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swirl Lesson 15: Base Graphics

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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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15: Base Graphics             
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| One of the greatest strengths of R, relative to other programming
| languages, is the ease with which we can create
| publication-quality graphics. In this lesson, you'll learn about
| base graphics in R.
...
  |=                                                         |   2%
| We do not cover the more advanced portions of graphics in R in
| this lesson. These include lattice, ggplot2 and ggvis.
...
  |===                                                       |   4%
| There is a school of thought that this approach is backwards,
| that we should teach ggplot2 first. See
| http://varianceexplained.org/r/teach_ggplot2_to_beginners/ for an
| outline of this view.
...
  |====                                                      |   7%
| Load the included data frame cars with data(cars).
> data(cars)
| That's the answer I was looking for.
  |=====                                                     |   9%
| To fix ideas, we will work with simple data frames. Our main goal
| is to introduce various plotting functions and their arguments.
| All the output would look more interesting with larger, more
| complex data sets.
...
  |======                                                    |  11%
| Pull up the help page for cars.
> ?cars
| All that hard work is paying off!
  |========                                                  |  13%
| As you can see in the help page, the cars data set has only two
| variables: speed and stopping distance. Note that the data is
| from the 1920s.
...
  |=========                                                 |  15%
| Run head() on the cars data.
> head(cars)
  speed dist
1     4    2
2     4   10
3     7    4
4     7   22
5     8   16
6     9   10
| All that practice is paying off!
  |==========                                                |  17%
| Before plotting, it is always a good idea to get a sense of the
| data. Key R commands for doing so include, dim(), names(),
| head(), tail() and summary().
...
  |===========                                               |  20%
| Run the plot() command on the cars data frame.
> plot(cars)
| Nice work!
  |=============                                             |  22%
| As always, R tries very hard to give you something sensible given
| the information that you have provided to it. First, R notes that
| the data frame you have given it has just two columns, so it
| assumes that you want to plot one column versus the other.
...
  |==============                                            |  24%
| Second, since we do not provide labels for either axis, R uses
| the names of the columns. Third, it creates axis tick marks at
| nice round numbers and labels them accordingly. Fourth, it uses
| the other defaults supplied in plot().
...
  |===============                                           |  26%
| We will now spend some time exploring plot, but many of the
| topics covered here will apply to most other R graphics
| functions. Note that 'plot' is short for scatterplot.
...
  |================                                          |  28%
| Look up the help page for plot().
> ?plot
| Keep working like that and you'll get there!
  |==================                                        |  30%
| The help page for plot() highlights the different arguments that
| the function can take. The two most important are x and y, the
| variables that will be plotted. For the next set of questions,
| include the argument names in your answers. That is, do not type
| plot(cars$speed, cars$dist), although that will work. Instead,
| use plot(x = cars$speed, y = cars$dist).
...
  |===================                                       |  33%
| Use plot() command to show speed on the x-axis and dist on the
| y-axis from the cars data frame. Use the form of the plot command
| in which vectors are explicitly passed in as arguments for x and
| y.
> plot(x = cars$speed, y = cars$dist)
| All that practice is paying off!
  |====================                                      |  35%
| Note that this produces a slightly different answer than
| plot(cars). In this case, R is not sure what you want to use as
| the labels on the axes, so it just uses the arguments which you
| pass in, data frame name and dollar signs included.
...
  |=====================                                     |  37%
| Note that there are other ways to call the plot command, i.e.,
| using the "formula" interface. For example, we get a similar plot
| to the above with plot(dist ~ speed, cars). However, we will wait
| till later in the lesson before using the formula interface.
...
  |=======================                                   |  39%
| Use plot() command to show dist on the x-axis and speed on the
| y-axis from the cars data frame. This is the opposite of what we
| did above.
> plot(x = cars$dist, y = cars$speed)
| Keep working like that and you'll get there!
  |========================                                  |  41%
| It probably makes more sense for speed to go on the x-axis since
| stopping distance is a function of speed more than the other way
| around. So, for the rest of the questions in this portion of the
| lesson, always assign the arguments accordingly.
...
  |=========================                                 |  43%
| In fact, you can assume that the answers to the next few
| questions are all of the form plot(x = cars$speed, y = cars$dist,
| ...) but with various arguments used in place of the ...
...
  |==========================                                |  46%
| Recreate the plot with the label of the x-axis set to "Speed".
> plot(x = cars$speed, y = cars$dist, xlab="Speed")
| Great job!
  |============================                              |  48%
| Recreate the plot with the label of the y-axis set to "Stopping
| Distance".
> plot(x = cars$speed, y = cars$dist, ylab="Stopping Distance")
| You are really on a roll!
  |=============================                             |  50%
| Recreate the plot with "Speed" and "Stopping Distance" as axis
| labels.
> plot(x = cars$speed, y = cars$dist, xlab="Speed", ylab="Stopping Distance")
| Excellent job!
  |==============================                            |  52%
| The reason that plots(cars) worked at the beginning of the lesson
| was that R was smart enough to know that the first element (i.e.,
| the first column) in cars should be assigned to the x argument
| and the second element to the y argument. To save on typing, the
| next set of answers will all be of the form, plot(cars, ...) with
| various arguments added.
...
  |================================                          |  54%
| For each question, we will only want one additional argument at a
| time. Of course, you can pass in more than one argument when
| doing a real project.
...
  |=================================                         |  57%
| Plot cars with a main title of "My Plot". Note that the argument
| for the main title is "main" not "title".
> plot(cars, main ="My Plot")
| All that practice is paying off!
  |==================================                        |  59%
| Plot cars with a sub title of "My Plot Subtitle".
> plot(cars, sub ="My Plot Subtitle")
| All that hard work is paying off!
  |===================================                       |  61%
| The plot help page (?plot) only covers a small number of the many
| arguments that can be passed in to plot() and to other graphical
| functions. To begin to explore the many other options, look at
| ?par. Let's look at some of the more commonly used ones. Continue
| using plot(cars, ...) as the base answer to these questions.
...
  |=====================================                     |  63%
| Plot cars so that the plotted points are colored red. (Use col =
| 2 to achieve this effect.)
> plot(cars, col =2)
| Keep up the great work!
  |======================================                    |  65%
| Plot cars while limiting the x-axis to 10 through 15.  (Use xlim
| = c(10, 15) to achieve this effect.)
> plot(cars, xlim=c(10,15))
| That's a job well done!
  |=======================================                   |  67%
| You can also change the shape of the symbols in the plot. The
| help page for points (?points) provides the details.
...
  |========================================                  |  70%
| Plot cars using triangles.  (Use pch = 2 to achieve this effect.)
> plot(cars, pch=2)
| All that hard work is paying off!
  |==========================================                |  72%
| Arguments like "col" and "pch" may not seem very intuitive. And
| that is because they aren't! So, many/most people use more modern
| packages, like ggplot2, for creating their graphics in R.
...
  |===========================================               |  74%
| It is, however, useful to have an introduction to base graphics
| because many of the idioms in lattice and ggplot2 are modeled on
| them.
...
  |============================================              |  76%
| Let's now look at some other functions in base graphics that may
| be useful, starting with boxplots.
...
  |=============================================             |  78%
| Load the mtcars data frame.
> data(mtcars)
| Perseverance, that's the answer.
  |===============================================           |  80%
| Anytime that you load up a new data frame, you should explore it
| before using it. In the middle of a swirl lesson, just type
| play(). This temporarily suspends the lesson (without losing the
| work you have already done) and allows you to issue commands like
| dim(mtcars) and head(mtcars). Once you are done examining the
| data, just type nxt() and the lesson will pick up where it left
| off.
...
  |================================================          |  83%
| Look up the help page for boxplot().
> ?boxplot
| You are really on a roll!
  |=================================================         |  85%
| Instead of adding data columns directly as input arguments, as we
| did with plot(), it is often handy to pass in the entire data
| frame. This is what the "data" argument in boxplot() allows.
...
  |==================================================        |  87%
| boxplot(), like many R functions, also takes a "formula"
| argument, generally an expression with a tilde ("~") which
| indicates the relationship between the input variables. This
| allows you to enter something like mpg ~ cyl to plot the
| relationship between cyl (number of cylinders) on the x-axis and
| mpg (miles per gallon) on the y-axis.
...
  |====================================================      |  89%
| Use boxplot() with formula = mpg ~ cyl and data = mtcars to
| create a box plot.
> boxplot(formula=mpg~cyl,data=mtcars)
| You are really on a roll!
  |=====================================================     |  91%
| The plot shows that mpg is much lower for cars with more
| cylinders. Note that we can use the same set of arguments that we
| explored with plot() above to add axis labels, titles and so on.
...
  |======================================================    |  93%
| When looking at a single variable, histograms are a useful tool.
| hist() is the associated R function. Like plot(), hist() is best
| used by just passing in a single vector.
...
  |=======================================================   |  96%
| Use hist() with the vector mtcars$mpg to create a histogram.
> hist(mtcars$mpg)
| All that hard work is paying off!
  |========================================================= |  98%
| In this lesson, you learned how to work with base graphics in R.
| The best place to go from here is to study the ggplot2 package.
| If you want to explore other elements of base graphics, then this
| web page (http://www.ling.upenn.edu/~joseff/rstudy/week4.html)
| provides a useful overview.
...
  |==========================================================| 100%

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