Swirl – R porgramming – Lesson 7 – Matrices and Data Frames

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1: R Programming
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1: Basic Building Blocks 2: Workspace and Files 3: Sequences of Numbers
4: Vectors 5: Missing Values 6: Subsetting Vectors
7: Matrices and Data Frames 8: Logic 9: Functions
10: lapply and sapply 11: vapply and tapply 12: Looking at Data
13: Simulation 14: Dates and Times 15: Base Graphics

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| In this lesson, we’ll cover matrices and data frames. Both represent ‘rectangular’ data types, meaning that
| they are used to store tabular data, with rows and columns.


|=== | 3%
| The main difference, as you’ll see, is that matrices can only contain a single class of data, while data
| frames can consist of many different classes of data.


|====== | 6%
| Let’s create a vector containing the numbers 1 through 20 using the `:` operator. Store the result in a
| variable called my_vector.

> my_vector <- c(1:20) | Give it another try. Or, type info() for more options. | You learned about the `:` operator in the lesson on sequences. If you wanted to create a vector containing the | numbers 1, 2, and 3 (in that order), you could use either c(1, 2, 3) or 1:3. In this case, we want the numbers | 1 through 20 stored in a variable called my_vector. Also, remember that you don't need the c() function when | using `:`. > my_vector(1:20)
Error: could not find function “my_vector”
> my_vector <- 1:20 | That's the answer I was looking for. |========= | 8% | View the contents of the vector you just created. > my_vector
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

| Excellent work!
|=========== | 11%
| The dim() function tells us the ‘dimensions’ of an object. What happens if we do dim(my_vector)? Give it a
| try.

> dim(my_vector)
NULL

| You nailed it! Good job!
|============== | 14%
| Clearly, that’s not very helpful! Since my_vector is a vector, it doesn’t have a `dim` attribute (so it’s just
| NULL), but we can find its length using the length() function. Try that now.

> length(my_vector)
[1] 20

| All that hard work is paying off!
|================= | 17%
| Ah! That’s what we wanted. But, what happens if we give my_vector a `dim` attribute? Let’s give it a try. Type
| dim(my_vector) <- c(4, 5). > dim(my_vector) <- c(4, 5) | Nice work! |==================== | 19% | It's okay if that last command seemed a little strange to you. It should! The dim() function allows you to get | OR set the `dim` attribute for an R object. In this case, we assigned the value c(4, 5) to the `dim` attribute | of my_vector. ... |======================= | 22% | Use dim(my_vector) to confirm that we've set the `dim` attribute correctly. > dim(my_vector)
[1] 4 5

| All that hard work is paying off!
|========================== | 25%
| Another way to see this is by calling the attributes() function on my_vector. Try it now.

> attributes(my_vector)
$dim
[1] 4 5

| That’s correct!
|============================= | 28%
| Just like in math class, when dealing with a 2-dimensional object (think rectangular table), the first number
| is the number of rows and the second is the number of columns. Therefore, we just gave my_vector 4 rows and 5
| columns.


|=============================== | 31%
| But, wait! That doesn’t sound like a vector any more. Well, it’s not. Now it’s a matrix. View the contents of
| my_vector now to see what it looks like.

> my_vector
[,1] [,2] [,3] [,4] [,5]
[1,] 1 5 9 13 17
[2,] 2 6 10 14 18
[3,] 3 7 11 15 19
[4,] 4 8 12 16 20

| All that practice is paying off!
|================================== | 33%
| Now, let’s confirm it’s actually a matrix by using the class() function. Type class(my_vector) to see what I
| mean.

> class(my_vector)
[1] “matrix”

| Your dedication is inspiring!
|===================================== | 36%
| Sure enough, my_vector is now a matrix. We should store it in a new variable that helps us remember what it
| is. Store the value of my_vector in a new variable called my_matrix.

> my_matrix <- my_vector | You're the best! |======================================== | 39% | The example that we've used so far was meant to illustrate the point that a matrix is simply an atomic vector | with a dimension attribute. A more direct method of creating the same matrix uses the matrix() function. ... |=========================================== | 42% | Bring up the help file for the matrix() function now using the `?` function. > ?matric()
Error in .helpForCall(topicExpr, parent.frame()) :
no methods for ‘matric’ and no documentation for it as a function
> ?matrix()
starting httpd help server … done

| Almost! Try again. Or, type info() for more options.

| The command ?matrix will do the trick.

> ?matrix

| That’s a job well done!
|============================================== | 44%
| Now, look at the documentation for the matrix function and see if you can figure out how to create a matrix
| containing the same numbers (1-20) and dimensions (4 rows, 5 columns) by calling the matrix() function. Store
| the result in a variable called my_matrix2.

> my_matrix2 <- matrix(data = 1:20, nrow = 4, ncol = 5, byrow = FALSE) | You got it right! |================================================= | 47% | Finally, let's confirm that my_matrix and my_matrix2 are actually identical. The identical() function will | tell us if its first two arguments are the same. Try it out. > identical(my_matrix, mymatrix2)
Error in identical(my_matrix, mymatrix2) : object ‘mymatrix2’ not found
> identical(my_matrix, my_matrix2)
[1] TRUE

| Keep working like that and you’ll get there!
|==================================================== | 50%
| Now, imagine that the numbers in our table represent some measurements from a clinical experiment, where each
| row represents one patient and each column represents one variable for which measurements were taken.


|====================================================== | 53%
| We may want to label the rows, so that we know which numbers belong to each patient in the experiment. One way
| to do this is to add a column to the matrix, which contains the names of all four people.


|========================================================= | 56%
| Let’s start by creating a character vector containing the names of our patients — Bill, Gina, Kelly, and
| Sean. Remember that double quotes tell R that something is a character string. Store the result in a variable
| called patients.

> patients <- c("Bill", "Gina", "Kelly", "Sean") | You got it right! |============================================================ | 58% | Now we'll use the cbind() function to 'combine columns'. Don't worry about storing the result in a new | variable. Just call cbind() with two arguments -- the patients vector and my_matrix. > cbind(pateints, my_matrix)
Error in cbind(pateints, my_matrix) : object ‘pateints’ not found
> cbind(patients, my_matrix)
patients
[1,] “Bill” “1” “5” “9” “13” “17”
[2,] “Gina” “2” “6” “10” “14” “18”
[3,] “Kelly” “3” “7” “11” “15” “19”
[4,] “Sean” “4” “8” “12” “16” “20”

| All that practice is paying off!
|=============================================================== | 61%
| Something is fishy about our result! It appears that combining the character vector with our matrix of numbers
| caused everything to be enclosed in double quotes. This means we’re left with a matrix of character strings,
| which is no good.


|================================================================== | 64%
| If you remember back to the beginning of this lesson, I told you that matrices can only contain ONE class of
| data. Therefore, when we tried to combine a character vector with a numeric matrix, R was forced to ‘coerce’
| the numbers to characters, hence the double quotes.


|===================================================================== | 67%
| This is called ‘implicit coercion’, because we didn’t ask for it. It just happened. But why didn’t R just
| convert the names of our patients to numbers? I’ll let you ponder that question on your own.


|======================================================================== | 69%
| So, we’re still left with the question of how to include the names of our patients in the table without
| destroying the integrity of our numeric data. Try the following — my_data <- data.frame(patients, my_matrix) > my_data <- data.frame(patients, my_matrix) | That's correct! |========================================================================== | 72% | Now view the contents of my_data to see what we've come up with. > my_data
patients X1 X2 X3 X4 X5
1 Bill 1 5 9 13 17
2 Gina 2 6 10 14 18
3 Kelly 3 7 11 15 19
4 Sean 4 8 12 16 20

| Your dedication is inspiring!
|============================================================================= | 75%
| It looks like the data.frame() function allowed us to store our character vector of names right alongside our
| matrix of numbers. That’s exactly what we were hoping for!


|================================================================================ | 78%
| Behind the scenes, the data.frame() function takes any number of arguments and returns a single object of
| class `data.frame` that is composed of the original objects.


|=================================================================================== | 81%
| Let’s confirm this by calling the class() function on our newly created data frame.

> class(my_data)
[1] “data.frame”

| You are amazing!
|====================================================================================== | 83%
| It’s also possible to assign names to the individual rows and columns of a data frame, which presents another
| possible way of determining which row of values in our table belongs to each patient.


|========================================================================================= | 86%
| However, since we’ve already solved that problem, let’s solve a different problem by assigning names to the
| columns of our data frame so that we know what type of measurement each column represents.


|============================================================================================ | 89%
| Since we have six columns (including patient names), we’ll need to first create a vector containing one
| element for each column. Create a character vector called cnames that contains the following values (in order)
| — “patient”, “age”, “weight”, “bp”, “rating”, “test”.

> names <- c("patient", "age", "weight", "bp", "rating", "test") | You're close...I can feel it! Try it again. Or, type info() for more options. | Make sure all of the names are lowercase, surrounded by double quotes, and separated with commas. Don't forget | to use the c() function. > cnames <- c("patient", "age", "weight", "bp", "rating", "test") | You nailed it! Good job! |============================================================================================== | 92% | Now, use the colnames() function to set the `colnames` attribute for our data frame. This is similar to the | way we used the dim() function earlier in this lesson. > colnames(my_data)
[1] “patients” “X1” “X2” “X3” “X4” “X5”

| You almost had it, but not quite. Try again. Or, type info() for more options.

| Try colnames(my_data) <- cnames. > colnames(my_data) <- cnames | You are really on a roll! |================================================================================================= | 94% | Let's see if that got the job done. Print the contents of my_data. > my_data
patient age weight bp rating test
1 Bill 1 5 9 13 17
2 Gina 2 6 10 14 18
3 Kelly 3 7 11 15 19
4 Sean 4 8 12 16 20

| That’s correct!
|==================================================================================================== | 97%
| In this lesson, you learned the basics of working with two very important and common data structures —
| matrices and data frames. There’s much more to learn and we’ll be covering more advanced topics, particularly
| with respect to data frames, in future lessons.


|=======================================================================================================| 100%
| Would you like to receive credit for completing this course on Coursera.org?

1: Yes
2: No

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