Stanford Future Bay Initiative
1
Introduction
1.1
Software setup
1.2
RStudio interface
1.3
R Markdown files
1.4
GitHub
1.5
Reading and saving files
1.6
Loops
1.7
Manipulating data
1.8
Plots
2
Populations
2.1
Geospatial data
2.2
ACS data
2.3
Decennial Data
2.4
Spatial subsets
2.5
Migration
3
Surveys
3.1
Microdata
3.2
Strings
4
Distributions
4.1
Equity analysis
4.2
Probability distributions
4.3
Monte Carlo simulations
5
Regression
5.1
Simple linear regression
5.2
Multiple regression
5.3
Autocorrelation
5.4
Survey regression
5.5
Training and testing data
6
Causality
6.1
Matching
6.2
Difference-in-differences
7
Web Applications
7.1
Dashboards
Shaping the Future of the Bay Area: Intro to Urban Data Analytics in R
6
Causality
In this chapter we’ll expand on the last chapter by covering:
Matching techniques to find similar records across multiple dimensions
Difference-in-differences models