EDH 7916: Contemporary Research in Higher Education

Spring 2020

A course in quantitative research workflow for students in the higher education administration program at the University of Florida

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Assignment 2

  

NOTE This assignment needs to be completed by the start of the second class. That means everything pushed to you remote GitHub repo before class starts.

Steps

  1. Create a new top-level directory (i.e., the same level as scripts, data, and figures) called tables. Add a README.md file with an informative title and description. See other folders for examples.

Using template.R:

  1. Rename it to organizing_hw_<your_last_name>.R.
  2. Fill in all relevant header information about the script.
  3. Load the {tidyverse} library
  4. Create objects/macros with the paths to the following directories:
    • data
    • figures
    • tables
  5. Include the old_to_new_score_ratio macro, but change it to a new value.
  6. Include the old_to_new_score() function from class as is (just cut and paste).
  7. Read in the updated data set, test_scores_updated.RDS, we made in class.
  8. Create a new column called test_scores_new_2 that converts the original test scores to updated values using your new ratio and the old_to_new_score() function.
  9. You need final rounded grades that don’t go above 100, but you also need to give two students (stu_ids 1 and 7) 2 points of extra credit. Create a last column, test_scores_final, in which you:
    1. Copy the results from test_scores_new_2
    2. Add 2 points to the score for students 1 and 7 only
    3. Round all grades to the nearest units digit (no decimals)
      NOTE You can do these in one, two, or three discrete steps. If you figure out how to do it in three, see if you can combine the steps into only one or two steps.
      HINT Use the information on brackets ([]) in Data types and structures to help.
  10. Save the updated data file in your data directory with a different name. You should now have three files: the original, the updated one we made in class, and the one you just made.
  11. Push everything to your GitHub repo (keep in mind that your new data sets won’t show up on your GitHub repo — that’s okay: I should be able to run your script and make them myself).

Remember, you can do these steps as many times as you want. I would encourage you to commit smaller changes and push to your remote GitHub repo often rather than wait until the last minute.