Book Image

The Clojure Workshop

By : Joseph Fahey, Thomas Haratyk, Scott McCaughie, Yehonathan Sharvit, Konrad Szydlo
Book Image

The Clojure Workshop

By: Joseph Fahey, Thomas Haratyk, Scott McCaughie, Yehonathan Sharvit, Konrad Szydlo

Overview of this book

The Clojure Workshop is a step-by-step guide to Clojure and ClojureScript, designed to quickly get you up and running as a confident, knowledgeable developer. Because of the functional nature of the language, Clojure programming is quite different to what many developers will have experienced. As hosted languages, Clojure and ClojureScript can also be daunting for newcomers because of complexities in the tooling and the challenge of interacting with the host platforms. To help you overcome these barriers, this book adopts a practical approach. Every chapter is centered around building something. As you progress through the book, you will progressively develop the 'muscle memory' that will make you a productive Clojure programmer, and help you see the world through the concepts of functional programming. You will also gain familiarity with common idioms and patterns, as well as exposure to some of the most widely used libraries. Unlike many Clojure books, this Workshop will include significant coverage of both Clojure and ClojureScript. This makes it useful no matter your goal or preferred platform, and provides a fresh perspective on the hosted nature of the language. By the end of this book, you'll have the knowledge, skills and confidence to creatively tackle your own ambitious projects with Clojure and ClojureScript.
Table of Contents (17 chapters)
Free Chapter
2
2. Data Types and Immutability

Summarizing Tennis Scores

In the previous chapter, we were able to generate some summary data from the tennis scores, using filter. If we wanted to know how many matches a particular player had won, we could filter out that player's victories and call count. While this approach works well when we are only interested in one player, it becomes cumbersome if we want more complete data. For example, if we needed to know the number of matches played or won by each of the players in the dataset, we would have to filter, for each query, the entire history of all the matches. The map and filter functions are extremely useful in many situations, but reducing a large collection down into a more compact report is not what they are best for.

Let's suppose that for each player, we need to know the number of matches played, won, and lost. We'll walk through two different ways to solve the problem in Clojure, the first using reduce and the second using group-by, one of Clojure&apos...