swirl学习之七——Matrices and Data Frames

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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             

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

| That's the answer I was looking for.

  |=====                                                       |   9%

| 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 job!

  |=======                                                     |  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

| That's correct!

  |=========                                                   |  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

| Excellent 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)| Great job!  |============                                                |  20%| 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....  |==============                                              |  23%| Use dim(my_vector) to confirm that we've set the `dim` attribute| correctly.> dim(my_vector)
| 5).

>

| Great job!

  |============                                                |  20%

| 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.

...

  |==============                                              |  23%

| Use dim(my_vector) to confirm that we've set the `dim` attribute
| correctly.

>
[1] 4 5

| All that practice is paying off!

  |===============                                             |  26%

| Another way to see this is by calling the attributes() function on
| my_vector. Try it now.

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


| Keep up the great work!

  |=================                                           |  29%

| 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!

  |=====================                                       |  34%

| 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're the best!

  |======================                                      |  37%

| 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

| Keep up the great work!

  |========================                                    |  40%

| 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.

...

  |==========================                                  |  43%

| Bring up the help file for the matrix() function now using the `?`
| function.

> ?matrix

| Perseverance, that's the answer.

  |===========================                                 |  46%

| 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)

| That's a job well done!

  |=============================                               |  49%

| 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 nailed it! Good job!

  |===============================                             |  51%

| 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.

...

  |=================================                           |  54%

| 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.

...

  |==================================                          |  57%

| 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 are doing so well!

  |====================================                        |  60%

| 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"

| That's correct!

  |======================================                      |  63%

| 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.

...

  |=======================================                     |  66%

| 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.

...

  |=========================================                   |  69%

| 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.

...

  |===========================================                 |  71%

| 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)

| You nailed it! Good job!

  |=============================================               |  74%

| 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!

  |==============================================              |  77%

| 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!

...

  |================================================            |  80%

| 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.

...

  |==================================================          |  83%

| Let's confirm this by calling the class() function on our newly
| created data frame.

> class(my_data)
[1] "data.frame"

| That's the answer I was looking for.

  |===================================================         |  86%

| 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.

...

  |=====================================================       |  89%

| 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.

...

  |=======================================================     |  91%

| 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")

| Nice work!

  |=========================================================   |  94%

| 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

| You are amazing!

  |==========================================================  |  97%

| 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

| You nailed it! Good job!

  |============================================================| 100%

| 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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