POLS 1600 - Political Research Methods

# POLS 1600 - Political Research Methods
## Week 01 - Introduction
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### Danilo Freire
### 23rd January 2019

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# Introduction

* Masters from the Graduate Institute Geneva

* PhD in Political Economy at King's College London

* Post-doctoral research fellow in the Political Theory Project

* Political violence, collective action, experimental methods, machine learning

* [http://danilofreire.github.io](http://danilofreire.github.io)
* [danilo_freire@brown.edu](mailto:danilo_freire@brown.edu)
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# What about you?

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# Introduce yourself

* Where do you come from?

* Tell me something I don't know about your city or country

* Why are you taking this course?
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# POLS 1600

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# Logistics

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* We meet every Monday, Wednesday and Friday at 13:00 in the [CIT Centre (Thomas Watson CTI) 227](http://brown.edu/Facilities/Facilities_Management/maps/index.php#building/WATSONCIT)

* All course information will be available at [http://danilofreire.github.io/pols1600](http://danilofreire.github.io/pols1600)

* Syllabus will probably be updated throughout the course (but not much)

* Office hours: at your convenience (just e-mail me first)

* Office address: [8 Fones Alley, first floor, office 114](https://goo.gl/maps/MRQNS9EC9Qv)
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# Textbooks

# Learning Objectives

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* Be a critical consumer of statistical information
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* Be a critical _producer_ of statistical information
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* Learn how to code
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* Learn how to use R for quantitative analysis
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* Learn the basics of data visualisation
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* Estimate the relationship among many variables of interest using statistical models (OLS)
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* .orange[Have a good time with your colleagues]
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# Grading

* **Problem Sets**: 40%

* **Final Project**: 50%
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* _No exams_
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* Read the materials and do the exercises _before_ class
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# Resources

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* This course is relatively light in mathematics...
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* ... but it is quite heavy in programming
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* .orange[Programming can be hard and frustrating...]
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* ... but it doesn't have to
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* R has a steep learning curve, so practice is fundamental 
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# Why programming?

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# Why programming?

# Why programming?

* Newspapers often use R to plot graphs and convey statistical information...and you can do it too
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# Freire (2018)

# Freire (2018)

# Freire (2018)

# Freire (2018)

```r
dataprep.out <-
        dataprep(df,
                 predictors = c("state.gdp.capita",
                                "state.gdp.growth.percent",
                                "population.projection.ln",
                                "years.schooling.imp"
                                ),
                 special.predictors = list(
                         list("homicide.rates", 1990:1998, "mean"),
                         list("proportion.extreme.poverty", 1990:1998, "mean"),
                         list("gini.imp", 1990:1998, "mean")
                         ),
                 predictors.op = "mean",
                 dependent     = "homicide.rates",
                 unit.variable = "code",
                 time.variable = "year",
                 unit.names.variable   = "state",
                 treatment.identifier  = 35,
                 controls.identifier   = c(11:17, 21:27, 31:33, 41:43, 50:53),
                 time.predictors.prior = c(1990:1998),
                 time.optimize.ssr     = c(1990:1998),
                 time.plot             = c(1990:2009)
)
```
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# Freire (2018)

# Freire (2018)

# Data visualisation in R

# Data visualisation in R

# Questions?

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# Homework

* Download and install R and RStudio:

* <https://cloud.r-project.org/>

* <https://www.rstudio.com/> (RStudio Desktop, free version)
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# See you on Friday!