start_time <- Sys.time() knitr::opts_chunk$set( collapse = TRUE, comment = "#>", warning = FALSE, message = FALSE, fig.width = 6, fig.height = 4, out.width = "100%", out.height = "60%" )
This vignette is meant to provide useRs with an visual, explorable
introduction to the capabilities of the igoR
package.
The analysis would be based on those provided on [@pevehouse2020]. For more information on the IGO data sets and additional downloads, see Intergovernmental Organizations (v3).
Note that the dyadic dataset is not provided in the package, due its size
(\~500 MB on Stata .dta
format). However, igo_dyadic()
function provides
similar results.
From @pevehouse2019:
The definition of an Intergovernmental Organization (IGO) on the original dataset is based on the following criteria:
An IGO must consist of at least three members of the COW-defined state system.
An IGO must hold regular plenary sessions at least once every ten years
An IGO must possess a permanent secretariat and corresponding headquarters.
The data sets begins to code an IGO by identifying the first year in which the organization functions. In some cases, individual members are listed by year of accession or signature.
Version 3.0 of the IGO data set uses the following criteria:
An organization is considered terminated when the following words were used to describe the context of the organization:
This section provides some quick analysis based on the figures of @pevehouse2020.
library(igoR) # Additional libraries library(ggplot2) library(dplyr)
ggplot2
theme:
theme_igoR
theme_igoR <- theme( axis.title = element_blank(), axis.line.x.bottom = element_line("black"), axis.line.y.left = element_line("black"), axis.text = element_text(color = "black", family = "sans"), axis.text.y.left = element_text(angle = 90), legend.position = "bottom", legend.title = element_blank(), legend.key = element_blank(), legend.key.width = unit(2, "cm"), legend.text = element_text(family = "sans", size = 13), legend.box.background = element_rect(color = "black", linewidth = 1), legend.spacing = unit(1.2 / 100, "npc"), plot.background = element_rect("grey90"), plot.margin = unit(rep(0.5, 4), "cm"), panel.background = element_rect("white"), panel.grid = element_blank(), panel.border = element_rect(fill = NA, colour = "grey90"), panel.grid.major.y = element_line("grey90") )
The following code extracts the number of IGOs and states included on this package. The years available are 1816 to 2014.
# Summarize igos_by_year <- igo_year_format3 %>% group_by(year) %>% summarise(value = n(), .groups = "keep") %>% mutate(variable = "Total IGOs") countries_by_year <- state_year_format3 %>% group_by(year) %>% summarise(value = n(), .groups = "keep") %>% mutate(variable = "Number of COW states") all_by_year <- rbind(igos_by_year, countries_by_year) %>% mutate(variable = factor(variable)) # Reverse values all_by_year$variable <- factor(all_by_year$variable, levels = rev(levels(all_by_year$variable)) ) # Plot ggplot(all_by_year, aes(x = year, y = value)) + geom_line(color = "black", aes(linetype = variable)) + scale_linetype_manual(values = c("solid", "dashed")) + geom_vline(xintercept = c(1945, 1989)) + ylim(0, 400) + theme_igoR
This plot shows how many IGOs were "born" and "died" on each year
# Births and deads by year df <- igo_search() births <- df %>% mutate(year = sdate) %>% group_by(year) %>% summarise(value = n(), .groups = "keep") %>% mutate(variable = "IGO Births") deads <- df %>% mutate(year = deaddate) %>% group_by(year) %>% summarise(value = n(), .groups = "keep") %>% mutate(variable = "IGO Deaths") births_and_deads <- rbind(births, deads) %>% filter(!is.na(year)) # Plot ggplot(births_and_deads, aes(x = year, y = value)) + geom_line(color = "black", aes(linetype = variable)) + scale_linetype_manual(values = c("solid", "dashed")) + ylim(0, 15) + theme_igoR
A plot with the number of IGOs by region. The definition of region is based on the original definition by @pevehouse2020.
# Extracted from analysis-jpr.do - See https://www.prio.org/JPR/Datasets/ # crossreg and universal codes not included Asia <- c( 550, 560, 570, 580, 590, 600, 610, 640, 650, 660, 670, 725, 750, 825, 1030, 1345, 1400, 1530, 1532, 2300, 2770, 3185, 3330, 3560, 3930, 4115, 4150, 4160, 4170, 4190, 4200, 4220, 4265, 4440 ) MiddleEast <- c( 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 490, 500, 510, 520, 1110, 1410, 1990, 2000, 2220, 3450, 3800, 4140, 4270, 4380 ) Europe <- c( 20, 300, 780, 800, 832, 840, 860, 1020, 1050, 1070, 1080, 1125, 1140, 1390, 1420, 1440, 1563, 1565, 1580, 1585, 1590, 1600, 1610, 1620, 1630, 1640, 1645, 1653, 1660, 1670, 1675, 1680, 1690, 1700, 1710, 1715, 1720, 1730, 1740, 1750, 1760, 1770, 1780, 1790, 1800, 1810, 1820, 1830, 1930, 1970, 1980, 2310, 2325, 2345, 2440, 2450, 2550, 2575, 2610, 2650, 2705, 2890, 2972, 3010, 3095, 3230, 3290, 3360, 3485, 3505, 3585, 3590, 3600, 3610, 3620, 3630, 3640, 3650, 3655, 3660, 3665, 3762, 3810, 3855, 3860, 3910, 4000, 4350, 4450, 4460, 4510, 4520, 4540 ) Africa <- c( 30, 40, 50, 60, 80, 90, 100, 110, 115, 120, 125, 130, 140, 150, 155, 160, 170, 180, 190, 200, 210, 225, 240, 250, 260, 280, 290, 690, 700, 710, 940, 1060, 1150, 1170, 1260, 1290, 1310, 1320, 1330, 1340, 1355, 1430, 1450, 1460, 1470, 1475, 1480, 1500, 1510, 1520, 1870, 2080, 2090, 2230, 2330, 2795, 3300, 3310, 3470, 3480, 3510, 3520, 3570, 3740, 3760, 3761, 3790, 3820, 3875, 3905, 3970, 4010, 4030, 4050, 4055, 4080, 4110, 4120, 4130, 4230, 4240, 4250, 4251, 4340, 4365, 4480, 4485, 4490, 4500, 4501, 4503 ) Americas <- c( 310, 320, 330, 340, 720, 760, 815, 875, 880, 890, 900, 910, 912, 913, 920, 950, 970, 980, 990, 1000, 1010, 1095, 1130, 1486, 1489, 1490, 1860, 1890, 1920, 1950, 2070, 2110, 2120, 2130, 2140, 2150, 2160, 2170, 2175, 2180, 2190, 2200, 2203, 2206, 2210, 2260, 2340, 2490, 2560, 2980, 3060, 3340, 3370, 3380, 3390, 3400, 3410, 3420, 3428, 3430, 3670, 3680, 3812, 3830, 3880, 3890, 3900, 3925, 3980, 4070, 4100, 4260, 4280, 4370 ) regions <- igo_search() regions$region <- NA regions <- regions %>% select(region, ionum) regions$region <- ifelse(regions$ionum %in% Africa, "Africa", regions$region) regions$region <- ifelse(regions$ionum %in% Americas, "Americas", regions$region) regions$region <- ifelse(regions$ionum %in% Asia, "Asia", regions$region) regions$region <- ifelse(regions$ionum %in% Europe, "Europe", regions$region) regions$region <- ifelse(regions$ionum %in% MiddleEast, "Middle East", regions$region)
After we have created a data frame with the regions, we can classify the IGOs by region.
# regions dataset created on previous chunk # All IGOs alligos <- igo_year_format3[, c("year", "ionum")] regionsum <- merge(alligos, regions) %>% group_by(year, region) %>% summarise(value = n(), .groups = "keep") %>% filter(!is.na(region)) # Order regionsum$region <- factor(regionsum$region, levels = c( "Asia", "Europe", "Africa", "Americas", "Middle East" ) ) # Plot ggplot(regionsum, aes(x = year, y = value)) + geom_line(color = "black", aes(linetype = region)) + scale_linetype_manual(values = c( "solid", "dashed", "dotted", "dotdash", "longdash" )) + guides(linetype = guide_legend(ncol = 2, byrow = TRUE)) + ylim(0, 80) + theme_igoR
Number of memberships of a country. We select here five countries on Asia: India, China, Pakistan, Indonesia and Bangladesh.
asia5_cntries <- c( "China", "India", "Pakistan", "Indonesia", "Bangladesh" ) # Five countries of Asia asia5_igos <- igo_state_membership( state = asia5_cntries, year = 1865:2014, status = c("Full Membership") ) asia5 <- asia5_igos %>% group_by(statenme, year) %>% summarise(values = n(), .groups = "keep") # Reorder asia5$statenme <- factor(asia5$statenme, levels = asia5_cntries ) # Plot ggplot(asia5, aes(x = year, y = values)) + geom_line(color = "black", aes(linetype = statenme)) + scale_linetype_manual(values = c( "solid", "dashed", "dotted", "dotdash", "longdash" )) + guides(linetype = guide_legend(ncol = 3, byrow = TRUE)) + theme(axis.title.y.left = element_text( family = "sans", size = 12, margin = margin(r = 6) )) + scale_y_continuous( "Number of memberships", breaks = c(0, 20, 40, 60, 80, 100), limits = c(0, 95), labels = as.character(c(0, 20, 40, 60, 80, 100)) ) + theme_igoR
Number of shared full memberships between Spain and four selected countries:
selected_countries <- c( "France", "Morocco", "China", "USA" ) Spain_Selected <- igo_dyadic("Spain", selected_countries) # Compute number of shared memberships Spain_Selected$values <- rowSums(Spain_Selected == 1) # Plot ggplot(Spain_Selected, aes(x = year, y = values)) + geom_line(color = "black", aes(linetype = statenme2)) + scale_linetype_manual(values = c( "solid", "dashed", "dotted", "dotdash" )) + guides(linetype = guide_legend(ncol = 2, byrow = TRUE)) + theme(axis.title.y.left = element_text( family = "sans", size = 10, margin = margin(r = 6) )) + scale_y_continuous( "Shared memberships w\ Spain", breaks = c(0, 20, 40, 60, 80, 100), limits = c(0, 110), labels = as.character(c(0, 20, 40, 60, 80, 100)) ) + theme_igoR + geom_vline(xintercept = 1939, alpha = 0.2) + annotate( "label", x = 1938, y = 60, size = 3, label = "Spanish \nCivil War" ) + geom_vline(xintercept = 1978, alpha = 0.2) + annotate( "label", x = 1970, y = 100, size = 3, label = "Constitution\nof Spain" )
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