Consult the general homework guidelines.

Due anytime Monday 2016-11-14.

Pick (at least) two of the topics below and do one of the exercise prompts listed, or something comparable.

Character data

Read and work the exercises in the Strings chapter or R for Data Science.

Writing functions

Pick one:

Work with the candy data

In 2015, we explored a dataset based on a Halloween candy survey (but it included many other odd and interesting questions). Work on something from this homework from 2015. It is good practice on basic data ingest, exploration, character data cleanup, and wrangling.

Work with a list

Work through and write up a lesson from the purrr tutorial:

Work with a nested data frame

Create a nested data frame and map a function over the list column holding the nested data. Use list extraction or other functions to pull interesting information out of these results and work your way back to a simple data frame you can visualize and explore.

Here’s a fully developed prompt for Gapminder:

Inspiration for the modelling and downstream inspiration

Report your process

You’re encouraged to reflect on what was hard/easy, problems you solved, helpful tutorials you read, etc. Give credit to your sources, whether it’s a blog post, a fellow student, an online tutorial, etc.

Submit the assignment

Follow instructions on How to submit homework


Check minus: One or more elements are missing or sketchy. Missed opportunities to complement code and numbers with a figure and interpretation. Technical problem that is relatively easy to fix. It’s hard to find the report in this crazy repo.

Check: Hits all the elements. No obvious mistakes. Pleasant to read. No heroic detective work required. Well done! This should be the most typical mark!

Check plus: Exceeded the requirements in number of dimensions. Developed novel tasks that were indeed interesting and “worked”. Impressive use of R – maybe involving functions, packages or workflows that weren’t given in class materials. Impeccable organization of repo and report. You learned something new from reviewing their work and you’re eager to incorporate it into your work.