Preface:
- Introductions:
- UI
- Big Data
- Data Science
- R and RStudio
- R as a calculator
- the concept of functions
- packages
- I need a good metaphor for packages and their relationship to base R
Toolkit Foundations:
- Visual Analysis
- introduce exploratory data analysis and the concept of inductive science
- introduce a curated ACS data set as one of the most foundational datasets in UI and Comp Soc. Sci
- explore a social question and answer it using ggplot charts
- the hope is to make them feel like they're off to the races doing cool stuff
- Reading Data
- Describe different types of data
- provide an intuition for flat text files
- using
readr - honorable mentions: excel, tvs, and json
- General Data Manipulation
- level set with data cleaning as a necessity (80/20 rule)
- introduce a scenario to frame the work
- this is inte‚nded to be a light-hearted imaginary role play
- the important ones: select(), filter(), mutate(),
- the helpful ones: arrange(), count()
- Visualizing Trends:
- the grammer of graphics
- what to visualize when?
- univariate
- bivariate
- going beyond two-variables
- Data Structures: vectors
- describe data types and vector nuances
- revisiting statistics:
- it's important to introduce vectors first because we must have a very good udnerstanding of what they are if we will be creating statstistics. Statistics are taking many numbers and making one from all of those.
- that is what we will do with
summarise()
- that is what we will do with
- it's important to introduce vectors first because we must have a very good udnerstanding of what they are if we will be creating statstistics. Statistics are taking many numbers and making one from all of those.
- the %>% for chainging functions
- this is important
- creating grouped summaries