Introduction

️✅ Learning objectives

• Explain what an API is.
• Decide whether this book is for you.

• We’ll talk a bit more about using LOs later in this chapter.
• We’ll pick up other information, but these are the key skills.
• Anything else you’d want to see in an intro?

What is an API?

API = “application programming interface”

• “application” = function, website… any computerized thing
• “programming” = can be used in code
• “interface” = way to interact with something

🤖 APIs allow computers to talk to one another

• Right level?

What are web APIs?

💻 Technically any function’s list of arguments is an API

“httr2 is a ground-up rewrite of httr that provides a pipeable API with an explicit request object that solves more problems felt by packages that wrap APIs” (httr2 docs, emphasis added)

🕸️ This book = web APIs

• Web API = API on the internet
• For rest of book, “API” means “web API”

• httr2 quote helpful/interesting?

What are some API examples?

• Cloud services ({paws})
• Government agencies (OpenFEC)
• Random data (sunrisesunset.io)
• Services (OpenAI)
• APIs.guru = API directory
  • 2,529 APIs as of 2024-01-22

• Number of examples good?
• Variety of examples good?
• Deep enough, or more-full usage examples?
• What would you expect in book (vs slides)?

What will I learn?

Part 1: Get started

• Get started with APIs
• Quickly parse API responses
• Tell APIs who you are

Part 2: Get more data

• Get a lot of data from APIs
• Scrape data from web pages
• Process other responses types

Part 3: Do more with APIs

• Do other things with APIs
• Find APIs
• Communicate with other types of APIs
• Learn more about APIs

• Any missing topics?

What won’t I learn?

• How can I access APIs with {httr} or {curl}?
• How can I create an API?
• How can I access APIs from other programming languages?

• Anything else people would expect?
• For now at least, {plumber} didn’t make the cut. Separate book?

Who should read this book?

• Data Analysts/Scientists
  • Access datasets
  • Enrich datasets
• Machine Learning Engineers
  • Feature engineering
• Shiny App Designers
  • Load/process data

• People who analyze data, not necessarily that job title.
• Does anybody fit into something other than those use cases?

Prerequisites

• Familiarity with R
  • R for Data Science
• Very few hard prerequisites
  • Will point to resources when helpful
• Helpful:
  • Advanced R to do more complicated things with retrieved data

• How many have read R4DS?
• How many have read Advanced R?
• How many have written a function?
• Accessed an API from R with httr?
• Accessed an API from R with httr2?
• Accessed an API from R some other way?
• Discuss level I can assume.

How are chapters structured?

• Learning Objectives ≈ skills
  • “After you read this chapter, you will be able to…”
• Test-Yourself Questions to check understanding
• Check online version for updates