讲解 lab3_jupyter_webscraping辅导数据结构语言程序

lab3_jupyter_webscraping

April 4, 2025

Webscraping in R

Estimated time needed: 15 minutes

0.1 Objectives

After completing this lab you will be able to:

• Understand HTML via coding practice

• Perform basic webscraping using rvest Table of Contents

<ul>

<li><a href="#Overview-of-HTML">Overview of HTML</a></li>

<li><a href="#The-rvest-library">The rvest library</a></li>

Table of Contents

<ul>

<li><a href="#Overview-of-HTML">Overview of HTML</a></li>

<li><a href="#The-rvest-library">The rvest library</a></li> Overview of HTML

• HTML stands for Hypertext Markup Language and it is used mainly for writing web pages.

• An HTML page consists of many organized HTML nodes or elements that tell a browser how to render its content.

• Each node or element has a start tag and an end tag with the same name and wraps some textual content.

One key feature of HTML is that nodes can be nested within other nodes, organizing into a tree-like structure like the folders in a file system. Below is a basic HTML node structure:

• <html> node is the root node,

• <html> node has two children: <head> and <body>.

• Since the <head> and <body> nodes have the same parent <html> node they are siblings to each other.

• Similarly, the <body> node has two child nodes, the <h1> and <p> nodes.

It is important to understand this tree-structure when writing a new HTML page or extracting data from an existing HTML page.

The revest library

The rvest package is a popular web scraping package for R. After rvest reads an HTML page, you can use the tag names to find the child nodes of the current node.

[ ]: library(rvest)

We also need to import httr library to get some HTML pages by sending HTTP GET request

[ ]: library(httr)

First let’s warm-up by reading HTML from the following character variable simple_html_text

[ ]: # A simple HTML document in a character variable

simple_html_text <- "

<html>

<body>

</body>

</html>"

Then use the read__html function to create the HTML root node, i.e., the html node by loading the simple_html_text

[ ]: # Create the root html node by reading the simple HTML string

root_node <- read_html(simple_html_text)

root_node

You can also check the type of root_node [ ]: class(root_node)

You can see the class is xml_node because rvest load HTML pages and convert them using XML format internally. XML has similar parent-child tree structure but more suitable for storing and tranporting data than HTML.

Next, let’s try to create a HTML node by loading a remote HTML page given a URL

[ ]: ibm_html_node <- read_html("http://www.ibm.com") ibm_html_node

Sometimes you want to download some HTML pages and analyze them offline, you could use download.file to do so:

Coding Exercise: in the code cell below, download a html node using any URL you like.

[ ]: # Write your code below. Don't forget to press Shift+Enter to execute the cell

Now you know how to read an HTML page from a character variable, a URL, or a local HTML file. Next let’s see how to parse and extract data from a specific node(s) starting from the root node

[ ]: simple_html_text <- "

<html>

<body>

</body>

</html>"

root_node <- read_html(simple_html_text)

root_node

Get the <body> node by using its parent node <html>

[ ]: # Get the child <body> node from <html> root_node

body_node <- html_node(root_node, "body")

body_node

You can see it has a child node paragraph <p>

Let’s get the content of the <p>

[ ]: # Get the child <p> node from its <body> node

p_node <- html_node(body_node, "p")

p_content<-html_text(p_node)

p_content

The <p> node also has style attribute with value color:red;, which means we want the browser to render its text using red color. To get an attribute of a node, we can use a function called html_attr("attribute name")

[ ]: # Use the p_node as the first argument to get its attribute

style_attr <- html_attr(p_node, "style")

style_attr

In the code cell below, the downloaded r.html file (from https://www.r-project.org) has an <img> node representing an image URL to R logo image (a relative path on its web server), let’s try to find the image URL and download it.

Your need to paste the relative path in <img> with the the https://www.r-project.org to get the full URL of the image, and use the GET function to request the image as bytes in the response

Then use writeBin() function to save the returned bytes into an image file.

Now, from the file list on the left, you should be able to find a saved r.png file.

In HTML, many tabluar data are stored in <table> nodes. Thus, it is very important to be able to extract data from <table> nodes and preferably convert them into R data frames.

Below is a sample HTML page contains a color table showing the supported HTML colors, and we want to load it as a R data frame so we can analyze it using data frame-related operations.

[ ]: table_url <- "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.

。cloud/IBM-DA0321EN-SkillsNetwork/labs/datasets/HTMLColorCodes.html"

Like other HTML nodes, let’s first get the <table> node using html_node function [ ]: root_node <-read_html(table_url)

table_node <- html_node(root_node, "table")

table_node

You can see the table node in a messy HTML format. Fortunately, you dont need to parse it by yourself, rvest provides a handy function called html_table() to convert <table> node into R dataframe.

But you could see the table headers were parsed as the first row, no worries, we could manually fix that

[ ]: names(color_data_frame)

That’s it for webscraping in R, there is a lot more you can do check out rvest.

0.2 Authors

Hi, this is Yan Luo the author of this notebook.

I hope you found it easy to learn how to do webscraping in R! Feel free to connect with us if you have any questions.

0.2.1 Other Contributors

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