Web scraping with rvest and SelectorGadget can be powerful and fun. Recently I have experimented with trying to scrape a table from the Chronicle of Higher Education that showed compensation for university CEO’s. With a certain amount of trial and error I used SelectorGadget to find the fields I wanted to scrape: name, university, and compensation.

But when I went through the steps in rvest to scrape those fields, I got nothing (in the form of a zero length vector of results). I know next to nothing about how javascript is used to populate a web page so my understanding of what is happening is almost non-existant. I see the results on the screen, but when I use the browser to look at the page source, I don’t see the individual compensation data. Here’s a Stack Overflow response that explains a little bit of what is going on:

The “View Source” only shows the source the server sent at the time the browser requested the particular webpage from the server. Therefore, since these changes were made on the client side, they don’t show up on the “View Source” because they’ve been made after the original page has been delivered.

In my case, javascript processed by my browser is adding html after the original page was sent. (Or at least I think that is what is going on.)

The solution is a “headless browser”. This is a program that functions like a web browser, but it’s purpose is to produce HTML rather than to put something on the screen (hence, headless, if one thinks of the screen as the head). This article by Brooke Watson goes over the steps you need to follow to scrape web data created via javascript (and also discusses other considerations related to web scraping). Another good source is this post by Florian Teschner. I also found a comprehensive list of headless browsers, although I haven’t looked beyond PhantomJS.

An Example

In this post I will work through scraping the Chronicle page as an example. First we have to get the HTML containing the CEO compensation table. To do that I will use PhantomJS as a headless browser. I installed it on my Windows computer at work via a PhantomJS install page. I also installed it on my Mac at home using HomeBrew. I did a little bit of googling first to assure myself that PhantomJS was was reputable and safe to install. The source code is available on GitHub. Note that the author has suspended development of PhantomJS as of March, 2018, for reasons that are discussed at the GitHub README.

To use PhantomJS, I’ll use the R system() function to run PhantomJS so that it processes a javascript file called public_fetch.js. This is based on example code cited above with the URL reference changed to point to the Chronicle of Higher Ed page and an output file for the resulting HTML called public_ceo_export.html. Here’s the javascript that PhantomJS will process:

var webPage = require('webpage');
var page = webPage.create();
var fs = require('fs');
var path = 'public_ceo_export.html';
page.open('https://www.chronicle.com/interactives/executive-compensation?cid=wsinglestory#id=table_public_2017', function (status) {
var content = page.content;
fs.write(path,content,'w');
phantom.exit();
});

We can call PhantomJS from within R:

# a macos version of running phantomjs
system("phantomjs public_fetch.js")

After this runs, public_ceo_export.html contains the HTML that contains the data table. Here’s what the beginning of the data table looks like in raw HTML:

<table id="interactive_table" class="interactive_table"><tbody><tr id="table-13623_157289_2017_public" class="result odd"><td class="col_0"><div class="ect_name"><div class="ect_head"><img class="fl headshot" src="//chronicle.s3.amazonaws.com/DI/exec-comp/james-r-ramsey.jpg" alt="James R. Ramsey"></div><span class="name">James R. Ramsey *</span><span class="college">University of Louisville</span></div><div class="ec_baractual" data-val="4290232" style="width: 100%;"><div class="ecba_notbase" style="width:98.70163198633547%;"></div></div><span class="ec_barlabel">$4,290,232</span><div class="ec_rowHover"><span class="ech_label">James R. Ramsey <em>University of Louisville, 2017</em></span><span class="ech_detail"><em>Total Compensation: </em>$4,290,232</span><span class="ech_detail ech_base"><em>Base Pay: </em>$55,703</span><span class="ech_detail ech_bonus"><em>Bonus Pay: </em>$0</span><span class="ech_detail ech_other"><em>Other Pay: </em>$4,233,739</span></div></td></tr><tr id="table-13790_100858_2017_public" class="result even"><td class="col_0"><div class="ect_name">

Yuck. The data is in there, but HTML is not easy for mere humans to decipher. Our next task is to use rvest to pull out the data elements we are looking for. In the HTML there are a lot of “class” labels which (I think) are there so that CSS can be used to produce a snazzy looking data table delivered to the user’s screen. Those names are confusing to read, but revest is going to make good use of them.

What class names are we looking for? I used SelectorGadget to tease out which elements I wanted to pull out of the HTML. Here is a video that demonstrates how to use both rvest and SelectorGadget. I won’t try to explain how to use SelectorGadget here.

Next we’ll look at some basic rvest code.

library(rvest)
library(tidyverse)
library(stringr)
# read the page that was written by the headless browser
public_page <- read_html("public_ceo_export.html")
# look for HTML bit with the ".name" and produce them as text
name <- public_page %>% html_nodes(".name") %>% html_text()
# look for HTML bit with the ".college" and produce list of universities
college <- public_page %>% html_nodes(".college") %>% html_text()
print(college[1:3]) # show we have something reasonable
base <- public_page %>%
html_nodes(".ech_base") %>%
html_text() %>%
str_replace_all("[[:alpha:]]|:| |\\$|,", "") %>%
as.numeric()
bonus <- public_page %>%
html_nodes(".ech_bonus") %>%
html_text() %>%
str_replace_all("[[:alpha:]]|:| |\\$|,", "") %>%
as.numeric()
other <- public_page %>%
html_nodes(".ech_other") %>%
html_text() %>%
str_replace_all("[[:alpha:]]|:| |\\$|,", "") %>%
as.numeric()
# .ech_detail retrieves all four elements so length is 200 rther than 50
# (I figured out .ech_detail by trial and error.)
# we need to pull out every fourth element to get the total
total <- public_page %>%
html_nodes(".ech_detail") %>%
html_text()
total <- total[seq(1, 200, 4)]
# Identify cases where adding up the bits is different than the "total"
if (!all(str_detect(total, "Total Compensation"))) print("something is wrong with total")
total <- total %>%
str_replace_all("[[:alpha:]]|:| |\\$|,", "") %>%
as.numeric()

The general pattern is that we get the HTML (loaded here into pubic_page), use html_nodes to pick out the items we want, and then convert the resulting items into text via html_text. For the numeric fields, we also need strip out commas and dollar signs and covert to numeric.

(str_replace uses regular expressions, which is a whole topic in and of itself. There is a giga-ton of info on the web about regular expressions. For an R user, I would suggest focusing on using it via the stringr package, so this page might be a good place to start.)

The Punch Line?

At this point, you are probably looking for the punchline: some insight about university CEO compensation. No such luck. What to do with the information after you have scraped it is a different topic. In this post I am just trying to find an illustration of where scrpaing might be used and lay down enough information so that if I actually want to do something like this again I’ll be able to recreate it.

When Is Scraping OK?

Just because you can do something doesn’t mean you may. You should check the terms of service of the web site you are going to scrape. Sometimes it is made clear that web scraping is off limits. For example, this bit from the FlightAware terms of service is admirably clear:

you will only access the FlightAware web site with a human-operated interactive web browser and not with any program, collection agent, or “robot” for the purpose of automated retrieval or display of content.

Okey dokey, no web scraping the FlightAware web site.

The “User Agreement” for the Chronicle web site seems to be more what one expects in the way of long and ambiguous (to the layperson at least) legalese. There is a restrictions on use section that seems to be restrictive indeed.

You may not modify, decompile, create derivative work(s) of, disassemble, retransmit, resell, distribute, compile, broadcast, sublicense, mirror, frame, rent, or otherwise use in any manner not expressly permitted herein, the Site or any of its content; use robots or spiders or other automated devices or manual processes to monitor or gather any data from the Site or its users; create course books or educational materials using any of the Site content; use the Site to “stalk” or harass; place a link to the internal pages of any password-protected portions of the Site on any Web site, intranet, or extranet without the prior written permission of The Chronicle….

Under the section on permitted uses,

We would like your access to the Site to be useful and convenient. Subject to the terms and conditions of this Agreement, you may download items of content on the Site for personal use during the term of this Agreement, except in any instance where we have expressly restricted the access to or copying of such materials.

So can I use web scraping to download data from this Chronicle article for my own use? I don’t really know. As a practical matter I assume that if I don’t republish the information I’m on fairly safe ground. An earlier version of this post had a screenshot to illustrate using SelectorGadget on the Chronicle CEO table. I decided that was not something they would regard as a fair use of their copyrighted and restricted material, so I deleted it before this post was published.

robots.txt

Another way to check on whether web scraping is welcomed or frowned upon is to check for a file called robots.txt at the top level of the site. This Wikipedia article describes the history of this tool. You can ask your browser to see robots text by just adding /robots.txt to the top level domain name for a site. For example, chronicle.com/robots.txt is fairly thin:

User-agent: yandex

User-agent: bingbot
User-agent: msnbot
Disallow:
Sitemap: https://www.chronicle.com/public/sitemap/chronicle-sitemap-index.xml
Crawl-delay: 20

User-agent: *
Disallow:
Sitemap: https://www.chronicle.com/public/sitemap/chronicle-sitemap-index.xml

There’s nothing after Disallow:. The robots.txt standard is aimed primarily at things like search engines crawling the internet looking for the information to add to their search algorithms. FlightAware has a long, elaborate robots.txt file. Yale’s robots.txt is even more elaborate.

There’s a semi-official site that describes robots.txt. I don’t know much more about how to interpret robots.txt, but it’s one more thing you can check if you are concerned about whether your web scraping is legit.

That wraps it up. Web sraping can be fun and useful. But keep an eye on whether scraping a particular site is legit and legal.