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15_project-dempeace.Rmd
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15_project-dempeace.Rmd
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# Joins and Merges, Wide and Long^[Module originally written by Shiro Kuriwaki, Connor Jerzak, and Yon Soo Park] {#dempeace}
### Motivation {-}
The "Democratic Peace" is one of the most widely discussed propositions in political science, covering the fields of International Relations and Comparative Politics, with insights to domestic politics of democracies (e.g. American Politics). The one-sentence idea is that democracies do not fight with each other. There have been much theoretical debate -- for example in earlier work, [Oneal and Russet (1999)](https://blackboard.angelo.edu/bbcswebdav/institution/LFA/CSS/Course%20Material/SEC6302/Readings/Lesson_3/Oneal-Russett.pdf) argue that the democratic peace is not due to the hegemony of strong democracies like the U.S. and attempt to distinguish between realist and what they call Kantian propositions (e.g. democratic governance, international organizations)^[[The Kantian Peace: The Pacific Benefits of Democracy, Interdependence, and International Organizations, 1885-1992. _World Politics_ 52(1):1-37](https://blackboard.angelo.edu/bbcswebdav/institution/LFA/CSS/Course%20Material/SEC6302/Readings/Lesson_3/Oneal-Russett.pdf)].
An empirical demonstration of the democratic peace is also a good example of a __Time Series Cross Sectional__ (or panel) dataset, where the same units (in this case countries) are observed repeatedly for multiple time periods. Experience in assembling and analyzing a TSCS dataset will prepare you for any future research in this area.
## Where are we? Where are we headed? {-}
Up till now, you should have covered:
* R basic programming
* Counting.
* Visualization.
* Objects and Classes.
* Matrix algebra in R
* Functions.
Today you will work on your own, but feel free to ask a fellow classmate nearby or the instructor. The objective for this session is to get more experience using R, but in the process (a) test a prominent theory in the political science literature and (b) explore related ideas of interest to you.
## Setting up
```{r, message=FALSE, warning=FALSE}
library(dplyr)
library(tidyr)
library(readr)
library(ggplot2)
```
## Create a project directory
First start a directory for this project. This can be done manually or through RStudio's Project feature(`File > New Project...`)
Directories is the computer science / programming name for folders. While advice about how to structure your working directories might strike you as petty, we believe that starting from some well-tested guides will go a long way in improving the quality and efficiency of your work.
Chapter 4 of Gentzkow and Shapiro's memo, [Code and Data for the Social Scientist](https://web.stanford.edu/~gentzkow/research/CodeAndData.pdf)] provides a good template.
## Data Sources
Most projects you do will start with downloading data from elsewhere. For this task, you'll probably want to track down and download the following:
* __Correlates of war dataset (COW):__ Find and download the Militarized Interstate Disputes (MIDs) data from the Correlates of War website: <http://www.correlatesofwar.org/data-sets>. Or a dyad-version on dataverse: <https://dataverse.harvard.edu/dataset.xhtml?persistentId=hdl:1902.1/11489>
* __PRIO Data on Armed Conflict:__ Find and download the Uppsala Conflict Data Program (UCDP) and PRIO dyad-year data on armed conflict(<https://www.prio.org>) or this link to to the flat csv file (<http://ucdp.uu.se/downloads/dyadic/ucdp-dyadic-171.csv>).
* __Polity:__ The Polity data can be downloaded from their website (<http://www.systemicpeace.org/inscrdata.html>). Look for the newest version of the time series that has the widest coverage.
## Example with 2 Datasets
Let's read in a sample dataset.
```{r, warning = FALSE, message = FALSE}
polity <- read_csv("data/input/sample_polity.csv")
mid <- read_csv("data/input/sample_mid.csv")
```
What does `polity` look like?
```{r}
unique(polity$country)
ggplot(polity, aes(x = year, y = polity2)) +
facet_wrap(~ country) +
geom_line()
head(polity)
```
MID is a dataset that captures a `dispute` for a given country and year.
```{r}
mid
```
## Loops
Notice that in the `mid` data, we have a start of a dispute vs. an end of a dispute.In order to combine this into the `polity` data, we want a way to give each of the interval years a row.
There are many ways to do this, but one is a loop. We go through one row at a time, and then for each we make a new dataset. that has `year` as a sequence of each year. A lengthy loop like this is typically slow, and you'd want to recast the task so you can do things with functions. But, a loop is a good place to start.
```{r}
mid_year_by_year <- data_frame(ccode = numeric(),
year = numeric(),
dispute = numeric())
for(i in 1:nrow(mid)) {
x <- data_frame(ccode = mid$ccode[i], ## row i's country
year = mid$StYear[i]:mid$EndYear[i], ## sequence of years for dispute in row i
dispute = 1)
mid_year_by_year <- rbind(mid_year_by_year, x)
}
head(mid_year_by_year)
```
## Merging
We want to combine these two datasets by merging. Base-R has a function called `merge`. `dplyr` has several types of `joins` (the same thing). Those names are based on SQL syntax.
Here we can do a `left_join` matching rows from `mid` to `polity`. We want to keep the rows in `polity` that do not match in `mid`, and label them as non-disputes.
```{r}
p_m <- left_join(polity,
distinct(mid_year_by_year),
by = c("ccode", "year"))
head(p_m)
```
Replace `dispute` = `NA` rows with a zero.
```{r}
p_m$dispute[is.na(p_m$dispute)] <- 0
```
Reshape the dataset long to wide
```{r}
p_m_wide <- pivot_wider(p_m,
id_cols = c(scode, ccode, country),
names_from = year,
values_from = polity2)
select(p_m_wide, 1:10)
```
## Main Project
Try building a panel that would be useful in answering the Democratic Peace Question, perhaps in these steps.
### Task 1: Data Input and Standardization {-}
Often, files we need are saved in the `.xls` or `xlsx` format. It is possible to read these files directly into `R`, but experience suggests that this process is slower than converting them first to `.csv` format and reading them in as `.csv` files.
`readxl`/`readr`/`haven` packages(<https://github.com/tidyverse/tidyverse>) is constantly expanding to capture more file types. In day 1, we used the package `readxl`, using the `read_excel()` function.
```{r}
```
### Task 2: Data Merging {-}
We will use data to test a version of the Democratic Peace Thesis (DPS). Democracies are said to go to war less because the leaders who wage wars are accountable to voters who have to bear the costs of war. Are democracies less likely to engage in militarized interstate disputes?
To start, let's download and merge some data.
* Load in the Militarized Interstate Dispute (MID) files. Militarized interstate disputes are hostile action between two formally recognized states. Examples of this would be threats to use force, threats to declare war, beginning war, fortifying a border with troops, and so on.
* Find a way to __merge__ the Polity IV dataset and the MID data. This process can be a bit tricky.
* An _advanced_ version of this task would be to download the dyadic form of the data and try merging that with polity.
```{r}
```
### Task 3: Tabulations and Visualization {-}
1. Calculate the mean Polity2 score by year. Plot the result. Use graphical indicators of your choosing to show where key events fall in this timeline (such as 1914, 1929, 1939, 1989, 2008). Speculate on why the behavior from 1800 to 1920 seems to be qualitatively different than behavior afterwards.
2. Do the same but only among state-years that were invovled in a MID. Plot this line together with your results from 1.
3. Do the same but only among state years that were _not_ involved in a MID.
4. Arrive at a tentative conclusion for how well the Democratic Peace argument seems to hold up in this dataset. Visualize this conclusion.
```{r}
```