Homework between Day 1 and Day 2

Last updated on 2026-04-22 | Edit this page

At the end of Day 1 you have the three skills that make up most day-to-day R work: importing data, transforming it with dplyr, and visualising it with ggplot2. The 48 hours between sessions is the most valuable part of the course. Use it to translate those skills into something that matters in your own work.

The assignment

Pick a dataset you work with regularly. It can be an Excel export, a CSV, or a .sav file you saved from SPSS. All three load into R with what we covered today.

Write an R script that does the following:

  1. Imports the data with the appropriate function (read_csv(), readxl::read_excel(), or haven::read_sav()).
  2. Transforms it in at least one way. Filter to a subset of rows with filter(), create a new column with mutate(), or aggregate with group_by() and summarise(). Pick whichever is useful for your data.
  3. Produces a summary table of something meaningful — counts by category, means by group, totals over time.
  4. Creates one chart with ggplot2 that shows something you would actually want to look at.

Save the script as a .R file inside your r-workshop project folder. Give it a name that describes what it does, like tourism-arrivals-2024.R.

Bring the script to Day 2. We will use the open-lab slot at the end of Day 2 to help you push your own projects further.

Guidance

  • Thirty to sixty minutes is plenty. This is practice, not a deliverable. Do not make it perfect. Do not pick a dataset that needs an hour of cleaning before you can use it.
  • Use what is in front of you. Your Day 1 notes, the course website, the code we ran together — all of it is fair game. Look things up.
  • Googling is fine. Asking R for help with ?function_name is fine. Using Claude or ChatGPT for syntax hints is fine and encouraged. The rule is that you type the code yourself once it works, so your fingers remember it.
  • If something breaks, keep the error message. Screenshot it or copy it into a comment at the top of your script. We will look at it together on Day 2.

What counts as done

You opened a dataset you already use at work and wrote R code that produced at least one thing you could show a colleague. Anything beyond that is bonus.

Optional stretch

If you finish the four steps above and want more, try one of these:

  • Write a second version of the summary table grouped by a different variable, and see how the picture changes.
  • Rewrite your chart three different ways — change the geom_*, the colour mapping, or the facet structure — and decide which version communicates best.
  • If your original dataset is in SPSS .sav format, export the same analysis to a CSV with write_csv() and re-import it. You have now made the move from closed to open data format.

Do not try to build an R Markdown report yet. R Markdown is the first topic on Day 2, and it is much faster to learn after you have seen why it matters than to piece together on your own from blog posts.