For the scripts created below. The entire console needs to be displayed. So the graphics and the script can be seen.
- At the top of your script, include housekeeping steps to first list the contents of the workspace
and then clear it out.
- Load the HW04 workspace that you downloaded from eCampus for the previous assignments.
You may use the file menu on the R console to browse for and load the workspace. When this is
completed, copy the line of code from the console window and paste it into your script so you
can load the workspace automatically the next time you want to run the script. After loading
the workspace, include a step that lists in the console the contents of the workspace again to
confirm the desired data file has been loaded.
- Include a statement that will cause R to send all of the graphics in this assignment directly to the
PDF file that you will be posting on WebAssign. NOTE: You may want to wait until you have all
of your graphics working properly before including this step so you can see the graphics in the
console while you are working on them.
- Create histograms of the PTRatio in the Oklahoma schools. Make sure the layout, labels, and
title all match the sample output.
- Create the first histogram using the default number of breaks.
- Create a vector that can be used to extend the length of the X axis and force breaks
every 5 pupils/teacher. You may find it helpful to use the statistics on the PTRatio
column to determine how far your sequence should go.
- Create a new histogram with break points at 5 pupils/teacher.
- Add a maroon line to the graph that shows the normal distribution density of the PTRatio
numbers. Feel free to use separate lines of code as necessary to create the vectors required for
- Draw a yellow vertical line on the previous histogram at the average PTRatio. Use a function to
determine the position of the line instead of hard coding in the mean value. Use a value from
the color palette to specify the color instead of using the color name.
- Create a new plot showing the number of teachers compared to the Pupil Teacher Ratio
(PTRatio). Use the plot character shown in the sample output on eCampus. The color of the
plot character is purple. Force the x axis to extend to 140.
- Add a colored fit line to the plot. Use a hex value to mix a color for the line that is FF parts Red,
99 parts Green and no Blue. In the console, show the summary statistics for the model that
created the fit line.
- Imbed the date and time of creation near the upper right hand corner of the graph area. The
coordinates of the time stamp location need only be close to the ones used for the sample
output. This time stamp must be based on system time so that it updates whenever your code
- Create a boxplot of the number of students in each grade (Columns Grade7 through Grade12).
Make sure the labels, etc. match the posted output. The inside of the boxes is light green.
- Add diamonds that can be colored and filled that represent the average number of students in
each grade from Tulsa County. You may create the vector of averages first to simplify the
function that is used to plot the diamonds. The background of the diamonds is dark green and
the border is red. Make the size of the diamonds larger than the default similar to what is
shown in the sample output. (TIP: Do not over-complicate the plotting of the diamonds. You
can plot a vector without supplying the x component of the points if the vector labels match the
data used to create the original graph as it does in this case.) You may find it helpful to consult
the R help on points in order to find out which plot character to use and how to specify the
Then On another Script complete the following
Create a function. The creation of a function is an iterative process. The first
step of that process (explained in #3 below) is to get the code working as standard R code with all of the
values hard coded. After the process has been successfully tested with the hard coded values, convert
the process to a function and replace the desired hard coded values with function parameters. You may
want to delay sending output to PDF until you have your manual process working as desired.
An exponentially weighted moving average (EMA) is frequently used in the finance industry to form a
stock price trend indicator while smoothing price action and filtering out noise. EMA for a given time
period is calculated using the following formula:
P is the closing price at the end of that time period and N is the number of periods to include in the
moving average. For those who may not know, the “a” looking character is the Greek letter alpha.
Along with the header, your R script will include two major sections. The first section will graph the EMA
of stock prices (adjusted closing price) using hard coded values. The second section will contain the
code to create the function, the appropriate graphics setup, and two calls to the function. The console
and PDF output should represent only the final submission of your R script after restarting R.
- The file cisco.csv contains stock price information for every day since Cisco stock started trading
publicly. Download the file from eCampus and become familiar with its contents. Write a line
of code that reads this file into a data frame in your R session for use in this assignment.
- When you define your PDF document and open the device, set the size to 11 X 8.5 inches to
match the size of a normal letter size sheet of paper turned so that it is wider than tall.
(Research the help documentation on pdf to determine the correct options to use.)
- Create and plot a 30 Day Exponential Moving Average with these steps:
- Go ahead and assign a value for the alpha component of the formula so the EMA code
does not get too long later on. If you wish you may also assign a scalar the value of 30
for our N value and use it in your code since this will be an argument to the function
later in the assignment.
- Create a vector populated with zeroes that will be used to contain the EMA values. The
length of this vector needs to be the same as the length of the vector of adjusted closing
prices from the Cisco data.
- One of the problems with moving averages is that they rely on a previous value and, at
some point in the beginning, there is no previous value. We are going to solve this
problem in this instance by populating the EMA on day 30 with the simple average
adjusted closing prices of the first 30 days in which Cisco stock was traded. Create an
assignment statement that puts the 30 day average in the 30th member of the EMA
- Use a loop to run the EMA formula repetitively from day 31 to the last day for which
Cisco stock prices are available. Each iteration of the loop will use the corresponding
day’s adjusted closing price to calculate the EMA value for that day and store the
calculated value in the appropriate place in the EMA vector.
- There are roughly 260 weekdays in a year. Plot a line showing the last 260 EMA values.
You may simply use the numbers 1 through 260 as the x values and then use the EMA
values for y. This line should be purple. The range of the Y axis is to be 0 to 35. To
make our program fully adaptive, we would need to use an algorithm to base the upper
limit on the maximum stock price but this assignment is going to take a long time
without adding this requirement so hard code the values of 0 and 35 for now. We will
provide an argument to adjust this in the function. Set the title and axis labels as
- At the bottom of the graph, add the EMA formula as shown. Use the point 0,2 as the
reference point for the formula line.
- Add to the graph a green2 line showing the actual adjusted closing prices for the last
260 days available in the Cisco data.
- Convert the completed code to a function including the code that will plot the graph. The first
argument to the function is to be the name of the vector containing the adjusted closing prices
so that we can analyze data from any stock without having to rewrite our function. The second
argument will be the number of days over which the EMA is computed. The default value will
be 30 like we used above. Be sure to replace all references to the price vector and the number
of days with the appropriate function argument. Note that any value in your original code that
was based on the 30 day average will use the N argument or an algebraic expression based on
- The third argument will set the upper limit for the Y axis. Make the default 35 and make sure
the graphing expressions use this argument. The graph title and formula line must be
constructed so they automatically change according to the value of N used in the function call.
- Set up your graphics for this section of the script so that there will be two columns of graphics
on the output page. We want to set the overall margins of the graphics within the page to be
1.5 inches on the top to allow space for punched holes and 0.5 inches on the bottom, right, and
left. Set the margins of the individual graph to be 2 lines on top, 4 lines left, 0 lines right and 4
- Call your function twice. The first call should have the reference to the adjusted closing price
column as its only argument. For the second call, add a value of 100 for the N argument. (When
you are testing your program, you should experiment with an N of 10 and a few other values to
see how the size of the EMA affects the graph. But, do not include the results of these tests in
your homework submission.)
- Write the system time in the bottom outer margin of the page. Left justify the time as shown in
- After you have finished writing and testing your code close R. Then reopen R and run your
script. Submit to WebAssign the script, console, and output from this final run.