[R: package swirl]Selection: 7 Matrices and Data Frames

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Selection: 7 Matrices and Data Frames

R version 3.5.2 (2018-12-20) – “Eggshell Igloo”
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library(‘swirl’)

| Hi! Type swirl() when you are ready to begin.

swirl()

| Welcome to swirl! Please sign in. If you’ve been here before, use the same name as you did then. If you are
| new, call yourself something unique.

What shall I call you? Michael

| Would you like to continue with one of these lessons?

1: R Programming Basic Building Blocks
2: No. Let me start something new.

Selection: 2

| Please choose a course, or type 0 to exit swirl.

1: R Programming
2: Take me to the swirl course repository!

Selection: 1

| Please choose a lesson, or type 0 to return to course menu.

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

Selection: 7

| | 0%

| 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 <- [1:20]
Error: unexpected ‘[’ in “my_vector <- [”
my_vector <- c(1:20)

| Try again. Getting it right on the first try is
| boring anyway! 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 <- 1:3

| Try again. Getting it right on the first try is
| boring anyway! 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 <- (1:3)

| Try again. Getting it right on the first try is
| boring anyway! 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

| You nailed it! Good job!

|======== | 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] 18 19 20

| Nice 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 are quite good my friend!

|============== | 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

| Keep up the great work!

|================= | 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)

| That’s correct!

|==================== | 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

| Nice work!

|========================== | 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

| You are amazing!

|============================ | 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

| You got it!

|================================== | 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”

| Perseverance, that’s the answer.

|===================================== | 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

| All that practice is paying off!

|======================================== | 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.

…matrix(my_matrix)

|========================================== | 42%
| Bring up the help file for the matrix() function now
| using the ? function.

?matrix

| You are amazing!

|============================================= | 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(my_matrix)

| Nice try, but that’s not exactly what I was hoping
| for. Try again. Or, type info() for more options.

| Call the matrix() function with three arguments –
| 1:20, the number of rows, and the number of columns.
| Be sure to specify arguments by their proper names
| and store the result in my_matrix2 (not in
| my_matrix).

my_matrix2 <- (1:20,4,5)
Error: unexpected ‘,’ in “my_matrix2 <- (1:20,”
my_matrix2 <- matrix(1:20,4,5)

| Perseverance, that’s the answer.

|================================================ | 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,my_matrix2)
[1] TRUE

| That’s a job well done!

|=================================================== | 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(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”

| You are doing so well!

|============================================================== | 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)

| All that practice is paying off!

|========================================================================== | 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

| Keep up the great work!

|============================================================================ | 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”

| That’s correct!

|===================================================================================== | 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”.

cnames <- c(‘patient’,‘age’,‘weight’,‘bp’,'rating,‘test’)
Error: unexpected symbol in “cnames <- c(‘patient’,‘age’,‘weight’,‘bp’,'rating,'test”
cnames <- c(‘patient’,‘age’,‘weight’,‘bp’,‘rating’,‘test’)

| You’re the best!

|============================================================================================== | 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(cnames)
NULL

| Not quite! Try again. Or, type info() for more
| options.

| Try colnames(my_data) <- cnames.

colnames(my_data) <- cnames

| Excellent work!

|================================================================================================ | 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

| Nice work!

|=================================================================================================== | 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

Selection: 2

| You are amazing!

| You’ve reached the end of this lesson! Returning to
| the main menu…