3. Wrangling

Data wrangling skills are among the most important to hone.

Artwork by @allison_horst.

Agenda

September 9 + 11, 2024

1. Tidy data
2. Data verbs

September 16, 2024

1. Relational data (_join)
2. pivoting

September 18, 2024

1. mapping

Readings

• Class notes: Data Wrangling

• Wickham (2017) Data transformation and Tidy data in R for Data Science.

• If you aren’t super comfortable with R yet, check out Workflow: basics in R for Data Science.

Reflection questions

• How and why is |> used? And how is it different from the layering symbol + used in ggplot()?

• What are the main data wrangling verbs?

• How do you distinguish the different _join functions? Are the _join keys formatted in the same way across the two datasets? Are the data recorded in the same way (e.g., is age birthday or age at recording?) ?

• What are some of the ways to distinguish a data verb from a typical function?

Ethics considerations

• What is Jan 31 plus one month? And why does it matter that every analysis we do is a series of decisions? Keeping in mind that each of us might make a different decision, and all decisions have consequences.

Slides

• In class slides for both 9/9/24 and 9/11/24.

• In class slides for 9/16/24.

• In class slides for 9/18/24.

• WS4 - verbs

• WS5 - joins

• WS6 - mapping

Additional Resources

• Posit cheatsheets

• tidyverse vignettes

• pivoting

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