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What if you had an idea for an ecological study, but the data you needed weren't available? What if you wanted to validate one of your metrics by comparing your estimates to outside sources? How are you?

Well, for one thing, you could get the data online. Web scraping (web harvesting or web data extraction) is a computer software technique that allows you to extract information from websites. When you want to extract data from a document, copy and paste the items you want. For a website, this is a little more difficult due to the way the information is formatted and stored, usually as HTML code. This is how scrapers work by parsing a website's HTML source code to extract and retrieve certain elements in the page's code.


Search engines use a certain type of scraper called a web crawler or search bot to crawl web pages and identify which websites they link to and what terms they use. That could mean that the first web scrapers existed Early nineties .

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Google and Facebook really took scraping to another level . Google scoured the web to catalog and make all information on the internet accessible. Lately, Facebook has been using scrapers to help people find connections and fill their social networks.


Well that depends on what you think the importance of legality is. While early-century court precedents set the tone for unscrupulous scraping of content, recent judgments have shifted towards a more conservative approach. If you have to agree to declarations of consent, the data is available for purchase or the data is behind a login, you are usually in a legal dark field. Even if none of these reservations are met, you could still be in hot water .


Here are some general ethical issues Points to note before scraping:

1) Respect the hosting site's wishes

Some websites may contain instructions for bots and scrapers that describe the items that can be scrapped and what items are prohibited. These websites contain robot.txt files that do not allow certain content to be scraped. Also, if you need to agree to the terms and conditions, read them carefully. Check if there is an API or if the data is otherwise available for download or sale.

2) Respect the hosting site bandwidth

Website hosting costs money and scraping takes up bandwidth. If you are familiar with Denial-of-Service-Attacken , Scraping, or sending bots to a website is similar. Write responsible programs that limit bandwidth usage. Wait a few seconds between requests and try to scrape outside of peak hours. After all, you only scratch what you need.

3) Respect the law

Some call it theft; some call it legitimate business practices. The fact that you can access the data doesn't mean you can use it for your research. Some data are more sensitive. Certain time critical data Is popular. For example, a successful bookmaker might want their bets to be listed publicly, but of course they don't want their competitors to know about it. If necessary, read the terms of the contract or simply subversive breast .

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Sample application

Below is a quick example of scraping data from one bedroom apartment listings in Manhattan with R. This code can be easily adapted for other apartment sizes, locations, and other amenities by setting a different search filter for Naked Apartments and the updated URL insert below.

1) Get the website url

URL<- http://www.nakedapartments.com/renter/listings/search?nids=23,211.6,21,203,191,194,18,24,76,204,205,10,14,195,1,5,25,93,206,22,17,207,13,155,16,72, 2,9,20,19,73,7,208,209,192,8,74,210,11,4,3,26,212,12 & tools = 3 & order = climb & sort = rent & page =

# Set the maximum number of search results pages. Currently set to 800.

so<- as.character(seq(1,800,by=1))
URLs<- paste0(url, s)

2) Scrape the lines of code

# load the libraries

require (RCurl)
Library (stringer)

THOSE<- getURL(urls,encoding=UTF-8″) # Specify encoding when dealing with non-latin characters

3) Parse the HTML to isolate the data


# Price and environment

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Listings<- (xpathSApply(PARSED, [PATH], xmlValue))

# Trim white space

Listings<- str_trim(listings)
Listings<- strsplit(listings, , )
Tabs<- matrix(unlist(listings), , 2, byrow=TRUE)
Column names (tabs)<- cbind(price, neighborhood)

#lat and long

Years<- (xpathSApply(PARSED, div[@id]/@data-latitude))
Long<- (xpathSApply(PARSED, div[@id]/@data-longitude))
Registerkarten1<- cbind(tabs, lat, long)
row.names(tabs1)<- seq(nrow(tabs1))

4) Clean up and insert elements into a data frame

it is<- data.frame(tabs1)
Lats<- as.numeric(tabs1[,3])
Long<- as.numeric(tabs1[,4])

Lats [Lats == 0]<- NA
lang [lang == 0]<- NA

mydf [, 3]<- lats
mydf [, 4]<- longs

price<- mydf[,1]
Price1<- gsub($, , as.character(price), fixed=TRUE)
Price2<- gsub(,, , as.character(price1), fixed=TRUE)
Price3<- as.numeric(price2)
mydf [, 1]<- price3
Head (mdf)

NEW<- mydf[complete.cases(mydf),]
Table (complete.cases (NEW))

Which<- tapply(NEW$price, NEW$neighborhood, mean)
p<- as.matrix(dat)



Textbooks & Chapters

HANRETTY, C. 2013. Scraping the Web for Arts and Humanities.


NAN, X. Web-Scraping mit R. In: ROAD2STAT, hrsg. 6. China R 2013 Peking.

LEE, B. K. 2010. Epidemiological Research and Web 2.0 - the user-driven web. Epidemiology, 21,760-3.

SIGNORINI, A., SEGRE, A. M. & POLGREEN, P.M. 2011. Using Twitter to Follow the Level of Disease Activity and Public Concern in the US During the Influenza A H1N1 Pandemic. PLoS One, 6, e19467.

CUNNINGHAM, J.A. 2012. Using Twitter to Measure Behavioral Patterns. Epidemiology, 23, 764-5.

CHEW, C. & EYSENBACH, G. 2010. Pandemics in the age of Twitter: content analysis of tweets during the H1N1 outbreak 2009. PLoS One, 5, e14118.

[On Ethics: Screen Scraping: How To Benefit From Your Rival's Data]

[On ethics: Depends on what the meaning of the word illegal means]

[About Ethics - Screen Scratch Complaint]

abstinence only education doesn't work

[Programming with Humanists: Considerations for Building an Army of Hacker Scholars]
http://blog.hartleybrody.com/web-scraping/ http://openbookpublishers.com/htmlreader/DHP/chap09.html#ch09

Web pages

[Charles DiMaggio on Web Scraping]

[Basics of Web Scraping - Part I of III]

[Remove Google Scholar]

[How to Buy a Used Car with an R]

[Scraper commercial website]

[Scrambled data commercial website]


TO two-day EPIC course covers the digital capture of big data

BARBERA, P. NYU Politics Data Lab Workshop: Scraping Twitter and Web Data using R. Department of Politics, 2013 New York University

STARKWEATHER, J. 2013. Five Easy Steps to Scraping Data from Web Pages. Benchmarks RSS matters.

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