Becoming Fluent in Data
I Introduction
Preface
Software
Why code?
Why R?
Why the Tidyverse?
Intro to R
Intro to Tidyverse
II Data
1
Foundations
1.1
Data is everywhere
1.2
Stories and Visuals
2
Structured Data
2.1
Tabular Data
2.2
Panel Data
2.3
Time data
3
Unstructured Data
3.1
Web Data
3.2
Text Data
3.3
Geo Data
4
Imperfect Data
4.1
Missing Data
4.2
Synthetic Data
III Analysis
5
Compare
5.1
Relationships
5.2
Comparing Means
5.3
Partitioning Variation
6
Model
6.1
Regression
6.2
Linear Models
6.3
Decomposing Differences
6.4
Logistic Models
6.5
Interactions
6.6
Marginal Effects
6.7
Generalized Models
6.8
Learning from Data
6.9
Common Tests Are Linear Models
7
Structure
7.1
Time
7.2
Longitudinal Data
7.3
Multilevel Models
7.4
Places
8
Reveal
8.1
Components
8.2
Clusters
8.3
Latent Variables
8.4
Exploratory Factors
8.5
Confirmatory Factors
8.6
Structural Equations
8.7
Items
9
Identify
9.1
Experiments
9.2
Matching and Balancing
9.3
Difference-in-Differences
9.4
Discontinuities
9.5
Instruments
10
Extract
10.1
Text Mining
References
10.2
Resources
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Becoming Fluent in Data
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6.4
Logistic Models
Binary outcomes and predicted probabilities