The world-wide web presents enormous amounts of data. Unfortunately, the majority of the data is not directly available for download. In response, web scraping exploits indirect means to harvest data from websites. In practice, web scrapping is not unique and is totally legal. For example, web browsers rely on the Hypertext Transfer Protocol (HTTP) to fetch data and so does web scrapping. The difference with web scrapping is that the user retrieves, selects and extracts website content and data intended for browser display. This article shows how web scraping works and presents tools available in the R programming language for both manual and automated web-scraping.
What is Web Scraping?
Web scraping involves getting a web page and and extracting data from it. Specifically, a message is sent to fetch a web page using Uniform Resource Locators (URLs). A basic URL structure appears below:1
The request components include the protocol, which is typically http or https for secure communications. The host is a target website. The default port is 80, but one can be set explicitly, as shown here. The resource path specifies the server path to the data and the query is the data request action or verb.
A web page request is a simple component of web scrapping. Next, a server response will deliver a status message with web page content. The web page content must be searched, be it manually or automatically. Hence, web page crawling is a key feature of web scrapping. Next, the web page content will be parsed, extracted and reformatted.
Web scrapping, for example, might first retrieve a web page and then extract contact names and phone numbers. In another instance, the focus might be to grab a research data table or a collection of tables. Finally, another effort might seek to extract intel using text strings found in multiple news or social media reports.
Web Page Content – The basics
The response to a URL request is the web page delivered in a
HTML document message. HTML stands for HyperText Markup Language and defines the content and structure of a webpage.
“Hyper Text” in HTML refers to hyperlinks that connect webpages to one another, either within a single website or between websites, to populate page content. Meanwhile, “markup” text includes special page elements or “CSS selectors” such as <head>, <title>, <section>, <body>, <div>, <table>, <p>, <img> and may others. Web crawling and scrapping involves finding and extracting from these elements as needed.
Other language technologies also populate the web page besides HTML. The technologies include CSS, which describe a webpage’s presentation appearance and JavaScript for web page functionality. An analyst inspects all web page content using a web browser tool to find what parts will be scraped.
Web Page Inspection
Inspect web page content using web browser tools to find what parts will be scraped. For example, the following web site image (https://oilprice.com/oil-price-charts) shows petroleum product prices from around the world. The lower half of the image shows the web site HTML content in your web browser inspector (click to enlarge):
The inspector is launched in FireFox by right-clicking the data table and choosing “Inspect Element.” Other web browsers have selections to “View Page Source.” The next image shows the page source for the first table (click to enlarge):
If you don’t know HTML, this may look daunting! But look close. All the table text and data is there to be grabbed. Meanwhile, another essential tool for web page inspection is SelectorGadget. SelectorGadget is an open source tool that makes it easy to identify and define CSS content. Just install the browser extension as instructed. The tool simplifies inspection of complicated web sites to just a few point-and-click actions and it makes it a breeze to locate content to scrape.
Available R Packages
Several R packages are available for webscrapping:
[table id=89 /]
rvest is a set of wrapper functions around the xml2 and httr packages. The rest of the tutorial focuses on rvest exclusively. The main functions are:
- read_html(): read a webpage into R as XML (document and nodes)
- html_nodes(): extract pieces out of HTML documents using XPath and/or CSS selectors
- html_attr(): extract attributes from HTML, such as
href - html_text(): extract text content
Web Scraping Examples
The first step is to load the rvest package and to read the target web page:
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library(rvest) url <- "https://oilprice.com/oil-price-charts" webpage <- read_html(url) webpage {xml_document} <html lang="en"> [1] <head>\n<script>\n (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||fun ... [2] <body id="pagetop" class="oilprices loggedout">\n<!-- Google Tag Manager (noscript) -->\ ... |
The first two lines above are the first HTML lines of the target web page. rvest has read or captured the entire HTML content of the target webpage. Next, the webpage nodes are read to extract to find data table content…the product names and prices. The rvest command html_nodes() is told to seek specific nodes as defined by CSS selectors used in the function calls. The selector string used was defined by SelectorGadget after clicking on a price or name element on the web page, The reaming command html_text() converts node content to text and other commands use basic R syntax to reshape the data for use in R:
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value <- webpage %>% html_nodes( css = ".last_price") %>% html_text() name <- webpage %>% html_nodes(., css = "td:nth-child(2)") %>% html_text() %>% .[c(41:72, 74, 76:77, 79, 81:82, 84:85, 87:89, 91, 94:96, 98:99, 101:103, 105:107, 109:112, 114:116, 118:120, 122:124, 126:128, 130:131, 133, 135:142, 145:160, 162:164, 166:167, 169:170, 172, 174, 176:178, 180:182, 184:186, 188:189, 191, 193:196, 198:199, 201:213)] prices <- tibble(name = name, last_price = value) prices # A tibble: 138 x 2 name last_price <chr> <chr> 1 WTI Crude 55.98 2 Brent Crude 66.25 3 Mars US 63.19 4 Opec Basket 64.28 5 Canadian Crude Index 42.29 6 DME Oman 66.73 7 Urals 63.07 8 Mexican Basket 57.39 9 Indian Basket 65.04 10 Western Canadian Select 43.76 11 Dubai 64.05 12 Brent Weighted Average 64.19 13 Louisiana Light 63.60 14 Coastal Grade A 45.00 15 Domestic Swt. @ Cushing 52.00 |
The next example uses the pipe operator to link node parsing, data extracting and formatting. In this case, the extraction is on the 13th data table returned by the html_table() function. The table scraping extracts the spot price and price change data for US crude oil blends:
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tables13 <- webpage %>% html_nodes("table") %>% html_table(fill = TRUE) %>% .[[13]] %>% .[c(3:18, 21:23, 26:27, 30:31, 34, 37, 40:42, 45:47, 50:52, 55:56, 59, 62:65,68:69, 72),] %>% `colnames<-`(c("index", "name", "last", "change", "percent", "updated")) %>% as.tibble() %>% separate(col = "percent", into = c("percent", "update"), sep = "\\(") %>% select(name, last, change, percent) %>% mutate(last = as.numeric(last)) %>% mutate(change = as.numeric(change)) %>% mutate(percent = as.numeric(substr(percent,1,5))) table13 A tibble: 44 x 4 name last change percent <chr> <dbl> <dbl> <dbl> 1 West Texas Sour 49.54 1.18 2.44 2 West Texas Intermediate 52.04 1.18 2.320 3 Upper Texas Gulf Coast 39.84 1.18 3.05 4 Texas Gulf Coast Light 50.54 1.18 2.39 5 South Texas Sour 45.93 1.18 2.64 6 North Texas Sweet 49.5 0.25 0.51 7 North Texas Sour 39.91 0.87 2.23 8 Eagle Ford Pipeline 52.04 1.18 2.320 9 Eagle Ford Condensate 51.04 1.18 2.37 10 Eagle Ford 53.49 1.18 2.260 11 Tx. Upper Gulf Coast 45.75 1 2.23 12 South Texas Light 45.75 1 2.23 13 W. Tx./N. Mex. Inter. 52 1 1.96 14 South Texas Heavy 45.5 1 2.25 15 W. Cen. Tx. Inter. 52 1 1.96 16 East Texas Sweet 49.25 1 2.070 17 Arkansas Sweet 47.75 0.25 0.53 18 Arkansas Sour 46.75 0.25 0.54 19 Arkansas Ex Heavy 42.75 0.25 0.59 20 Buena Vista 65.21 1.11 1.73 21 Midway-Sunset 60.53 1.11 1.87 22 Williston Sweet 45.75 0.75 1.67 23 Williston Sour 41.86 0.75 1.82 24 Utah Black Wax 39.11 0.51 1.32 25 Four Corners 49.68 0.5 1.02 26 Colorado D-J Basin 50 0.75 1.52 27 Colorado South East 41.25 0.25 0.61 28 Colorado West 47.41 0.51 1.09 29 NW Kansas Sweet 42.25 0.25 0.6 30 SW Kansas Sweet 42.75 0.25 0.59 31 South Central Kansas 46.5 2 4.49 32 Delhi/N. Louisiana 49 1 2.08 33 South Louisiana 50.5 1 2.02 34 North Louisiana Sweet 47.75 0.25 0.53 35 Michigan Sour 44 1 2.33 36 Michigan Sweet 48.75 1 2.09 37 Nebraska Sweet 46.51 0.51 1.11 38 Oklahoma Sweet 52.04 1.18 2.320 39 Oklahoma Sour 37.5 0.25 0.67 40 Western Oklahoma Swt. 51.25 1 1.99 41 Oklahoma Intermediate 51 1 2 42 Wyoming General Sour 42.1 0.75 1.81 43 Wyoming General Sweet 48.75 0.75 1.56 44 Central Montana 46.98 0.51 1.1 |