How to create semi-circle parliament graphs with the ggparliament package in R

Packages we will need:


Check out part 1 of this blog where you can follow along how to scrape the data that we will use in this blog. It will create a dataset of the current MPs in the Irish Dail.

In this blog, we will use the ggparliament package, created by Zoe Meers.

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With this dataset of the 33rd Dail, we will reduce it down to get the number of seats that each party holds.

If we don’t want to graph every party, we can lump most of the smaller parties into an “other” category. We can do this with the fct_lump_n() function from the forcats package. I want the top five biggest parties only in the graph. The rest will be colored as “Other”.

Click here to read more about the forcats pacakge and dealing with factors in R.

dail_33 %>% 
  mutate(party_groups  = fct_lump_n(party, n = 5,
         other_level = "Other"))-> dail_lump_count

Next we want to count the number of members per party.

dail_lump_count %>% 
  group_by(party_groups) %>% 
  count() %>%  
  arrange(desc(n)) -> dail_count
  <fct>        <int>
1 Fianna Fail     38
2 Sinn Fein       37
3 Fine Gael       35
4 Independent     19
5 Other           19
6 Green Party     12

Before we graph, I found the hex colors that represent each of the biggest Irish political party. We can create a new party color variables with the case_when() function and add each color.

dail_count %<>% 
  mutate(party_color = case_when(party_groups == "Fianna Fail" ~ "#66bb66",
                                 party_groups == "Fine Gael" ~ "#6699ff",
                                 party_groups == "Green Party" ~ "#44532a",
                                 party_groups == "Independent" ~ "#8e2420",
                                 party_groups == "Sinn Fein" ~ "#326760",
                                 party_groups == "Other" ~ "#ee9f27"))

Now we can dive into the ggparliament package.

We use the parliamenet_data() function to create coordinates for our graph: these are the x and y variables we will plot out.

We feed in the data.frame of all the seat counts into the election_data argument.

We specifiy the type as “semi-circle“. Other options are “horseshoe” and “opposing_benches“.

We can change how many circles we want stacked on top of each other.

I tried it with three and it looked quite strange. So play around with this parl_rows argument to see what suits your data best

And last we feed in the number of seats that each party has with the n we summarised above.

dail_33_coord <- parliament_data(election_data = dail_count,
                                 type = "semicircle", 
                                 parl_rows = 6,  
                                 party_seats = dail_count$n) 

If we view the dail_33_coord data.frame we can see that the parliament_data() function calculated new x and y coordinate variables for the semi-circle graph.

I don’t know what the theta variables is for… But there it is also … maybe to make circular shapes?

We feed the x and y coordinates into the ggplot() function and then add the geom_parliament_seat() layer to produce our graph!

Click here to check out the PDF for the ggparliament package

dail_33_coord %>% 
  ggplot(aes(x = x,
             y = y,
             colour = party_groups)) +
  geom_parliament_seats(size = 20) -> dail_33_plot

And we can make it look more pretty with bbc_style() plot and colors.

Click here to read more about the BBC style graphs.

dail_33_plot +  bbplot::bbc_style() + 
  ggtitle("33rd Irish Parliament") +
  theme(text = element_text(size = 50),
                      legend.title = element_blank(),
                      axis.text.x = element_blank(),
                      axis.text.y = element_blank()) +  
  scale_colour_manual(values = dail_33_coord$party_color,
                    limits = dail_33_coord$party_groups)
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Create a dataset of Irish parliament members


This blogpost will walk through how to scrape and clean up data for all the members of parliament in Ireland.

Or we call them in Irish, TDs (or Teachtaí Dála) of the Dáil.

We will start by scraping the Wikipedia pages with all the tables. These tables have information about the name, party and constituency of each TD.

On Wikipedia, these datasets are on different webpages.

This is a pain.

However, we can get around this by creating a list of strings for each number in ordinal form – from1st to 33rd. (because there have been 33 Dáil sessions as of January 2023)

We don’t need to write them all out manually: “1st”, “2nd”, “3rd” … etc.

Instead, we can do this with the toOrdinal() function from the package of the same name.

dail_sessions <- sapply(1:33,toOrdinal)

Next we can feed this vector of strings with the beginning of the HTML web address for Wikipedia as a string.

We paste the HTML string and the ordinal number strings together with the stri_paste() function from the stringi package.

This iterates over the length of the dail_sessions vector (in this case a length of 33) and creates a vector of each Wikipedia page URL.

dail_wikipages <- stri_paste("",
           dail_sessions, "_D%C3%A1il")

Now, we can take the most recent Dáil session Wikipedia page and take the fifth table on the webpage using `[[`(5)

We rename the column names with select().

And the last two mutate() lines reomve the footnote numbers in ( ) [ ] brackets from the party and name variables.

dail_wikipages[33] %>%  
  read_html() %>%
  html_table(header = TRUE, fill = TRUE) %>% 
  `[[`(5) %>% 
  rename("ble" = 1, "party" = 2, "name" = 3, "constituency" = 4) %>% 
  select(-ble) %>% 
  mutate(party = gsub(r"{\s*\([^\)]+\)}","",as.character(party))) %>% 
  mutate(name = sub("\\[.*", "", name)) -> dail_33

Last we delete the first row. That just contais a duplicate of the variable names.

dail_33 <- dail_33[-1,]

We want to delete the fadas (long accents on Irish words). We can do this across all the character variables with the across() function.

The stri_trans_general() converts all strings to LATIN ASCII, which turns string to contain only the letters in the English language alphabet.

dail_33 %<>% 
  mutate(across(where(is.character), ~ stri_trans_general(., id = "Latin-ASCII"))) 

We can also separate the first name from the second names of all the TDs and create two variables with mutate() and separate()

dail_33 %<>% 
  mutate(name = str_replace(name, "\\s", "|")) %>% 
  separate(name, into = c("first_name", "last_name"), sep = "\\|") 

With the first_name variable, we can use the new pacakge by Kalimu. This guesses the gender of the name. Later, we can track the number of women have been voted into the Dail over the years.

Of course, this will not be CLOSE to 100% correct … so later we will have to check each person manually and make sure they are accurate.


gender = findGivenNames(dail_33$name, progress = TRUE)

gender %>% 
  select(probability, gender)  -> gen_variable

gen_variable %<>% 
  select(name, gender) %>% 
  mutate(name = str_to_sentence(name))

dail_33 %<>% 
  left_join(gen_variable, by = "name") 

Create date variables and decade variables that we can play around with.

dail_df$date_2 <- as.Date(dail_df$date, "%Y-%m-%d")

dail_df$year <- format(dail_df$date_2, "%Y")

dail_df$month <- format(dail_df$date_2, "%b")

dail_df %>% 
  mutate(decade = substr(year, 1, 3)) %>% 
  mutate(decade = paste0(decade, "0s"))

In the next blog, we will graph out the various images to explore these data in more depth. For example, we can make a circle plot with the composition of the current Dail with the ggparliament package.

We can go into more depth with it in the next blog… Stay tuned.

Scraping and wrangling UN peacekeeping data with tidyr package in R

Packages we will need:


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("")
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(!$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(!$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 %>% 

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(!, 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, "", "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")

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)) %>% 
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")

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, "", "iso2c"))) %>% 
  mutate(cown = countrycode::countrycode(contributing_country, "", "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!