Parikshit is a marketer with a deep passion for data. He spends his free time learning how to make better use of data to make marketing decisions. We will teach you from ground up on how to scrape the web with R, and will take you through fundamentals of web scraping with examples from R.
The first step towards scraping the web with R requires you to understand HTML and web scraping fundamentals. HTML is behind everything on the web. Our goal here is to briefly understand how Syntax rules, browser presentation, tags and attributes help us learn how to parse HTML and scrape the web for the information we need. Before we scrape anything using R we need to know the underlying structure of a webpage.
You can basically open any HTML document using a text editor like notepad. HTML tells a browser how to show a webpage, what goes into a headline, what goes into a text, etc.
The underlying marked up structure is what we need to understand to actually scrape it. Looking at this source code might seem like a lot of information to digest at once, let alone scrape it! The next section exactly shows how to see this information better. Those are tags that HTML uses, and each of those tags have their own unique property.
All you need to take away form this section is that a page is structured with the help of HTML tags, and while scraping knowing these tags can help you locate and extract the information easily. It is the first step towards scraping the web as well.
And in the code below, we will parse HTML in the same way we would parse a text document and read it with R. Remember, scraping is only fun if you experiment with it.
Share in comments if you found something interesting or feel stuck somewhere. In HTML we have a document hierarchy of tags which looks something like. Given that, I just wanted to give you a barebones look at scraping, this code looks like a good illustration. However, in reality, our code is a lot more complicated. But fortunately, we have a lot of libraries that simplify web scraping in R for us. This does not give us the PDF documents, though. Now that we have the HTML content we do a little exploratory data analysis to see how everything is organized and decide how we want to download all the defendant documents.
We want to know if there is a one-to-one or one-to-many relationship between any cases and documents. When we look at the documents column we can see that some people have the same associated documents. When I last ran this code there were unique document names in the column, but we really need to see how many unique document download links there are. Hmm, so the HTML table had document names but unique download links. We want to keep everything organized by defendant, so we are going to loop through each row of the table and capture the download links per defendant, then while downloading we will save documents to a folder unique to each defendant even if that means the same document ends up in multiple folders.
Now we can iterate through each element of this list a row from the HTML table and do whatever we want. Wow that seems like a lot of work to get to those documents! I just launched the code right now and waiting to see what happens the code is still running! Is there a way to isolate the text from each article into a separate R object? I would not recommend doing that though.
In most cases it is better to keep the data in a list and iterate through it with something like purrr::map or base::lapply. Sure, a loop works for that. Alternatively use purrr::map for that as well. I think you are right, I just assumed this was public data that can be scraped. I don't hink the question needs to be deleted, but if you want to access more than the first page, you should get in contact with them first.
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