Fariss Human Rights Data with Animation
Fariss Data
Is neat and complete.
load("FarissHRData.RData")
skimr::skim(HR.Data)| Name | HR.Data |
| Number of rows | 11717 |
| Number of columns | 27 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 26 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| COW_YEAR | 0 | 1 | FALSE | 11717 | 100: 1, 100: 1, 100: 1, 100: 1 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| X | 0 | 1.00 | 5859.72 | 3382.90 | 1.00 | 2930.00 | 5860.00 | 8789.00 | 11718.00 | ▇▇▇▇▇ |
| YEAR | 0 | 1.00 | 1984.78 | 20.44 | 1946.00 | 1968.00 | 1987.00 | 2002.00 | 2017.00 | ▅▆▆▇▇ |
| CIRI | 6787 | 0.42 | 390.52 | 170.85 | 101.00 | 242.00 | 380.00 | 530.00 | 698.00 | ▇▇▇▇▇ |
| COW | 0 | 1.00 | 461.21 | 254.40 | 2.00 | 255.00 | 461.00 | 663.00 | 990.00 | ▆▆▇▆▃ |
| DISAP | 6798 | 0.42 | 1.68 | 0.62 | 0.00 | 2.00 | 2.00 | 2.00 | 2.00 | ▁▁▂▁▇ |
| KILL | 6800 | 0.42 | 1.33 | 0.76 | 0.00 | 1.00 | 2.00 | 2.00 | 2.00 | ▃▁▅▁▇ |
| POLPRIS | 6793 | 0.42 | 1.13 | 0.84 | 0.00 | 0.00 | 1.00 | 2.00 | 2.00 | ▆▁▅▁▇ |
| TORT | 6794 | 0.42 | 0.79 | 0.75 | 0.00 | 0.00 | 1.00 | 1.00 | 2.00 | ▇▁▇▁▃ |
| Amnesty | 6140 | 0.48 | 2.70 | 1.12 | 1.00 | 2.00 | 3.00 | 3.00 | 5.00 | ▃▇▇▅▂ |
| State | 4802 | 0.59 | 2.39 | 1.16 | 1.00 | 1.00 | 2.00 | 3.00 | 5.00 | ▇▇▇▃▂ |
| HRW | 11271 | 0.04 | 3.03 | 1.11 | 1.00 | 2.00 | 3.00 | 4.00 | 5.00 | ▂▅▇▅▂ |
| hathaway | 9484 | 0.19 | 2.74 | 1.15 | 1.00 | 2.00 | 3.00 | 3.00 | 5.00 | ▃▆▇▅▂ |
| ITT | 10258 | 0.12 | 3.51 | 2.03 | 1.00 | 1.00 | 4.00 | 6.00 | 6.00 | ▇▃▃▃▆ |
| genocide | 1261 | 0.89 | 0.03 | 0.16 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| rummel | 6077 | 0.48 | 0.20 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| massive_repression | 5541 | 0.53 | 0.05 | 0.21 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| executions | 7613 | 0.35 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| negative_sanctions | 7613 | 0.35 | 0.69 | 0.46 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| mass_killing | 0 | 1.00 | 0.13 | 0.34 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| killing_low | 6176 | 0.47 | 45.34 | 2306.65 | 0.00 | 0.00 | 0.00 | 0.00 | 171441.00 | ▇▁▁▁▁ |
| killing_best | 11397 | 0.03 | 1855.35 | 27941.40 | 25.00 | 42.00 | 99.50 | 251.25 | 500000.00 | ▇▁▁▁▁ |
| killing_high | 11397 | 0.03 | 3043.11 | 44714.85 | 25.00 | 62.00 | 149.00 | 397.75 | 800000.00 | ▇▁▁▁▁ |
| killing_present | 6176 | 0.47 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| id | 0 | 1.00 | 5859.00 | 3382.55 | 1.00 | 2930.00 | 5859.00 | 8788.00 | 11717.00 | ▇▇▇▇▇ |
| theta_mean | 0 | 1.00 | 0.05 | 1.52 | -3.77 | -0.99 | -0.07 | 0.96 | 5.14 | ▂▇▇▂▁ |
| theta_sd | 0 | 1.00 | 0.36 | 0.19 | 0.18 | 0.23 | 0.27 | 0.47 | 1.31 | ▇▂▁▁▁ |
# HR.Data$year <- as.Date(HR.Data$YEAR, format = "%Y")What do we have?
library(gganimate)
p1 <- p1 + transition_reveal(YEAR) # this is kind of a hack, the year isn't right
animate(p1, nframes = 200)
COW.Codes <- distinct(COW.Codes) # Duplicates exist in the COW Code master file
COW.Codes$Continent <- cut(COW.Codes$CCode, breaks = c(0, 100, 200, 400, 600, 700, 900,1000), labels = c("N. America", "S. America", "Europe", "Africa", "Med/Mid", "Asia", "Oceania"), right = FALSE)
HR.Data.Named <- left_join(HR.Data, COW.Codes, by = c("COW" = "CCode"))Now I have something to work with that is labeled.
( ggplot(HR.Data.Named) + aes(x=YEAR, y=theta_mean, color=as.factor(StateAbb), label = StateAbb) + geom_line(alpha = 0.3) + scale_color_viridis_d(guide=FALSE) + labs(x="Year", y="Human Rights Dynamic Factor Score", title = "The Evolution of Human Rights with Normed Scores", caption = "See Fariss and Schnakenberg (2014)" ) + theme_economist_white() + facet_wrap(vars(Continent)) -> p2 )
p2
Now let’s look at that.
p2 <- p2 + transition_reveal(YEAR)
animate(p2, nframes = 200)
North America
HR.Data.Named %>% filter(COW < 100) %>% ggplot(.) + aes(x=YEAR, y=theta_mean, color=as.factor(StateAbb), label = StateAbb) + geom_line(alpha = 0.3) + scale_color_viridis_d(guide=FALSE) + labs(x="Year", y="Human Rights Dynamic Factor Score", title = "The Evolution of Human Rights with Normed Scores", caption = "See Fariss and Schnakenberg (2014)", subtitle = "North America") + theme_economist_white()
An Animation for North America
HR.Data.Named %>% filter(COW < 100) %>% ggplot(.) + aes(x=YEAR, y=theta_mean, color=as.factor(StateAbb), label = StateNme) + geom_label_repel(x=2010, size=2, direction = "y", hjust = 1, nudge_x = 20) + geom_line(alpha = 0.3) + scale_color_viridis_d(guide=FALSE) + labs(x="Year", y="Human Rights Dynamic Factor Score", title = "The Evolution of Human Rights with Normed Scores", caption = "See Fariss and Schnakenberg (2014)", subtitle = "North America") + theme_economist_white() + transition_reveal(YEAR) -> NA.p
animate(NA.p, nframes=200)
South America
HR.Data.Named %>% filter(COW < 200 & COW > 100) %>% ggplot(.) + aes(x=YEAR, y=theta_mean, color=as.factor(StateAbb), label = StateNme) + geom_line(alpha = 0.3) + scale_color_viridis_d(guide=FALSE) + labs(x="Year", y="Human Rights Dynamic Factor Score", title = "The Evolution of Human Rights with Normed Scores", caption = "See Fariss and Schnakenberg (2014)", subtitle = "South America") + theme_economist()
Animation for South America
HR.Data.Named %>% filter(COW < 200 & COW >= 100) %>% ggplot(.) + aes(x=YEAR, y=theta_mean, color=as.factor(StateAbb), label = StateNme) + geom_label_repel(x=2010, size=2, direction = "y", hjust = 1, nudge_x = 20) + geom_line(alpha = 0.3) + scale_color_viridis_d(guide=FALSE) + labs(x="Year", y="Human Rights Dynamic Factor Score", title = "The Evolution of Human Rights with Normed Scores", caption = "See Fariss and Schnakenberg (2014)", subtitle = "South America") + theme_economist() + transition_reveal(YEAR) -> SA.p
animate(SA.p, nframes=200)
Europe
HR.Data.Named %>% filter(COW < 400 & COW >= 200) %>% ggplot(.) + aes(x=YEAR, y=theta_mean, color=as.factor(StateAbb), label = StateNme) + geom_line(alpha = 0.3) + scale_color_viridis_d(guide=FALSE) + labs(x="Year", y="Human Rights Dynamic Factor Score", title = "The Evolution of Human Rights with Normed Scores", caption = "See Fariss and Schnakenberg (2014)", subtitle = "Europe") + theme_economist()
Animate Europe
HR.Data.Named %>% filter(COW < 400 & COW >= 200) %>% ggplot(.) + aes(x=YEAR, y=theta_mean, color=as.factor(StateAbb), label = StateNme) + geom_label_repel(x=2010, size=2, direction = "y", hjust = 1, nudge_x = 20) + geom_line(alpha = 0.3) + scale_color_viridis_d(guide=FALSE) + labs(x="Year", y="Human Rights Dynamic Factor Score", title = "The Evolution of Human Rights with Normed Scores", caption = "See Fariss and Schnakenberg (2014)", subtitle = "Europe") + theme_economist() + transition_reveal(YEAR) -> EU.p
animate(EU.p, nframes=200)
Africa
HR.Data.Named %>% filter(COW < 600 & COW >= 400) %>% ggplot(.) + aes(x=YEAR, y=theta_mean, color=as.factor(StateAbb), label = StateNme) + geom_line(alpha = 0.3) + scale_color_viridis_d(guide=FALSE) + labs(x="Year", y="Human Rights Dynamic Factor Score", title = "The Evolution of Human Rights with Normed Scores", caption = "See Fariss and Schnakenberg (2014)", subtitle = "Africa") + theme_economist()
Animate Africa
HR.Data.Named %>% filter(COW < 600 & COW >= 400) %>% ggplot(.) + aes(x=YEAR, y=theta_mean, color=as.factor(StateAbb), label = StateNme) + geom_label_repel(x=2010, size=2, direction = "y", hjust = 1, nudge_x = 20) + geom_line(alpha = 0.3) + scale_color_viridis_d(guide=FALSE) + labs(x="Year", y="Human Rights Dynamic Factor Score", title = "The Evolution of Human Rights with Normed Scores", caption = "See Fariss and Schnakenberg (2014)", subtitle = "Africa") + theme_economist() + transition_reveal(YEAR) -> AF.p
animate(EU.p, nframes=200)
Middle East / North Africa
HR.Data.Named %>% filter(COW < 700 & COW >= 600) %>% ggplot(.) + aes(x=YEAR, y=theta_mean, color=as.factor(StateAbb), label = StateNme) + geom_line(alpha = 0.3) + scale_color_viridis_d(guide=FALSE) + labs(x="Year", y="Human Rights Dynamic Factor Score", title = "The Evolution of Human Rights with Normed Scores", caption = "See Fariss and Schnakenberg (2014)", subtitle = "Middle East and North Africa") + theme_economist()
Animate Mid East / North Africa
HR.Data.Named %>% filter(COW < 700 & COW >= 600) %>% ggplot(.) + aes(x=YEAR, y=theta_mean, color=as.factor(StateAbb), label = StateNme) + geom_label_repel(x=2010, size=2, direction = "y", hjust = 1, nudge_x = 20) + geom_line(alpha = 0.3) + scale_color_viridis_d(guide=FALSE) + labs(x="Year", y="Human Rights Dynamic Factor Score", title = "The Evolution of Human Rights with Normed Scores", caption = "See Fariss and Schnakenberg (2014)", subtitle = "Middle East and North Africa") + theme_economist() + transition_reveal(YEAR) -> MENA.p
animate(MENA.p, nframes=200)
Asia
HR.Data.Named %>% filter(COW < 900 & COW >= 700) %>% ggplot(.) + aes(x=YEAR, y=theta_mean, color=as.factor(StateAbb), label = StateNme) + geom_line(alpha = 0.3) + scale_color_viridis_d(guide=FALSE) + labs(x="Year", y="Human Rights Dynamic Factor Score", title = "The Evolution of Human Rights with Normed Scores", caption = "See Fariss and Schnakenberg (2014)", subtitle = "Asia") + theme_economist()
Animate Asia
HR.Data.Named %>% filter(COW < 900 & COW >= 700) %>% ggplot(.) + aes(x=YEAR, y=theta_mean, color=as.factor(StateAbb), label = StateNme) + geom_label_repel(x=2015, size=2, direction = "y", hjust = 1, nudge_x = 20) + geom_line(alpha = 0.3) + scale_color_viridis_d(guide=FALSE) + labs(x="Year", y="Human Rights Dynamic Factor Score", title = "The Evolution of Human Rights with Normed Scores", caption = "See Fariss and Schnakenberg (2014)", subtitle = "Asia") + theme_economist() + transition_reveal(YEAR) -> Asia.p
animate(Asia.p, nframes=200)
Oceania
HR.Data.Named %>% filter(COW >= 900) %>% ggplot(.) + aes(x=YEAR, y=theta_mean, color=as.factor(StateAbb), label = StateNme) + geom_line(alpha = 0.3) + scale_color_viridis_d(guide=FALSE) + labs(x="Year", y="Human Rights Dynamic Factor Score", title = "The Evolution of Human Rights with Normed Scores", caption = "See Fariss and Schnakenberg (2014)", subtitle = "Oceania") + theme_economist()
Animate Oceania
HR.Data.Named %>% filter(COW >= 900) %>% ggplot(.) + aes(x=YEAR, y=theta_mean, color=as.factor(StateAbb), label = StateNme) + geom_label_repel(x=2010, size=2, direction = "y", hjust = 1, nudge_x = 20) + geom_line(alpha = 0.3) + scale_color_viridis_d(guide=FALSE) + labs(x="Year", y="Human Rights Dynamic Factor Score", title = "The Evolution of Human Rights with Normed Scores", caption = "See Fariss and Schnakenberg (2014)", subtitle = "Oceania") + theme_economist() + transition_reveal(YEAR) -> OC.p
animate(OC.p, nframes=200)
Robert W. Walker
Associate Professor of Quantitative Methods
My research interests include causal inference, statistical computation and data visualization.