POL 478H1 F

class: center, middle, inverse, title-slide

# POL 478H1 F
## Intro to Graphics
### Olga Chyzh [www.olgachyzh.com]

---

## Visualizing Large Datasets

The challenge---aggregate to a suitable level

Useful commands: `summarise`, `filter`, `mutate`

---
## Example 1

Question: Are democracies more vulnerable to terrorist attacks?

Motivation: 
 
- Civil liberties (privacy, freedom of movement) facilitate communication and planning;
 
- Freedom of the press amplifies the audience effect of attacks.

---
## Load the Data

```r
library(classdata)
data("terr_attacks")
str(terr_attacks)
```

```
## 'data.frame':	16120 obs. of  10 variables:
##  $ country    : chr  "Afghanistan" "Albania" "Algeria" "Angola" ...
##  $ ccode      : num  700 339 615 540 160 371 900 305 373 692 ...
##  $ cabb       : chr  "AFG" "ALB" "DZA" "AGO" ...
##  $ year       : int  2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 ...
##  $ type       : chr  "Armed Assault" "Armed Assault" "Armed Assault" "Armed Assault" ...
##  $ num_attacks: num  2 0 80 22 0 0 0 0 2 0 ...
##  $ GDPpc      : num  NA 2454 3617 2214 7776 ...
##  $ population : num  20531160 3060173 31590320 15562791 37471535 ...
##  $ tradeofgdp : num  NA 57.4 58.7 150.3 21.9 ...
##  $ polity2    : int  NA 5 -3 -3 8 5 10 10 -7 -8 ...
```

---
## Step 1: Explore the Data

- The variables of interest are `num_attacks` and `polity2`;

```r
summary(terr_attacks[,c("num_attacks","polity2")])
```

- Check how each variable is coded, recode as needed
  
  - Address any missingness
  
      - There are 320 missing values on the `polity2` variable.

- It is safe to assume that regimes with "unknown" regime types are not democracies.

```r
terr_attacks$dem<-"Autocracy"
terr_attacks$dem[terr_attacks$polity2>7]<-"Democracy"
```

*The `polity2` variable is obtained from (the Polity V Project)[http://www.systemicpeace.org/inscrdata.html]. Go to its website to check out its codebook.

---

## Step 2: Set Up the Data

- The unit of analysis in the `terr_attacks` data is country-year.

- Our research question asks for a comparison among country-types (democracies vs non-democracies).

- Need to *aggregate* data by regime type, make regime type the unit of analysis.

---
## Data Management Tool #2: Aggregating

.pull-left[
<img src="./country_yr.png" width="250px" style="display: block; margin: auto;" />
]

.pull-right[
<img src="./country.png" width="250px" style="display: block; margin: auto;" />
]

---

## Aggregate by Year

.pull-left[
<img src="./country_yr.png" width="250px" style="display: block; margin: auto;" />
]

.pull-right[
<img src="./yr.png" width="150px" style="display: block; margin: auto;" />
]

---

## Aggregate by Regime Type

.pull-left[
<img src="./country_yr.png" width="250px" style="display: block; margin: auto;" />
]

.pull-right[
<img src="./reg_type.png" width="150px" style="display: block; margin: auto;" />
]

---
## Aggregating using `summarise`

```r
library(tidyverse)
library(magrittr)
# by country
terr_attacks %>% select(country, year, num_attacks, everything()) %>% group_by(country) %>% summarise(num_attacks=sum(num_attacks), .groups="keep")

#by year
terr_attacks %>% select(country, year, num_attacks, everything()) %>% group_by(year) %>% summarise(num_attacks=sum(num_attacks), .groups="keep") 
```

```r
library(tidyverse)
library(magrittr)
#by regime
attks <-terr_attacks %>%
  group_by(dem) %>%
  summarise(`num_attacks`=mean(`num_attacks`),.groups="keep")
```

*Note that `%>%` is called the pipe operator and means "then", i.e. the code above says "group by country, then summarise the number of armed assaults"
---

## `num_attacks` by Regime Type

```r
library(ggplot2)
#Set theme options:
theme_set(theme_grey() + theme(panel.background = element_rect(fill = NA, color = 'black'))+ theme(axis.text=element_text(size=10),
					axis.title=element_text(size=12,face="bold")))

ggplot(data=attks, aes(x=dem,y=num_attacks))+geom_bar(stat="identity", fill="gray80", width=.5)+ylab("Average Number of Attacks")+scale_x_discrete("Regime Type")+coord_flip()
```

---
## Your Turn

- Aggregate the data to show the mean and the median GDP/cap for each country over the entire time-period, i.e. your unit of analysis will be country rather than country-year.

Hint: Use `?summarise` to find out how to get the median.

---
## Adding Nuance

Are democracies more vulnerable to certain types of attacks?

- Need to aggregate by regime *and* attack type

```r
attks1 <-terr_attacks %>%
  group_by(dem, type) %>%
  summarise(`num_attacks`=mean(`num_attacks`),.groups="keep")
```

---
## Your Turn

- Plot number attacks by regime type. Facet by the type of attack.

- Are democracies more vulnerable to certain types of attacks? Which ones? Why do you think that is?

---
## RColorBrewer

Pre-set color schemes:

```r
library(RColorBrewer)
display.brewer.all(n=NULL, type="all", select=NULL, exact.n=TRUE, 
colorblindFriendly=TRUE)
```

---

## Over Time

```r
attks2 <-terr_attacks %>%
  group_by(dem, type, year) %>%
  summarise(`num_attacks`=mean(`num_attacks`),.groups="keep")
attks2$type<-recode(attks2$type,"Bombing/Explosion"="Explosion",
                    "Facility/Infrastructure Attack"="Facility Attack",
                    "Hostage Taking (Kidnapping)"="Kidnapping",
                    "Hostage Taking (Barricade Incident)"="Barricade")

ggplot(data=attks2, aes(x=year,y=num_attacks, fill=dem))+
  geom_bar(stat="identity", position="fill")+
  facet_wrap(~type)+ylab("Proportion")+
  scale_x_continuous(breaks=seq(from=2001,to=2014,by=6))+scale_fill_brewer(name="Regime type", palette="Dark2")+theme_classic()
```

```r
ggplot(data=attks2, aes(x=year,y=num_attacks, fill=dem))+
  geom_bar(stat="identity")+
  facet_wrap(~type, scale="free")+ylab("Number of Attacks")+scale_fill_brewer(name="Regime type", palette="Dark2")+
  scale_x_continuous(breaks=seq(from=2001,to=2014,by=6))+theme_classic()
```

---
## Line Plots vs Bar Graphs

- What can you tell from a line plot that you could not from the corresponding bar graph?

- Why overlay a line plot with a scatter plot?

- What are the advantages of plotting separate lines vs a single line (proportion)?

- Why do we need a horizontal line at y=0.5?

```r
ggplot(data=attks2, aes(x=year,y=num_attacks, color=dem))+
  geom_line(size=1)+geom_point(size=1)+
  facet_wrap(~type, scale="free")+ylab("Number of Attacks")+scale_colour_brewer(name="Regime type", palette="Dark2")+
  scale_x_continuous(breaks=seq(from=2001,to=2014,by=6))+theme_classic()+theme(legend.position="bottom")

attks3<-attks2 %>% pivot_wider(names_from=dem,values_from=num_attacks) %>% mutate(prop_dem=Democracy/(Autocracy+Democracy))
ggplot(data=attks3, aes(x=year,y=prop_dem))+
  geom_line(size=1)+geom_point(size=1)+
  facet_wrap(~type)+
  geom_hline(aes(yintercept=.5), linetype=2)+
  ylab("Proportion of Democracies")+scale_colour_brewer(name="Regime type", palette="Dark2")+ scale_x_continuous(breaks=seq(from=2001,to=2014,by=6))+theme_classic()
```

---
## Utilize Proximity

- If you wanted to know *what attack type is equally likely in both an autocracy and a democracy?* which plot makes this easier to answer?

.pull-left[
<img src="05_more_graphics_files/figure-html/unnamed-chunk-18-1.png" width="450px" style="display: block; margin: auto;" />
]
.pull-right[
<img src="05_more_graphics_files/figure-html/unnamed-chunk-19-1.png" width="450px" style="display: block; margin: auto;" />
]

---
## Your Turn

Make the two plots from the previous slide.

---

## What We Learned

- Dealing with missing data

- Aggregating using `summarise`

- Bar graphs, line plots

- Raw numbers vs. proportions

- Utilizing proximity

- Fill, system of coordinates