Use && instead of &

To determine if both dice are the same, the move_square() function uses if statements.

if (is_double[1] & is_double[2] & is_double[3]) {
    current <- 11 # Go To Jail - square 11 == Jail
}

The & operator will always evaluate both its arguments. That is, if you type x & y, R will always try to work out what x and y are. There are some cases where this is inefficient. For example, if x is FALSE, then x & y will always be FALSE, regardless of the value of y. Thus, you can save a little processing time by not calculating it. The && operator takes advantage of this trick, and doesn't bother to calculate y if it doesn't make a difference to the overall result.

In this code, if is_double[1] is FALSE we don't need to evaluate is_double[2] or is_double[3], so we can get a speedup by swapping & for &&.

One thing to note is that && only works on single logical values, i.e., logical vectors of length 1 (like you would pass into an if condition), but & also works on vectors of length greater than 1.

This is a part of the course

“Writing Efficient R Code”

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Exercise instructions

Create an improved_move() function, which is similar to move(), but uses &&.
Using microbenchmark(), compare improved_move() to the previous version move().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Example data
is_double

# Define the previous solution
move <- function(is_double) {
    if (is_double[1] & is_double[2] & is_double[3]) {
        current <- 11 # Go To Jail
    }
}

# Define the improved solution
improved_move <- function(is_double) {
    if (is_double[1] ___ is_double[2] ___ is_double[3]) {
        current <- 11 # Go To Jail
    }
}

# microbenchmark both solutions
# Very occassionally the improved solution is actually a little slower
# This is just random chance
microbenchmark(___, ___, times = 1e5)

Edit and Run Code