# Programming in R

These notes are based on Chapter 3-5 of JMR — Jones, O., Maillardet, R., & Robinson, A. (2009). Introduction to scientific programming and simulation using R. CRC Press.

## Function

Just like functions in mathematics, functions in R take arguments and return a value. For example, if we have a mathematical function: $f:x\mapsto \sin(x^2+1)$ the corresponding R function can be written as f <- function(x) sin(x^2+1). To use multiple lines for the definition, use a pair of brackets { } and return keyword as follows:

f <- function(x) {
tmp <- x^2 # tmp is a dummy name
tmp <- tmp + 1
tmp <- sin(tmp)
return(tmp)
}

Notice the use of # for comments.

A returned value need not be a scalr number. It may be a vector, list, or even more complex R object.

## for loop

### JMR 3.9.2

Let $$h(x,n) = 1 + x + x^2+\dots+ x^n = \sum_{i=0}^n x^i$$. Write an R program to calculate $$h(x,n)$$ using for loop.

h <- function(x,n) {
sum <- 0
for (i in 0:n) {
sum <- sum + x^i
}
return(sum)
}

### JMR 3.9.3

The function $$h(x,n)$$ from JMR 3.9.2 is the finite sum of a geometric sequence. It has the following explicit formula, for $$x\neq1$$, $h(x,n) = \frac{1-x^{n+1}}{1-x} .$ Test your program from JMR 3.9.2 against this formula using the following values

$$x$$ $$n$$ $$h(x,n)$$ $$\frac{1-x^{n+1}}{1-x}$$
0.3 55 1.4285714 1.4285714
6.6 8 4.243335510^{6} 4.243335510^{6}

You may use format(h(6.6,8), scientific=FALSE) for a nicer look.

## while loop

### JMR 3.9.4

First write a program that achieves the same result as in JMR 3.9.2 but using a while loop. Then write a program that does this using vector operations (and no loops).

h2 <- function(x,n) {
sum <- 0
i <- 0
while (i <= n) {
sum <- sum + x^i
i <- i + 1
}
return(sum)
}
x <- 6.6
n <- 8
h2(x,n)
##  4243336
sum(x^(1:n)) # Just one line!
##  4243335

Vector operations are faster and cool. We should use them when performance is required (e.g., Size of the Mandelbrot set). But, if they run for a second or two, I recommend using loops to be explicit about what you are doing.

## if statement

### Are you prime?

Write a function that returns whether an integer $$n\geq2$$ is prime. (Recall that $$n\geq2$$ is prime if it has only two factors: $$1$$ and $$n$$.)

prime <- function(n) {
if (n > 2) {
for (m in 2:(n/2)) {
# for (m in 2:(sqrt(n))) {
if (n %% m == 0) {
return(FALSE)
}
}
}
return(TRUE)
}
prime(7)
##  TRUE

We may replace n/2 with sqrt(n) and make it more efficient because if $$n=m_1m_2$$, at least one of $$m_1$$ and $$m_2$$ must be less than or equal to $$\sqrt{n}$$.

### JMR 3.9.8

How does program provided in JMR p.32 behave if a2=0 and/or a1=0? Using if statements, modify the program so that it gives sensible answers for all possible (numerical) inputs.

a2 <- 0
a1 <- 2
a0 <- 1
discrim <- a1^2 - 4*a2*a0
if (discrim > 0) {
roots <- c( (-a1 + sqrt(a1^2 - 4*a2*a0))/(2*a2),
(-a1 - sqrt(a1^2 - 4*a2*a0))/(2*a2) )
} else {
if (discrim == 0) {
roots <- -a1/(2*a2)
} else {
roots <- c()
}
}
roots
##   NaN -Inf

When using the quadratic formula, a problematic case is a2=0, which makes the function no longer quadratic but linear. So, let’s separate this case using if.

if (a2 != 0) {
if (discrim > 0) {
roots <- c( (-a1 + sqrt(a1^2 - 4*a2*a0))/(2*a2),
(-a1 - sqrt(a1^2 - 4*a2*a0))/(2*a2) )
} else {
if (discrim == 0) {
roots <- -a1/(2*a2)
} else {
roots <- c()
}
}
} else if (a1 != 0) {
roots <- -a0/a1
} else {
roots <- c()
}
roots
##  -0.5

## Recursive function

A recursive function is a function that calls itself. Although powerful and useful because many algorithms are recursive in nature, it sometimes takes time to follow the flow of executions.

### JMR 5.5.1

A factorial function $$f(n)$$ (commonly denoted by $$n!$$) is defined as follows: $f(n) = \begin{cases} n\cdot(n-1)\cdots1 & \text{for } n \geq 1 \\ 1 & \text{for } n = 0 \end{cases}$

Notice the recursion for $$n\geq1$$, $f(n) = n\cdot(n-1)\cdots1 = n\cdot f(n-1) .$ That is, to return a value for an argument $$n$$, the factorial function calls itself with another argument $$n-1$$.

Let’s implement this simple recursive function.

factorial <- function(n) {
if (n >= 1) {
return(n*factorial(n-1)) # calling itself!
} else {
return(1)
}
}

factorial(5)
##  120

It is illuminating, but the use of recursion for factorial with fixed $$n$$ seems like overkill as we can simply use for loop. The next example is more practical.

### Which of you are prime?

Write a function that returns a subset of $$\{2,3,\dots,n\}$$ which contains only primes.

An easy solution is to use a for loop to call prime() defined in Are you prime? $$n-1$$ times.

primesieve1 <- function(n) {
S <- c()
for (i in 2:n) {
if (prime(i)) {
S <- c(S, i)
}
}
return(S)
}

According to JMR 5.5.2, however, there is a lot more efficient algorithm called the Sieve of Eratosthenes, which uses a recursive function.

1. Set $$p = 2$$, $$P = \{\}$$ and $$C = \{2,3,\dots,n\}$$.
2. Append $$p$$ to $$P$$ and remove all multiples of $$p$$ from $$C$$.
3. Set $$p = \min(C)$$ and $$n = \max(C)$$.
4. Return $$P+C$$ if $$p > \sqrt{n}$$. Otherwise, go back to #1.
primesieve2 <- function(P,C) {
p <- C # faster than min(C)
n <- C[length(C)] # faster than max(C)
if (p > sqrt(n)) {
return(c(P,C))
} else {
P <- c(P, p) # append p
C <- C[C%%p != 0] # removing multiples of p
return(primesieve2(P,C))
}
}

Let’s see how faster the recursive algorithm performs for the same result.

n = 1e5
tic()
r1 = primesieve1(n)
toc()
## 24.688 sec elapsed
tic()
r2 = primesieve2(c(), 2:n)
toc()
## 0.022 sec elapsed
if (identical(r1,r2)) {
cat("Both produce the same set containing", length(r2), "primes." )
} else {
cat("Well, something went wrong...")
}
## Both produce the same set containing 9592 primes.

We will see another recursive function in Numerical Integration

## Plotting

For practice, let’s draw a hyperbolic tangent with three fixed points at $$x = 0, \pm1.915$$. $y = 2\tanh(x) = 2\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}}$

x <- c(-1.915,0,1.915)
y <- c(-1.915,0,1.915)
plot(x, y, pch=19, xlim=c(-2,2), ylim=c(-2,2))
title(expression(y==2*tanh(x)))
curve(2*tanh(x), from=-3, to=3, add=TRUE)
curve(x*1, from=-3, to=3, add=TRUE)
text(-1.55, -.8, "fixed point at -1.915")
text(0, -.2, "fixed point at 0", pos=4)
text(1.55, .8, "fixed point at 1.915")
arrows(-1.8, -1, -1.9, -1.8, length=0.1)
arrows(1.8, 1, 1.9, 1.8, length=0.1) # RStudio

The interface of RStudio may look like this.