R for reproducible scientific analysis

Vectorisation

Learning Objectives

To understand vectorised operations in R.

One of the nice features of R is that most of its functions are vectorized, that is the function will operate on all elements of a vector without needing to loop through and act on each element one at a time. This makes writing code more concise, easy to read, and less error prone.

x <- 1:4
x * 2

[1] 2 4 6 8

The multiplication happened to each element of the vector.

Let's try this on the pop column of the gapminder dataset.

Challenge 1

Make a new column in the gapminder dataframe that contains population in units of millions of people. Check the head or tail of the dataframe to make sure it worked.

Challenge 2

Create a subset of the gapminder dataset countaining entries only for Australia.

Calculate the mean GDP (GDP per capita multiplied by total population) for Australia over all years on record.

Refresh your ggplot skils by plotting mean GDP against year.

We can also add two vectors together:

y <- 6:9
x + y

[1]  7  9 11 13

Each element of x was added to its corresponding element of y:

x:  1  2  3  4
    +  +  +  +
y:  6  7  8  9
---------------
    7  9 11 13

Challenge 3

What do you think will happen if you add (or subtract, multiply, divide etc.) vectors of different lengths?

Try it. What does x + c(1,3) give you? Why?

Just as we saw in the previous lesson, vectors will recycle:

x + c(1,3)

[1] 2 5 4 7

Like so:

x:  1  2  3  4
    +  +  +  +
y:  1  3  1  3
---------------
    2  5  4  7

Comparison operators also apply element-wise, as we saw in the subsetting lesson:

x > 2

[1] FALSE FALSE  TRUE  TRUE

Logical operations are also vectorised:

a <- x > 3
b <- x < 3

[1] FALSE FALSE FALSE  TRUE

[1]  TRUE  TRUE FALSE FALSE

a | b

[1]  TRUE  TRUE FALSE  TRUE

a & b

Tip: some useful functions for logical vectors

any() will return TRUE if any element of a vector is TRUE all() will return TRUE if all elements of a vector are TRUE

[1] FALSE FALSE FALSE FALSE

Many functions also operate on element-wise on vectors:

x <- 1:4
sin(x)

[1]  0.8414710  0.9092974  0.1411200 -0.7568025

Vectorised operations also work element wise on matrices:

m <- matrix(1:12, nrow=3, ncol=4)
m * -1

     [,1] [,2] [,3] [,4]
[1,]   -1   -4   -7  -10
[2,]   -2   -5   -8  -11
[3,]   -3   -6   -9  -12

Note that * gives you element-wise multiplication!

m * m

     [,1] [,2] [,3] [,4]
[1,]    1   16   49  100
[2,]    4   25   64  121
[3,]    9   36   81  144

To do matrix multiplication, we need to use the %*% operator:

m %*% t(m)   # The t() function returns the transpose of a matrix

     [,1] [,2] [,3]
[1,]  166  188  210
[2,]  188  214  240
[3,]  210  240  270

For more on matrix algebra, see the Quick-R reference guide

Challenge 4

We're interested in looking at the sum of the following sequence of fractions:

 x = 1/(1^2) + 1/(2^2) + 1/(3^2) + ... + 1/(n^2)

This would be tedious to type out, and impossible for high values of n. Can you use vectorisation to solve for x, when n=100? How about when n=10,000?

Challenge 5

Given the following matrix:

m <- matrix(1:12, nrow=3, ncol=4)
m

     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12

Write down what you think will happen when you run:

m ^ -1
m * c(1, 0, -1)
m > c(0, 20)

Did you get the output expected? If not, ask a helper!