Seminar 1: Quantitative Data Management

Lecture Slides

Data for lab:

• London Crime data (.zip)
• LSOA Indices of Deprivation (.csv)
• LSOA Population Density (.xlsx)

In-class code:

• 1_process-metro.R
• 2_process-deprivation.R

Note: We did not finish the activity, so in week 3 we will use a pre-built project scaffold!

Required readings:

• Quartz. The Quartz guide to bad data. https://github.com/Quartz/bad-data-guide
  • “An exhaustive reference to problems seen in real-world data along with suggestions on how to resolve them.”
  • Don’t read this thoroughly, but browse through it a bit!

Further reading and resources:

• Wickham, H. and Grolemund, G. (2022) R for Data Science. Sebastopol, CA: O’Reilly. https://r4ds.hadley.nz/

• Robinson, D. Tidy Tuesday live screencasts. https://www.youtube.com/@safe4democracy/streams

  • Livestreamed data cleaning and analysis videos. See also https://www.youtube.com/@JuliaSilge

• Data Carpentry. (2022) Social Science Curriculum. https://datacarpentry.org/lessons/#social-science-curriculum

  • Recommended workshop rather than reading; look for upcoming workshops!