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Hands-On Reactive Programming with Clojure

You're reading from   Hands-On Reactive Programming with Clojure Create asynchronous, event-based, and concurrent applications

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789346138
Length 298 pages
Edition 2nd Edition
Languages
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Authors (2):
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Konrad Szydlo Konrad Szydlo
Author Profile Icon Konrad Szydlo
Konrad Szydlo
Leonardo Borges Leonardo Borges
Author Profile Icon Leonardo Borges
Leonardo Borges
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Table of Contents (20) Chapters Close

Title Page
Copyright and Credits
About Packt
Contributors
Preface
1. What is Reactive Programming? FREE CHAPTER 2. A Look at Reactive Extensions 3. Asynchronous Programming and Networking 4. Introduction to core.async 5. Creating Your Own CES Framework with core.async 6. Building a Simple ClojureScript Game with Reagi 7. The UI as a Function 8. A New Approach to Futures 9. A Reactive API to Amazon Web Services 10. Reactive Microservices 11. Testing Reactive Apps 12. Concurrency Utilities in Clojure 1. Appendix - The Algebra of Library Design 2. Other Books You May Enjoy Index

Gathering stats about ages


Now that we can safely calculate the average age of a number of pirates, it might be interesting to take this further and calculate the median and standard deviation of the pirates' ages, in addition to their average age.

We already have a function to calculate the average; so, let's just create the ones to calculate the median and the standard deviation of a list of numbers, as follows:

(defn median [& ns] 
  (let [ns (sort ns) 
        cnt (count ns) 
        mid (bit-shift-right cnt 1)] 
    (if (odd? cnt) 
      (nth ns mid) 
      (/ (+ (nth ns mid) (nth ns (dec mid))) 2)))) 
 
(defn std-dev [& samples] 
  (let [n (count samples) 
   mean (/ (reduce + samples) n) 
   intermediate (map #(Math/pow (- %1 mean) 2) samples)] 
    (Math/sqrt 
     (/ (reduce + intermediate) n)))) 

With these functions in place, we can write the code that will gather all of the stats for us:

  (let  [a       (some-> (pirate-by-name "Jack Sparrow")    age) 
         b       (some-> (pirate-by-name "Blackbeard")      age) 
         c       (some-> (pirate-by-name "Hector Barbossa") age) 
         avg     (avg a b c) 
         median  (median a b c) 
         std-dev (std-dev a b c)] 
    {:avg avg 
     :median median 
     :std-dev std-dev}) 
 
  ;; {:avg 56.666668, 
  ;;  :median 60, 
  ;;  :std-dev 12.472191289246473} 

This implementation is fairly straightforward. First, we retrieve all of the ages that we're interested in and bind them to the locals a, b, and c. We then reuse the values when calculating the remaining stats. Finally, we gather all of the results in map, for easy access.

By now, you probably know where we're headed. What if any of those values is nil? Consider the following code:

  (let  [a       (some-> (pirate-by-name "Jack Sparrow")    age) 
         b       (some-> (pirate-by-name "Davy Jones")      age) 
         c       (some-> (pirate-by-name "Hector Barbossa") age) 
         avg     (avg a b c) 
         median  (median a b c) 
         std-dev (std-dev a b c)] 
    {:avg avg 
     :median median 
     :std-dev std-dev}) 
  ;; NullPointerException   clojure.lang.Numbers.ops (Numbers.java:961) 

The second binding, b, returns nil, as we don't have any information about Davy Jones. As such, it causes the calculations to fail. Like before, we can change our implementation to protect us from such failures, as follows:

  (let  [a       (some-> (pirate-by-name "Jack Sparrow")    age) 
         b       (some-> (pirate-by-name "Davy Jones")      age) 
         c       (some-> (pirate-by-name "Hector Barbossa") age) 
         avg     (when (and a b c) (avg a b c)) 
         median  (when (and a b c) (median a b c)) 
         std-dev (when (and a b c) (std-dev a b c))] 
    (when (and a b c) 
      {:avg avg 
       :median median 
       :std-dev std-dev})) 
  ;; nil 

This time, it's even worse than when we only had to calculate the average. The code is checking for nil values in four extra spots—before calling the three stats functions, and just before gathering the stats into the resulting map.

Can we do better?

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