about reading XML data.

Getting ready

Make sure you have downloaded the files for this chapters and the files cd_catalog.xml and WorldPopulation-wiki.htm are in working directory of R. If the XML package is not already installed in your R environment, install the package now, as follows:

> install.packages("XML") 

How to do it...

XML data can be read by following these steps:

  1. Load the library and initialize:
> library(XML) 
> url <- "cd_catalog.xml"
  1. Parse the XML file and get the root node:
> xmldoc <- xmlParse(url) 
> rootNode <- xmlRoot(xmldoc)
> rootNode[1]
  1. Extract the XML data:
> data <- xmlSApply(rootNode,function(x) xmlSApply(x, xmlValue)) 
  1. Convert the extracted data into a data frame:
> cd.catalog <- data.frame(t(data),row.names=NULL) 
  1. Verify the results:
> cd.catalog[1:2,] 

How it works...

The xmlParse function returns an object of the XMLInternalDocument class, which is a C-level internal data structure.

The xmlRoot() function gets access to the root node and its elements. Let us check the first element of the root node:

> rootNode[1] 

$CD
<CD>
<TITLE>Empire Burlesque</TITLE>
<ARTIST>Bob Dylan</ARTIST>
<COUNTRY>USA</COUNTRY>
<COMPANY>Columbia</COMPANY>
<PRICE>10.90</PRICE>
<YEAR>1985</YEAR>
</CD>
attr(,"class")
[1] "XMLInternalNodeList" "XMLNodeList"

To extract data from the root node, we use the xmlSApply() function iteratively over all the children of the root node. The xmlSApply function returns a matrix.

To convert the preceding matrix into a data frame, we transpose the matrix using the t() function and then extract the first two rows from the cd.catalog data frame:

> cd.catalog[1:2,] 
TITLE ARTIST COUNTRY COMPANY PRICE YEAR
1 Empire Burlesque Bob Dylan USA Columbia 10.90 1985
2 Hide your heart Bonnie Tyler UK CBS Records 9.90 1988

There's more...

XML data can be deeply nested and hence can become complex to extract. Knowledge of XPath is helpful to access specific XML tags. R provides several functions, such as xpathSApply and getNodeSet, to locate specific elements.

Extracting HTML table data from a web page

Though it is possible to treat HTML data as a specialized form of XML, R provides specific functions to extract data from HTML tables, as follows:

> url <- "WorldPopulation-wiki.htm" 
> tables <- readHTMLTable(url)
> world.pop <- tables[[6]]

The readHTMLTable() function parses the web page and returns a list of all the tables that are found on the page. For tables that have an id attribute, the function uses the id attribute as the name of that list element.

We are interested in extracting the "10 most populous countries", which is the fifth table, so we use tables[[6]].

Extracting a single HTML table from a web page

A single table can be extracted using the following command:

> table <- readHTMLTable(url,which=5) 

Specify which to get data from a specific table. R returns a data frame.

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