/tidy-lesson-plan

Primary LanguageR

Sample lesson

The instructions are to:

Prepare a 15-minute lesson on a topic related to R, RStudio products, or data science and submit it to the examiner at least two days before your scheduled examination. Your submission should include (but is not restricted to):

  1. A learner persona characterizing the audience for the lesson.
  2. A concept map showing the mental model you intend to convey.
  3. At least one formative assessment (such as a multiple choice question or Parsons Problem).
  4. Any notebooks or slides you would use to support delivery of the lesson.

Lesson concept

My plan is to teach a sample lesson about extracting data from a rectangle using the dplyr package.

Learner persona

Firstly, let's outline a learner persona.

General background - a specific example

  • Jo is British-Nigerian, born and raised in London
  • They now live in the US, having moved to attend medical school
  • In their free time they enjoy baking, reading, and dancing

Starting point

  • Jo has familiarity with data analysis, and has familarity with base R from their previous academic experience
  • Jo is aware of the tidyverse, but doesn't have experience using it
  • Jo has taught themselves aspects of R in the past, and is confident that they can do it again

Needs

  • Jo has volunteered to assist with some academic research, and will be the primary data analyst for the project, with limited support from others
  • Jo knows that they may be sent several versions of the data, as their collaborators will be continually collecting results and re-providing the data, and doesn't want to have to re-code everything each time
  • Jo knows their results will need to be reproducible as they will submit it to a journal

Special considerations

  • As a medical student, they don't have a lot of time to learn extra material, and they will only want to focus on what's needed for them to get their research done.

Pitch

"In this lesson, we're going to cover one of the most important elements of data analysis: how to work with tablular data to extract just the bits of it that you need. We'll cover the two main applications: selecting columns and filtering rows, and show how to use both to get the subsets of data you need for your work."