## 4.4 Application: The Collatz Conjecture

Take any positive integer greater than 1. Apply the following rule, which we will call the Collatz Rule:

- If the integer is even, divide it by 2;
- if the integer is odd, multiply it by 3 and add 1.

Now apply the rule to the resulting number, then apply the rule again to the number you get from that, and so on.

For example, start with 13. We proceed as follows:

- 13 is odd, so compute \(3 \times 13 + 1 = 40\).
- 40 is even, so compute \(40/2 = 20\).
- 20 is even, so compute \(20/2 = 10\).
- 10 is even, so compute \(10/2 = 5\).
- 5 is odd, so compute \(3 \times 5+ 1 = 16\)
- 16 is even, so compute \(16/2 = 8\).
- 8 is even, so compute \(8/2 = 4\).
- 4 is even, so compute \(4/2 = 2\).
- 2 is even, so compute \(2/2 = 1\).
- 1 is odd, so compute \(3 \times 1 + 1 = 4\).
- 4 is even, so compute \(4/2 = 2\).
- 2 is even, so compute \(2/2 = 1\).

If we keep going, then we will cycle forever:

\[4, 2, 1, 4, 2, 1, \ldots\]
In mathematics the *Collatz Conjecture* is the conjecture that for *any* initial positive number, every Collatz Sequence (the sequence formed by repeated application of the Collatz Rule) eventually contains a 1, after which it must cycle forever. No matter how large a number we begin with, we have always found that it returns to 1, but mathematicians do not know if this will be so for *any* initial number.

A sequence of Collatz numbers can bounce around quite remarkably before descending to 1. Our goal in this section is to write a function called `collatz()`

that will compute the Collatz sequence for any given initial number and draw a graph of the sequence as well.

First, let’s make a function just for the Collatz Rule itself:

```
collatzRule <- function(m) {
if ( m %% 2 == 0) {
return(m/2)
} else {
return(3*m + 1)
}
}
```

(Recall that `m %% 2`

is the remainder of `m`

after it is divided by 2. If this is 0, then `m`

is even.)

Next let’s try to get a function going that will print out Collatz numbers:

```
collatz <- function(n) {
# n is the initial number
while ( n > 1 ) {
cat(n, " ", sep = "")
# get the next number and call it n:
n <- collatzRule(n)
}
}
```

Let’s try it out:

`collatz(13)`

`## 13 40 20 10 5 16 8 4 2`

So far, so good, but if we are going to graph the numbers, then we should store them in a vector. The problem is that we don’t know how long the vector needs to be.

One possible solution is to add to the vector as we go, like this:

```
collatz <- function(n) {
numbers <- numeric()
while ( n > 1 ) {
# stick n onto the end of numbers:
numbers <- c(numbers, n)
n <- collatzRule(n)
}
print(numbers)
}
```

Try it out:

`collatz(13)`

`## [1] 13 40 20 10 5 16 8 4 2`

This looks good. There are two problems, though, if the Collatz sequence happens to go on for a very long time.

**Computation Time**: the user doesn’t know when the sequence will end—if ever!—so she won’t know whether a delay in production of the output is due to a long sequence or a problem with the program itself. as the sequence gets longer, the computation-time is made even longer by the way the following line of code works:`numbers <- c(numbers, n)`

R cannot actually “stick” a new element onto the end of a vector. What it actually does is to move to a new place in memory and create an entirely new vector consisting of all the elements of

`numbers`

followed by the number`n`

. R then assigns the name`numbers`

to this value, freeing up the old place in memory where the previous`numbers`

vector lived, but when a vector is very long copying can take a long time.**Memory Problems**Once the`numbers`

vector gets long enough it will use all of the memory in the computer that is available to R. R will crash.

In order to get around this problem, we should impose a limit on the number of Collatz numbers that we will compute. We’ll set the limit at 10,000. The user can change the limit, but should exercise caution in doing so. Also, we’ll initialize our `numbers`

vector to have a length set to this limit. We can then assign values to elements of an *already existing* vector: this is much faster than copying entire vectors from scratch.

```
collatz <- function(n, limit = 10000) {
# collatz numbers will go in this vector
numbers <- numeric(limit)
# keep count of how many numbers we have made:
counter <- 0
while ( n > 1 & counter < limit) {
# need to make a new number
counter <- counter + 1
# put the current number into the vector
numbers[counter] <- n
# make next Collatz number
n <- collatzRule(n)
}
# find how many Collatz numbers we made:
howMany <- min(counter, limit)
# print them out:
print(numbers[1:howMany])
}
```

Again let’s try it:

`collatz(257)`

```
## [1] 257 772 386 193 580 290 145 436 218 109 328 164 82 41
## [15] 124 62 31 94 47 142 71 214 107 322 161 484 242 121
## [29] 364 182 91 274 137 412 206 103 310 155 466 233 700 350
## [43] 175 526 263 790 395 1186 593 1780 890 445 1336 668 334 167
## [57] 502 251 754 377 1132 566 283 850 425 1276 638 319 958 479
## [71] 1438 719 2158 1079 3238 1619 4858 2429 7288 3644 1822 911 2734 1367
## [85] 4102 2051 6154 3077 9232 4616 2308 1154 577 1732 866 433 1300 650
## [99] 325 976 488 244 122 61 184 92 46 23 70 35 106 53
## [113] 160 80 40 20 10 5 16 8 4 2
```

Things are working pretty well, but since the sequence of numbers might get pretty long, perhaps we should only print out the length of the sequence, and leave it to the reader to say whether the sequence itself should be shown.

```
collatz <- function(n, limit = 10000) {
numbers <- numeric(limit)
counter <- 0
while ( n > 1 & counter < limit) {
counter <- counter + 1
numbers[counter] <- n
n <- collatzRule(n)
}
howMany <- min(counter, limit)
cat("The Collatz sequence has ", howMany, " elements.\n", sep = "")
show <- readline("Do you want to see it (y/n)? ")
if ( show == "y" ) {
print(numbers[1:howMany])
}
}
```

Next let’s think about the plot. We’ll use the plotting system in the **ggplot2** package (Wickham et al. 2018).

`library(ggplot2)`

Later on we will make a serious study of plotting with **ggplot2**, but for now let’s just get the basic idea of plotting a set of points. First, let’s get a small set of points to plot:

```
xvals <- c(1, 2, 3, 4, 5)
yvals <- c(3, 7, 2, 4, 3)
```

`xvals`

contains the x-coordinates of our points, and `yvals`

contains the corresponding y-coordinates.

We set up a plot as follows:

`ggplot(mapping = aes(x = xvals, y = yvals))`

The `ggplot()`

function sets up a basic two-dimensional grid. The `mapping`

parameter explains how data will be “mapped” to particular positions on the plot. In this case it has been set to:

`aes(x = xvals, y = yvals)`

`aes`

is short for “aesthetics”, which has to do with how somethings *looks*. The expression means that `xvals`

will be located on the x-axis and `yvals`

will be located on the y-axis of the plot.

Now let’s see what we get if we actually run the call to the `ggplot()`

function (see Figure 4.2):

`ggplot(mapping = aes(x = xvals, y = yvals))`

The plot is blank! Why is this? Well, although `ggplot()`

has been told what values are to be represented on the plot and where they might go, it has not yet been told how they should be shaped: it has not been told their *geometry*, you might say. We can add a geometry to the plot to get a picture (see Figure 4.3:

```
ggplot(mapping = aes(x = xvals, y = yvals)) +
geom_point()
```

The geometry determines a lot about the look of the plot. In order to have the points connected by lines we could add `geom_line()`

(see Figure 4.4:

```
ggplot(mapping = aes(x = xvals, y = yvals)) +
geom_point() +
geom_line()
```

We’ll choose a scatterplot with connecting lines for our graph of the sequence. With a little more work we can get nice labels for the x and y-axes, and a title for the graph. Our `collatz()`

function now looks like:

```
collatz <- function(n, limit = 10000) {
# record initial numer because will change n
initial <- n
numbers <- numeric(limit)
counter <- 0
while ( n > 1 & counter < limit) {
counter <- counter + 1
numbers[counter] <- n
n <- collatzRule(n)
}
howMany <- min(counter, limit)
steps <- 1:howMany
cat("The Collatz sequence has ", howMany, " elements.\n", sep = "")
show <- readline("Do you want to see it (y/n)? ")
if ( show == "y" ) {
print(numbers[steps])
}
# use initial value to make plot title:
plotTitle <- paste0("Collatz Sequence for n = ", initial)
# make the plot
ggplot(mapping = aes(x = steps, y = numbers[steps])) +
geom_point() + geom_line() +
labs( x = "Step", y = "Collatz Value at Step",
title = plotTitle)
}
```

Try this version a few times, like so:

`collatz(257)`

It’s quite remarkable how much the sequence can rise and fall before hitting 1.

A further issue worth considering is that our collatz() function depends on the previously-defined function `collatzRule()`

. In order for it to work correctly, R would need to be able to find the name `collatzRule`

somewhere on the search path. If the user hasn’t already defined the `collatzRule()`

function, then a call to `collatz() will fail.

One solution is simply to remind users of the need to define both `collatzRule()`

and `collatz()`

prior to running `collatz()`

. Another solution—perhaps the kinder one—is to define the `collatzRule()`

function in the body of `collatz()`

. Let’s adopt this approach.

The final version of the `collatz()`

function (with some validation thrown in for good measure) appears below.

```
collatz <- function(n, limit = 10000) {
# add some validation:
n <- suppressWarnings(as.integer(n))
isValid <- !is.na(n) && n > 1
if (!isValid ) {
return(cat("Need an integer bigger than 1. Try again."))
}
# define collatzRule:
collatzRule <- function(m) {
if ( m %% 2 == 0) {
return(m/2)
} else {
return(3*m + 1)
}
}
# On with the show!
# Record initial number because we will change it:
initial <- n
numbers <- numeric(limit)
counter <- 0
while ( n > 1 & counter < limit) {
counter <- counter + 1
numbers[counter] <- n
n <- collatzRule(n)
}
howMany <- min(counter, limit)
cat("The Collatz sequence has ", howMany, " elements.\n", sep = "")
show <- readline("Do you want to see it (y/n)? ")
if ( show == "y" ) {
print(numbers[1:howMany])
}
plotTitle <- paste0("Collatz Sequence for n = ", initial)
steps <- 1:howMany
ggplot(mapping = aes(x = steps, y = numbers[1:howMany])) +
geom_point() + geom_line() +
labs( x = "Step", y = "Collatz Value at Step",
title = plotTitle)
}
```

### References

Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, and Kara Woo. 2018. *Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics*. https://CRAN.R-project.org/package=ggplot2.