1. Thinking in Clojure
Mar 29, 2013

2. Jumping in
 We’ll quickly go through Clojure’s data types, some
basic functions, and basic syntax
 Then we’ll get to the good stuff!
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3. Clojure’s data types
 Clojure has:
 Lists, enclosed in parentheses and separated by spaces or commas:
(a 17 "Plenty of parentheses")
 Functions: (fn [x] (first(rest x))
 Numbers: All Java numeric types, plus ratios and exact decimals:
5, 5.3, 5.3e30, 077, 0xFF00FF, 3/5, 5.3M
 Strings, as in Java: "She said "Hello""
 Characters: a, 5, n, newline, tab, etc.
 The booleans true and false
 nil, equivalent to Java’s null
 Symbols, which stand for themselves: :meadow, :CIS-554
 Vectors: [5 :a "hi!"]
 Maps: {:one 1, :two 2}
 Sets: #{:prolog :clojure}
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4. Some basic Clojure functions
 Syntax of a function call: (function args)
 Basic operations—sequences (seq) are lists, sets, maps, vectors:

(quote arg) or 'arg, to keep arg from being evaluated
 (first seq) is the first element in the sequence (or nil)
 (rest seq) is what remains when the first element is removed (or nil)
 (cons arg seq) returns a new sequence with arg as its first element
 (= args) tests whether its args are equal
 (empty? seq), (list? seq), (seq? arg), (nil? arg) are more tests
 Basic arithmetic
 (+ args), (- args), (* args), (/ args) , (< args), etc.
 Basic logic
 (and args), (or args), (not arg), (if condition result1 result2)
 Defining values

(def name value) defines the name to be the given value
 (defn name argv value) is shorthand for (def name argv value),
where argv is a vector
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5. Functions and special forms
 The arguments to a function are evaluated before the function is called
 Example: (* 2 (+ 3 4))
The function * is called with the arguments 2 and 7
 A special form looks just like a function, but it gets its arguments
unevaluated
 The special form itself decides when and whether to evaluate its arguments
 quote does not evaluate its argument
 if evaluates its first argument, then decides which of the second and third
arguments to evaluate
 Clojure allows you to define your own special forms
 This means you can define your own control structures
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6. A typical Clojure function
 (defn first-double-letter
"Returns the first doubled letter in a string, or nil."
[s]
(if (< (count s) 2)
nil
(if (= (first s) (second s))
(first s)
(first-double-letter (rest s)) ) ) )
 user=> (first-double-letter "Pennsylvania")
n
user=> (first-double-letter '(1 2 3 4 3 5 5 4 6))
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7. It’s easier with cond
 cond is an if … then … else if … then … else … construct:
(cond test1 result1 test2 result2 … testN resultN)
 It requires an even number of parameters (one result for each test)
 The symbol :else may be used as the last test
 (defn first-double-letter
"Returns the first doubled letter in a string, or nil."
[s]
(cond (< (count s) 2) nil
(= (first s) (second s)) (first s)
:else (first-double-letter (rest s)) ) )
 user=> (first-double-letter "Pennsylvania")
n
user=> (first-double-letter '(1 2 3 4 3 5 5 4 6))
5
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8. It’s all about recursion
 Some rules of doing recursion:
1. Handle the base cases directly (without recursion)
2. Recur only with a simpler case
3. Don’t use global variables
4. Don’t “look down” into the recursion—that will just confuse you
 In Clojure you are almost always working with a list or some similar sequence
 Lisp programmers say, “Do something with the head, and recur with the tail”
 Clojure’s terms for “head” and “tail” are “first” and “rest”
 This pretty much covers rules 1 and 2 above
 Clojure doesn’t have global variables
 This covers rule #3 above
 Rule 4 always holds. Think about what you are doing now, not what
some recursive call is doing
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9. Functional programming
 Clojure is functional—what does that mean?
 Functions are like functions in math—called with the same arguments,
they always return the same result
 This means: No “global variables,” no dependence on external values, and no
side effects!
 Functions are values, or first-class objects
 Functions can be passed as parameters to functions, returned as the value of
functions, created as needed, stored in data structures, and there are
operations on functions that produce new functions
 The “blub paradox” applies—the value added is substantial, but not obvious
to an imperative or object-oriented programmer
 Data is immutable (like strings in Java)
 Clojure’s data structures are designed to make this efficient

Immutable data greatly simplifies concurrent programming
 Because data is immutable, loops are unnecessary (use recursion instead!)
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10. Costs and benefits
 Costs of functional programming
 It’s weird and unfamiliar
 How can you do anything without objects, mutable variables, or loops?
 (Loops are used primarily to change the values of things)
 As a manager, functional programmers are hard to find (and expensive!)
 Clojure, and Lisp dialects generally, have too many parentheses!
 Benefits of functional programs
 Easier to write correct programs
 “Yeah, right!” – “No, really! All data is local and immutable.”
 Easier to write unit tests, because function values depend only on inputs
 Much easier to write concurrent programs
 Operations on collections make code simpler and more concise
 The simpler foundation means less syntax and fewer special cases
 Some operations, such as equality testing, are really fast
 But it’s still weird!
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11. Easier to write correct programs
 Programs are easier to write when all data is local
 When relevant values can be changed elsewhere in the program—possibly in
many places—it’s harder to see all the connections
 Functions in a functional language get all relevant input from the parameter list
 Unit testing is easier, because there are no dependencies on functions that
may or may not have been called previously
 There is no need for a setUp method
 Functional programming supports powerful operations on sequences
 The imperative and object-oriented programming styles have been characterized
as “word by word” programming
 Some sequence operations, such as membership testing, are provided for you
 In a functional language, any function can be a sequence operation
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12. The problem of state
 Nonfunctional programming language are “stateful” or “have state”
 The state of a program is given by (1) the values of all the variables throughout the
program, and (2) the current locus of execution
 That can be a huge amount of information to keep track of!
 Object-oriented programmers try to control complexity by having objects be
responsible for their own state, and “loosely coupled” (not very dependent on)
other objects
 Methods often have “side effects,” that is, they modify state
 Functional languages try to avoid having state at all
 This isn’t always easy
 Purely functional languages cannot have side effects
 Since I/O is a side effect, this is an even more difficult restriction
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13. Maintaining state, functionally
 Sometimes you just need state
 Consider an adventure game
 You need to keep track of where you are, where other objects are, what you are
holding, which paths are blocked or open, etc.—this is your state
 You do not need to keep track of permanent, immutable data; for example, most paths
between rooms are fixed and unchanging—this isn’t part of the state
 In a functional program, a “state” is just an immutable (and usually just one)
data item
 The data item can be quite complex, such as a dictionary
 States are immutable, but you can always create a “new” state that is a variation
of a given state
 With carefully designed data structures, not as much storage is required as you might
expect
 So the functional solution to maintaining state is: Pass one state into a function,
get a new (and different) state back!
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14. Clojure’s I/O compromise
 A purely functional program has no side effects
 I/O is a side effect
 Therefore: A purely functional program cannot do I/O!
 In Clojure, all functions return a value
 (print args) and (println args) return nil
 Clojure allows side effects in two well-defined places:
 (do args) evaluates all its arguments in order, but returns only the value of the last one
 When a function (fn argv args) is called, the arguments are evaluated in order, but only
the last value is returned
 Example:
 (defn powers "Computes cube and square" []
(def n (read))
(println (* n n n))
(println (* n n))
n)
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15. Lists are immutable
Here is a typical list:
my-list A B C
w
Here is
(cons 'w my-list)
Here is (rest my-list)
Notice that my-list remains unchanged
Vectors, hash maps, sorted maps, hash sets, and sorted sets
are similarly immutable
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16. Functions are just values
 user=> (cons 'w '(a b c))
(w a b c)
 user=> (defn swap-args [f x y] (f y x))
#'user/swap-args
 user=> (swap-args cons '(a b c) 'w)
(w a b c)
 user=> (defn apply-n-times [f x n]
"Apply f to x, n times: f(f(f..(n)...))"
(if (zero? n)
x
(apply-n-times f (f x) (dec n)) ) )
#'user/apply-n-times
 user=> (apply-n-times (fn [x] (* 2 x)) 1 10)
1024
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17. Collatz, the hard way
 Definition:
 collatz(1) = 1
collatz(n) = collatz(n / 2) if n is even
collatz(n) = collatz(3 * n + 1)
 user=> (defn collatz [n]
(let [ do-even (fn [n] (collatz (/ n 2)))
do-odd (fn [n] (collatz (inc (* 3 n)))) ]
(print n " ")
(if (= n 1) 1
(if (even? n) (do-even n) (do-odd n)) ) ) )
 #'user/collatz
user=> (collatz 7)
7 22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1 1
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18. map, filter, and reduce
 Here are three powerful functions you will find in almost any
functional programming language
 map – apply a function to every element of a sequence, returning a
sequence of results
 user=> (map even? '(3 1 4 1 6))
 (false false true false true)
 filter – apply a predicate to every element of a sequence, returning a
sequence of those that satisfy the predicate

user=> (filter even? '(3 1 4 1 6))
 (4 6)
 reduce – use a function to reduce a sequence to a single value
 user=> (reduce * '(3 1 4 1 6))
 72
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19. The real problem with state
 For decades we’ve been dealing with mutable state
 Mutable state + concurrency = nondeterminism
 We use threads and locks and semaphores and so on
 These are complicated, unsafe, and inefficient
 As Herb Sutter points out in The Free Lunch is Over, we have hit a 3 GHz barrier
 Since 2003, computers have not gotten faster
 We still want them faster
 Concurrency is the only solution
 Functional languages, with immutable state, provide a partial solution
 As Martin Odersky points out, you can hide from concurrency for a while yet…but
not forever
 Important consequences:
 All newer languages are gaining functional and concurrent features
 Older languages, such as Java, are also trying to integrate these features
 “You can run, but you can’t hide!”
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20. Oh, and by the way…
 Clojure has
 Infinite sequences
 Lazy sequences
 Exact decimal arithmetic
 Function composition
 Function currying
 Macros
 And lots more
 No version of Lisp has ever become mainstream
 They just get mugged in dark alleys and their ideas stolen!
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21. The End
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