May 12, 2020

Our course so far

  • Solve simple problems
    • Decomposition
    • Pattern matching
    • Abstraction
  • Systems Analysis
    • Identify the system parts and processes
    • Use R to simulate a system
    • See what happens in the long term
    • See the effect of changing rates or initial conditions

Computational Thinking

We know that Computational Thinking has four parts

  • Decomposition
  • Pattern recognition
  • Abstraction
  • Algorithm design

Today we will talk about patterns


in the dictionary

  • a repeated decorative design: a wallpaper pattern
  • an arrangement or sequence regularly found in comparable objects: the house had been built on the usual pattern
  • a regular form or sequence discernible in certain actions or situations: the problems followed a repeated pattern

Science is about Patterns

  • The goal of Science is to understand Nature
  • We find the laws that explain Patterns in Nature
  • The first step is to find Patterns
    • Day & Night, Winter & Summer are Patterns
    • Understanding seasons allows us to survive
    • Some food makes us healthy
    • Understanding which food makes us sick is essential to survive

Patterns in Technology

Patterns in Technology

Patterns in Nature

Patterns in Nature

Patterns in Nature

Patterns in Nature

Patterns in Art

Patterns in Decoration

Patterns in Music

Patterns in movement: repetitions

Patterns in programs

When we see a pattern we can make

  • loops, if we need to repeat the pattern in one place
    • Like drawing each angle in a star
  • functions, if the pattern is used in several places
    • Like drawing several stars in different places.

There is another kind of pattern

Recursive patterns in the Nature

Recursive patterns in Trees

Recursive patterns in the Rivers

Recursive patterns in the Body

A Pattern can be part of itself

Sometimes the easiest way to define a pattern is to use the same pattern.

For example:

  • A tree has a trunk and branches. Each branch is a trunk with branches.
    • In other words, each branch is a little tree
    • Real trees or phylogenetic trees
  • Rivers are made of small rivers
  • Blood vessels connect smaller blood vessels

Recursive functions

To represent these patterns we use functions that are part of themselves

example<-function(input) {

Example of recursive function

One useful mathematical function is factorial

The factorial of a \(n\) is \(n!\), defined as \[n! = n\cdot(n-1)!\]

That is, the factorial of n is n times the factorial of n-1

How do we write it in R?

Let’s write our own factorial function

factorial <- function(n) {
    ans <- n * factorial(n-1)

There is an error here. Can you find it?

Use the debugger to see what is happening

factorial(4) visually

factorial(4) visually

factorial(4) visually

factorial(4) visually

factorial(4) visually

Exit condition

Each recursive function needs an exit condition

That is, some rule to decide when to finish

There is always an easy case that does not require recursion

For example, the factorial of 1 is \(1! = 1\).

Exit condition for factorial

factorial <- function(n) {
    if(n <= 1) {
        ans <- 1
    } else {
        ans <- n * factorial(n-1)

Correct factorial(4)

Example of recursive function to find minimum

find_min <-function(vector) {
    n <- length(vector)
    if(n==1) {
    } else {
        a <- find_min(vector[1:(n/2)])
        b <- find_min(vector[(n/2+1):n])
        if(a<b) {
        } else {

find_min(c(12, 10, 15, 11, 13))

The simple cases return a single value

Then the “parent” chooses between two options

Then once again…

…and we get the final value

Another Example

tree <- function(level) {
  save turtle position
  if(level<=1) {
    draw branch
  } else {
  recover turtle position

Improved Example

  • Branch color depends on level
  • Line width depends on level
  • Lower levels have smaller length
  • Branch angle changes with level

Online material