Coding/Learn/haskell/haskell data structures trees
Learn Haskell & Pure Functional Programming
01.Introduction to Haskell & GHC
02.Syntax, Comments & Program Layout
03.Basic Types & Immutability
04.Arithmetic, Precedence & Infix Functions
05.Strings, Lists & the Cons Operator (:)
06.Booleans, Equality & Logical Operators
07.Conditional Expressions (if then else)
08.Function Guards (| condition = result)
09.Pattern Matching on Functions
10.Case Expressions (case of)
11.Local Bindings with let ... in
12.Auxiliary Bindings with where Clauses
13.Function Signatures & Referential Transparency
14.Currying & Partial Function Application
15.Anonymous Lambda Expressions (\x -> expr)
16.Higher-Order Functions: map, filter, foldl & foldr
17.Function Composition (.) & Application ($)
18.Recursion Patterns & Tail Recursion
19.Tuples & Pair Operations (fst, snd)
20.List Functions: head, tail, take, drop & null
21.Arithmetic Sequences & List Ranges
22.List Comprehensions
23.Zipping & Combining Lists (zip, zipWith)
24.Lazy Evaluation & Infinite Streams
25.Type Inference & Type Variables
26.Standard Typeclasses: Eq, Ord, Show & Num
27.Defining Custom Typeclasses & Instances
28.Type Synonyms (type) & newtype Wrappers
29.Algebraic Data Types (data ADT)
30.Record Syntax & Named Fields
31.The Maybe Type & Null Safety
32.The Either Type for Detailed Errors
33.Pattern Matching on Maybe & Either
34.Domain Errors & Validation Pipelines
35.Recursive Data Structures: Binary Trees
36.Strictness, seq & Bang Patterns (!)
37.The Functor Typeclass & fmap (<$>)
38.Applicative Functors: pure & (<*>)
39.The Monad Typeclass & Bind Operator (>>=)
40.Do Notation & Monadic Desugaring
41.The Three Monad Laws
42.Monadic Utilities: sequence, mapM, forM & when
43.The IO Monad & Pure/Impure Separation
44.File I/O: Reading & Writing Files
45.Modules, Exports & Qualified Imports
46.Monad Transformers: Reader, Writer & State
47.Concurrency, forkIO & MVars
48.Property-Based Testing with QuickCheck
49.Unit Testing & Test Suites (HUnit / Hspec)
50.Monadic Parsing & Parser Combinators
51.Production Architecture: Cabal & Stack
52.Comprehensive Functional Practice Challenges
53.Final Knowledge Assessment Quiz
54.Capstone Project: Domain Expression Evaluator & AST Engine
55.Verified Haskell Developer Certificate
Course Certificate0%

Complete all lessons to earn

LearnRecursive Data Structures: Binary Trees
35 / 55 0%
Lesson 35 of 55
10 mins read

Recursive Data Structures: Binary Trees

PreviousNext Lesson
In Plain English

Recursive data structures define types in terms of themselves. Implement a generic Binary Search Tree (Tree a = Empty | Node a (Tree a) (Tree a)) and search algorithms.

Deep Dive: How It Works

Recursive ADT: data Tree a = Empty | Node a (Tree a) (Tree a) deriving (Show, Eq).

Tree Insertion: insertTree x Empty = Node x Empty Empty; recursively navigate left or right child based on comparison.

Tree Traversal: inOrder Empty = []; inOrder (Node val l r) = inOrder l ++ [val] ++ inOrder r.

PointCore Rules to Remember

Point star
Recursive Type Definitions: Build trees, graphs, and nested AST expressions natively.
Point star
Pure Immutable Trees: Path copying allows sharing unchanged tree branches with zero memory overhead.

Live Interactive Example

Hit Run Code to see it live
Binary Search Tree Insertion and Traversal
Python 3.12
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Output Console
Click "Run Code" to view the rendered output.
How it works: inOrder traversal of the BST produces the elements in sorted order.

Your Turn: Micro Challenge

No pressure! Edit the starter code below and test your solution with instant feedback.

Micro Exercise

Construct Singleton Tree Node

  • PointCreate `t = Node 42 Empty Empty`.
  • PointPrint `"Tree: "` followed by `show t`.
1
2
3
4
5
6
7
8
Sandbox Output
Click "Run & Check" to test your solution.

Finished reading and practicing?

Mark this lesson as completed to update your course progress.

PreviousNext Lesson
Overview100 SheetLearnCertificates