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!

Lump groups together and create “other” category with forcats package

Packages we will need:

library(tidyverse)
library(forcats)
library(tidytext)
library(ggthemes)
library(democracyData)
library(magrittr)

For this blog, we are going to look at the titles of all countries’ heads of state, such as Kings, Presidents, Emirs, Chairman … understandably, there are many many many ways to title the leader of a country.

First, we will download the PACL dataset from the democracyData package.

Click here to read more about this super handy package:

If you want to read more about the variables in this dataset, click the link below to download the codebook by Cheibub et al.

pacl <- redownload_pacl()

We are going to look at the npost variable; this captures the political title of the nominal head of stage. This can be King, President, Sultan et cetera!

pacl %>% 
  count(npost) %>% 
  arrange(desc(n))

If we count the occurence of each title, we can see there are many ways to be called the head of a country!

"president"                         3693
"prime minister"                    2914
"king"                               470
"Chairman of Council of Ministers"   229
"premier"                            169
"chancellor"                         123
"emir"                               117
"chair of Council of Ministers"      111
"head of state"                       90
"sultan"                              67
"chief of government"                 63
"president of the confederation"      63
""                                    44
"chairman of Council of Ministers"    44
"shah"                                33

# ... with 145 more rows

155 groups is a bit difficult to meaningfully compare.

So we can collapse some of the groups together and lump all the titles that occur relatively seldomly – sometimes only once or twice – into an “other” category.

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First, we use grepl() function to take the word president and chair (chairman, chairwoman, chairperson et cetera) and add them into broader categories.

Also, we use the tolower() function to make all lower case words and there is no confusion over the random capitalisation.

 pacl %<>% 
  mutate(npost = tolower(npost)) %>% 
  mutate(npost = ifelse(grepl("president", npost), "president", npost)) %>% 
  mutate(npost = ifelse(grepl("chair", npost), "chairperson", npost))

Next, we create an "other leader type" with the fct_lump_prop() function.

We specifiy a threshold and if the group appears fewer times in the dataset than this level we set, it is added into the “other” group.

pacl %<>% 
  mutate(regime_prop = fct_lump_prop(npost,
                                   prop = 0.005,
                                   other_level = "Other leader type")) %>% 
  mutate(regime_prop = str_to_title(regime_prop)) 

Now, instead of 155 types of leader titles, we have 10 types and the rest are all bundled into the Other Leader Type category

President            4370
Prime Minister       2945
Chairperson           520
King                  470
Other Leader Type     225
Premier               169
Chancellor            123
Emir                  117
Head Of State          90
Sultan                 67
Chief Of Government    63
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The forcast package has three other ways to lump the variables together.

First, we can quickly look at fct_lump_min().

We can set the min argument to 100 and look at how it condenses the groups together:

pacl %>% 
  mutate(npost = tolower(npost)) %>% 
 
  mutate(post_min = fct_lump_min(npost,
                                   min = 100,
                                   other_level = "Other type")) %>% 
  mutate(post_min = str_to_title(post_min)) %>% 
  count(post_min) %>% 
  arrange(desc(n))
President       4370
Prime Minister  2945
Chairperson      520
King             470
Other Type       445
Premier          169
Chancellor       123
Emir             117

We can see that if the post appears fewer than 100 times, it is now in the Other Type category. In the previous example, Head Of State only appeared 90 times so it didn’t make it.

Next we look at fct_lump_lowfreq().

This function lumps together the least frequent levels. This one makes sure that “other” category remains as the smallest group. We don’t add another numeric argument.

pacl %>% 
  mutate(npost = tolower(npost)) %>% 
  mutate(post_lowfreq  = fct_lump_lowfreq(npost,
                                   other_level = "Other type")) %>% 
  mutate(post_lowfreq = str_to_title(post_lowfreq)) %>% 
  count(post_lowfreq) %>% 
  arrange(desc(n))
President       4370
Prime Minister  2945
Other Type      1844

This one only has three categories and all but president and prime minister are chucked into the Other type category.

Last, we can look at the fct_lump_n() to make sure we have a certain number of groups. We add n = 5 and we create five groups and the rest go to the Other type category.

pacl %>% 
  mutate(npost = tolower(npost)) %>% 
  mutate(post_n  = fct_lump_n(npost,
                                n = 5,
                                other_level = "Other type")) %>% 
  mutate(post_n = str_to_title(post_n)) %>% 
  count(post_n) %>% 
  arrange(desc(n))
President       4370
Prime Minister  2945
Other Type       685
Chairperson      520
King             470
Premier          169
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Next we can make a simple graph counting the different leader titles in free, partly free and not free Freedom House countries. We will use the download_fh() from DemocracyData package again

fh <- download_fh()

We will use the reorder_within() function from tidytext package.

Click here to read the full blog post explaining the function from Julia Silge’s blog.

First we add Freedom House data with the inner_join() function

Then we use the fct_lump_n() and choose the top five categories (plus the Other Type category we make)

pacl %<>% 
  inner_join(fh, by = c("cown", "year")) %>% 
  mutate(npost  = fct_lump_n(npost,
                  n = 5,
                  other_level = "Other type")) %>%
  mutate(npost = str_to_title(npost))

Then we group_by the three Freedom House status levels and count the number of each title:

pacl %<>% 
  group_by(status) %>% 
  count(npost) %>% 
  ungroup() %>% 

Using reorder_within(), we order the titles from most to fewest occurences WITHIN each status group:

pacl %<>%
  mutate(npost = reorder_within(npost, n, status)) 

To plot the columns, we use geom_col() and separate them into each Freedom House group, using facet_wrap(). We add scales = "free y" so that we don’t add every title to each group. Without this we would have empty spaces in the Free group for Emir and King. So this step removes a lot of clutter.

pacl_colplot <- pacl %>%
  ggplot(aes(fct_reorder(npost, n), n)) +
  geom_col(aes(fill = npost), show.legend = FALSE) +
  facet_wrap(~status, scales = "free_y") 

Last, I manually added the colors to each group (which now have longer names to reorder them) so that they are consistent across each group. I am sure there is an easier and less messy way to do this but sometimes finding the easier way takes more effort!

We add the scale_x_reordered() function to clean up the names and remove everything from the underscore in the title label.

pacl_colplot + scale_fill_manual(values = c("Prime Minister___F" = "#005f73",
                                "Prime Minister___NF" = "#005f73",
                                "Prime Minister___PF" = "#005f73",
                                
                               "President___F" = "#94d2bd",
                               "President___NF" = "#94d2bd",
                               "President___PF" = "#94d2bd",
                               
                               "Other Type___F" = "#ee9b00",
                               "Other Type___NF" = "#ee9b00",
                               "Other Type___PF" = "#ee9b00",
                               
                               "Chairperson___F" = "#bb3e03",
                               "Chairperson___NF" = "#bb3e03",
                               "Chairperson___PF" = "#bb3e03",
                               
                               "King___F" = "#9b2226",
                               "King___NF" = "#9b2226",
                               "King___PF" = "#9b2226",
                               
                               "Emir___F" = "#001219", 
                               "Emir___NF" = "#001219",
                               "Emir___PF" = "#001219")) +
  scale_x_reordered() +
  coord_flip() + 
  ggthemes::theme_fivethirtyeight() + 
  themes(text = element_size(size = 30))

In case you were curious about the free country that had a chairperson, Nigeria had one for two years.

pacl %>%
  filter(status == "F") %>% 
  filter(npost == "Chairperson") %>% 
  select(Country = pacl_country) %>% 
  knitr::kable("latex") %>%
  kableExtra::kable_classic(font_size = 30)

References

Cheibub, J. A., Gandhi, J., & Vreeland, J. R. (2010). Democracy and dictatorship revisited. Public choice143(1), 67-101.