swirl 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
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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.
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|==                                                        |   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.
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|===                                                       |   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
| You're the best!
|=====                                                     |   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
| You got it!
|======                                                    |  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
| Excellent 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
| Great job!
|==========                                                |  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)
| You are really on a roll!
|===========                                               |  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.
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|=============                                             |  22%
| Use dim(my_vector) to confirm that we've set the dim attribute
| correctly.
> dim(my_vector)
[1] 4 5
| You nailed it! Good job!
|==============                                            |  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.
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|==================                                        |  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
| Nice work!
|===================                                       |  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"
| You are doing so well!
|=====================                                     |  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
| Nice work!
|=======================                                   |  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.
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|========================                                  |  42%
| Bring up the help file for the matrix() function now using the
| ? function.
> ?matrix
| You nailed it! Good job!
|==========================                                |  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(1:20, nrow=4,ncol=5,byrow = FALSE,)
| That's correct!
|===========================                               |  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
| You are amazing!
|=============================                             |  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.
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|===============================                           |  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")
| That's a job well done!
|==================================                        |  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"
| 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.
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|=====================================                     |  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.
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|=======================================                   |  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.
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|========================================                  |  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)
|==========================================                |  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
|============================================              |  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!
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|=============================================             |  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"
| Excellent job!
|================================================          |  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.
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|====================================================      |  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")
| That's correct!
|=====================================================     |  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)<-cnames
| Nice 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
| 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.
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|==========================================================| 100%