GUELPH The Ontario government is investing $2.5 million through the On… Province Ramps Up Production of Ontario-Made Ventilators
TORONTO - The Ontario government launched a new voluntary interactive sc… Ontario Launches New COVID-19 Screening Tool to Help Protect Students an… We can see there’s still something strange going on with unicode, but that’s a problem for another day:
WEB SCRAPER CLICK BUTTON CODE
Mutate(plain_text = str_remove_all(plain_text, regex("Table of Contents(.*?)Related TopicsĪnd that’s it! Here are the first five results from when I ran the code to prepare this blog post. But it works, and I’m a strong proponent of third-best solutions that work! # would you like updates to the console? This code chunk does all that using, again, a workmanlike for loop and some decidedly un- tidy indexing. For each link we tell RSelenium to load the page, we extract some relevant information using css selectors, and we put that information into a results tibble. Scraping the web data is pretty easy in comparison to what we’ve done so far. This is what we’ve been waiting for! Now we can finally start scraping some web data! Here are the first five links from when I ran the code: URL We only care about the client, so we’ll extract it into another variable called rDC like so: # Start Selenium using Firefox on port 4567įilter(str_detect(value, "news\\.ontario\\.ca/en"), Assuming it worked, rsDriver() returned a list named rD that contains a server and a client. This function gives us a lot of options, but we’ll settle for specifying the port (4567, for aesthetic purposes) and browser (Firefox, because it was the first one I tried and it worked).
We’ll move on the PhantomJS once we’ve got this step working.įirst, we start a Selenium server and browser using the command rsDriver(). I found it helpful to start this way because I could actually see what was happening. Next we’ll set up RSelenium to automatically manage an instance of Firefox. With this knowledge, we’re ready to get started! Normally this is the exact opposite of what you want in a web browser (it’s no good knowing that your computer is looking at cat gifs if you can’t see them), but here it’s perfect since we just want to automate basic tasks and extract the results. “Headless” here means that it doesn’t connect to any kind of graphical user interface–it just runs in the background, doing web stuff. PhantomJS is a scriptable headless web browser.
WEB SCRAPER CLICK BUTTON DOWNLOAD
You can download it through its CRAN page, or as usual through R’s package manager. RSelenium is an R package that does exactly what its name might suggest, and lets us interact with Selenium through R. There’s just something aesthetically pleasing about using a complex technological workaround to solve a dumb UI problem! We’ll be using it for a very simple purpose: connecting to a browser instance, navigating it to the Ontario Newsroom, and then having our computer click the ridiculous button 1,000 times to load 10,000 media releases. That’s it!”, and frankly it’s tough to improve on that summary. Selenium’s developers claim that “Selenium automates browsers. Some New Tools: Selenium, RSelenium, and PhantomJSįor this tutorial we’ll need three new tools: Selenium, RSelenium, and PhantomJS. It’s like putting your entire library catalogue online but only showing users the last 10 books you happened to put on the shelves.īasic web-scraping techniques based on pure html or css won’t work here, and our only other option is to sit there clicking the ridiculous button for hours on end. This is, and I cannot stress this enough, dumb: if you want to find a press release from last year, you will have to click the ridiculous button over 300 times. Since I’m interested in applying machine learning techniques to public policy questions, I decided to try my hand at createing a dataset of publicly available press releases issued by the Government of Ontario.īut while Ontario’s press releases are technically available online, the Ontario Newsroom is a Javascript-enabled nightmare that shows results 10 at a time in reverse chronological order each time you click a button. I’ve been working through the excellent (and free!!) book Supervised Machine Learning for Text Analysis in R byĮmil Hvitfeldt and Julia Silge, and wanted to try out some of their techniques on a new dataset. Motivation: Scraping Government of Ontario News Releases
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