Learn R from STATA
1
Welcome
2
Introduction
2.1
Who is this book for?
2.2
Who isn’t this book for?
2.3
Setting up R
3
Why switch to R
3.1
R advantages
3.2
In conclusion…
4
Going Through a Project from Start to Finish
4.1
Loading packages
4.2
Loading and prepping the data
4.3
Visualization
4.4
Modeling
4.5
Reporting
4.6
Exercises
5
Dealing with .dta
5.1
Using haven to import STATA files
5.2
Dealing with errors
5.3
But there’s still problems
5.4
Other data formats
5.4.1
.Rdata
5.4.2
.csv and other delimited files
5.4.3
Excel files
5.4.4
.json
5.5
Using multiple dataframes
6
Merging and Wrangling
6.1
base R
6.2
dplyr
6.3
merge.stats
6.4
Into the tidyverse
6.4.1
filter()
6.4.2
mutate()
6.4.3
group_by()
6.4.4
select()
6.4.5
%>%
6.4.6
Stringing it Together
7
One Stop Modeling: Zelig
7.1
Simulation and Counterfactuals
8
Visualizing and Describing your Data
8.1
ggplot2
8.1.1
Destriptive visualitation
8.1.2
Communicating Results
8.1.3
Other Vizualizations
8.2
base-R
9
Linear Regression
9.1
Which way should you write your model?
9.2
Viewing the results
10
MLE
10.1
Binary Dependent Variables
10.2
Counts
10.3
Rare-events and Zero-inflation
11
Bayesian Models
11.1
Using STAN in R
12
Panel Models
12.1
Fixed-Effects
12.1.1
Adjusting Standard Errors
12.2
Random-Effects
12.2.1
PML
12.3
Mixed Models
12.4
Multilevel/Hierarchical Models
12.4.1
lmer
and
stan_lmer
13
Missing Data
13.1
Amelia II
13.2
k-NN Imputation
13.3
Machine Learning for Missing Data
14
Matching
14.1
The MarchIt Package
15
GIS
16
A Brief Introduction to Coding
16.1
Why Learn to Code?
16.2
Coding Basics
16.2.1
The Internet is your Friend
16.3
ifelse()
16.4
Writing a Function
16.5
Loops
16.5.1
Loops in Loops
16.6
Parallelization: What it is, Why it’s Awesome, and How to Use it.
Learn R from STATA
15
GIS