Midterm

Every student should master a basic data-science toolkit that will be applicable to a wide array of potential research projects. The midterm gives you an opportunity to practice and apply the skills we’ve discussed so far. It takes the form of an extended exercise using real data I collected for a recent project; you’ll read the data into R, perform data-wrangling, report summary information, and create plots. It is somewhat open-ended, in that there may be multiple valid ways to write code to accomplish the same task, and you’ll have the option to dig further into different parts of the data.

Assignment

It will be due in two phases:

• First, you complete it to the best of your ability and submit it by class on Tuesday, October 18 (week 8)
  • Open-notes, open-book, open-lectures, open-internet, open-everything-but-one-another.
  • Please ask me questions (via Slack DM) if you get really stuck (= you spend 10+ minutes on an individual issue)
  • It’s okay if you don’t complete every part of the midterm—it’s designed to push you hard
• Then, you review your answers based on an “answer key” by class on Thursday, October 27 (week 9)
  • “answer key” is in quotes because of its open-ended nature
  • You’ll review your answers with a randomly assigned partner: Mack+Soobin, Gianina+Sen
  • What you submit is your revised answers, plus a metacognitive reflection

For the metacognitive reflection, write a paragraph that describes your progress as a learner in this course, using the evidence provided by reviewing your midterm answers. There is no set structure, but you may want to think about questions like:

• What do you understand better about coding itself (not specific R things but “tips and tricks”) now than you did at the start of the semester?
• What was the hardest thing for you about this midterm?
• What do you still need to improve on?
• How will what you’ve learned from this midterm help you approach uncertain data structures in your project?

Submission

Our class GitHub organization has a midterm repository, which you will fork. For the first submission, you’ll need to complete midterm.Rmd to the best of your ability and knit it to midterm.md; for the second submission, you’ll have those two files plus reflection.md (which you can create as an .Rmd initially if you want to embed the output of R code). You will not contribute your fork to the upstream remote, so you don’t need to rename any files. You are free to create R Notebooks in the process of figuring out your ‘final’ code, as long as your repo also has the required files. Because your midterm repo comes from our class GitHub organization repo, it’ll be visible to other students; please do not look at your fellow students’ midterm repositories before you have submitted yours.

Grading

The midterm will be graded for effort, because I know enough about all of you by now that I can safely trust you all to work hard. You will mostly create your own feedback based on the “answer key” and your discussion with your partner, but please don’t hesitate to ask me for feedback as well. Don’t worry about making the Markdown file look pretty (unlike our To-dos, where you’re encouraged to pay attention to formatting); just pay attention to the content of your code.

Last words of advice

• Assume that some googling might be necessary
• You don’t need any packages other than what’s in tidyverse (ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, forcats), but you might find yourself using functions we haven’t discussed within those packages.
  • Each package has a homepage at <package-name>.tidyverse.org (e.g., https://ggplot2.tidyverse.org/)
  • To see the full list of functions in a package, run (e.g.) ?purrr and click “index” at the bottom
  • Don’t forget the cheatsheets!
• Use R Notebooks to fiddle around with different ways of writing code! It’s really useful to look at what you’ve already tried and what that code yielded
  • Once you’re confident about a piece of code, ‘lock it in’ by adding it to your main midterm.Rmd document
• You’ve got this!!!