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Pre-class FAQs

Thanks to everyone who has filled out the pre-class welcome survey (see the link in iCollege)! I’m excited to get to know you all more this semester!

Lots of you had similar questions and concerns, so I’ve consolidated the common ones here in a list of FAQs.

Can I reach out if I have questions?

ABSOLUTELY. DO NOT ATTEMPT TO DO THIS CLASS WITHOUT EVER ASKING A SINGLE QUESTION.

Make sure you read this about building a community and this about the 30-minute rule and how to ask for help.

How hard will it be to reach you? Will you have regular office hours?

Hopefully not hard at all! I’m extremely responsive on Slack and e-mails.

I don’t have a set time every week for student hours (see here for why I don’t call them office hours). Instead, I use an appointment system. If you want to meet with me online, visit my Calendly page and sign up for a slot. There’s also a link to it on the homepage and syllabus page of the class website.

I’ll also be on campus on Thursdays if you want to meet IRL. Use the in-person slot at Calendly to sign up for a time to meet me on campus.

How much time should I expect to spend on this class?

Learning a new programming langauge is tough initially! As you start working through the primers, you might feel intimidated and feel like it’s taking up all your time.

Everyone learns at different speeds, so it’s hard to give an exact number of hours. In general, though, the first couple weeks will take longer than normal because of the initial learning curve. To compensate for that, the assignments are shorter and easier. Like, in exercise 1, you make one simple plot and one simple table—that’s it.

With more and more practice and time, you’ll get the hang of the syntax and it’ll get easier and faster—I promise! Push through the first few weeks and you should be good!

And remember the 30-minute rule! If you find yourself spiraling into a problem andsinking hours into it, stop.

Will we cover {X}?

• Geospatial data and visualizations? Absolutely! Maps are one of my favorite kinds of visualization and we have a whole session on them.
• Interactive visualizations? Yep!
• Animated visualizations? Not directly, but we’ll briefly mention the {gganimate} package, which is surprisingly easy to use—it’s just regular ggplot code with a few extra lines to animate it

I don’t have a background in anything related to PMAP (public management and policy)—will I be lost?

That’s 1000% fine! I’ve designed this class to be accessible and useful for people working with any kind of data. Lots of the examples we’ll play with will often be related to political science-y things, but not always. And the kind of data doesn’t actually matter. When learning new statistical programming and visualization tools and techniques, the industry standard is to work with toy datasets. For instance, in your first exercise you’ll make a plot of car mileage. I know nothing about cars at all, but I (along with everyone else in the R world) use that dataset all the time for quick little tests of plots or models or code or whatever, since it’s built into R. I also often use data about penguins and lots of other domains in assignments and examples.

Don’t worry about the precise nature of the data. In your final project, you’ll make visualizations with whatever data you want. For all the other assignments and exercises and mini-projects, just go with the data I’ve provided. It’s all actual real world data (refugee inflows, unemployment, election results, texts of books from the 19th century, counts and locations of rat sightings in NYC, etc.). If it’s not related to your field, don’t worry—the principles of code and data visualization and graphic design still apply.