# PS 811 Website

*2022-11-15*

# 1 Course Description

This is the class website for PS-811 for the 2022 fall semester.

PS 811 is a one-credit hour, pass/fail course taught to incoming Ph.D. students in the political science department. This course will be taught remotely; however, RM 3218 in the Social Sciences building has been reserved if you would like to listen to lecture and work on the problem sets together in the room during the class time window.

In modern political science, you will be required to perform or understand quantitative research. Often this research is conducted using one of several programming languages. The language most often used is R. R is a special-purpose programming language designed for statistical and mathematical computing, though it can function similarly to a general-purpose language. In addition to R, the typesetting language is often used in academia to write and submit articles. Data management can take many forms, but the reproducibility of your research must always be considered. This course will provide you with data management/hygiene techniques and tools that will provide a useful starting point for your own organizational style.

In your academic career, you will also find it necessary to produce papers, presentations, websites, and reproducible research. This course is intended to give you a brief but sound footing to start your academic journey with the tools necessary to accomplish these tasks. In addition to R, we will cover Markdown, , presentation styles, Git, and will touch on other programming, computing, and research-related topics that are relevant to this core material1.

— Class GitHub: https://github.com/jbr1987/ps811_fa22

## 1.1 Basic Reference Code

```
#### ####
# Operators
+ # add
- # subtract
/ # divide
* # multiply
^ # exponent
| # or
& # and
< # less than
> # greater than
<- # assign
= # assign
== # equals
<= # less than or equal to
>= # greater than or equal to
%*% # matrix multiplication
t() # transpose a matrix
inv() # inverse of a matrix
#### ####
# Data Types
## There are 4 main data types for R
data.frame()
matrix()
list()
vector()
#### ####
# Read Data into Memory (RAM)
## csv
<- read.csv("datatablename.csv", ...) # base R
df
## data.table fread
<- fread("datatablename.csv", ...)
df
## delim
<- read.delim("datatablename.csv", sep = "\t", ...) # base R df
```