07 Feb 2015
A Practical Introduction to Functional Programming in Ruby
The following is an article written by Mary Rose Cook. My only contributions were the translation of the Python examples into Ruby and slight changes to the text.
A Guide Rope
Functional code is characterised by one thing: the absence of side effects. It doesn’t rely on data outside the current function, and it doesn’t change data that exists outside the current function. Every other “functional” thing can be derived from this property. Use it as a guide rope as you learn.
This is an unfunctional function:
This is a functional style function:
Don’t Iterate Over Lists
Use Map, Reduce and Filter
Map takes a function and a collection of items. It makes a new, empty collection, runs the function on each item in the original collection and inserts each return value into the new collection. It returns the new collection.
This is a simple map that takes a list of names and returns a list of the lengths of those names:
This is a map that squares every number in the passed collection:
The unfunctional code below takes a list of real names and replaces them with randomly assigned code names.
This can be rewritten as a map:
Excercise 1. Try rewriting the code below as a map. It takes a list of real names and replaces them with code names produced using a more robust strategy.
Reduce takes a function and a collection of items. It returns a value that is created by combining the items.
This is a simple reduce. It returns the sum of all the items in the collection.
Reduce takes a block of code with two arguments:
x is the current element
of the array, and ‘a’ is called the accumulator. The value of
a is the
return value of the block of code that ran on the previous element of the
a the when the block is run for the first element in the array?
That depends. In most implementations the default is for
a to initialize
to the first element in the array. The initial value can also be set by
passing an optional parameter to reduce.
This code counts how often the word ‘Sam’ appears in a list of strings:
This is the same code written as a reduce:
Why Are Map and Reduce Better?
- They are often one-liners
- The important parts of the iteration – the collection, the operation and the return value – are always in the same places in every map and reduce.
- The code in a loop may affect variables defined before it or code that runs after it. By convention, maps and reduces are functional.
- map and reduce are elemental operations. Every time a person reads a
forloop, they have to work through the logic line by line. There are few structural regularities they can use to create a scaffolding on which to hang their understanding of the code. In contrast, map and reduce are at once building blocks that can be combined into complex algorithms, and elements that the code reader can instantly understand and abstract in their mind. “Ah, this code is transforming each item in this collection. It’s throwing some of the transformations away. It’s combining the remainder into a single output.”
- Map and reduce have many friends that provide useful, tweaked versions of
their basic behaviour. For example:
Excercise 2. Try rewriting the code below using map, reduce and filter. Filter takes a function and a collection. It returns a collection of every item for which the function returned True.
If this seems tricky, try not thinking about the operations on the data. Think of the states the data will go through, from the list of people dictionaries to the average height.
Don’t try and bundle multiple transformations together. Put each on a separate line and assign the result to a descriptively-named variable. Once the code works, condense it.
Write Declaratively, Not Imperatively
The program below runs a race between three cars. At each time step, each car may move forwards or it may stall. At each time step, the program prints out the paths of the cars so far. After five time steps, the race is over.
This is some sample output:
- -- -- -- -- --- --- -- --- ---- --- ---- ---- ---- -----
This is the program:
This example is going to be functionalized in steps.
To understand this program, the reader just reads the main loop. “If there is time left, run a step of the race and draw. Check the time again.” If the reader wants to understand more about what it means to run a step of the race, or draw, they can read the code in those functions.
There are no comments any more. The code describes itself.
Splitting code into functions is a great, low brain power way to make code more readable.
This technique uses functions, but it uses them as sub-routines. They parcel up code. The code is not functional in the sense of the guide rope. The functions in the code use state that was not passed as arguments. They affect the code around them by changing external variables, rather than by returning values. To check what a function really does, the reader must read each line carefully. If they find an external variable, they must find its origin. They must see what other functions change that variable.
This is a functional version of the car code:
The code is still split into functions, but the functions are functional. There
are three signs of this. First, there are no longer any shared variables.
car_positions get passed straight into
race(). Second, functions
take parameters. Third, no variables are instantiated inside functions. All
data changes are done with return values.
race() recurses with the result of
run_step_of_race(). Each time a step generates a new state, it is passed
immediately into the next step.
Now, here are two functions,
zero() takes a string,
s. If the first character is
'0', it returns the
rest of the string. If it is not, it returns
nil, the default return value of
one() does the same, but for a first character of
Imagine a function called
rule_sequence(). It takes a string and a list of rule
functions of the form of
one(). It calls the first rule on the
nil is returned, it takes the return value and calls the second
rule on it. Unless
nil is returned, it takes the return value and calls the
third rule on it. And so forth. If any rule returns
nil. Otherwise, it returns the return value of the final rule.
This is some sample input and output:
This is the imperative version of
Exercise 3. The code above uses a loop to do its work. Make it more declarative by rewriting it as a recursion.
In the previous section, some imperative loops were rewritten as recursions that called out to auxiliary functions. In this section, a different type of imperative loop will be rewritten using a technique called a pipeline.
The loop below performs transformations on dictionaries that hold the name, incorrect country of origin and active status of some bands.
Worries are stirred by the name of the function: “format” is very vague. Upon closer inspection of the code, these worries begin to claw. Three things happen in the same loop. The ‘country’ key gets set to ‘Canada’. Punctuation is removed from the band name. The band name gets capitalized. It is hard to tell what the code is intended to do and hard to tell if it does what it appears to do. The code is hard to reuse, hard to test and hard to parallelize.
Compare it with this:
This code is easy to understand. It gives the impression that the auxiliary functions are functional because they seem to be chained together. The output from the previous one comprises the input to the next. If they are functional, they are easy to verify. They are also easy to reuse, easy to test and easy to parallelize.
The job of
pipeline_each() is to pass the bands, one at a time, to a
transformation function, like
set_canada_as_country(). After the function has
been applied to all the bands,
pipeline_each() bundles up the transformed
bands. Then, it passes each one to the next function.
Let’s look at the transformation functions.
Each one associates a key on a band with a new value. Everything seems fine.
Band dictionary originals are protected from mutation when a key is associated
with a new value. But there are two other potential mutations in the code
strip_punctuation_from_name(), the unpunctuated name is generated
delete() on the original name. In
capitalized name is generated by calling
title() on the original name. If
replace() and title() are not functional, strip_punctuation_from_name() and
capitalize_names() are not functional. capitalize_names()` are not
Everything seems fine. Band dictionary originals are protected from mutation
when a key is associated with a new value. But there are two other potential
mutations in the code above. In
unpunctuated name is generated by calling
delete() on the original name. In
capitalize_names(), the capitalized name is generated by calling
on the original name. If
upcase() are not functional,
capitalize_names() are not functional.
upcase() do not mutate the strings they operate
on. When, for example,
delete() operates on a band name string, the original
band name is copied and
delete() is called on the copy. Phew.
Exercise 4. Try and write the
pipeline_each function. Think about the order of operations. The bands in the array are passed, one band at a time, to the first transformation function. The bands in the resulting array are passed, one band at a time, to the second transformation function. And so forth.
Functional code co-exists very well with code written in other styles. The transformations in this article can be applied to any code base in any language. Try applying them to your own code.
Think of Mary, Isla and Sam. Turn iterations of lists into maps and reduces.
Think of the race. Break code into functions. Make those functions functional. Turn a loop that repeats a process into a recursion.
Think of the bands. Turn a sequence of operations into a pipeline.