APIs present researchers with a diverse set of data sources through a standardised access mechanism: send a pasted together HTTP request, receive JSON or XML in return. Today we tap into a range of APIs to get comfortable sending queries and processing responses.
These are the slides from the final class in Web Scraping through R: Web scraping for the humanities and social sciences
This week we explore how to use APIs in R, focusing on the Google Maps API. We then attempt to transfer this approach to query the Yandex Maps API. Finally, the practice section includes examples of working with the YouTube V2 API, a few 'social' APIs such as LinkedIn and Twitter, as well as APIs less off the beaten track (Cricket scores, anyone?).
I enjoyed teaching this course and hope to repeat and improve on it next year. When designing the course I tried to cram in everything I wish I had been taught early on in my PhD (resulting in information overload, I fear). Still, hopefully it has been useful to students getting started with digital data collection, showing on the one hand what is possible, and on the other giving some idea of key steps in achieving research objectives.
Download the .Rpres file to use in Rstudio here
A regular R script with code-snippets only can be accessed here
Slides from the first session here
Slides from the second session here
Slides from the third session here
UPDATE March 2015:
New 2015 version of slides here
PDFs of slides available here
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ReplyDeleteThank you very much for sharing this. Your slides really help me learn the basics of digital data collection, which is crucial part of my research which applied text mining.
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