Wrangling and graphing UN Secretaries-General data with R

Packages we will need:

library(tidyverse)
library(janitor)
library(rvest)
library(countrycode)
library(magrittr)
library(lubridate)
library(ggflags)
library(scales)

According to Urquhart (1995) in his article, “Selecting the World’s CEO”,

From the outset, the U.N. secretary
general has been an important part of the
institution, not only as its chief executive,
but as both symbol and guardian of the
original vision of the organization.
There, however, specific agreement has
ended. The United Nations, like any
important organization, needs strong and
independent leadership, but it is an inter-
governmental organization, and govern
ments have no intention of giving up
control of it. While the secretary-general
can be extraordinarily useful in times of
crisis, the office inevitably embodies
something more than international coop
eration, sometimes even an unwelcome
hint of supranationalism. Thus, the atti-
tude of governments toward the United
Nations’ chief and only elected official is
and has been necessarily ambivalent.

(Urquhart, 1995: 21)

So who are these World CEOs? We’ll examine more in this dataset.

First, we will scrape the data from the Wikipedia

sg_html <- read_html("https://en.wikipedia.org/wiki/Secretary-General_of_the_United_Nations")
sg_tables <- sg_html %>% html_table(header = TRUE, fill = TRUE)
sg <- sg_tables[[2]]

The table we scrape is a bit of a hot mess in this state …. but we can fix it

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We can first use the clean_names() function from the janitor package

A quick way to clean up the table and keep only the rows with the names of the Secretaries-General is to use the distinct() function. Last we filter out the rows and select out the columns we don’t want.

sg %>% 
  clean_names() %>% 
  distinct(no, .keep_all = TRUE) %>% 
  filter(no != "–") %>% 
  select(!c(portrait, ref))-> sg_clean

Already we can see a much cleaner table. However, the next problem is that the names and their years of birth / death are in one cell.

Also the dates in office are combined together.

So we can use the separate() function from tidyr to make new variables for each piece of information.

First we will separate the name of the Secretary-General from their date of birth and death.

We supply the two new variable names to the into = argument.

We then use the regex code pattern [()] to indicate where we want to separate the character string into two separate columns. In this instance the regex pattern is for what is after the round brackets (

I want to remove the original cluttered varaible so remove = TRUE

sg_clean %<>% 
  separate(
    col = secretary_general_born_died,
    into = c("sec_gen", "born_died"),
    sep = '[()]',
    remove = TRUE) 

We can repeat this step to create a separate born and died variable. This time the separator symbol is a hyphen And so we do not need regex pattern; we can just indicate a hyphen.

sg_clean %<>% 
  separate(
    col = born_died,
    into = c("born", "died"),
    sep = '–',
    remove = TRUE)  

And I want to ignore the “present” variable, so I extract the numbers with the parse_number() function, converting things from characters to numbers

sg_clean %<>% 
  mutate(born = parse_number(born))

Last, we repeat with the dates in office. This is also easily seperated by indicating the hyphen.

sg_clean %<>% 
  separate(
    col = dates_in_office,
    into = c("start_office", "end_office"),
    sep = '–',
    remove = TRUE)  

We convert the word “present” to the actual present date

sg_clean %<>% 
  mutate(end_office = ifelse(end_office == "present", "5 May 2022", end_office))

We use the lubridate dmy() function to convert the character strings to date class variables.

sg_clean %<>% 
  mutate(start_office = dmy(start_office),
         end_office = dmy(end_office))

We can calculate the length of time that each Secretary-General was in office with the difftime() function.

sg_clean %<>% 
  mutate(duration_days = difftime(end_office, start_office, units = "days"),
         duration_years = round(duration_days / 365, 2),
         duration_years = as.integer(duration_years))

Next we can compare the different durations and see which Secretary-General was longest or shortest in office.

sg_clean %>% 
  mutate(duration_days = difftime(end_office, start_office)) %>%  
  mutate(iso2 = tolower(countrycode::countrycode(country_of_origin, "country.name", "iso2c"))) %>% 
  ggplot(aes(x = forcats::fct_reorder(sec_gen, duration_days), y = duration_days)) + 
  geom_bar(aes(fill = un_regional_group), stat = "identity", width = 0.7) + 
  coord_flip() + bbplot::bbc_style() + 
  ggflags::geom_flag(aes(x = sec_gen, y = -100, country = iso2), size = 12) +
  scale_fill_manual(values = le_palette) +
  labs(title = "Longest serving UN Secretaries General",
       subtitle = ("Source: Wikipedia")) + 
  xlab("") + ylab("") 

We can make a quick pie-chart to compare regions. We can see that Secretaries-General from the West have had the most time in office

sg_text <- sg_count %>% 
  arrange(desc(un_regional_group)) %>%
  mutate(prop = sum_days / sum(sg_count$sum_days) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )

sg_text %>% 
  count(un_regional_group)

sg_text %>%
  mutate(region = case_when(un_regional_group == "Western European & others" ~ "Europe",
         un_regional_group == "Latin American& Caribbean" ~ "Latin America",
         un_regional_group == "Asia & Pacific" ~ "Asia", 
         TRUE ~ as.character(un_regional_group))) %>% 
  ggplot(aes(x = "", y = prop, fill = region)) +
  geom_bar(stat = "identity", width = 1) +
  geom_text(aes(y = ypos + 1, label = round(prop, 0)), color = "white", size = 15) +
  coord_polar("y", start = 0) +
  theme_void() +
  ggtitle("Length of Secretaries General in office across regions") + 
  scale_fill_manual(values = le_palette) + 
  theme(legend.title = element_blank(),
        legend.text = element_text(size = 20), 
        plot.title = element_text(size = 30))

We can create a Gantt-like chart to track the timeline for the different men (all men!)

Click here to read more about timelines in R

sg_clean %>% 
  mutate(region = case_when(un_regional_group == "Western European & others" ~ "Europe",un_regional_group == "Latin American& Caribbean" ~ "Latin America",un_regional_group == "Asia & Pacific" ~ "Asia", TRUE ~ as.character(un_regional_group))) %>%
  ggplot(aes(x = as.Date(start_office), 
             y = no, 
             color = region)) +
  geom_segment(aes(xend = as.Date(end_office), 
                   yend = no, alpha = 0.9,
                   color = region), size = 9)  +
  geom_text(aes(label = sec_gen), 
            color = "black", 
            alpha = 0.7,
            size = 8, show.legend = FALSE) +
  bbplot::bbc_style() +
  scale_color_manual(values = le_palette) + 
  scale_x_date(breaks = scales::breaks_pretty(15))
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References

Urquhart, B. (1995). Selecting the world’s CEO: Remembering the Secretaries-General. Foreign Affairs, 21-26.

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Scraping and wrangling UN peacekeeping data with tidyr package in R

Packages we will need:

library(tidyverse)
library(rvest)
library(magrittr)
library(tidyr)
library(countrycode)
library(democracyData)
library(janitor)
library(waffle)

For this blog post, we will look at UN peacekeeping missions and compare across regions.

Despite the criticisms about some operations, the empirical record for UN peacekeeping records has been robust in the academic literature

“In short, peacekeeping intervenes in the most difficult
cases, dramatically increases the chances that peace will
last, and does so by altering the incentives of the peacekept,
by alleviating their fear and mistrust of each other, by
preventing and controlling accidents and misbehavior by
hard-line factions, and by encouraging political inclusion”
(Goldstone, 2008: 178).

The data on the current and previous PKOs (peacekeeping operations) will come from the Wikipedia page. But the variables do not really lend themselves to analysis as they are.

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Once we have the url, we scrape all the tables on the Wikipedia page in a few lines

pko_members <- read_html("https://en.wikipedia.org/wiki/List_of_United_Nations_peacekeeping_missions")
pko_tables <- pko_members %>% html_table(header = TRUE, fill = TRUE)

Click here to read more about the rvest package for scraping data from websites.

pko_complete_africa <- pko_tables[[1]]
pko_complete_americas <- pko_tables[[2]]
pko_complete_asia <- pko_tables[[3]]
pko_complete_europe <- pko_tables[[4]]
pko_complete_mena <- pko_tables[[5]]

And then we bind them together! It’s very handy that they all have the same variable names in each table.

rbind(pko_complete_africa, pko_complete_americas, pko_complete_asia, pko_complete_europe, pko_complete_mena) -> pko_complete

Next, we will add a variable to indicate that all the tables of these missions are completed.

pko_complete %<>% 
  mutate(complete = ifelse(!is.na(pko_complete$Location), "Complete", "Current"))

We do the same with the current missions that are ongoing:

pko_current_africa <- pko_tables[[6]]
pko_current_asia <- pko_tables[[7]]
pko_current_europe <- pko_tables[[8]]
pko_current_mena <- pko_tables[[9]]

rbind(pko_current_europe, pko_current_mena, pko_current_asia, pko_current_africa) -> pko_current

pko_current %<>% 
  mutate(complete = ifelse(!is.na(pko_current$Location), "Current", "Complete"))

We then bind the completed and current mission data.frames

rbind(pko_complete, pko_current) -> pko

Then we clean the variable names with the function from the janitor package.

pko_df <-  pko %>% 
  janitor::clean_names()

Next we’ll want to create some new variables.

We can make a new row for each country that is receiving a peacekeeping mission. We can paste all the countries together and then use the separate function from the tidyr package to create new variables.

 pko_df %>%
  group_by(conflict) %>%
  mutate(location = paste(location, collapse = ', ')) %>% 
  separate(location,  into = c("country_1", "country_2", "country_3", "country_4", "country_5"), sep = ", ")  %>% 
  ungroup() %>% 
  distinct(conflict, .keep_all = TRUE) %>% 

Next we can create a new variable that only keeps the acroynm for the operation name. I took these regex codes from the following stack overflow link

pko_df %<>% 
  mutate(acronym = str_extract_all(name_of_operation, "\\([^()]+\\)")) %>% 
  mutate(acronym = substring(acronym, 2, nchar(acronym)-1)) %>% 
  separate(dates_of_operation, c("start_date", "end_date"), "–")

I will fill in the end data for the current missions that are still ongoing in 2022

pko_df %<>% 
  mutate(end_date = ifelse(complete == "Current", 2022, end_date)) 

And next we can calculate the duration for each operation

pko_df %<>% 
  mutate(end_date = as.integer(end_date)) %>% 
  mutate(start_date = as.integer(start_date)) %>% 
  mutate(duration = ifelse(!is.na(end_date), end_date - start_date, 1)) 

I want to compare regions and graph out the different operations around the world.

We can download region data with democracyData package (best package ever!)

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pacl <- redownload_pacl()

pacl %>% 
  select(cown = pacl_cowcode,
        un_region_name, un_continent_name) %>% 
  distinct(cown, .keep_all = TRUE) -> pacl_region

We join the datasets together with the inner_join() and add Correlates of War country codes.

pko_df %<>% 
  mutate(cown = countrycode(country_1, "country.name", "cown")) %>%   mutate(cown = ifelse(country_1 == "Western Sahara", 605, 
                       ifelse(country_1 == "Serbia", 345, cown))) %>% 
  inner_join(pacl_region, by = "cown")

Now we can start graphing our duration data:

pko_df %>% 
  ggplot(mapping = aes(x = forcats::fct_reorder(un_region_name, duration), 
                       y = duration, 
                       fill = un_region_name)) +
  geom_boxplot(alpha = 0.4) +
  geom_jitter(aes(color = un_region_name),
              size = 6, alpha = 0.8, width = 0.15) +
  coord_flip() + 
  bbplot::bbc_style() + ggtitle("Duration of Peacekeeping Missions")
Years

We can see that Asian and “Western Asian” – i.e. Middle East – countries have the longest peacekeeping missions in terns of years.

pko_countries %>% 
  filter(un_continent_name == "Asia") %>%
  unite("country_names", country_1:country_5, remove = TRUE,  na.rm = TRUE, sep = ", ") %>% 
  arrange(desc(duration)) %>% 
  knitr::kable("html")
Start End Duration Region Country
1949 2022 73 Southern Asia India, Pakistan
1964 2022 58 Western Asia Cyprus, Northern Cyprus
1974 2022 48 Western Asia Israel, Syria, Lebanon
1978 2022 44 Western Asia Lebanon
1993 2009 16 Western Asia Georgia
1991 2003 12 Western Asia Iraq, Kuwait
1994 2000 6 Central Asia Tajikistan
2006 2012 6 South-Eastern Asia East Timor
1988 1991 3 Southern Asia Iran, Iraq
1988 1990 2 Southern Asia Afghanistan, Pakistan
1965 1966 1 Southern Asia Pakistan, India
1991 1992 1 South-Eastern Asia Cambodia, Cambodia
1999 NA 1 South-Eastern Asia East Timor, Indonesia, East Timor, Indonesia, East Timor
1958 NA 1 Western Asia Lebanon
1963 1964 1 Western Asia North Yemen
2012 NA 1 Western Asia Syria

Next we can compare the decades

pko_countries %<>% 
  mutate(decade = substr(start_date, 1, 3)) %>% 
  mutate(decade = paste0(decade, "0s")) 

And graph it out:

pko_countries %>% 
  ggplot(mapping = aes(x = decade, 
                       y = duration, 
                       fill = decade)) +
  geom_boxplot(alpha = 0.4) +
  geom_jitter(aes(color = decade),
              size = 6, alpha = 0.8, width = 0.15) +
   coord_flip() + 
  geom_curve(aes(x = "1950s", y = 60, xend = "1940s", yend = 72),
  arrow = arrow(length = unit(0.1, "inch")), size = 0.8, color = "black",
   curvature = -0.4) +
  annotate("text", label = "First Mission to Kashmir",
           x = "1950s", y = 49, size = 8, color = "black") +
  geom_curve(aes(x = "1990s", y = 46, xend = "1990s", yend = 32),
             arrow = arrow(length = unit(0.1, "inch")), size = 0.8, color = "black",curvature = 0.3) +
  annotate("text", label = "Most Missions after the Cold War",
           x = "1990s", y = 60, size = 8, color = "black") +

  bbplot::bbc_style() + ggtitle("Duration of Peacekeeping Missions")
Years

Following the end of the Cold War, there were renewed calls for the UN to become the agency for achieving world peace, and the agency’s peacekeeping dramatically increased, authorizing more missions between 1991 and 1994 than in the previous 45 years combined.

We can use a waffle plot to see which decade had the most operation missions. Waffle plots are often seen as more clear than pie charts.

Click here to read more about waffle charts in R

To get the data ready for a waffle chart, we just need to count the number of peacekeeping missions (i.e. the number of rows) in each decade. Then we fill the groups (i.e. decade) and enter the n variable we created as the value.

pko_countries %>% 
  group_by(decade) %>% 
  count() %>%  
  ggplot(aes(fill = decade, values = n)) + 
  waffle::geom_waffle(color = "white", size= 3, n_rows = 8) +
  scale_x_discrete(expand=c(0,0)) +
  scale_y_discrete(expand=c(0,0)) +
  coord_equal() +
  labs(title = "Number of Peacekeeper Missions") + bbplot::bbc_style() 
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If we want to add more information, we can go to the UN Peacekeeping website and download more data on peacekeeping troops and operations.

We can graph the number of peacekeepers per country

Click here to learn more about adding flags to graphs!

le_palette <- c("#5f0f40", "#9a031e", "#94d2bd", "#e36414", "#0f4c5c")

pkt %>%
  mutate(contributing_country = ifelse(contributing_country == "United Republic of Tanzania", "Tanzania",ifelse(contributing_country == "Côte d’Ivoire", "Cote d'Ivoire", contributing_country))) %>% 
  mutate(iso2 = tolower(countrycode::countrycode(contributing_country, "country.name", "iso2c"))) %>% 
  mutate(cown = countrycode::countrycode(contributing_country, "country.name", "cown")) %>% 
  inner_join(pacl_region, by = "cown") %>% 
  mutate(un_region_name = case_when(grepl("Africa", un_region_name) ~ "Africa",grepl("Eastern Asia", un_region_name) ~ "South-East Asia",
 un_region_name == "Western Africa" ~ "Middle East",TRUE ~ as.character(un_region_name))) %>% 
  filter(total_uniformed_personnel > 700) %>% 
  ggplot(aes(x = reorder(contributing_country, total_uniformed_personnel),
             y = total_uniformed_personnel)) + 
  geom_bar(stat = "identity", width = 0.7, aes(fill = un_region_name), color = "white") +
  coord_flip() +
  ggflags::geom_flag(aes(x = contributing_country, y = -1, country = iso2), size = 8) +
  # geom_text(aes(label= values), position = position_dodge(width = 0.9), hjust = -0.5, size = 5, color = "#000500") + 
  scale_fill_manual(values = le_palette) +
  labs(title = "Total troops serving as peacekeepers",
       subtitle = ("Across countries"),
       caption = "         Source: UN ") +
  xlab("") + 
  ylab("") + bbplot::bbc_style()

We can see that Bangladesh, Nepal and India have the most peacekeeper troops!