R Course
1
Preface
1.1
Notation Conventions
1.2
Acknowledgements
2
Introduction
2.1
What is R?
2.2
The R Ecosystem
2.3
Bibliographic Notes
3
R Basics
3.0.1
Other IDEs
3.1
File types
3.2
Simple calculator
3.3
Probability calculator
3.4
Getting Help
3.5
Variable Asignment
3.6
Missing
3.7
Piping
3.8
Vector Creation and Manipulation
3.9
Search Paths and Packages
3.10
Simple Plotting
3.11
Object Types
3.12
Data Frames
3.13
Exctraction
3.14
Augmentations of the data.frame class
3.15
Data Import and Export
3.15.1
Import from WEB
3.15.2
Import From Clipboard
3.15.3
Export as CSV
3.15.4
Export non-CSV files
3.15.5
Reading From Text Files
3.15.6
Writing Data to Text Files
3.15.7
.XLS(X) files
3.15.8
Massive files
3.15.9
Databases
3.16
Functions
3.17
Looping
3.18
Apply
3.19
Recursion
3.20
Strings
3.21
Dates and Times
3.21.1
Dates
3.21.2
Times
3.21.3
lubridate Package
3.22
Complex Objects
3.23
Vectors and Matrix Products
3.24
Bibliographic Notes
3.25
Practice Yourself
4
data.table
4.1
Make your own variables
4.2
Join
4.3
Reshaping data
4.3.1
Wide to long
4.3.2
Long to wide
4.4
Bibliographic Notes
4.5
Practice Yourself
5
Exploratory Data Analysis
5.1
Summary Statistics
5.1.1
Categorical Data
5.1.2
Continous Data
5.2
Visualization
5.2.1
Categorical Data
5.2.2
Continuous Data
5.3
Mixed Type Data
5.3.1
Alluvial Diagram
5.4
Bibliographic Notes
5.5
Practice Yourself
6
Linear Models
6.1
Problem Setup
6.2
OLS Estimation in R
6.3
Inference
6.3.1
Testing a Hypothesis on a Single Coefficient
6.3.2
Constructing a Confidence Interval on a Single Coefficient
6.3.3
Multiple Regression
6.3.4
ANOVA (*)
6.3.5
Testing a Hypothesis on a Single Contrast (*)
6.4
Bibliographic Notes
6.5
Practice Yourself
7
Generalized Linear Models
7.1
Problem Setup
7.2
Logistic Regression
7.2.1
Logistic Regression with R
7.3
Poisson Regression
7.4
Extensions
7.5
Bibliographic Notes
7.6
Practice Yourself
8
Linear Mixed Models
8.1
Problem Setup
8.1.1
Non-Linear Mixed Models
8.1.2
Generalized Linear Mixed Models (GLMM)
8.2
Mixed Models with R
8.2.1
A Single Random Effect
8.2.2
Multiple Random Effects
8.2.3
A Full Mixed-Model
8.3
Serial Correlations
8.4
Extensions
8.4.1
Cluster Robust Standard Errors
8.4.2
Linear Models for Panel Data
8.4.3
Testing Hypotheses on Correlations
8.5
Relation to Other Estimators
8.5.1
Fixed Effects in the Econometric Literature
8.5.2
Relation to Generalized Least Squares (GLS)
8.5.3
Relation to Conditional Gaussian Fields
8.5.4
Relation to Empirical Risk Minimization (ERM)
8.5.5
Relation to M-Estimation
8.5.6
Relation to Generalize Estimating Equations (GEE)
8.5.7
Relation to MANOVA
8.5.8
Relation to Seemingly Unrelated Equations (SUR)
8.6
Bibliographic Notes
8.7
Practice Yourself
9
Multivariate Data Analysis
9.1
Signal Detection
9.1.1
Hotelling’s T2 Test
9.1.2
Various Types of Signal to Detect
9.1.3
Simes’ Test
9.1.4
Signal Detection with R
9.2
Signal Counting
9.3
Signal Identification
9.3.1
Signal Identification in R
9.4
Signal Estimation (*)
9.5
Bibliographic Notes
9.6
Practice Yourself
10
Supervised Learning
10.1
Problem Setup
10.1.1
Common Hypothesis Classes
10.1.2
Common Complexity Penalties
10.1.3
Unbiased Risk Estimation
10.1.4
Collecting the Pieces
10.2
Supervised Learning in R
10.2.1
Linear Models with Least Squares Loss
10.2.2
SVM
10.2.3
Neural Nets
10.2.4
Classification and Regression Trees (CART)
10.2.5
K-nearest neighbour (KNN)
10.2.6
Linear Discriminant Analysis (LDA)
10.2.7
Naive Bayes
10.2.8
Random Forrest
10.2.9
Boosting
10.3
Bibliographic Notes
10.4
Practice Yourself
11
Unsupervised Learning
11.1
Dimensionality Reduction
11.1.1
Principal Component Analysis
11.1.2
Dimensionality Reduction Preliminaries
11.1.3
Latent Variable Generative Approaches
11.1.4
Purely Algorithmic Approaches
11.1.5
Dimensionality Reduction in R
11.2
Clustering
11.2.1
Latent Variable Generative Approaches
11.2.2
Purely Algorithmic Approaches
11.2.3
Clustering in R
11.3
Bibliographic Notes
11.4
Practice Yourself
12
Plotting
12.1
The graphics System
12.1.1
Using Existing Plotting Functions
12.1.2
Exporting a Plot
12.1.3
Fancy graphics Examples
12.2
The ggplot2 System
12.2.1
Extensions of the ggplot2 System
12.3
Interactive Graphics
12.3.1
Plotly
12.4
Other R Interfaces to JavaScript Plotting
12.5
Bibliographic Notes
12.6
Practice Yourself
13
Reports
13.1
knitr
13.1.1
Installation
13.1.2
Pandoc Markdown
13.1.3
Rmarkdown
13.1.4
BibTex
13.1.5
Compiling
13.2
bookdown
13.3
Shiny
13.3.1
Installation
13.3.2
The Basics of Shiny
13.3.3
Beyond the Basics
13.3.4
shinydashboard
13.4
flexdashboard
13.5
Bibliographic Notes
13.6
Practice Yourself
14
Sparse Representations
14.1
Sparse Matrix Representations
14.1.1
Coordinate List Representation
14.1.2
Compressed Row Oriented Representation
14.1.3
Compressed Column Oriented Representation
14.1.4
Sparse Algorithms
14.2
Sparse Matrices and Sparse Models in R
14.2.1
The Matrix Package
14.2.2
The glmnet Package
14.2.3
The MatrixModels Package
14.2.4
The SparseM Package
14.3
Beyond Sparsity
14.4
Bibliographic Notes
14.5
Practice Yourself
15
Memory Efficiency
15.1
Efficient Computing from RAM
15.1.1
Summary Statistics from RAM
15.2
Computing from a Database
15.3
Computing From Efficient File Structrures
15.3.1
bigmemory
15.3.2
bigstep
15.4
ff
15.5
matter
15.6
iotools
15.7
HDF5
15.8
DelayedArray
15.9
Computing from a Distributed File System
15.10
Bibliographic Notes
15.11
Practice Yourself
16
Parallel Computing
16.1
Implicit Parallelism
16.2
Explicit Parallelism
16.2.1
Caution: Implicit with Explicit Parallelism
16.3
Bibliographic Notes
16.4
Practice Yourself
17
Numerical Linear Algebra
17.1
LU Factorization
17.2
Cholesky Factorization
17.3
QR Factorization
17.4
Singular Value Factorization
17.5
Iterative Methods
17.6
Solving the OLS Problem
17.7
Numerical Libraries for Linear Algebra
17.7.1
OpenBlas
17.7.2
MKL
17.8
Bibliographic Notes
17.9
Practice Yourself
18
Convex Optimization
18.1
Theoretical Backround
18.2
Optimizing with R
18.2.1
The optim Function
18.2.2
The nloptr Package
18.2.3
minqa Package
18.3
Bibliographic Notes
18.4
Practice Yourself
19
RCpp
19.1
Bibliographic Notes
19.2
Practice Yourself
20
Debugging Tools
20.1
Bibliographic Notes
20.2
Practice Yourself
21
Econometrics
21.1
Bibliographic Notes
21.2
Practice Yourself
22
Psychometrics
22.1
Bibliographic Notes
22.2
Practice Yourself
23
The Hadleyverse
23.1
readr
23.2
dplyr
23.3
tidyr
23.4
reshape2
23.5
stringr
23.6
anytime
23.7
Biblipgraphic Notes
23.8
Practice Yourself
24
Causal Inferense
24.1
Causal Inference From Designed Experiments
24.1.1
Design of Experiments
24.1.2
Randomized Inference
24.2
Causal Inference from Observational Data
24.2.1
Principal Stratification
24.2.2
Instrumental Variables
24.2.3
Propensity Scores
24.2.4
Direct Lieklihood
24.2.5
Regression Discontinuity
24.3
Bibliographic Notes
24.4
Practice Yourself
Published with bookdown
R (BGU course)
Chapter 21
Econometrics
TODO
VAR
Robust and Clustered SEs.
GEE
TSLS
21.1
Bibliographic Notes
21.2
Practice Yourself