Welcome! In this video you'll be introduced to creating matrices in R. After creating some
sample data in vector form we will take a look at using the matrix function to create a matrix.
Then we will look at a couple functions which we can use in R to create row and column names which in the next video
we will use as a way to index into the matrix.
And then finally we'll look at a couple ways of viewing the matrix after it's been created.
So notice that we have three vectors of information that make up our example data.
Q1.mu and Q2.mu represent the first and second quarter mean stock price for 4 different companies.
While stock.sym is going to correspond to the company stock symbols.
Now we'll be using the first two vectors, quarterly stock prices, to represent the two columns of our matrix.
Whereas we'll use stock.sym as the row names. So next let's create the matrix which will be a 4x2 matrix by calling upon the matrix function.
Notice that the first input is going be a concatenation of the two mean stock prices.
The result is going to be a vector itself, so the second input "nrow = 4"
is used to declare how you want that vector of information cut up.
So in this instance we want 4 rows to be created from that single concatenated vector.
Then the output is going to be saved as stocks.
Then the output is going to be saved as stocks. So if we run all of this,
will see that we have our information stored as "Values" while our matrix, stocks,
is saved as "Data" in global environment.
So notice that after we run all of this, that in our Global Environment,
we have two different categories of information.
One is gonna be Data; the other one Values. So our individual vectors were saved as values in the Environment,
whereas stocks, which is the matrix that we created, is recognized in the R Environment as Data.
Now that we've created a matrix let's form row and column names.
So to do this,
we can call upon the rownames and columnnames functions.
So as shown here we're going to designate row names for stocks, our 4x2 matrix,
as the stock.sym vector.
Then for column names we're going to create another character vector. It's going to be made up of Q1, Q2, and remember when creating character
vectors you have to use quotation marks around each of your elements.
Let's run both of these.
Now after running these it may seem like nothing took place; but what actually happened is that it added
row and column names to the matrix itself. So to view your matrix
you can simply run a line (of code) with the name of the matrix. So for example, if we run stocks we can see in the console that we have our 4x2 matrix
with row names and column names. Now another way that you can view something that you've
created an R is to look over in the Environment window
and notice that there is a little grid that's showing up after stocks. So if you click on this,
notice that another pane opens over top of your scripting window,
and you can view the contents that way.
Now though you can click on the grid as we've done to produce the stocks matrix,
notice that in the Console window,
that the view function was called upon
whenever we clicked on that grid to produce the matrix. What's nice in RStudio is that some of the point and click options that you have
will still be run in the Console so you can see what functions are actually used.
So this concludes the video on an introduction to matrices.
In the next video we will look at some additional options when using the matrix function in R. We will also look at a couple ways of
indexing into a matrix. And we'll also be looking at some other functions which are very useful
if you're working with data structures in the form of matrices.
We hope you found this useful, and happy coding!
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