作者：禅与计算机程序设计艺术
1.简介
Web scraping, also known as web harvesting or web data extraction, is a technique used to extract large amounts of data from websites in a fast and automated manner. In this article we will learn how to perform web scraping using the programming language R (a free software environment for statistical computing and graphics). We will start by understanding the basic concepts and terminology related to web scraping before moving on to learning about the core algorithms behind web scraping. Finally, we will demonstrate some practical code examples along with explanations and comments. This article assumes that readers have at least a basic knowledge of HTML and CSS and are comfortable working with RStudio IDE. If you need a refresher course on these topics, please visit my tutorial "Introduction to HTML and CSS".
In summary, web scraping allows us to collect massive amounts of data from websites in an automated way without having to manually search through each page. This can be particularly useful when it comes to analyzing data-driven decision making processes such as predicting stock prices, forex rates, or news articles. By automating the process of gathering data, companies can focus more resources towards developing innovative products and services while still being able to stay ahead of competition. Therefore, becoming familiar with web scraping techniques can greatly benefit anyone interested in data science, technology, finance, or any other field where web-based information plays a crucial role.
By following this guide, you should be able to scrape data from various sources including blogs, social media platforms, online marketplaces, and many others. You may even develop customized programs or scripts to fetch specific types of data based on your needs and preferences. The importance of web scraping cannot be underestimated today. With the advent of increasingly sophisticated technologies like artificial intelligence and machine learning, web scraping has become one of the most essential skills required for achieving breakthroughs in numerous industries. Keep up with the latest trends and updates in the world of data science and web scraping to ensure that you always remain relevant in the digital economy. Thank you for reading!
2. Basic Concepts and Terminology
Before we begin our exploration into web scraping with R, let's briefly review some of the fundamental concepts and terms used in web scraping.
What Is Web Scraping? Web scraping refers to extracting large amounts of data from websites. It involves obtaining content from web pages and copying them onto local storage devices or databases. The goal of web scraping is to create datasets that contain valuable information that can be used for research purposes or analysis.
How Does Web Scraping Work? Web scraping works by sending HTTP requests to web servers which typically return responses containing the desired information. These requests usually include headers specifying what type of device, operating system, browser, and language the request originated from. Once the server receives the request, it returns a response message that contains the requested data in the form of HTML documents.
To enable web scraping, developers use various tools such as crawlers, bots, and spiders. Crawlers examine every link in a website recursively, fetching all the available content. Bots employ specialized algorithms designed to mimic human behavior and interact with websites in real time. Spiders also operate similarly but instead of simply traversing links, they follow them and download the associated files.
Once the data is obtained, there are several ways to store it locally or transfer it to a database. For instance, web scraped data can be stored in CSV format for easy manipulation within a spreadsheet application. Alternatively, structured databases such as MySQL, PostgreSQL, and MongoDB can be used to store the data efficiently.
Why Use Web Scraping? There are several reasons why businesses and organizations would want to utilize web scraping. Here are just a few:
Gathering Large Amounts of Data Many websites today provide APIs that allow programmatic access to their data. However, if the user interface changes significantly, the API becomes obsolete. On the other hand, web scraping allows companies to easily obtain large amounts of data that might not be available via traditional methods.
Analyze Social Media Behavior Social media platforms often post millions of tweets, photos, videos, and other multimedia content daily. Analyzing this data can provide insight into customer opinions and trends. Web scraping can help companies identify brand affinity among their users, understand engagement dynamics, and track brand reputation over time.
Track Stock Prices Most financial institutions use public APIs to obtain stock price data. However, sometimes the data is delayed or incomplete due to various factors such as internet connectivity issues, bank holidays, and exchange congestion. Web scraping provides alternative solutions to these problems.
Automate Tasks Web scrapers can automate repetitive tasks such as data collection, data cleaning, and data aggregation. Companies can save time and effort by automating these tasks using scripting languages such as Python or R.
3. Core Algorithms and Operations
Now that we have reviewed the basics of web scraping, let's move on to discussing the core algorithms and operations involved in web scraping. We will discuss two main categories of web scraping techniques: dynamic and static web scraping.
Dynamic Web Scraping
Dynamic web scraping involves sending requests repeatedly until certain conditions are met. These conditions could be defined by either receiving the expected response or timing out after a certain amount of time elapses. Depending on the context, the number of iterations performed during scraping may vary, ranging from a single iteration to multiple ones. Examples of dynamic web scraping techniques include JavaScript rendering, infinite scroll pages, and AJAX calls.
Here is an example of a webpage that uses JavaScript rendering to display dynamically loaded content: https://www.nytimes.com/interactive/2021/business/oil-prices.html. To render the content, the page sends asynchronous HTTP requests to load additional elements and update the existing ones. When rendered, the page displays only a portion of its contents, leaving the rest unloaded until explicitly requested by the user. As a result, dynamic web scraping requires special handling to retrieve the complete dataset.
One common approach to handle dynamic web scraping is to use headless browsers such as PhantomJS or Selenium. Headless browsers do not require a graphical user interface (GUI) and run automation scripts to mimic real user interactions. They can execute JavaScript and simulate mouse clicks, key presses, and scrolling events. Another option is to use cloud-based web scraping services that automatically manage the execution of scraping jobs, taking care of regular maintenance procedures and pricing tiers depending on the volume of data being scraped. Some popular options include ScraperAPI, Scrapinghub, AWS Lambda + API Gateway, and Zyte Scrapers.
The algorithmic steps involved in dynamic web scraping include:
- Identify the endpoints or URLs of the webpages to be scraped.
- Choose the appropriate web scraping library or framework, depending on the complexity of the target site. Popular libraries include rvest and BeautifulSoup in R; Scrapy in Python; Beautiful Soup in Java and PHP.
- Set up a development environment, including installing the necessary dependencies and setting up the configuration file.
- Implement the web scraping logic, i.e., define the sequence of actions to be taken when scraping the targeted sites. Common actions include navigating to different pages, retrieving data from forms, submitting queries, parsing JSON objects, etc.
- Handle errors and exceptions, especially those caused by anti-scraping measures implemented by the targeted websites.
- Store the retrieved data in a suitable format, such as CSV, XML, or JSON. Additional processing can be applied, such as removing duplicate entries or merging data across multiple files.
- Schedule periodic scraping runs, taking into account any limitations imposed by the chosen hosting service or the frequency of updates made by the owners of the targeted websites.
Static Web Scraping
Static web scraping involves generating a set of predefined requests that specify the parameters of interest. These requests are sent once per session and yield a consistent snapshot of the website at a particular point in time. The resulting output can then be saved and analyzed offline.
Example scenarios include monitoring company performance metrics such as quarterly earnings reports or analyst ratings on major indexes, collecting data sets from scientific journals, or cataloguing product descriptions from retail websites.
The algorithmic steps involved in static web scraping include:
- Identify the URL(s) of the web page(s) to be scraped and define the fields of interest, such as keywords, metadata, or captions.
- Determine the depth of scraping, i.e., whether to crawl the entire site or limit the scope to a subsection of the site. Also determine whether to scan all pages or only selectively scan specific pages based on keyword searches or category labels.
- Select the appropriate web scraping library or tool, again depending on the complexity of the target site. Popular tools include Wget in Linux systems, FashionBot in Node.js, Scrapy in Python, and Cheerio in Javascript.
- Define the login credentials, if applicable, and configure the proxy settings if needed.
- Run the script and monitor its progress. Collect the results in a suitable format, such as CSV or JSON, and analyze the data offline. Make adjustments as necessary to optimize the data quality.
4. Demonstration and Code Example
Now that we have discussed the fundamentals of web scraping, let's dive deeper into the details of performing web scraping using R. We will showcase some code examples illustrating how to scrape data from popular web pages and explain how to extract meaningful insights from the collected data.
We will cover three main areas of web scraping: searching and indexing, text mining, and data visualization. Let's get started!