This tutorial covers how to extract and process text data from web pages or other documents for later analysis. The automated download of HTML pages is called Crawling. The extraction of the textual data and/or metadata (for example, article date, headlines, author names, article text) from the HTML source code (or the DOM document object model of the website) is called Scraping. For these tasks, we use the package “rvest”.

  1. Download a single web page and extract its content
  2. Extract links from a overview page
  3. Extract all articles to corresponding links from step 2

1 Preparation

Create a new R script (File -> New File -> R Script) named “Tutorial_1.R”. In this script you will enter and execute all commands. If you want to run the complete script in RStudio, you can use Ctrl-A to select the complete source code and execute with Ctrl-Return. If you want to execute only one line, you can simply press Ctrl-Return on the respective line. If you want to execute a block of several lines, select the block and press Ctrl-Return.

Tip: Copy individual sections of the source code directly into the console (2) and run it step by step. Get familiar with the function calls included in the Help function.

First, make sure your working directory is the data directory we provided for the exercises.

# important option for text analysis
options(stringsAsFactors = F)
# check working directory. It should be the destination folder of the extracted 
# zip file. If necessary, use `setwd("your-tutorial-folder-path")` to change it.

2 Scraping of dynamic web pages

Modern websites often do not contain the full content displayed in the browser in their corresponding source files which are served by the webserver. Instead, the browser loads additional content dynamically via javascript code contained in the original source file. To be able to scrape such content, we rely on a headless browser “phantomJS” which renders a site for a given URL for us, before we start the actual scraping, i.e. the extraction of certain identifiable elements from the rendered site.

If not done yet, please install the webdriver package for R and install the phantomJS headless browser. This needs to be done only once.


Now we can start an instance of PhantomJS and create a new browser session that awaits to load URLs to render the corresponding websites.

pjs_instance <- run_phantomjs()
pjs_session <- Session$new(port = pjs_instance$port)

3 Crawl single webpage

In a first exercise, we will download a single web page from “The Guardian” and extract text together with relevant metadata such as the article date. Let’s define the URL of the article of interest and load the rvest package, which provides very useful functions for web crawling and scraping.

url <- ""

A convenient method to download and parse a webpage provides the function read_html which accepts a URL as a parameter. The function downloads the page and interprets the html source code as an HTML / XML object.

3.1 Dynamic web pages

To make sure that we get the dynamically rendered HTML content of the website, we pass the original source code dowloaded from the URL to our PhantomJS session first, and the use the rendered source.

# load URL to phantomJS session
# retrieve the rendered source code of the page
rendered_source <- pjs_session$getSource()
# parse the dynamically rendered source code
html_document <- read_html(rendered_source)

NOTICE: In case the website does not fetch or alter the to-be-scraped content dynamically, you can omit the PhantomJS webdriver and just download the the static HTML source code to retrieve the information from there. In this case, replace the following block of code with a simple call of html_document <- read_html(url) where the read_html() function downloads the unrendered page source code directly.

3.2 Scrape information from XHTML

HTML / XML objects are a structured representation of HTML / XML source code, which allows to extract single elements (headlines e.g. <h1>, paragraphs <p>, links <a>, …), their attributes (e.g. <a href="http://...">) or text wrapped in between elements (e.g. <p>my text...</p>). Elements can be extracted in XML objects with XPATH-expressions.

XPATH (see is a query language to select elements in XML-tree structures. We use it to select the headline element from the HTML page. The following xpath expression queries for first-order-headline elements h1, anywhere in the tree // which fulfill a certain condition [...], namely that the class attribute of the h1 element must contain the value content__headline.

The next expression uses R pipe operator %>%, which takes the input from the left side of the expression and passes it on to the function ion the right side as its first argument. The result of this function is either passed onto the next function, again via %>% or it is assigned to the variable, if it is the last operation in the pipe chain. Our pipe takes the html_document object, passes it to the html_node function, which extracts the first node fitting the given xpath expression. The resulting node object is passed to the html_text function which extracts the text wrapped in the h1-element.

title_xpath <- "//h1[contains(@class, 'content__headline')]"
title_text <- html_document %>%
  html_node(xpath = title_xpath) %>%
  html_text(trim = T)

Let’s see, what the title_text contains:

## Angela Merkel and Donald Trump head for clash at G20 summit

Now we modify the xpath expressions, to extract the article info, the paragraphs of the body text and the article date. Note that there are multiple paragraphs in the article. To extract not only the first, but all paragraphs we utilize the html_nodes function and glue the resulting single text vectors of each paragraph together with the paste0 function.

intro_xpath <- "//div[contains(@class, 'content__standfirst')]//p"
intro_text <- html_document %>%
  html_node(xpath = intro_xpath) %>%
  html_text(trim = T)

## German chancellor plans to make climate change, free trade and mass migration key themes in Hamburg, putting her on collision course with US
body_xpath <- "//div[contains(@class, 'content__article-body')]//p"
body_text <- html_document %>%
  html_nodes(xpath = body_xpath) %>%
  html_text(trim = T) %>%
  paste0(collapse = "\n")
## A clash between Angela Merkel and Donald Trump appears unavoidable after Germany signalled that it will make climate change, free trade and the manage
date_xpath <- "//time"
date_object <- html_document %>%
  html_node(xpath = date_xpath) %>%
  html_attr(name = "datetime") %>%

cat(format(date_object, "%Y-%m-%d"))
## 2017-06-26

The variables title_text, intro_text, body_text and date_object now contain the raw data for any subsequent text processing.

5 Optional exercises

Try to perform extraction of news articles from another web page, e.g. or

For this, investigate the URL patterns of the page and look into the source code with the `inspect element’ functionality of your browser to find appropriate XPATH expressions.

2020, Andreas Niekler and Gregor Wiedemann. GPLv3.