To work with APIs in R, we need to bring in some libraries. Often, we can immediately use the data we get from an API, which saves us time and frustration. While there are certainly libraries out there that make parsing HTML text easy, these are all cleaning steps that need to be taken before we even get our hands on the data we want! When a programmer scrapes a web page, they receive the data in a messy chunk of HTML. Why is this valuable? Contrast the API approach to pure web scraping. That computer will then read our code, process the request, and return nicely-formatted data that can be easily parsed by existing R libraries. From our perspective as the requester, we will need to write code in R that creates the request and tells the computer running the API what we need.
Once this computer receives a data request, it will do its own processing of the data and send it to the computer that requested it. When a website like Facebook sets up an API, they are essentially setting up a computer that waits for data requests. In this tutorial, we’ll specifically be working with web APIs, where two different computers - a client and server - will interact with each other to request and provide data, respectively.ĪPIs offer data scientists a polished way to request clean and curated data from a website. "API" is a general term for the place where one computer program interacts with another, or with itself. If you're not confident in your R abilities yet, you can check out our free, interactive R fundamentals and R intermediate courses:Īlready have the R programming skills you need? Great! Let's dive into accessing APIs with R. You don't need any API experience, but you will need to be familiar with the fundamentals of R to follow along.
In this tutorial, we're going to cover the basics of accessing an API using the R programming language. Many large websites like Reddit, Twitter and Facebook offer APIs so that data analysts and data scientists can access interesting data. Instead of having to download a data set, APIs allow programmers to request data directly from certain websites through what's called an Application Programming Interface (hence, "API"). And while websites like Kaggle offer free data sets to interested data scientists, APIs are another very common way to access and acquire interesting data.
Interesting data sets are the fuel of a good data science project.
0 kommentar(er)
