3. Wrangling

Data wrangling skills are among the most important to hone.

Figure 1: Artwork by @allison_horst.

Agenda

September 14, 2021

1. Tidy data
2. Data verbs

September 16, 2021

1. Relational data (_join)
2. pivoting
3. mapping
4. lubridate

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 9/14/21 and 9/16/21.

• WU4 - verbs

• WU5 - joining

Additional Resources

• RStudio cheatsheets

• tidyverse vignettes

• pivoting