Get started with the igoR package

This vignette is meant to provide useRs with an visual, explorable introduction to the capabilities of the igoR package.

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.

Definitions

From J. Pevehouse, McManus, and Nordstrom (2019):

What is an IGO?

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.

When does an IGO actually begin?

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.

When does an IGO die?

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:
- Replaced;
- Succeeded;
- Superseded;
- Integrated;
- Merged;
- Dies.

Analysis

This section provides some quick analysis based on the figures of J. C. Pevehouse et al. (2020).

Initial Setup


library(igoR)

# Additional libraries
library(ggplot2)
library(dplyr)

Custom 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", size = 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")
)

IGOs overview

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(size = 0.8, color = "black", aes(linetype = variable)) +
  scale_linetype_manual(values = c("solid", "dashed")) +
  geom_vline(xintercept = c(1945, 1989)) +
  ylim(0, 400) +
  theme_igoR

Figure 1. IGOs and states in the world system, 1816-2014

IGO Births and deaths

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(size = 0.8, color = "black", aes(linetype = variable)) +
  scale_linetype_manual(values = c("solid", "dashed")) +
  ylim(0, 15) +
  theme_igoR

Figure 2. Birth and death rates of IGOs, 1816-2014

IGOs across regions

A plot with the number of IGOs by region. The definition of region is based on the original definition by J. C. Pevehouse et al. (2020).

Regions definition

# 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(size = 0.8, 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

Figure 3. IGO counts across regions, 1816-2014

Selected Countries: Asia

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(size = 0.8, 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

Figure 4. IGO membership: five states in Asia, 1865-2014

Shared memberships

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(size = 0.8, 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"
  )

Figure 5. Number of IGOs with full shared memberships with Spain (selected countries), 1816-2014