**Embracing the Random | R**, and kindly contributed to R-bloggers)

(TL;DR: You can add vectors that have the same number of elements.)

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You want to know how to rhyme, you better learn how to add

It’s mathematicsMos Def in ‘Mathematics’

Last

time,

we learnt about **scalar multiplication**. Let’s get to adding vectors

together!

# Today’s topic: vector addition

We will follow the notation in Goodfellow, Ian, et

al.. If our vector

has elements that are real numbers, then we can say that

is a **-dimensional** vector.

We can also say that lies in some set of **all vectors
that have the same dimensions as itself**. This might be a bit abstract at first,

but it’s not too bad at all.

Let’s define two vectors:

They have two elements. They are, therefore, two-dimensional vectors.

What are some other two dimensional vectors made up of real numbers? We

could have:

There are **infinitely many** vectors with two elements that we could come up

with! How can we describe this infinite set of vectors made up of real

numbers? We can say that our two-dimensional vectors made up of real

numbers lie in this set:

What in the world does this mean?

## Cartesian products

To understand what the above product means, let’s use a simplified

example. Let’s define sets of integers like so:

What is the result of ? We can depict this operation in a

matrix:

What we’re doing here is taking an arbitrary element from our first set,

. We are then pairing it up with a second arbitrary element from the

set, . The entries in the matrix form a set of **ordered pairs
(i.e. the order of elements in the pair of numbers matter)** which can

be defined like so:

This is called the **Cartesian product** of two sets. Translating the

equation into plain English gives us this:

Now let’s replace the terminology of **ordered pairs** with the word

**vectors**. If we depict the vectors as row vectors, our matrix now

looks like this:

Now let’s replace sets and with the set of real numbers,

. The Cartesian product then becomes:

We now have a set of ordered pairs which are drawn from the **Cartesian
product of the set of real numbers with itself!** If we again replace

the term

**‘ordered pairs’**with

**‘vectors’**, we now have the

**set**

of all two dimensional, real number vectors. We can finally say this about the vectors we defined earlier:

of all two dimensional, real number vectors

Hooray!

Now it’s not difficult to see that is the set formed by

taking the **-ary Cartesian product** of the set of real numbers. We

can now quite easily generalise to vectors of dimensions.

## Get to the point! When can we add vectors?

If we have vectors of **equal dimensions**, we simply **add the elements
of the vectors, one element at a time!** The resulting vector is a vector of

the

**same dimensions as the vectors that were just added**.

Let’s use our example vectors from before:

We know that . We now know that

. Let’s add them:

Easy!

# How can we add vectors in R?

Some operations on vectors are defined differently to those that are

defined in linear algebra.

When we have two `numeric`

vectors with the same number of elements, we

can add them just like we saw above:

```
x <- c(1, 2, 3)
y <- c(2, 3, 4)
x + y
```

```
## [1] 3 5 7
```

But what if we try to add two `numeric`

vectors with different numbers

of elements?

```
x <- c(1, 2)
y <- c(1, 1, 1, 1)
x + y
```

```
## [1] 2 3 2 3
```

What the hell just happened? This isn’t what we have just learnt! How is it

possible to add these two vectors given the mathematical definition of

vector addition?

If we look at the help page for `+`

, we can find our answer:

```
?`+`
```

The binary operators return vectors containing the result of the

element by element operations. If involving a zero-length vector the

result has length zero. Otherwise,the elements of shorter vectors

are recycled as necessary (with a warning when they are recycled only

fractionally).

This is what R is doing:

- For the first two elements of the
**longer**, R is adding`vector`

the**shorter**to it in the above mathematically defined`vector`

way. - When it runs out of elements in the
**shorter**, R goes`vector`

back to the first element of the**shorter**and adds this`vector`

to the next element of the**longer**.`vector`

- R continues like this until it runs out of elements in the
**longer**.

`vector`

(Man, that’s a lot of use of the word `vector`

!)

In this way, R allows you to add vectors of unequal length. We also told

that some other operators also behave in this way:

The operators are + for addition, – for subtraction, * for

multiplication, / for division and ^ for exponentiation.

These aren’t our focus here so let’s move on.

# What about adding scalars to vectors?

In our standard mathematical definition, we can’t add scalars to

vectors. But we can in R!

We simply add the scalar to our vector, one element at a time:

```
x <- 1
y <- c(1, 2, 3)
x + y
```

```
## [1] 2 3 4
```

# Conclusion

We learnt how to add vectors. We also learnt a little bit more about

sets. We learnt that R can behave differently to our mathematical

definitions of vector addition. Let’s now move onto **dot products**!

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**Embracing the Random | R**.

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