Screenscraping

Screenscraping is a highly informal method of parsing HTML output for data.

The right way to work with programs on non-local hosts is through simple RPC (remote procedure calls) or APIs (Application Program Interfaces) that may include more complex objects or datastructures. In fact, the contents of webpages has changed dramatically from the simple HTML we would have seen even a few years ago, to highly dynamic containers of elaborate Javascript programs that assemble their information payload client-side, often from multiple sources. For such pages, API access is likely to be the only sensible way, and reverse engineering such output would be a major project. However many less sophisticated sites give us simpler output and we need to work with what we have: the contents of a Webpage. Taking a server's HTML output and parsing the relevant data-elements from it is therefore often called screenscraping. It is generally used

when data is available only through a Web-server;
when no formal specifications for the data and or procedures exist or are not easily available;
when robust access and reproducible behaviour are of less importance than speed of deployment.

Screen scraping is therefore highly informal and ad hoc - a quick and dirty solution to common tasks.

Retrieving

The first issue to address is how to retrieve the data. Let us assume it is textual data - I could not imagine that it would be less work to try to parse images than to contact the maintainers of the data and negotiate a proper interface.

Web browser

Simply navigate to a page, then save it as HTML or text-only. It's often easier to work with HTML because the markup may simplify parsing. Let's access the domain information for the yeast Mbp1 cell cycle regulator at Uniprot. The UniProt ID of the protein is P39678 and we can directly access information for a protein at Uniprot with this knowledge:

http://www.uniprot.org/uniprot/P39678

What I would like to do from this page is to access the start and end coordinates for the annotated domains. Let's have a look at the HTML source first.

It is certainly quite messy - but it seems well enough structured to work with it. We could copy and paste it and take it apart ...

wget

Better is to download the page directly. wget is a Unix command line interface to network protocols. It is simple to use to download the Mbp1 Uniprot page:

wget -O - http://www.uniprot.org/uniprot/P39678

If a file name is specified instead of "-", the output will be written to that file instead of STDOUT. If -O is not specified, the output will be written to a file in the local directory with the same name as the file on the server.

curl

curl is an alternative to wget. For a comparison of the two see here. Its syntax is "cleaner" unix, supporting redirects and pipes.

curl http://www.uniprot.org/uniprot/P39678 > P39678.html
head P39678.html

Perl

backticks

The easiest way to use Perl to retrieve a Web server document is actually through wget or curl. Consider the following code:

use strict;
use warnings;
 
my $url = 'http://www.uniprot.org/uniprot/P39678';
my $out = `curl $url`;
print $out;

exit();

The fun part is in the backticks: strings in backticks are executed as system commands and the resulting output from STDOUT is assigned to a variable.

LWP

A much more flexible way to use Perl to interact with Webservers is LWP (example) (Library for WWW in Perl) (also see here - with a link to an online book on the topic). Typical uses include sites into which you have to login, accept cookies or otherwise interact in more complex ways with the server. While wget will retrieve the contents of an URL, LWP simulates much of the behaviour of a browser.

Neither Javascript nor any plugins will work through LWP. That's not to say it can't be done, just not with LWP.

PHP

PHP has inbuilt support to retrieve HTML documents. Here is an example that retrieves this page and parses only the PHP section from it.

<html>
    <head>
        <title>Example</title>
    </head>
    <body>
<?php

$source = "http://biochemistry.utoronto.ca/steipe/abc/index.php/Screenscraping";

$raw = file_get_contents($source);
# print($raw);

preg_match("/(PHP<\/span><\/h4>)(.*?)(<h4>)/s", $raw, $matches);

echo $matches[1] . $matches[2] . "\n";

?>
    </body>
</html>

The modifier s after the matching pattern allows matching across newline boundaries. Otherwise matches would only be retrieved if they were completely contained within a single line.

Parsing

To parse anything meaningful from the raw code you have retrieved, you will need Regular_Expressions ...

R

Screenscraping with R is almost ridiculously easy with the XML package and the readHTMLTable() function^[1] ...

# screenscraping.R
# sample code to demonstrate the R XML package and
# readHTMLTable()
#
# Boris Steipe for BCB410
# 2014

setwd("my/R/working/directory")

if (!require(XML, quiet =TRUE)) { 
    install.packages("XML") 
    library(XML)
}

# Fetch the datatable for Ebola cases and deaths timeline from the
# Wikipedia page on the 2014 outbreak.

data <- readHTMLTable("http://en.wikipedia.org/wiki/Ebola_virus_epidemic_in_West_Africa")

# That's all we need to do to parse the page and return all table data 
# for further processing. The return value is a list of dataframes. Most
# likely the dataframes will contain columns of factors.

data

# The dataframes can be easily accessed as named elements of the list:
data$`Ebola cases and deaths by country and by date - 1 August to present.`
data$`Archived Ebola cases and deaths by country. - 22 March to 30 July`

# Extract the first three columns for these tables: 
d1 <- data$`Ebola cases and deaths by country and by date - 1 August to present.`[1:3]
d1 <- d1[2:nrow(d1),1:3] # slice off the first row
d2 <- data$`Archived Ebola cases and deaths by country. - 22 March to 30 July`[1:3]
d2 <- d2[2:nrow(d1),1:3]
d1 <-  rbind(d1, d2)

# 
# Let's clean up the data:
# First we convert the factors to strings, (column by column
# otherwise R gets confused)

d1[,1] <- as.character(d1[,1])
d1[,2] <- as.character(d1[,2])
d1[,3] <- as.character(d1[,3])

d1

# We see that some cells contain linebreaks. Lets remove these
# rows.
d1 <- d1[apply(d1[,2:3], 1, function(row) length(grep("\\n",row))==0),]
d1

# Now remove the comma from numbers and convert them to numeric
# gsub() will help us:
# the expression we need is as.numeric(gsub(",","", data)) ...
apply(d1[,2:3],2,function(x) as.numeric(gsub(",","", x)))
d1[,2:3] <- apply(d1[,2:3], 2, function(x) as.numeric(gsub(",","", x)))


# finally we need to convert the dates ...
apply(d1[,1], 2, function(x) as.integer(as.Date(x, format="%d %b %Y")))
d1[,1] <- as.integer(as.Date(d1[,1], format="%d %b %Y"))
# ... and start with day 1:
d1[,1] <- d1[,1] - min(d1[,1]) + 1


# Done. Plot this:

plot(d1[,1:2])

plot(d1[,1], log(d1[,2]))

# Case fatality ratio:
plot(d1[,1], d1[,3]/d1[,2])

# Done.

Automation

What if you want to extract data from multiple pages? What if the data is dynamically generated as a result to a query and you don't know the URL? What if you simply need much more control over your data retrieval? You will need to write some code that emulates user behaviour, essentially a bot, or spider. Note that there may be legal issues involved in doing so^[2].

Here is a simple example for real XML parsing that pulls some data on language distributions in Canada from Ethnologue using R's XML package^[3].

# EthnoSpider.R
# sample code to demonstrate accessing multiple URLs
# via XML tree parsing functions
#
# Boris Steipe for BCB410
# 2014

setwd("my/R/working/directory")

if (!require(XML, quiet =TRUE)) { install.packages("XML") }
library(XML)

country_page <- htmlParse("http://www.ethnologue.com/country/CA/languages", isURL = TRUE)
pages <- getNodeSet(country_page, "//a[@href]")

urls <- c()
for (i in 1:length(pages)) {
	if (length(grep("/language/...$", xmlAttrs(pages[[i]])))>0) {
		urls <- c(urls, paste("http://www.ethnologue.com", xmlAttrs(pages[[i]]), sep=""))
	}
}

urls

#... etc.

Beyond regular expressions: Xpath

One of the problems we encounter is that HTML cannot be parsed with regular expressions. Of course, it often works, but for robust applications it is highly not advisable. Use an XML parser instead.

TBC

Exercises

TBD

Notes and references

↑ Note that readHTMLTable() returns dataframes and by default turns strings into factors. This R code converts the factors in a very pedestrian way, but for more principled approaches to avoid getting factors in the first place, or to convert them if you do, see here at stackoverflow.
↑ For a discussion of the legal issues see e.g. Web scraping on Wikipedia
↑ For a quick introduction to the package see http://www.omegahat.org/RSXML/shortIntro.pdf. The vignettte is here: http://cran.r-project.org/web/packages/XML/XML.pdf. Type ??xml in R to see the available functions.

Screenscraping

Contents

Retrieving

Web browser

wget

curl

Perl

backticks

LWP

PHP

Parsing

R

Automation

Beyond regular expressions: Xpath

Exercises

Notes and references

Further reading and resources

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