5  Resources

5.1 Data Organization in Spreadsheets

• This is the paper we discussed - it is free to read and I think it’s a must read if you do any work with spreadsheets.

5.2 Tidy Data

Some say that tidy data is a bit of a cult. But I think the principles are sound and helpful ways to think about organizing data.

• Tidy Data (from the tidyr package) - this is a vignette from the {tidyr} package explaining the virtues of tidy data.
• Tidy data for efficiency, reproducibility and collaboration - this blog from Openscapes is a nice, gentle introduction to tidy data.
• How to Share Data for Collaboration - more guidelines on how to share data with statisticians from Jeff Leek (DaSL overlord) and Shannon Ellis.

5.3 R Packages

• {janitor} - this will always have a place in my heart because of janitor::clean_names().
• {tidyr} - this tidyverse package anticpiates and provides solutions for many spreadsheet issues.

5.4 Python Resources

• Python for Data Science: Tidy Data - this chapter outlines some of the Python tools for cleaning data: .melt(), .wide_to_long(), .stack()/unstack(), .pivot() and others.
• Tidy Data with Pandas - lesson from Library Carpentry which also discusses Python methods for tidying data.