Okay, So from Victor's to changing them entrance to matrices and data frames, we have gone a long way already, and in this lesson we are getting a step closer to data frames because we'll be focusing on matrices.

You already know what a matrix is.

You're so one.

In the previous lesson, we made a one dimensional object, an atomic vector eloquently called a into a two dimensional matrix, still eloquently called a by giving it a dimensions argument.

What does this tell us about matrices?

Well, a few things first, when it comes to storing data, matrices are a natural extension to Victor's.

While Victor's air one dimensional collections of data matrices are two dimensional, Ari's two matrices have a fixed number of rows and columns, and three matrices, in the same way as vectors, can contain only one basic data type.

This makes sense.

You create a matrix from a victor.

You can think of it as a curving of victor into rose or columns, and just as a victor can literally store doubles characters or logic ALS, so can a matrix.

All right, let's learn to new methods for creating a matrix.

The major X function in the are behind and see bind functions.

Using the major X function to create a matrix is very straightforward.

Matrix takes at least two arguments.

Victor.

It can structure into a matrix and an argument specifying the number of rows the new object must have called in a row.

Alternatively, you can pass the number of columns you want your matrix to have by specifying in coal.

Finally, you pick out a name for your matrix and save it to an object.

So my Matrix is gold MTR eggs and it contains the numbers from 1 to 12 I said and wrote 23 So you can see our inference that since I have 12 values in my victor, I probably want four columns.

If I said ankle to before and leave and Roland specified, we'll still get the same result for Mt.

Rx.

Awesome!

All right, so I have mentioned in the previous lesson that when we use the major X function will be able to decide the way our data is organized into a matrix.

So what does that mean?

Well, notice how the sequence of values ISS entered by columns Our starts by filling the first column, then moves on to the second and so on.

What if I wanted my data to be organized by row?

However Well, if that's the case, I can use the buyer Oh, arguments in the matrix function and said it too.

True, instead of its default folks like this print Mt.

Eriks.

Oh, that looks nicer.

Right?

Okay, So I mentioned there is another way to create a matrix in our and that is with the r Bind and see bind functions.

Our bind stands for row, bind and sit behind.

You guessed it for Columbine.

These methods often seem more intuitive to people.

But if you grasp the matrix function well enough, it will serve you just as well.

Okay, so let's create two new vectors.

I will call the 1st 1 USA and never possum numbers into it.

1.31 point 51.21 point four and 1.5.

I will create a second factor called d E, and it will hold the numbers.

0.20 point for 0.70 point eight and 0.8 Again.

Fantastic.

These are just made up numbers reflecting the amount of non government organizations in the U.

S.

A.

And in Germany in millions.

Now that I have my data ready, I can bind it into a matrix.

Let's use the sea bind to do that.

NGO.

This is the name I want my matrix to half see blind and in the parentheses.

I will pass the vectors.

I won't find it.

Print NGO to see how that worked and it worked beautifully.

Fantastic.

Okay, notice how the bind function took the names of the victors and bind it and save them of the column names.

Well, you can change that if that doesn't work for you.

But first, let's give names to our nameless Rose.

Naming the components of The Matrix is just like naming a victor.

But instead of using the names function matrices recognized the coal names and roll names functions.

Let's try row names Pastor Data and create a vector of row names.

Print the Matrix again.

Now that is a lot more informative than before, don't you think?

Okay, there's just one problem.

I would very much prefer to have my years as columns and the data points as rose, but I don't want to do the whole thing all over again.

Okay?

Luckily, there is the transposed based function that lets has changed the orientation of her data.

Okay, I'm just gonna go ahead and I'm going to save my NGO Matrix as its transposed version and very have it a fuller named and operational matrix.

And if I wanted to enter another role for the data, for instance about India's NGOs, I could easily do it with E r.

Bind function like this.

There you go.

Another row.

I did virtually effortlessly.

Okay, Awesome.

Fantastic.

Let's wrap it up here.

And the next lesson will be more on the practical side of things.

So I'm gonna catch you there form or videos like this one, please subscribe.