Exercises: Chapter 3 (import)

[wendtke]

Heavily revise Brooke's material OR create new activity. The GBA in-class activity is too long and probably beyond the the scope of this class.

[wendtke]

@SmogDr Do you want to keep the Ebola and measles data or pick different online data for import practice and class activity? We could use Brooke's in-class activity on measles as the structure for a homework assignment, but do you want to use pollution/respiratory health data or something like that?

[SmogDr]

@wendtke I would prefer to use a more engineer-y dataset for that activity. Is this Chapter 3? If you can point me to the specific activity/teaching then I can probably come up with a good alternative dataset.

[wendtke]

@SmogDr Yes, this is Chapter 3 (data import and wrangling). I started brainstorming activity ideas in this commit. I think the in-class activity can include, with a degree of "hand-holding":

• Import local and/or online data file and assign to object name -- package::function() of choice depends on file type.. Do your students typically work with csv, xls/xlsx, MATLAB?
• Examine object structure and column classes with str(), tibble::glimpse(), class(), and the like
• Think about and discuss(?) what kind of data wrangling needs to be done and sketch out the desired dataframe result --or at least list the desired changes (e.g., clean up variable names)
• Practice a few dplyr verbs within pipeline to clean up dataframe (e.g., rename() column, compute new variable with mutate())
• Bonus question: introduce janitor package, especially the clean_names() function to clean all variable names at once

Then, for Chapter 3 homework, students can use an R Markdown template I create (unless you want a lesson or two on Rmd added to Weeks 1-2 set-up) to import and wrangle local or online data related to engineering. The homework outline would have instructions but no "hand-holding" demonstrations or function hints.

What are your thoughts? We can discuss this tomorrow, if you'd like.

[wendtke]

@SmogDr provided Fort Collins ozone data. I will add data, code, and related information from email thread to cloud folder (when latter is created).

Additional Ch 3 exercise ideas:

• Find the maximum and minimum values for the month (and compare them to the standard, even though it’s only a 1-hr measurement)
• Count how many values are above 0.07 in the dataset
• Mutate the sample_measurement column to convert “parts-per-million” into “parts-per-billion” units (i.e., multiply by 1000)