Introduction to R Statistical Analysis Software

This is the home repository for the R-review class for incoming Ph.D. students in 2023.

Syllabus

• You can find the Syllabus here in PDF format.
• You are free to use this tex file as a LaTex template for yourself to learn more about LaTex.

Data

In this class, we will mostly use the public dataset, and I will either send the data to you through email or I will guide you to download the data during the lecture.

Code

In this class, I will provide you with an R-script file with Skeleton Code for you to start. And I will also provide an R-markdown file provided with a more detailed explanation.

• R script is the place for you to play with the code.
• R markdown and the rendered html file are the study guides.
  • Note: You need to Knit the Rmd file to re-render the html file to open on your own browser: Use Chrome. Don't use Safari.

Other Materials

• We will not use slides very often except in the first lecture. If we use a slide, it is just for transition. I upload the source code for the first lecture in LaTex-Beamer here. Feel free to use it as a template as your own purpose.

• For the rest of the class, our class notes will be provided using R markdown, which can be found in this repository and also on Canvas.

• The lectures will be recorded, and the videos will be shared on YouTube with an unlisted link.

• Useful tools with source code have been published here.

• Lecture notes can also be found on my personal website.

Lecture Structure

The following is a tentative lecture structure, and I might adjust it based on the teaching progress:

Date	Topics	Links
8/14/2023	Lec0: Get familiar with R + Lec1: Data Types and Data Structures in R	Lecture 0, Lecture 1, recodings
8/15/2023	Control Structures, Functions, Basic Data Manipulation:base, tidyverse, data.table	Lecture 2, recordings
8/16/2023	Advanced Data Manipulation, Simple Data Visualizaion, Simple Econometrics, First Comprehensive Project.	Lecture 3, recordings
8/17/2022	Econometrics Review with R with replication of day 1,2,3's materials.	Lecture 4; recordings
8/18/2022	Data Visualization: ggplot2; Final Review Project	Lecture 5; recordings