1. Web Scraping In R Tutorial
2. Web Scraping In Resume
3. Web Scraping In React

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 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():

Web application for scraping. R has a fantastic library for web/dashboard development – `Shiny`. It’s far easier to use than anything similar in Python, so we’ll stick with it. To start, create a new R file and paste the following code inside. I would like to scrape information from a webpage. There is a login screen, and when I am logged in, I can access all kinds off pages from which I would like to scrape information (such as the last name of a player, the object.lastName). I am using R and the packages rvest and httr. Scraping books The `rvest` package is used in R to perform web scraping tasks. It’s very similar to `dplyr`, a well-known data analysis package, due to the pipe operator’s usage and the behavior in general. We know how to get to certain elements, but how to implement this logic in R?

Web Scraping In R Tutorial

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

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.

Web Scraping In Resume

Here is a summary:

Web Scraping In React

• 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.