Monadic Adventures

Chapter Goals

The goal of this chapter will be to learn about monad transformers, which provide a way to combine side-effects provided by different monads. The motivating example will be a text adventure game that can be played on the console in NodeJS. The various side-effects of the game (logging, state, and configuration) will all be provided by a monad transformer stack.

Project Setup

This module's project introduces the following new dependencies:

  • ordered-collections, which provides data types for immutable maps and sets
  • transformers, which provides implementations of standard monad transformers
  • node-readline, which provides FFI bindings to the readline interface provided by NodeJS
  • optparse, which provides applicative parsers for processing command-line arguments

How To Play The Game

To run the project, use spago run

By default, you will see a usage message:

Monadic Adventures! A game to learn monad transformers

Usage: run.js (-p|--player <player name>) [-d|--debug]
  Play the game as <player name>

Available options:
  -p,--player <player name>
                           The player's name <String>
  -d,--debug               Use debug mode
  -h,--help                Show this help text

To provide command line arguments, you can either call spago run with the -a option to pass additional arguments directly to your application or call spago bundle-app, which will create an index.js file that can be run directly with node.

For example, to provide the player name using the -p option:

$ spago run -a "-p Phil"
$ spago bundle-app 
$ node index.js -p Phil

From the prompt, you can enter commands like look, inventory, take, use, north, south, east, and west. There is also a debug command, which can print the game state when the --debug command line option is provided.

The game is played on a two-dimensional grid, and the player moves by issuing commands north, south, east, and west. The game contains a collection of items that can either be in the player's possession (in the user's inventory) or on the game grid at some location. Items can be picked up by the player using the take command.

For reference, here is a complete walkthrough of the game:

$ spago run -a "-p Phil"

> look
You are at (0, 0)
You are in a dark forest. You see a path to the north.
You can see the Matches.

> take Matches
You now have the Matches

> north
> look
You are at (0, 1)
You are in a clearing.
You can see the Candle.

> take Candle
You now have the Candle

> inventory
You have the Candle.
You have the Matches.

> use Matches
You light the candle.
Congratulations, Phil!
You win!

The game is very simple, but the aim of the chapter is to use the transformers package to build a library that will enable rapid development of this type of game.

The State Monad

We will start by looking at some of the monads provided by the transformers package.

The first example is the State monad, which provides a way to model mutable state in pure code. We have already seen an approach to a mutable state provided by the Effect monad. State provides an alternative.

The State type constructor takes two type parameters: the type s of the state and the return type a. Even though we speak of the "State monad", the instance of the Monad type class is actually provided for the State s type constructor for any type s.

The Control.Monad.State module provides the following API:

get     :: forall s.             State s s
gets    :: forall s. (s -> a) -> State s a
put     :: forall s. s        -> State s Unit
modify  :: forall s. (s -> s) -> State s s
modify_ :: forall s. (s -> s) -> State s Unit

Note that these API signatures are presented in a simplified form using the State type constructor for now. The actual API involves MonadState, which we'll cover in the later "Type Classes" section of this chapter, so don't worry if you see different signatures in your IDE tooltips or on Pursuit.

Let's see an example. One use of the State monad might be to add the values in an array of integers to the current state. We could do that by choosing Int as the state type s and using traverse_ to traverse the array, with a call to modify for each array element:

import Data.Foldable (traverse_)
import Control.Monad.State
import Control.Monad.State.Class

sumArray :: Array Int -> State Int Unit
sumArray = traverse_ \n -> modify \sum -> sum + n

The Control.Monad.State module provides three functions for running a computation in the State monad:

evalState :: forall s a. State s a -> s -> a
execState :: forall s a. State s a -> s -> s
runState  :: forall s a. State s a -> s -> Tuple a s

Each function takes an initial state of type s and a computation of type State s a. evalState only returns the return value, execState only returns the final state, and runState returns both, expressed as a value of type Tuple a s.

Given the sumArray function above, we could use execState in PSCi to sum the numbers in several arrays as follows:

> :paste
… execState (do
…   sumArray [1, 2, 3]
…   sumArray [4, 5]
…   sumArray [6]) 0
… ^D


  1. (Easy) What is the result of replacing execState with runState or evalState in our example above?

  2. (Medium) A string of parentheses is balanced if it is obtained by either concatenating zero-or-more shorter balanced strings or wrapping a shorter balanced string in a pair of parentheses.

    Use the State monad and the traverse_ function to write a function

    testParens :: String -> Boolean

    which tests whether or not a String of parentheses is balanced by keeping track of the number of opening parentheses that have not been closed. Your function should work as follows:

    > testParens ""
    > testParens "(()(())())"
    > testParens ")"
    > testParens "(()()"

    Hint: you may like to use the toCharArray function from the Data.String.CodeUnits module to turn the input string into an array of characters.

The Reader Monad

Another monad provided by the transformers package is the Reader monad. This monad provides the ability to read from a global configuration. Whereas the State monad provides the ability to read and write a single piece of mutable state, the Reader monad only provides the ability to read a single piece of data.

The Reader type constructor takes two type arguments: a type r which represents the configuration type, and the return type a.

The Control.Monad.Reader module provides the following API:

ask   :: forall r. Reader r r
local :: forall r a. (r -> r) -> Reader r a -> Reader r a

The ask action can be used to read the current configuration, and the local action can be used to run a computation with a modified configuration.

For example, suppose we were developing an application controlled by permissions, and we wanted to use the Reader monad to hold the current user's permissions object. We might choose the type r to be some type Permissions with the following API:

hasPermission :: String -> Permissions -> Boolean
addPermission :: String -> Permissions -> Permissions

Whenever we wanted to check if the user had a particular permission, we could use ask to retrieve the current permissions object. For example, only administrators might be allowed to create new users:

createUser :: Reader Permissions (Maybe User)
createUser = do
  permissions <- ask
  if hasPermission "admin" permissions
    then map Just newUser
    else pure Nothing

To elevate the user's permissions, we might use the local action to modify the Permissions object during the execution of some computation:

runAsAdmin :: forall a. Reader Permissions a -> Reader Permissions a
runAsAdmin = local (addPermission "admin")

Then we could write a function to create a new user, even if the user did not have the admin permission:

createUserAsAdmin :: Reader Permissions (Maybe User)
createUserAsAdmin = runAsAdmin createUser

To run a computation in the Reader monad, the runReader function can be used to provide the global configuration:

runReader :: forall r a. Reader r a -> r -> a


In these exercises, we will use the Reader monad to build a small library for rendering documents with indentation. The "global configuration" will be a number indicating the current indentation level:

type Level = Int

type Doc = Reader Level String
  1. (Easy) Write a function line that renders a function at the current indentation level. Your function should have the following type:

    line :: String -> Doc

    Hint: use the ask function to read the current indentation level. The power function from Data.Monoid may be helpful too.

  2. (Easy) Use the local function to write a function

    indent :: Doc -> Doc

    which increases the indentation level for a block of code.

  3. (Medium) Use the sequence function defined in Data.Traversable to write a function

    cat :: Array Doc -> Doc

    which concatenates a collection of documents, separating them with new lines.

  4. (Medium) Use the runReader function to write a function

    render :: Doc -> String

    which renders a document as a String.

You should now be able to use your library to write simple documents as follows:

render $ cat
  [ line "Here is some indented text:"
  , indent $ cat
      [ line "I am indented"
      , line "So am I"
      , indent $ line "I am even more indented"

The Writer Monad

The Writer monad allows accumulating a secondary value in addition to the return value of a computation.

A common use case is to accumulate a log of type String or Array String, but the Writer monad is more general than this. It can accumulate a value in any monoid, so it might be used to keep track of an integer total using the Additive Int monoid or to track whether any of several intermediate Boolean values were true using the Disj Boolean monoid.

The Writer type constructor takes two type arguments: a type w that should be an instance of the Monoid type class, and the return type a.

The key element of the Writer API is the tell function:

tell :: forall w a. Monoid w => w -> Writer w Unit

The tell action appends the provided value to the current accumulated result.

As an example, let's add a log to an existing function using the Array String monoid. Consider our previous implementation of the greatest common divisor function:

gcd :: Int -> Int -> Int
gcd n 0 = n
gcd 0 m = m
gcd n m = if n > m
            then gcd (n - m) m
            else gcd n (m - n)

We could add a log to this function by changing the return type to Writer (Array String) Int:

import Control.Monad.Writer
import Control.Monad.Writer.Class

gcdLog :: Int -> Int -> Writer (Array String) Int

We only have to change our function slightly to log the two inputs at each step:

    gcdLog n 0 = pure n
    gcdLog 0 m = pure m
    gcdLog n m = do
      tell ["gcdLog " <> show n <> " " <> show m]
      if n > m
        then gcdLog (n - m) m
        else gcdLog n (m - n)

We can run a computation in the Writer monad by using either of the execWriter or runWriter functions:

execWriter :: forall w a. Writer w a -> w
runWriter  :: forall w a. Writer w a -> Tuple a w

Just like in the case of the State monad, execWriter only returns the accumulated log, whereas runWriter returns both the log and the result.

We can test our modified function in PSCi:

> import Control.Monad.Writer
> import Control.Monad.Writer.Class

> runWriter (gcdLog 21 15)
Tuple 3 ["gcdLog 21 15","gcdLog 6 15","gcdLog 6 9","gcdLog 6 3","gcdLog 3 3"]


  1. (Medium) Rewrite the sumArray function above using the Writer monad and the Additive Int monoid from the monoid package.

  2. (Medium) The Collatz function is defined on natural numbers \( n \) as \( n / 2 \) when \( n \) is even and \( 3 n + 1 \) when \( n \) is odd. For example, the iterated Collatz sequence starting at \( 10 \) is as follows:

    10, 5, 16, 8, 4, 2, 1, ...

    It is conjectured that the iterated Collatz sequence always reaches \( 1 \) after some finite number of applications of the Collatz function.

    Write a function that uses recursion to calculate how many iterations of the Collatz function are required before the sequence reaches \( 1 \).

    Modify your function to use the Writer monad to log each application of the Collatz function.

Monad Transformers

Each of the three monads above: State, Reader, and Writer, are also examples of so-called monad transformers. The equivalent monad transformers are called StateT, ReaderT, and WriterT, respectively.

What is a monad transformer? Well, as we have seen, a monad augments PureScript code with some type of side effect, which can be interpreted in PureScript by using the appropriate handler (runState, runReader, runWriter, etc.) This is fine if we only need to use one side-effect. However, it is often useful to use more than one side-effect at once. For example, we might want to use Reader together with Maybe to express optional results in the context of some global configuration. Or we might want the mutable state provided by the State monad together with the pure error tracking capability of the Either monad. This is the problem solved by monad transformers.

Note that we have already seen that the Effect monad partially solves this problem. Monad transformers provide another solution, and each approach has its own benefits and limitations.

A monad transformer is a type constructor parameterized by a type and another type constructor. It takes one monad and turns it into another monad, adding its own variety of side-effects.

Let's see an example. The monad transformer version of the State monad is StateT, defined in the Control.Monad.State.Trans module. We can find the kind of StateT using PSCi:

> import Control.Monad.State.Trans
> :kind StateT
Type -> (Type -> Type) -> Type -> Type

This looks quite confusing, but we can apply StateT one argument at a time to understand how to use it.

The first type argument is the type of the state we wish to use, as was the case for State. Let's use a state of type String:

> :kind StateT String
(Type -> Type) -> Type -> Type

The next argument is a type constructor of kind Type -> Type. It represents the underlying monad, which we want to add the effects of StateT to. For the sake of an example, let's choose the Either String monad:

> :kind StateT String (Either String)
Type -> Type

We are left with a type constructor. The final argument represents the return type, and we might instantiate it to Number for example:

> :kind StateT String (Either String) Number

Finally, we are left with something of kind Type, which means we can try to find values of this type.

The monad we have constructed – StateT String (Either String) – represents computations that can fail with an error and use mutable state.

We can use the actions of the outer StateT String monad (get, put, and modify) directly, but to use the effects of the wrapped monad (Either String), we need to "lift" them over the monad transformer. The Control.Monad.Trans module defines the MonadTrans type class, which captures those type constructors which are monad transformers, as follows:

class MonadTrans t where
  lift :: forall m a. Monad m => m a -> t m a

This class contains a single member, lift, which takes computations in any underlying monad m and lifts them into the wrapped monad t m. In our case, the type constructor t is StateT String, m is the Either String monad, so lift provides a way to lift computations of type Either String a to computations of type StateT String (Either String) a. This means that we can use the effects of StateT String and Either String side-by-side, as long as we use lift every time we use a computation of type Either String a.

For example, the following computation reads the underlying state and then throws an error if the state is the empty string:

import Data.String (drop, take)

split :: StateT String (Either String) String
split = do
  s <- get
  case s of
    "" -> lift $ Left "Empty string"
    _ -> do
      put (drop 1 s)
      pure (take 1 s)

If the state is not empty, the computation uses put to update the state to drop 1 s (that is, s with the first character removed) and returns take 1 s (that is, the first character of s).

Let's try this in PSCi:

> runStateT split "test"
Right (Tuple "t" "est")

> runStateT split ""
Left "Empty string"

This is not very remarkable since we could have implemented this without StateT. However, since we are working in a monad, we can use do notation or applicative combinators to build larger computations from smaller ones. For example, we can apply split twice to read the first two characters from a string:

> runStateT ((<>) <$> split <*> split) "test"
(Right (Tuple "te" "st"))

We can use the split function with a handful of other actions to build a basic parsing library. In fact, this is the approach taken by the parsing library. This is the power of monad transformers – we can create custom-built monads for various problems, choose the side-effects we need, and keep the expressiveness of do notation and applicative combinators.

The ExceptT Monad Transformer

The transformers package also defines the ExceptT e monad transformer, corresponding to the Either e monad. It provides the following API:

class MonadError e m where
  throwError :: forall a. e -> m a
  catchError :: forall a. m a -> (e -> m a) -> m a

instance Monad m => MonadError e (ExceptT e m)

runExceptT :: forall e m a. ExceptT e m a -> m (Either e a)

The MonadError class captures those monads that support throwing and catching errors of some type e, and an instance is provided for the ExceptT e monad transformer. The throwError action can indicate failure, like Left in the Either e monad. The catchError action allows us to continue after an error is thrown using throwError.

The runExceptT handler is used to run a computation of type ExceptT e m a.

This API is similar to that provided by the exceptions package and the Exception effect. However, there are some important differences:

  • Exception uses actual JavaScript exceptions, whereas ExceptT models errors as a pure data structure.
  • The Exception effect only supports exceptions of one type, namely JavaScript's Error type, whereas ExceptT supports errors of any type. In particular, we are free to define new error types.

Let's try out ExceptT by using it to wrap the Writer monad. Again, we are free to use actions from the monad transformer ExceptT e directly, but computations in the Writer monad should be lifted using lift:

import Control.Monad.Except
import Control.Monad.Writer

writerAndExceptT :: ExceptT String (Writer (Array String)) String
writerAndExceptT = do
  lift $ tell ["Before the error"]
  _ <- throwError "Error!"
  lift $ tell ["After the error"]
  pure "Return value"

If we test this function in PSCi, we can see how the two effects of accumulating a log and throwing an error interact. First, we can run the outer ExceptT computation of type by using runExceptT, leaving a result of type Writer (Array String) (Either String String). We can then use runWriter to run the inner Writer computation:

> runWriter $ runExceptT writerAndExceptT
Tuple (Left "Error!") ["Before the error"]

Note that only those log messages that were written before the error was thrown get appended to the log.

Monad Transformer Stacks

As we have seen, monad transformers can be used to build new monads on top of existing monads. For some monad transformer t1 and some monad m, the application t1 m is also a monad. That means we can apply a second monad transformer t2 to the result t1 m to construct a third monad t2 (t1 m). In this way, we can construct a stack of monad transformers, which combine the side-effects provided by their constituent monads.

In practice, the underlying monad m is either the Effect monad, if native side-effects are required, or the Identity monad, defined in the Data.Identity module. The Identity monad adds no new side-effects, so transforming the Identity monad only provides the effects of the monad transformer. The State, Reader, and Writer monads are implemented by transforming the Identity monad with StateT, ReaderT, and WriterT, respectively.

Let's see an example in which three side effects are combined. We will use the StateT, WriterT, and ExceptT effects, with the Identity monad on the bottom of the stack. This monad transformer stack will provide the side effects of mutable state, accumulating a log, and pure errors.

We can use this monad transformer stack to reproduce our split action with the added feature of logging.

type Errors = Array String

type Log = Array String

type Parser = StateT String (WriterT Log (ExceptT Errors Identity))

split :: Parser String
split = do
  s <- get
  lift $ tell ["The state is " <> s]
  case s of
    "" -> lift $ lift $ throwError ["Empty string"]
    _ -> do
      put (drop 1 s)
      pure (take 1 s)

If we test this computation in PSCi, we see that the state is appended to the log for every invocation of split.

Note that we have to remove the side-effects in the order in which they appear in the monad transformer stack: first, we use runStateT to remove the StateT type constructor, then runWriterT, then runExceptT. Finally, we run the computation in the Identity monad by using unwrap.

> runParser p s = unwrap $ runExceptT $ runWriterT $ runStateT p s

> runParser split "test"
(Right (Tuple (Tuple "t" "est") ["The state is test"]))

> runParser ((<>) <$> split <*> split) "test"
(Right (Tuple (Tuple "te" "st") ["The state is test", "The state is est"]))

However, if the parse is unsuccessful because the state is empty, then no log is printed at all:

> runParser split ""
(Left ["Empty string"])

This is because of how the side-effects provided by the ExceptT monad transformer interact with the side-effects provided by the WriterT monad transformer. We can address this by changing the order in which the monad transformer stack is composed. If we move the ExceptT transformer to the top of the stack, then the log will contain all messages written up until the first error, as we saw earlier when we transformed Writer with ExceptT.

One problem with this code is that we have to use the lift function multiple times to lift computations over multiple monad transformers; for example, the call to throwError has to be lifted twice, once over WriterT and a second time over StateT. This is fine for small monad transformer stacks but quickly becomes inconvenient.

Fortunately, as we will see, we can use the automatic code generation provided by type class inference to do most of this "heavy lifting" for us.


  1. (Easy) Use the ExceptT monad transformer over the Identity functor to write a function safeDivide which divides two numbers, throwing an error (as the String "Divide by zero!") if the denominator is zero.

  2. (Medium) Write a parser

    string :: String -> Parser String

    which matches a string as a prefix of the current state or fails with an error message.

    Your parser should work as follows:

    > runParser (string "abc") "abcdef"
    (Right (Tuple (Tuple "abc" "def") ["The state is abcdef"]))

    Hint: you can use the implementation of split as a starting point. You might find the stripPrefix function useful.

  3. (Difficult) Use the ReaderT and WriterT monad transformers to reimplement the document printing library, which we wrote earlier using the Reader monad.

    Instead of using line to emit strings and cat to concatenate strings, use the Array String monoid with the WriterT monad transformer, and tell to append a line to the result. Use the same names as in the original implementation but ending with an apostrophe (').

Type Classes to the Rescue!

When we looked at the State monad at the start of this chapter, I gave the following types for the actions of the State monad:

get    :: forall s.             State s s
put    :: forall s. s        -> State s Unit
modify :: forall s. (s -> s) -> State s Unit

In reality, the types given in the Control.Monad.State.Class module are more general than this:

get    :: forall m s. MonadState s m =>             m s
put    :: forall m s. MonadState s m => s        -> m Unit
modify :: forall m s. MonadState s m => (s -> s) -> m Unit

The Control.Monad.State.Class module defines the MonadState (multi-parameter) type class, which allows us to abstract over "monads which support pure mutable state". As one would expect, the State s type constructor is an instance of the MonadState s type class, but there are many more interesting instances of this class.

In particular, there are instances of MonadState for the WriterT, ReaderT, and ExceptT monad transformers provided in the transformers package. Each has an instance for MonadState whenever the underlying Monad does. In practice, this means that as long as StateT appears somewhere in the monad transformer stack, and everything above StateT is an instance of MonadState, then we are free to use get, put, and modify directly without the need to use lift.

Indeed, the same is true of the actions we covered for the ReaderT, WriterT, and ExceptT transformers. transformers defines a type class for each of the major transformers, allowing us to abstract over monads that support their operations.

In the case of the split function above, the monad stack we constructed is an instance of each of the MonadState, MonadWriter, and MonadError type classes. This means that we don't need to call lift at all! We can just use the actions get, put, tell, and throwError as if they were defined on the monad stack itself:

split :: Parser String
split = do
  s <- get
  tell ["The state is " <> show s]
  case s of
    "" -> throwError ["Empty string"]
    _ -> do
      put (drop 1 s)
      pure (take 1 s)

This computation looks like we have extended our programming language to support the three new side-effects of mutable state, logging, and error handling. However, everything is still implemented using pure functions and immutable data under the hood.


The control package defines a number of abstractions for working with computations that can fail. One of these is the Alternative type class:

class Functor f <= Alt f where
  alt :: forall a. f a -> f a -> f a

class Alt f <= Plus f where
  empty :: forall a. f a

class (Applicative f, Plus f) <= Alternative f

Alternative provides two new combinators: the empty value, which provides a prototype for a failing computation, and the alt function (and its alias, <|>), which provides the ability to fall back to an alternative computation in the case of an error.

The Data.Array module provides two useful functions for working with type constructors in the Alternative type class:

many :: forall f a. Alternative f => Lazy (f (Array a)) => f a -> f (Array a)
some :: forall f a. Alternative f => Lazy (f (Array a)) => f a -> f (Array a)

There is also an equivalent many and some for Data.List

The many combinator uses the Alternative type class to repeatedly run a computation zero-or-more times. The some combinator is similar but requires at least the first computation to succeed.

In the case of our Parser monad transformer stack, there is an instance of Alternative induced by the ExceptT component, which supports failure by composing errors in different branches using a Monoid instance (this is why we chose Array String for our Errors type). This means that we can use the many and some functions to run a parser multiple times:

> import Data.Array (many)

> runParser (many split) "test"
(Right (Tuple (Tuple ["t", "e", "s", "t"] "")
              [ "The state is \"test\""
              , "The state is \"est\""
              , "The state is \"st\""
              , "The state is \"t\""

Here, the input string "test" has been repeatedly split to return an array of four single-character strings, the leftover state is empty, and the log shows that we applied the split combinator four times.

Monad Comprehensions

The Control.MonadPlus module defines a subclass of the Alternative type class, called MonadPlus. MonadPlus captures those type constructors which are both monads and instances of Alternative:

class (Monad m, Alternative m) <= MonadPlus m

In particular, our Parser monad is an instance of MonadPlus.

When we covered array comprehensions earlier in the book, we introduced the guard function, which could be used to filter out unwanted results. In fact, the guard function is more general and can be used for any monad, which is an instance of MonadPlus:

guard :: forall m. Alternative m => Boolean -> m Unit

The <|> operator allows us to backtrack in case of failure. To see how this is useful, let's define a variant of the split combinator which only matches upper case characters:

upper :: Parser String
upper = do
  s <- split
  guard $ toUpper s == s
  pure s

Here, we use a guard to fail if the string is not upper case. Note that this code looks very similar to the array comprehensions we saw earlier – using MonadPlus in this way, we sometimes refer to constructing monad comprehensions.


We can use the <|> operator to backtrack to another alternative in case of failure. To demonstrate this, let's define one more parser, which matches lower case characters:

lower :: Parser String
lower = do
  s <- split
  guard $ toLower s == s
  pure s

With this, we can define a parser which eagerly matches many upper case characters if the first character is upper case, or many lower case character if the first character is lower case:

> upperOrLower = some upper <|> some lower

This parser will match characters until the case changes:

> runParser upperOrLower "abcDEF"
(Right (Tuple (Tuple ["a","b","c"] ("DEF"))
              [ "The state is \"abcDEF\""
              , "The state is \"bcDEF\""
              , "The state is \"cDEF\""

We can even use many to fully split a string into its lower and upper case components:

> components = many upperOrLower

> runParser components "abCDeFgh"
(Right (Tuple (Tuple [["a","b"],["C","D"],["e"],["F"],["g","h"]] "")
              [ "The state is \"abCDeFgh\""
              , "The state is \"bCDeFgh\""
              , "The state is \"CDeFgh\""
              , "The state is \"DeFgh\""
              , "The state is \"eFgh\""
              , "The state is \"Fgh\""
              , "The state is \"gh\""
              , "The state is \"h\""

Again, this illustrates the power of reusability that monad transformers bring – we were able to write a backtracking parser in a declarative style with only a few lines of code, by reusing standard abstractions!


  1. (Easy) Remove the calls to the lift function from your implementation of the string parser. Verify that the new implementation type checks, and convince yourself it should.

  2. (Medium) Use your string parser with the some combinator to write a parser asFollowedByBs that recognizes strings consisting of several copies of the string "a" followed by several copies of the string "b".

  3. (Medium) Use the <|> operator to write a parser asOrBs that recognizes strings of the letters a or b in any order.

  4. (Difficult) The Parser monad might also be defined as follows:

    type Parser = ExceptT Errors (StateT String (WriterT Log Identity))

    What effect does this change have on our parsing functions?

The RWS Monad

One particular combination of monad transformers is so common that it is provided as a single monad transformer in the transformers package. The Reader, Writer, and State monads are combined into the reader-writer-state or simply RWS monad. This monad has a corresponding monad transformer called the RWST monad transformer.

We will use the RWS monad to model the game logic for our text adventure game.

The RWS monad is defined in terms of three type parameters (in addition to its return type):

type RWS r w s = RWST r w s Identity

Notice that the RWS monad is defined as its own monad transformer by setting the base monad to Identity, which provides no side-effects.

The first type parameter, r, represents the global configuration type. The second, w, represents the monoid, which we will use to accumulate a log, and the third, s, is the type of our mutable state.

In the case of our game, our global configuration is defined in a type called GameEnvironment in the Data.GameEnvironment module:

type PlayerName = String

newtype GameEnvironment = GameEnvironment
  { playerName    :: PlayerName
  , debugMode     :: Boolean

It defines the player name and a flag that indicates whether or not the game is running in debug mode. These options will be set from the command line when we come to run our monad transformer.

The mutable state is defined in a type called GameState in the Data.GameState module:

import Data.Map as M
import Data.Set as S

newtype GameState = GameState
  { items       :: M.Map Coords (S.Set GameItem)
  , player      :: Coords
  , inventory   :: S.Set GameItem

The Coords data type represents points on a two-dimensional grid, and the GameItem data type is an enumeration of the items in the game:

data GameItem = Candle | Matches

The GameState type uses two new data structures: Map and Set, which represent sorted maps and sorted sets, respectively. The items property is a mapping from coordinates of the game grid to sets of game items at that location. The player property stores the current coordinates of the player, and the inventory property stores a set of game items currently held by the player.

The Map and Set data structures are sorted by their keys, and can be used with any key type in the Ord type class. This means that the keys in our data structures should be totally ordered.

We will see how the Map and Set structures are used as we write the actions for our game.

For our log, we will use the List String monoid. We can define a type synonym for our Game monad, implemented using RWS:

type Log = L.List String

type Game = RWS GameEnvironment Log GameState

Implementing Game Logic

Our game will be built from simple actions defined in the Game monad by reusing the actions from the Reader, Writer, and State monads. At the top level of our application, we will run the pure computations in the Game monad and use the Effect monad to turn the results into observable side-effects, such as printing text to the console.

One of the simplest actions in our game is the has action. This action tests whether the player's inventory contains a particular game item. It is defined as follows:

has :: GameItem -> Game Boolean
has item = do
  GameState state <- get
  pure $ item `S.member` state.inventory

This function uses the get action defined in the MonadState type class to read the current game state and then uses the member function defined in Data.Set to test whether the specified GameItem appears in the Set of inventory items.

Another action is the pickUp action. It adds a game item to the player's inventory if it appears in the current room. It uses actions from the MonadWriter and MonadState type classes. First of all, it reads the current game state:

pickUp :: GameItem -> Game Unit
pickUp item = do
  GameState state <- get

Next, pickUp looks up the set of items in the current room. It does this by using the lookup function defined in Data.Map:

  case state.player `M.lookup` state.items of

The lookup function returns an optional result indicated by the Maybe type constructor. If the key does not appear in the map, the lookup function returns Nothing; otherwise, it returns the corresponding value in the Just constructor.

We are interested in the case where the corresponding item set contains the specified game item. Again we can test this using the member function:

    Just items | item `S.member` items -> do

In this case, we can use put to update the game state and tell to add a message to the log:

          let newItems = M.update (Just <<< S.delete item) state.player state.items
              newInventory = S.insert item state.inventory
          put $ GameState state { items     = newItems
                                , inventory = newInventory
          tell (L.singleton ("You now have the " <> show item))

Note that there is no need to lift either of the two computations here because there are appropriate instances for both MonadState and MonadWriter for our Game monad transformer stack.

The argument to put uses a record update to modify the game state's items and inventory fields. We use the update function from Data.Map, which modifies a value at a particular key. In this case, we modify the set of items at the player's current location, using the delete function to remove the specified item from the set. inventory is also updated, using insert to add the new item to the player's inventory set.

Finally, the pickUp function handles the remaining cases by notifying the user using tell:

    _ -> tell (L.singleton "I don't see that item here.")

As an example of using the Reader monad, we can look at the code for the debug command. This command allows the user to inspect the game state at runtime if the game is running in debug mode:

  GameEnvironment env <- ask
  if env.debugMode
    then do
      state :: GameState <- get
      tell (L.singleton (show state))
    else tell (L.singleton "Not running in debug mode.")

Here, we use the ask action to read the game configuration. Again, note that we don't need to lift any computation, and we can use actions defined in the MonadState, MonadReader, and MonadWriter type classes in the same do notation block.

If the debugMode flag is set, the tell action is used to write the state to the log. Otherwise, an error message is added.

The remainder of the Game module defines a set of similar actions, each using only the actions defined by the MonadState, MonadReader, and MonadWriter type classes.

Running the Computation

Since our game logic runs in the RWS monad, it is necessary to run the computation to respond to the user's commands.

The front-end of our game is built using two packages: optparse, which provides applicative command line parsing, and node-readline, which wraps NodeJS' readline module, allowing us to write interactive console-based applications.

The interface to our game logic is provided by the function game in the Game module:

game :: Array String -> Game Unit

To run this computation, we pass a list of words entered by the user as an array of strings and run the resulting RWS computation using runRWS:

data RWSResult state result writer = RWSResult state result writer

runRWS :: forall r w s a. RWS r w s a -> r -> s -> RWSResult s a w

runRWS looks like a combination of runReader, runWriter, and runState. It takes a global configuration and an initial state as an argument and returns a data structure containing the log, the result, and the final state.

The front-end of our application is defined by a function runGame, with the following type signature:

runGame :: GameEnvironment -> Effect Unit

This function interacts with the user via the console (using the node-readline and console packages). runGame takes the game configuration as a function argument.

The node-readline package provides the LineHandler type, which represents actions in the Effect monad, which handle user input from the terminal. Here is the corresponding API:

type LineHandler a = String -> Effect a

foreign import setLineHandler
  :: forall a
   . Interface
  -> LineHandler a
  -> Effect Unit

The Interface type represents a handle for the console and is passed as an argument to the functions which interact with it. An Interface can be created using the createConsoleInterface function:

import Node.ReadLine as RL

runGame env = do
  interface <- RL.createConsoleInterface RL.noCompletion

The first step is to set the prompt at the console. We pass the interface handle, and provide the prompt string and indentation level:

  RL.setPrompt "> " interface

In our case, we are interested in implementing the line handler function. Our line handler is defined using a helper function in a let declaration, as follows:

    lineHandler :: GameState -> String -> Effect Unit
    lineHandler currentState input = do
      case runRWS (game (split (wrap " ") input)) env currentState of
        RWSResult state _ written -> do
          for_ written log
          RL.setLineHandler (lineHandler state) $ interface
      RL.prompt interface
      pure unit

The let binding is closed over both the game configuration, named env, and the console handle, named interface.

Our handler takes an additional first argument, the game state. This is required since we need to pass the game state to runRWS to run the game's logic.

The first thing this action does is to break the user input into words using the split function from the Data.String module. It then uses runRWS to run the game action (in the RWS monad), passing the game environment and current game state.

Having run the game logic, which is a pure computation, we need to print any log messages to the screen and show the user a prompt for the next command. The for_ action is used to traverse the log (of type List String) and print its entries to the console. Finally, setLineHandler is used to update the line handler function to use the updated game state, and the prompt is displayed again using the prompt action.

The runGame function finally attaches the initial line handler to the console interface and displays the initial prompt:

  RL.setLineHandler (lineHandler initialGameState) interface
  RL.prompt interface


  1. (Medium) Implement a new command cheat, which moves all game items from the game grid into the user's inventory. Create a function cheat :: Game Unit in the Game module, and use this function from game.

  2. (Difficult) The Writer component of the RWS monad is currently used for two types of messages: error messages and informational messages. Because of this, several parts of the code use case statements to handle error cases.

    Refactor the code to use the ExceptT monad transformer to handle the error messages and RWS to handle informational messages. Note: There are no tests for this exercise.

Handling Command Line Options

The final piece of the application is responsible for parsing command line options and creating the GameEnvironment configuration record. For this, we use the optparse package.

optparse is an example of applicative command line option parsing. Recall that an applicative functor allows us to lift functions of arbitrary arity over a type constructor representing some type of side-effect. In the case of the optparse package, the functor we are interested in is the Parser functor (imported from the optparse module Options.Applicative, not to be confused with our Parser that we defined in the Split module), which adds the side-effect of reading from command line options. It provides the following handler:

customExecParser :: forall a. ParserPrefs -> ParserInfo a -> Effect a

This is best illustrated by example. The application's main function is defined using customExecParser as follows:

main = OP.customExecParser prefs argParser >>= runGame 

The first argument is used to configure the optparse library. In our case, we simply configure it to show the help message when the application is run without any arguments (instead of showing a "missing argument" error) by using OP.prefs OP.showHelpOnEmpty, but the Options.Applicative.Builder module provides several other options.

The second argument is the complete description of our parser program:

  argParser :: OP.ParserInfo GameEnvironment
  argParser = (env <**> OP.helper) parserOptions

  parserOptions = fold 
    [ OP.fullDesc
    , OP.progDesc "Play the game as <player name>"
    , OP.header "Monadic Adventures! A game to learn monad transformers" 

Here combines a Parser with a set of options for how the help message is formatted. env <**> OP.helper takes any command line argument Parser named env and automatically adds a --help option. Options for the help message are of type InfoMod, which is a monoid, so we can use the fold function to add several options together.

The interesting part of our parser is constructing the GameEnvironment:

  env :: OP.Parser GameEnvironment
  env = gameEnvironment <$> player <*> debug

  player :: OP.Parser String
  player = OP.strOption $ fold 
    [ OP.long "player"
    , OP.short 'p'
    , OP.metavar "<player name>"
    , "The player's name <String>"

  debug :: OP.Parser Boolean
  debug = OP.switch $ fold 
    [ OP.long "debug"
    , OP.short 'd'
    , "Use debug mode"

player and debug are both Parsers, so we can use our applicative operators <$> and <*> to lift our gameEnvironment function, which has the type PlayerName -> Boolean -> GameEnvironment over Parser. OP.strOption constructs a command line option that expects a string value and is configured via a collection of Mods folded together. OP.flag works similarly but doesn't expect an associated value. optparse offers extensive documentation on different modifiers available to build various command line parsers.

Notice how we used the notation afforded by the applicative operators to give a compact, declarative specification of our command line interface. In addition, it is simple to add new command line arguments by adding a new function argument to runGame and then using <*> to lift runGame over an additional argument in the definition of env.


  1. (Medium) Add a new Boolean-valued property cheatMode to the GameEnvironment record. Add a new command line flag -c to the optparse configuration, enabling cheat mode. The cheat command from the previous exercise should be disallowed if cheat mode is not enabled.


This chapter was a practical demonstration of the techniques we've learned so far, using monad transformers to build a pure specification of our game and the Effect monad to build a front-end using the console.

Because we separated our implementation from the user interface, it would be possible to create other front-ends for our game. For example, we could use the Effect monad to render the game in the browser using the Canvas API or the DOM.

We have seen how monad transformers allow us to write safe code in an imperative style, where the type system tracks effects. In addition, type classes provide a powerful way to abstract over the actions provided by a monad, enabling code reuse. We used standard abstractions like Alternative and MonadPlus to build useful monads by combining standard monad transformers.

Monad transformers are an excellent demonstration of expressive code that can be written by relying on advanced type system features such as higher-kinded polymorphism and multi-parameter type classes.