1 Introduction

This tutorial introduces how to extract and process text data from social media sites, web pages, or other documents for later analysis. Parts of this tutorial builds on and uses from Wiedemann and Niekler (2017). The entire code for the sections below can be downloaded here.

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.

Preparation and session set up

As all calculations and visualizations in this tutorial rely on R, it is necessary to install R and RStudio. If these programs (or, in the case of R, environments) are not already installed on your machine, please search for them in your favorite search engine and add the term download. Open any of the first few links and follow the installation instructions (they are easy to follow, do not require any specifications, and are pretty much self-explanatory).

In addition, certain packages need to be installed so that the scripts shown below are executed without errors. Before turning to the code below, please install the packages by running the code below this paragraph. If you have already installed the packages mentioned below, then you can skip ahead ignore this section. To install the necessary packages, simply run the following code - it may take some time (between 1 and 5 minutes to install all of the packages so you do not need to worry if it takes some time).

# clean current workspace
# set options
options(stringsAsFactors = F)
# install libraries
install.packages(c("rvest", "readtext"))

For web crawling and scraping, we use the package “rvest” and to extract text data from various formats such as PDF, DOC, DOCX and TXT files with the “readtext” package. The tasks described int his section consist of:

  1. Download a single web page and extract its content
  2. Extract links from a overview page and extract articles
  3. Extract text data from PDF and other formats on disk

Before we can start with with web crawlign and scraping, we need to create a new R Project (File -> New Project -> Existing directory) in the provided tutorial folder. Then 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 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 <- "https://www.theguardian.com/world/2017/jun/26/angela-merkel-and-donald-trump-head-for-clash-at-g20-summit"

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.

html_document <- read_html(url)

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 https://en.wikipedia.org/wiki/XPath) 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.

4 Read various file formats

In case you have already a collection of text data files on your disk, you can import them into R in a very convenient provided by the readtext package. The package depends on some other programs or libraries in your system, to provide extraction for Word- and PDF-documents. So there may be some hurdles to install the package.

But once it is successfully installed, it allows for very easy extraction of text data from various file formats. Fist, we request a list of files in the directory to extract text from. For demonstration purpose, we provide in data/documents a random selection of various text formats.

data_files <- list.files(path = "data/documents", full.names = T, recursive = T)
# View first file paths
head(data_files, 3)
## character(0)

The readtext function from the package with the same name, detects automatically file formats of the given files list and extracts the content into a data.frame. The parameter docvarsfrom allows you to set metadata variables by splitting path names. In our example, docvar3 contains a source type variable derived from the sub folder name.

extracted_texts <- readtext(data_files, docvarsfrom = "filepaths", dvsep = "/")
# View first rows of the extracted texts
# View beginning of tenth extracted text
substr(trimws(extracted_texts$text[10]) , 0, 100)

Again, the extracted_texts can be written by write.csv2 to disk for later use.

How to cite this tutorial

Schweinberger, Martin. 2020. Web Crawling using R. Brisbane: The University of Queensland. url: https://slcladal.github.io/webcrawling.html.


Wiedemann, Gregor, and Andreas Niekler. 2017. “Hands-on: A Five Day Text Mining Course for Humanists and Social Scientists in R.” Berlin: Proceedings of the 1st Workshop Teaching NLP for Digital Humanities (Teach4DH@GSCL 2017). https://tm4ss.github.io/docs/index.html.