# Web scraping and parsing

# Basic scraping with rvest

rvest (opens new window) is a package for web scraping and parsing by Hadley Wickham inspired by Python's Beautiful Soup (opens new window). It leverages Hadley's xml2 (opens new window) package's libxml2 (opens new window) bindings for HTML parsing.

As part of the tidyverse, rvest is piped (opens new window). It uses

  • xml2::read_html to scrape the HTML of a webpage,
  • which can then be subset with its html_node and html_nodes functions using CSS or XPath selectors, and
  • parsed to R objects with functions like html_text and html_table.

To scrape the table of milestones from the Wikipedia page on R (opens new window), the code would look like


url <- 'https://en.wikipedia.org/wiki/R_(programming_language)'

        # scrape HTML from website
url %>% read_html() %>% 
    # select HTML tag with class="wikitable"
    html_node(css = '.wikitable') %>% 
    # parse table into data.frame
    html_table() %>%
    # trim for printing
    dplyr::mutate(Description = substr(Description, 1, 70))

##    Release       Date                                                  Description
## 1     0.16            This is the last alpha version developed primarily by Ihaka 
## 2     0.49 1997-04-23 This is the oldest source release which is currently availab
## 3     0.60 1997-12-05 R becomes an official part of the GNU Project. The code is h
## 4   0.65.1 1999-10-07 First versions of update.packages and install.packages funct
## 5      1.0 2000-02-29 Considered by its developers stable enough for production us
## 6      1.4 2001-12-19 S4 methods are introduced and the first version for Mac OS X
## 7      2.0 2004-10-04 Introduced lazy loading, which enables fast loading of data 
## 8      2.1 2005-04-18 Support for UTF-8 encoding, and the beginnings of internatio
## 9     2.11 2010-04-22                          Support for Windows 64 bit systems.
## 10    2.13 2011-04-14 Adding a new compiler function that allows speeding up funct
## 11    2.14 2011-10-31 Added mandatory namespaces for packages. Added a new paralle
## 12    2.15 2012-03-30 New load balancing functions. Improved serialization speed f
## 13     3.0 2013-04-03 Support for numeric index values 231 and larger on 64 bit sy

While this returns a data.frame, note that as is typical for scraped data, there is still further data cleaning to be done: here, formatting dates, inserting NAs, and so on.

Note that data in a less consistently rectangular format may take looping or other further munging to successfully parse. If the website makes use of jQuery or other means to insert content, read_html may be insufficient to scrape, and a more robust scraper like RSelenium may be necessary.

# Using rvest when login is required

I common problem encounter when scrapping a web is how to enter a userid and password to log into a web site.

In this example which I created to track my answers posted here to stack overflow. The overall flow is to login, go to a web page collect information, add it a dataframe and then move to the next page.


#Address of the login webpage

#create a web session with the desired login address
pgform<-html_form(pgsession)[[2]]  #in this case the submit is the 2nd form
filled_form<-set_values(pgform, email="*****", password="*****")
submit_form(pgsession, filled_form)

#pre allocate the final results dataframe.

#loop through all of the pages with the desired info
for (i in 1:5)
  #base address of the pages to extract information from
  url<-paste0(url, i)
  page<-jump_to(pgsession, url)

  #collect info on the question votes and question title
  summary<-html_nodes(page, "div .answer-summary")
  question<-matrix(html_text(html_nodes(summary, "div"), trim=TRUE), ncol=2, byrow = TRUE)

  #find date answered, hyperlink and whether it was accepted
  dateans<-html_node(summary, "span") %>% html_attr("title")
  hyperlink<-html_node(summary, "div a") %>% html_attr("href")
  accepted<-html_node(summary, "div") %>% html_attr("class")

  #create temp results then bind to final results 
  rtemp<-cbind(question, dateans, accepted, hyperlink)
  results<-rbind(results, rtemp)

#Dataframe Clean-up
names(results)<-c("Votes", "Answer", "Date", "Accepted", "HyperLink")
results$Accepted<-ifelse(results$Accepted=="answer-votes default", 0, 1)

The loop in this case is limited to only 5 pages, this needs to change to fit your application. I replaced the user specific values with ******, hopefully this will provide some guidance for you problem.

# Remarks

Scraping refers to using a computer to retrieve the code of a webpage. Once the code is obtained, it must be parsed into a useful form for further use in R.

Base R does not have many of the tools required for these processes, so scraping and parsing are typically done with packages. Some packages are most useful for scraping (RSelenium, httr, curl, RCurl), some for parsing (XML, xml2), and some for both (rvest).

A related process is scraping a web API, which unlike a webpage returns data intended to be machine-readable. Many of the same packages are used for both.

# Legality

Some websites object to being scraped, whether due to increased server loads or concerns about data ownership. If a website forbids scraping in it Terms of Use, scraping it is illegal.