Apr 20, 2021 2) Octoparse Octoparse is a web scraping tool easy to use for both coders and non-coders and popular for eCommerce data scraping. It can scrape web data at a large scale (up to millions) and store it in structured files like Excel, CSV, JSON for download.

1. Rvest Tutorial
2. Free Web Scraper
3. R Web Scraping Wikipedia

• May 22, 2020 For a deeper look at the legal concerns, see the 2018 publications Legality and Ethics of Web Scraping by Krotov and Silva and Twenty Years of Web Scraping and the Computer Fraud and Abuse Act by Sellars. Now it's time to scrape! After assessing the above, I came up with a project. My goal was to extract addresses for all Family Dollar stores.
• Web scraping is the process of collecting the data from the World Wide Web and transforming it into a structured format. Typically web scraping is referred to an automated procedure, even though formally it includes a manual human scraping. We distinguish several techniques of web scraping.

rvest is new package that makes it easy to scrape (or harvest) data from html web pages, inspired by libraries like beautiful soup. It is designed to work with magrittr so that you can express complex operations as elegant pipelines composed of simple, easily understood pieces. Install it with:

Rvest Tutorial

rvest in action

To see rvest in action, imagine we’d like to scrape some information about The Lego Movie from IMDB. We start by downloading and parsing the file with html():

To extract the rating, we start with selectorgadget to figure out which css selector matches the data we want: strong span. (If you haven’t heard of selectorgadget, make sure to read vignette('selectorgadget') - it’s the easiest way to determine which selector extracts the data that you’re interested in.) We use html_node() to find the first node that matches that selector, extract its contents with html_text(), and convert it to numeric with as.numeric():

We use a similar process to extract the cast, using html_nodes() to find all nodes that match the selector:

The titles and authors of recent message board postings are stored in a the third table on the page. We can use html_node() and [[ to find it, then coerce it to a data frame with html_table():

Other important functions

• If you prefer, you can use xpath selectors instead of css: html_nodes(doc, xpath = '//table//td')).

• Extract the tag names with html_tag(), text with html_text(), a single attribute with html_attr() or all attributes with html_attrs().

• Detect and repair text encoding problems with guess_encoding() and repair_encoding().

• Navigate around a website as if you’re in a browser with html_session(), jump_to(), follow_link(), back(), and forward(). Extract, modify and submit forms with html_form(), set_values() and submit_form(). (This is still a work in progress, so I’d love your feedback.)

To see these functions in action, check out package demos with demo(package = 'rvest').

1.2 Web Scraping Can Be Ugly

Free Web Scraper

Depending on what web sites you want to scrape the process can be involved and quite tedious. Many websites are very much aware that people are scraping so they offer Application Programming Interfaces (APIs) to make requests for information easier for the user and easier for the server administrators to control access. Most times the user must apply for a “key” to gain access.

For premium sites, the key costs money. Some sites like Google and Wunderground (a popular weather site) allow some number of free accesses before they start charging you. Even so the results are typically returned in XML or JSON which then requires you to parse the result to get the information you want. In the best situation there is an R package that will wrap in the parsing and will return lists or data frames.

Here is a summary:

R Web Scraping Wikipedia

• First. Always try to find an R package that will access a site (e.g. New York Times, Wunderground, PubMed). These packages (e.g. omdbapi, easyPubMed, RBitCoin, rtimes) provide a programmatic search interface and return data frames with little to no effort on your part.

• If no package exists then hopefully there is an API that allows you to query the website and get results back in JSON or XML. I prefer JSON because it’s “easier” and the packages for parsing JSON return lists which are native data structures to R. So you can easily turn results into data frames. You will ususally use the rvest package in conjunction with XML, and the RSJONIO packages.

• If the Web site doesn’t have an API then you will need to scrape text. This isn’t hard but it is tedious. You will need to use rvest to parse HMTL elements. If you want to parse mutliple pages then you will need to use rvest to move to the other pages and possibly fill out forms. If there is a lot of Javascript then you might need to use RSelenium to programmatically manage the web page.