In this lab, you will begin to get oriented with R and work with some data.

How to complete this assignment.

  • Attempt each exercise in order.

  • In each code chunk, if you see “# INSERT CODE HERE”, then you are expected to add some code to create the intended output (Make sure to erase “# INSERT CODE HERE” and place your code in its place).

  • If my instructions say to “Run the code below…” then you do not need to add any code to the chunk.

  • Many exercises may require you to type some text below the code chunk, interpreting the output and answering the questions.

  • Please follow the Davidson Honor Code and rules from the course syllabus regarding seeking help with this assignment.

How to submit this assignment.

  • When you are finished, click the “Knit” button at the top of this panel. If there are no errors, an word file should pop up after a few seconds.

  • Take a look at the resulting word file that pops up. Make sure everything looks correct, your name is listed at the top, and that there is no ‘junk’ code or output.

  • Save the word file (to your local computer, and/or to a cloud location) as: Lab 10 “Insert Your Name”.

  • Use this link to upload your word file to my Google Drive folder. Do not upload the original .Rmd version.

  • This assignment is due Thursday, August 11, 2022, no later than 9:30 am Eastern. Points will be deducted for late submissions.

  • TIP: Start early so that you can troubleshoot any issues with knitting to word.

Grading Rubric

There are 6 possible points on this assignment.

Baseline (C level work)

  • Your .Rmd file knits to word without errors.
  • You answer questions correctly but do not use complete sentences.
  • There are typos and ‘junk code’ throughout the document.
  • You do not put much thought or effort into the Reflection answers.

Average (B level work)

  • You use complete sentences to answer questions.
  • You attempt every exercise/question.

Advanced (A level work)

  • Your code is simple and concise.
  • Unnecessary messages from R are hidden from being displayed in the word.
  • Your document is typo-free.
  • At the discretion of the instructor, you give exceptionally thoughtful or insightful responses.

Exercise 1. (6 points)

In this problem, you will generate simulated data, and then perform PCA and K-means clustering on the data.

  1. Generate a simulated data set with 20 observations in each of three classes (i.e. 60 observations total), and 50 variables (Hint: There are a number of functions in R that you can use to generate data. One example is the rnorm() function; runif() is another option. Be sure to add a mean shift to the observations in each class so that there are three distinct classes.)

  2. Perform PCA on the 60 observations and plot the first two principal component score vectors. Use a different color to indicate the observations in each of the three classes. If the three classes appear separated in this plot, then continue on to part (C). If not, then return to part (A) and modify the simulation so that there is greater separation between the three classes. Do not continue to part (C) until the three classes show at least some separation in the first two principal component score vectors.

  3. Perform K-means clustering of the observations with \(K = 3\). How well do the clusters that you obtained in K-means clustering compare to the true class labels (Hint: You can use the table() function in R to compare the true class labels to the class labels obtained by clustering. Be careful how you interpret the results: K-means clustering will arbitrarily number the clusters, so you cannot simply check whether the true class labels and clustering labels are the same.)?

  4. Perform K-means clustering with \(K = 2\). Describe your results.

  5. Now perform K-means clustering with \(K = 4\), and describe your results.

  6. Now perform K-means clustering with \(K = 3\) on the first two principal component score vectors, rather than on the raw data. That is, perform K-means clustering on the 60 × 2 matrix of which the first column is the first principal component score vector, and the second column is the second principal component score vector. Comment on the results.

#insert code here