Consult the general homework guidelines.
Due anytime Friday 2016-10-21.
Remember the sampler concept. Your homework should serve as your own personal cheatsheet in the future for canonical tasks. Make things nice – your future self will thank you!
You can work with the gapminder data or take this chance to play with something else. In which case, you’ll have to create comparable tasks for yourself.
Drop Oceania. Filter the Gapminder data to remove observations associated with the
continent of Oceania. Additionally, remove unused factor levels. Provide concrete information on the data before and after removing these rows and Oceania; address the number of rows and the levels of the affected factors.
Reorder the levels of
continent. Use the forcats package to change the order of the factor levels, based on a principled summary of one of the quantitative variables. Consider experimenting with a summary statistic beyond the most basic choice of the median. While you’re here, practice writing to file and reading back in (see next section).
Characterize the (derived) data before and after your factor re-leveling.
arrange(). Does merely arranging the data have any effect on, say, a figure?
arrange(). Especially, what effect does this have on a figure?
These explorations should involve the data, the factor levels, and some figures.
Experiment with one or more of
write_csv()/read_csv() (and/or TSV friends),
dput()/dget(). Create something new, probably by filtering or grouped-summarization of Gapminder. I highly recommend you fiddle with the factor levels, i.e. make them non-alphabetical (see previous section). Explore whether this survives the round trip of writing to file then reading back in.
Remake at least one figure, in light of something you learned in the recent class meetings about visualization design and color. Maybe juxtapose before and after and reflect on the differences. Use the country or continent color scheme that ships with Gapminder. Consult the guest lecture from Tamara Munzner and everything here.
ggsave() to explicitly write a figure to file. Then use
![Alt text](/path/to/img.png) to embed it in your report. Things to play around with:
ggsave(), such as width, height, resolution or text scaling.
ggsave(..., plot = p). Show a situation in which this actually matters.
You have 6 weeks of R Markdown and GitHub experience now. You’ve reviewed 4 peer assignments. Surely there are aspects of your current repo organization that could be better. Deal with that. Ideas:
hw03 dplyr verbs
Rmdbut that could be
md? Convert it.
Make a deeper exploration of the forcats packages, i.e. try more of the factor level reordering functions.
Revalue a factor
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.
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.