This vignette introduces the main data sets and functions in igoR through visual examples.

The examples build on analyses from Pevehouse et al. (2020). For more information about the IGO data sets and additional downloads, see Intergovernmental Organizations (version 3).

The original dyad-year data set is not included because of its size (about 500 MB in Stata `.dta` format). The `igo_dyadic()` function derives comparable joint membership results from the included data.

## Definitions

From Pevehouse et al. (2019):

> ### What is an IGO?

> The original data set defines an intergovernmental organization (IGO) using the following criteria:

> 1. An IGO must consist of at least three members of the Correlates of War state system.
> 2. An IGO must hold regular plenary sessions at least once every ten years.
> 3. An IGO must possess a permanent secretariat and corresponding headquarters.

> ### When does an IGO begin?

> The data set begins coding an IGO in the first year the organization functions. In some cases, individual members are recorded by year of accession or signature.

> ### When does an IGO cease to exist?

> Version 3.0 of the IGO data set uses the following criteria:

> - An organization is considered terminated when its description includes one of the following terms: `replaced`, `succeeded`, `superseded`, `integrated`, `merged` or `dies`.

## Analysis

This section reproduces selected analyses based on figures in Pevehouse et al. (2020).

### Initial setup

    library(igoR)

    # Load helper packages.
    library(ggplot2)
    library(dplyr)

First, create a custom `ggplot2::theme()` object named `theme_igor`. All figures in this vignette use this theme.

    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, hjust = 0.5),
      legend.position = "bottom",
      legend.title = element_blank(),
      legend.key = element_blank(),
      legend.key.width = unit(2, "cm"),
      legend.text = element_text(family = "sans", size = 11.5),
      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")
    )

### IGOs overview

The following code counts IGOs and states in the Correlates of War (COW) system by year. The data cover 1816 to 2014.

    # Summarize values by year.
    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 <- igos_by_year %>%
      bind_rows(countries_by_year) %>%
      # Label the plot.
      mutate(
        variable = factor(
          variable,
          levels = c("Total IGOs", "Number of COW states")
        )
      )

    # Plot the results.
    ggplot(all_by_year, aes(x = year, y = value)) +
      geom_line(color = "black", aes(linetype = variable)) +
      scale_x_continuous(limits = c(1800, 2014)) +
      scale_linetype_manual(values = c("solid", "dashed")) +
      geom_vline(xintercept = c(1945, 1989)) +
      ylim(0, 400) +
      theme_igor

Figure 1: IGOs and COW states in the state system, 1816-2014

### IGO starts and endings

This plot shows how many IGOs started or ended in each year.

    # Summarize IGO starts and endings 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 <- births %>%
      bind_rows(deads) %>%
      filter(!is.na(year))

    # Plot the results.
    ggplot(births_and_deads, aes(x = year, y = value)) +
      geom_line(color = "black", aes(linetype = variable)) +
      scale_linetype_manual(values = c("solid", "dashed")) +
      scale_x_continuous(
        limits = c(1815, 2015),
        breaks = seq(1815, 2015, by = 25)
      ) +
      ylim(0, 15) +
      theme_igor

Figure 2: IGO starts and endings, 1816-2014

### IGOs across regions

This plot shows the number of IGOs by region. The region definitions are based on Pevehouse et al. (2020) and the complementary replication data set (PRIO 2020).

IGOs across regions: codes

    # Cross-regional and universal codes are 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
    )

    middle_east <- 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
    )

    # `africa`, `americas`, `asia`, `europe` and `middle_east` were created in the
    # previous chunk, which is collapsed for readability.

    regions <- igo_search() %>%
      mutate(
        region = case_when(
          ionum %in% africa ~ "Africa",
          ionum %in% americas ~ "Americas",
          ionum %in% asia ~ "Asia",
          ionum %in% europe ~ "Europe",
          ionum %in% middle_east ~ "Middle East",
          TRUE ~ NA
        )
      ) %>%
      select(ioname, region)

The next step joins the region classifications to the IGO-year data and counts IGOs by region.

    # The `regions` data set was created in the previous chunk.

    # Select all IGOs.
    alligos <- igo_year_format3 %>%
      select(ioname, year)

    regionsum <- alligos %>%
      left_join(regions) %>%
      group_by(year, region) %>%
      summarise(value = n(), .groups = "keep") %>%
      filter(!is.na(region)) %>%
      # Prepare for plotting.
      mutate(
        region = factor(
          region,
          levels = c(
            "Asia",
            "Europe",
            "Africa",
            "Americas",
            "Middle East"
          )
        )
      )

    # Plot the results.
    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) +
      scale_x_continuous(
        limits = c(1815, 2015),
        breaks = seq(1815, 2015, by = 25)
      ) +
      theme_igor

Figure 3: IGO counts across regions, 1816-2014

### Selected states in Asia

This plot shows the number of full IGO memberships for five states in Asia: India, China, Pakistan, Indonesia and Bangladesh.

    asia5_cntries <- c("China", "India", "Pakistan", "Indonesia", "Bangladesh")

    # Use five states in Asia.
    asia5_igos <- igo_state_membership(
      state = asia5_cntries,
      year = 1865:2014,
      status = "Full Membership"
    )

    asia5 <- asia5_igos %>%
      group_by(statenme, year) %>%
      summarise(values = n(), .groups = "keep") %>%
      mutate(statenme = factor(statenme, levels = asia5_cntries))

    # Plot the results.
    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_x_continuous(
        limits = c(1865, 2015),
        breaks = seq(1865, 2015, by = 25)
      ) +
      scale_y_continuous(
        "Number of memberships",
        breaks = seq(0, 100, 20),
        limits = c(0, 100)
      ) +
      theme_igor

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

### Joint memberships

The final plot counts full joint memberships in dyad-year data for Spain and four selected states.

    selected_countries <- c("France", "Morocco", "China", "USA")

    spain_selected <- igo_dyadic("Spain", selected_countries)

    # Compute the number of full joint memberships.
    spain_selected <- spain_selected %>%
      rowwise() %>%
      mutate(values = sum(c_across(aaaid:wassen) == 1))

    # Plot the results.
    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_x_continuous(
        limits = c(1815, 2015),
        breaks = seq(1815, 2015, by = 25)
      ) +
      scale_y_continuous(
        "Number of memberships",
        breaks = seq(0, 110, 20),
        limits = c(0, 110)
      ) +
      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 joint memberships with Spain (selected states), 1816-2014

## References

Pevehouse, Jon C. W., Timothy Nordstrom, Roseanne W. McManus, and Anne Spencer Jamison. 2020. “Tracking Organizations in the World: The Correlates of War IGO Version 3.0 Datasets.” Journal of Peace Research 57 (3): 492–503. https://doi.org/10.1177/0022343319881175.
Pevehouse, Jon, Roseanne McManus, and Timothy Nordstrom. 2019. Codebook for Correlates of War 3 International Governmental Organizations Data Set Version 3.0.https://correlatesofwar.org/wp-content/uploads/IGO-Codebook_v3_short-copy.pdf .
PRIO. 2020. Replication Datasets: Journal of Peace Research. Peace Research Institute Oslo; Online. https://www.prio.org/journals/jpr/replicationdata.