Examining Ireland’s foreign policy in pictures with R

Packages we will need:

library(peacesciencer)  
library(forcats)
library(ggflags)
library(tidyverse)
library(magrittr)
library(waffle)
library(bbplot)
library(rvest)

In January 2015, the Irish government published a review of Ireland’s foreign policy. The document, The Global Island: Ireland’s Foreign Policy for a Changing World offers a perspective on Ireland’s place in the world.

In this blog, we will graph out some of the key features of Ireland’ foreign policy and so we can have a quick overview of the key relationships and trends.

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First, we will look at the aid that Ireland gives to foreign countries. This read.csv(file.choose()) will open up the file window and you can navigate to the file and data that you can download from DAC OECD website: https://data.oecd.org/oda/net-oda.htm

dac <- read.csv(file.choose())

We will filter only Ireland and clean the names with the clean_names() function from the janitor package:

dac %<>% 
  filter(Donor == "Ireland") %>% 
  clean_names()

And change the variables, adding the Correlates of War codes and cleaning up some of the countries’ names.

dac %<>% 
  mutate(cown = countrycode(recipient_2, "country.name", "cown"),
         aid_amount = value*1000000) %>%  
  select(country = recipient_2, cown,
         year, time, aid_type, value, aid_amount) %>%
  mutate(cown = ifelse(country == "West Bank and Gaza Strip", 6666,
         ifelse(country == "Serbia", 345, 
         ifelse(country == "Micronesia", 987,cown))))%>%
  filter(!is.na(cown)) 

Next we can convert dataframe to wider format so we have a value column for each aid type

dac %>% 
  distinct(country, cown, year, time, aid_type, value, .keep_all = TRUE)  %>%  
  pivot_wider(names_from = "aid_type", values_from = "aid_amount") %>% 
  mutate(across(where(is.numeric), ~ replace_na(., 0))) %>% 
  clean_names() -> dac_wider

And we graph out the three main types of aid:

dac_wider %>%
  group_by(year) %>% 
  summarise(total_humanitarian = sum(humanitarian_aid, na.rm = TRUE),
  total_technical = sum(technical_cooperation, na.rm = TRUE),
  total_development_food_aid = sum(development_food_aid)) %>% 
  ungroup() %>% 
  pivot_longer(!year, names_to = "aid_type", values_to = "aid_value") %>% 
  ggplot(aes(x = year, y = aid_value, groups = aid_type)) + 
  geom_line(aes(color = aid_type), size = 2, show_guide  = FALSE) +
  geom_point(aes(color = aid_type), fill = "white", shape = 21, size = 3, stroke = 2) +
  bbplot::bbc_style()  +
  scale_y_continuous(labels = scales::comma) + 
  scale_x_discrete(limits = c(2010:2018)) +
  labs(title = "Irish foreign aid by aid type (2010 - 2018)",
       subtitle = ("Source: OECD DAC")) +
  scale_color_discrete(name = "Aid type", 
        labels = c("Development and Food", "Humanitarian", "Technical"))

We will look at total ODA aid:

dac %>% 
  count(aid_type) %>% 
  arrange(desc(n)) %>% 
  knitr::kable(format = "html")
aid_type n
Imputed Multilateral ODA 2298
Memo: ODA Total, excl. Debt 1292
Memo: ODA Total, Gross disbursements 1254
ODA: Total Net 1249
Grants, Total 1203
Technical Cooperation 541
ODA per Capita 532
Humanitarian Aid 518
ODA as % GNI (Recipient) 504
Development Food Aid 9

And get some pretty hex colours:

pal_10 <- c("#001219","#005f73","#0a9396","#94d2bd","#e9d8a6","#ee9b00","#ca6702","#bb3e03","#ae2012","#9b2226")

And download some regime, democracy, region and continent data from the PACL datase with the democracyData() package

pacl <- redownload_pacl() 

pacl %<>% 
  mutate(regime_name = ifelse(regime == 0, "Parliamentary democracies",
         ifelse(regime == 1, "Mixed democracies",
         ifelse(regime == 2, "Presidential democracies",
         ifelse(regime == 3, "Civilian autocracies",
         ifelse(regime == 4, "Military dictatorships",
         ifelse(regime ==  5,"Royal dictatorships", regime))))))) %>%
  mutate(regime = as.factor(regime)) 

pacl %<>% 
  select(year, country = pacl_country, 
         democracy, regime_name,
         region_name = un_region_name, 
         continent_name = un_continent_name)

pacl %<>% 
  mutate(cown = countrycode(country, "country.name", "cown")) %>% 
  select(!country)

Summarise the total aid for each country across the years and choose the top 20 countries

dac %>% 
  filter(aid_type == "Memo: ODA Total, Gross disbursements") %>% 
  group_by(country) %>% 
  summarise(total_country_aid = sum(aid_amount, na.rm = TRUE)) %>% 
  ungroup() %>% 
  top_n(n = 20) %>% 
  mutate(cown = countrycode::countrycode(country, "country.name", "cown")) %>% 
  inner_join(pacl, by = "cown") %>%  
  mutate(region_name = ifelse(country == "West Bank and Gaza Strip", "Western Asia", region_name)) %>% 
  mutate(region_name = ifelse(region_name == "Western Asia", "Middle East", region_name)) %>% 
  mutate(country = ifelse(country == "West Bank and Gaza Strip", "Palestine",
  ifelse(country == "Democratic Republic of the Congo", "DR Congo",
  ifelse(country == "Syrian Arab Republic", "Syria", country)))) %>% 
  mutate(iso2 = tolower(countrycode::countrycode(country, "country.name", "iso2c"))) %>% 
  ggplot(aes(x = forcats::fct_reorder(country, total_country_aid), y = total_country_aid)) + 
  geom_bar(aes(fill = region_name), stat = "identity", width = 0.7) + 
  coord_flip() + bbplot::bbc_style() + 
  geom_flag(aes(x = country, y = -100, country = iso2), size = 12) +
  scale_fill_manual(values = pal_10) +
  labs(title = "Ireland's largest ODA foreign aid recipients, 2010 - 2018",
       subtitle = ("Source: OECD DAC")) + 
  xlab("") + ylab("") + 
  scale_x_continuous(labels = scales::comma)

We can make a waffle plot to look at the different types of regimes to which the Irish government gave aid over the decades

 dac %>% 
  mutate(decade = substr(year, 1, 3)) %>% 
  mutate(decade = paste0(decade, "0s")) %>% 
  group_by(decade) %>% 
  count(regime_name) %>% 
  ggplot(aes(fill = regime_name, values = n)) +
  geom_waffle(color = "white", size = 0.3, n_rows = 10, flip = TRUE) +
  facet_wrap(~decade, nrow = 1, strip.position = "bottom") + 
  bbplot::bbc_style()  +
  scale_fill_manual(values = pal_10) +
   scale_x_discrete(breaks = round(seq(0, 1, by = 0.2),3)) +
  labs(title = "Ireland's ODA foreign aid recipient regime types since 1945",
       subtitle = ("Source: OECD DAC"))  

Next, we will download dyadic foreign policy similarity measures from peacesciencer.

Peacesciencer package has tools and data sets for the study of quantitative peace science. 

Click here to read more about the peacesciencer package by Steven Miller

fp_similar_df <- peacesciencer::create_dyadyears() %>% 
  add_gwcode_to_cow() %>% 
  add_fpsim()	

I am only looking at dyadic foreign policy similarity with Ireland, so filter by Ireland’s Correlates of War code, 205.

Click here to find out all countries’ COW code

fp_similar_df %<>% 
  filter(ccode1 == 205)

Data on alliance portfolios comes from the Correlates of War and is used to calculate similarity of foreign policy positions (see Altfeld & Mesquita, 1979).

The assumption is that similar alliance portfolios are the result of similar foreign policy positions.

With increasing in level of commitment, the strength of alliance commitments can be:

  1. no commitment
  2. entente
  3. neutrality or nonaggression pact
  4. defense pact

We will map out alliance similarity. This will use the measurement calculated with Cohen’s Kappa. Check out Hage’s (2011) article to read more about the different ways to measure alliance similarity.

Next we can look at UN similarity.

The UN voting variable calculates three values:

1 = Yes

2 = Abstain

3 = No

Based on these data, if two countries in a similar way on the same UN resolutions, this is a measure of the degree to which dyad members’ foreign policy positions are similar.

fp_similarity_df %>% 
  mutate(country = countrycode::countrycode(ccode2, "cown", "country.name")) %>% 
  select(country, ccode2, year,
         un_similar = kappavv) %>% 
  filter(year > 1989) %>% 
  filter(!is.na(country)) %>%
  mutate(iso2 = tolower(countrycode::countrycode(country, "country.name", "iso2c"))) %>% 
  group_by(country) %>% 
  mutate(avg_un = mean(un_similar, na.rm = TRUE)) %>%
  distinct(country, avg_un, iso2, .keep_all = FALSE) %>% 
  ungroup() %>% 
  top_n(n = 10)  -> top_un_similar

And graph out the top ten

  top_un_similar %>%
  ggplot(aes(x = forcats::fct_reorder(country, avg_un), 
             y = avg_un)) + 
  geom_bar(stat = "identity",
           width = 0.7, 
           color = "#0a85e5", 
           fill = "#0a85e5") +
  ggflags::geom_flag(aes(x = country, y = 0, country = iso2), size = 15) +
  coord_flip() + bbplot::bbc_style()  +
  ggtitle("UN voting similarity with Ireland since 1990")

If we change the top_n() to negative, we can get the bottom 10

top_n(n = -10)

We can quickly scrape data about the EU countries with the rvest package


eu_members_html <- read_html("https://en.wikipedia.org/wiki/European_Union")
eu_members_tables <- eu_members_html %>% html_table(header = TRUE, fill = TRUE)

eu_member <- eu_members_tables[[6]]

eu_member %<>% 
  janitor::clean_names()

eu_member %>% distinct(state) %>%  pull(state) -> eu_state

Last we are going to look at globalization scores. The data comes from the the KOF Globalisation Index. This measures the economic, social and political dimensions of globalisation. Globalisation in the economic, social and political fields has been on the rise since the 1970s, receiving a particular boost after the end of the Cold War.

Click here for data that you can download comes from the KOF website

kof %>%
  filter(country %in% eu_state) -> kof_eu

And compare Ireland to other EU countries on financial KOF index scores. We will put Ireland in green and the rest of the countries as grey to make it pop.

Ireland appears to follow the general EU trends and is not an outlier for financial globalisation scores.

kof_eu %>% 
  ggplot(aes(x = year,  y = finance, groups = country)) + 
  geom_line(color = ifelse(kof_eu$country == "Ireland",     "#2a9d8f", "#8d99ae"),
  size = ifelse(kof_eu$country == "Ireland", 3, 2), 
  alpha = ifelse(kof_eu$country == "Ireland", 0.9, 0.3)) +
  bbplot::bbc_style() + 
  ggtitle("Financial Globalization in Ireland, 1970 to 2020", 
          subtitle = "Source: KOF")

References

Häge, F. M. (2011). Choice or circumstance? Adjusting measures of foreign policy similarity for chance agreement. Political Analysis19(3), 287-305.

Dreher, Axel (2006): Does Globalization Affect Growth? Evidence from a new Index of Globalizationcall_made, Applied Economics 38, 10: 1091-​1110.

How to tidy up messy Wikipedia data with dplyr in R

Packages we will need:

library(rvest)
library(magrittr)
library(tidyverse)
library(waffle)
library(wesanderson)
library(ggthemes)
library(countrycode)
library(forcats)
library(stringr)
library(tidyr)
library(janitor)
library(knitr)

To see another blog post that focuses on cleaning messy strings and dates, click here to read

We are going to look at Irish embassies and missions around the world. Where are the embassies, and which country has the most missions (including embassies, consulates and representational offices)?

Let’s first scrape the embassy data from the Wikipedia page. Here is how it looks on the webpage.

It is a bit confusing because Ireland does not have a mission in every country. Argentina, for example, is the embassy for Bolivia, Paraguay and Uruguay.

Also, there are some consulates-general and other mission types.

Some countries have more than one mission, such as UK, Canada, US etc. So we are going to try and clean up this data.

Click here to read more about scraping data with the rvest package

embassies_html <- read_html("https://en.wikipedia.org/wiki/List_of_diplomatic_missions_of_Ireland")

embassies_tables <- embassies_html %>% html_table(header = TRUE, fill = TRUE)

We will extract the data from the different continent tables and then bind them all together at the end.

africa_emb <- embassies_tables[[1]]

africa_emb %<>% 
  mutate(continent = "Africa")

americas_emb <- embassies_tables[[2]]

americas_emb %<>% 
  mutate(continent = "Americas")

asia_emb <- embassies_tables[[3]]

asia_emb %<>% 
  mutate(continent = "Asia")

europe_emb <- embassies_tables[[4]]

europe_emb %<>% 
  mutate(continent = "Europe")

oceania_emb <- embassies_tables[[5]]

oceania_emb %<>% 
  mutate(continent = "Oceania")

Last, we bind all the tables together by rows, with rbind()

ire_emb <- rbind(africa_emb, 
                 americas_emb,
                 asia_emb,
                 europe_emb,
                 oceania_emb)

And clean up the names with the janitor package

ire_emb %<>% 
  janitor::clean_names() 

There is a small typo with a hypen and so there are separate Consulate General and Consulate-General… so we will clean that up to make one single factor level.

ire_emb %<>% 
  mutate(mission = ifelse(mission == "Consulate General", "Consulate-General", mission))

We can count out how many of each type of mission there are

ire_emb %>% 
  group_by(mission) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  knitr::kable(format = "html")
mission n
Embassy 69
Consulate-General 17
Liaison office 1
Representative office 1

A quick waffle plot

ire_emb %>% 
  group_by(mission) %>%
  count() %>% 
  arrange(desc(n)) %>% 
  ungroup() %>% 
  ggplot(aes(fill = mission, values = n)) +
  geom_waffle(color = "white", size = 1.5, 
              n_rows = 20, flip = TRUE) + 
  bbplot::bbc_style() +
  scale_fill_manual(values= wes_palette("Darjeeling1", n = 4))

We can remove the notes in brackets with the sub() function.

Square brackets equire a regex code \\[.*

ire_emb %<>% 
  select(!ref) %>%
  mutate(host_country = sub("\\[.*", "", host_country))

We delete the subheadings from the concurrent_accreditation column with the str_remove() function from the stringr package

ire_emb %<>%
  mutate(concurrent_accreditation = stringr::str_remove(concurrent_accreditation, "International Organizations:\n")) %>% 
  mutate(concurrent_accreditation = stringr::str_remove(concurrent_accreditation, "Countries:\n"))

After that, we will tackle the columns with many countries. The many variables in one cell violates the principles of tidy data.

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For example, we saw above that Argentina is the embassy for three other countries.

We will use the separate() function from the tidyr package to make a column for each country that shares an embassy with the host country.

This separate() function has six arguments:

First we indicate the column with will separate out with the col argument

Next with into, we write the new names of the columns we will create. Nigeria has the most countries for which it is accredited to be the designated embassy with nine. So I create nine accredited countries columns to accommodate this max number.

The point I want to cut up the original column is at the \n which is regex for a large space

I don’t want to remove the original column so I set remove to FALSE

ire_emb %<>%
  separate(
    col = "concurrent_accreditation",
    into = c("acc_1", "acc_2", "acc_3", "acc_4", "acc_5", "acc_6", "acc_7", "acc_8", "acc_9"),
    sep = "\n",
    remove = FALSE,
    extra = "warn",
    fill = "warn") %>% 
  mutate(across(where(is.character), str_trim)) 

Some countries have more than one type of mission, so I want to count each type of mission for each country and create a new variable with the distinct() and pivot_wider() functions

Click here to read more about turning long to wide format data

With the across() function we can replace all numeric variables with NA to zeros

Click here to read more about the across() function

ire_emb %>% 
  group_by(host_country, mission) %>% 
  mutate(number_missions = n())  %>% 
  distinct(host_country, mission, .keep_all = TRUE) %>% 
  ungroup() %>% 
  pivot_wider(!c(host_city, concurrent_accreditation:count_accreditation), 
              names_from = mission, 
              values_from = number_missions) %>% 
  janitor::clean_names() %>% 
  mutate(across(where(is.numeric), ~ replace_na(., 0))) %>% 
  select(!host_country) -> ire_wide

Before we bind the two datasets together, we need to only have one row for each country.

ire_emb %>% 
  distinct(host_country, .keep_all = TRUE) -> ire_dist

And bind them together:

ire_full <- cbind(ire_dist, ire_wide) 
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We can graph out where the embassies are with the geom_polygon() in ggplot

First we download the map data from dplyr and add correlates of war codes so we can easily join the datasets together with right_join()

First, we add correlates of war codes

Click here to read more about the countrycode package

ire_full %<>%
    mutate(cown = countrycode(host_country, "country.name", "cown")) 
world_map <- map_data("world")

world_map %<>% 
  mutate(cown = countrycode::countrycode(region, "country.name", "cown"))

I reorder the variables with the fct_relevel() function from the forcats package. This is just so they can better match the color palette from wesanderson package. Green means embassy, red for no mission and orange for representative office.

ire_full %>%
  right_join(world_map, by = "cown") %>% 
  filter(region != "Antarctica") %>% 
  mutate(mission = ifelse(is.na(mission), replace_na("No Mission"), mission)) %>% 
  mutate(mission = forcats::fct_relevel(mission,c("No Mission", "Embassy","Representative office"))) %>%
  ggplot(aes(x = long, y = lat, group = group)) + 
  geom_polygon(aes(fill = mission), color = "white", size = 0.5)  -> ire_map

And we can change how the map looks with the ggthemes package and colors from wesanderson package

  ire_map + ggthemes::theme_map() +
  theme(legend.key.size = unit(3, "cm"),
        text = element_text(size = 30),
        legend.title = element_blank()) + 
  scale_fill_manual(values = wes_palette("Darjeeling1", n = 4))

And we can count how many missions there are in each country

US has the hightest number with 8 offices, followed by UK with 4 and China with 3

ire_full %>%
  right_join(world_map, by = "cown") %>% 
  filter(region != "Antarctica") %>% 
  mutate(sum_missions = rowSums(across(embassy:representative_office))) %>% 
  mutate(sum_missions = replace_na(sum_missions, 0)) %>%  
  ggplot(aes(x = long, y = lat, group = group)) + 
  geom_polygon(aes(fill = as.factor(sum_missions)), color = "white", size = 0.5)  +
  ggthemes::theme_map() +
  theme(legend.key.size = unit(3, "cm"),
        text = element_text(size = 30),
        legend.title = element_blank()) + 
scale_fill_brewer(palette = "RdBu") + 
  ggtitle("Number of Irish missions in each country",
          subtitle = "Source: Wikipedia")

Last we can count the number of accredited countries that each embassy has. Nigeria has the most, in charge of 10 other countries across northern and central Africa.

ire_full %>% 
  right_join(world_map, by = "cown") %>% 
  filter(region != "Antarctica") %>%
  mutate(count_accreditation = str_count(concurrent_accreditation, pattern = "\n")) %>% 
  mutate(count_accreditation = replace_na(count_accreditation, -1)) %>%  
  ggplot(aes(x = long, y = lat, group = group)) + 
  geom_polygon(aes(fill = as.factor(count_accreditation)), color = "white", size = 0.5)  +
  ggthemes::theme_fivethirtyeight() +
  theme(legend.key.size = unit(1, "cm"),
        text = element_text(size = 30),
        legend.title = element_blank()) + 
  ggtitle("Number of Irish missions in extra accreditations",
          subtitle = "Source: Wikipedia")
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Comparing proportions across time with ggstream in R

Packages we need:

library(tidyverse)
library(ggstream)
library(magrittr)
library(bbplot)
library(janitor)

We can look at proportions of energy sources across 10 years in Ireland. Data source comes from: https://www.seai.ie/data-and-insights/seai-statistics/monthly-energy-data/electricity/

Before we graph the energy sources, we can tidy up the variable names with the janitor package. We next select column 2 to 12 which looks at the sources for electricity generation. Other rows are aggregates and not the energy-related categories we want to look at.

Next we pivot the dataset longer to make it more suitable for graphing.

We can extract the last two digits from the month dataset to add the year variable.

elec %<>% 
  janitor::clean_names()

elec[2:12,] -> elec

el <- elec %>% 
  pivot_longer(!electricity_generation_g_wh, 
               names_to = "month", values_to = "value") %>% 

substrRight <- function(x, n){
  substr(x, nchar(x) - n + 1, nchar(x))}

el$year <- substrRight(el$month, 2)

el %<>% select(year, month, elec_type = electricity_generation_g_wh, elec_value = value) 

First we can use the geom_stream from the ggstream package. There are three types of plots: mirror, ridge and proportion.

First we will plot the proportion graph.

Select the different types of energy we want to compare, we can take the annual values, rather than monthly with the tried and trusted group_by() and summarise().

Optionally, we can add the bbc_style() theme for the plot and different hex colors with scale_fill_manual() and feed a vector of hex values into the values argument.

el %>% 
  filter(elec_type == "Oil" | 
           elec_type == "Coal" |
           elec_type == "Peat" | 
           elec_type == "Hydro" |
           elec_type == "Wind" |
           elec_type == "Natural Gas") %>% 
  group_by(year, elec_type) %>%
  summarise(annual_value = sum(elec_value, na.rm = TRUE)) %>% 
  ggplot(aes(x = year, 
             y = annual_value,
             group = elec_type,
             fill = elec_type)) +
  ggstream::geom_stream(type = "proportion") + 
  bbplot::bbc_style() +
  labs(title = "Comparing energy proportions in Ireland") +
  scale_fill_manual(values = c("#f94144",
                               "#277da1",
                               "#f9c74f",
                               "#f9844a",
                               "#90be6d",
                               "#577590"))

With ggstream::geom_stream(type = "mirror")

With ggstream::geom_stream(type = "ridge")

Without the ggstream package, we can recreate the proportion graph with slightly different looks

https://giphy.com/gifs/filmeditor-clueless-movie-l0ErMA0xAS1Urd4e4

el %>% 
  filter(elec_type == "Oil" | 
           elec_type == "Coal" |
           elec_type == "Peat" | 
           elec_type == "Hydro" |
           elec_type == "Wind" |
           elec_type == "Natural Gas") %>% 
  group_by(year, elec_type) %>%
  summarise(annual_value = sum(elec_value, na.rm = TRUE)) %>% 
  ggplot(aes(x = year, 
             y = annual_value,
             group = elec_type,
             fill = elec_type)) +
  geom_area(alpha=0.8 , size=1.5, colour="white") +
  bbplot::bbc_style() +
  labs(title = "Comparing energy proportions in Ireland") +
  theme(legend.key.size = unit(2, "cm")) + 
  scale_fill_manual(values = c("#f94144",
                               "#277da1",
                               "#f9c74f",
                               "#f9844a",
                               "#90be6d",
                               "#577590"))

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Create density plots with ggridges package in R

Packages we will need:

library(tidyverse)
library(ggridges)
library(ggimage)  # to add png images
library(bbplot)   # for pretty graph themes

We will plot out the favourability opinion polls for the three main political parties in Ireland from 2016 to 2020. Data comes from Louwerse and Müller (2020)

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Before we dive into the ggridges plotting, we have a little data cleaning to do. First, we extract the last four “characters” from the date string to create a year variable.

I took this quick function from a StackOverflow response:

substrRight <- function(x, n){
  substr(x, nchar(x)-n+1, nchar(x))}

polls_csv$year <- substrRight(polls_csv$Date, 4)

Next, pivot the data from wide to long format.

More information of pivoting data with dplyr can be found here. I tend to check it at least once a month as the arguments refuse to stay in my head.

I only want to take the main parties in Ireland to compare in the plot.

polls <- polls_csv %>%
  select(year, FG:SF) %>% 
  pivot_longer(!year, names_to = "party", values_to = "opinion_poll")

I went online and found the logos for the three main parties (sorry, Labour) and saved them in the working directory I have for my RStudio. That way I can call the file with the prefix “~/**.png” rather than find the exact location they are saved on the computer.

polls %>% 
  filter(party == "FF" | party == "FG" | party == "SF" ) %>% 
  mutate(image = ifelse(party=="FF","~/ff.png",
 ifelse(party=="FG","~/fg.png", "~/sf.png"))) -> polls_three

Now we are ready to plot out the density plots for each party with the geom_density_ridges() function from the ggridges package.

We will add a few arguments into this function.

We add an alpha = 0.8 to make each density plot a little transparent and we can see the plots behind.

The scale = 2 argument pushes all three plots togheter so they are slightly overlapping. If scale =1, they would be totally separate and 3 would have them overlapping far more.

The rel_min_height = 0.01 argument removes the trailing tails from the plots that are under 0.01 density. This is again for aesthetics and just makes the plot look slightly less busy for relatively normally distributed densities

The geom_image takes the images and we place them at the beginning of the x axis beside the labels for each party.

Last, we use the bbplot package BBC style ggplot theme, which I really like as it makes the overall graph look streamlined with large font defaults.

polls_three %>% 
  ggplot(aes(x = opinion_poll, y = as.factor(party))) +  
  geom_density_ridges(aes(fill = party), 
                      alpha = 0.8, 
                      scale = 2,
                      rel_min_height = 0.01) + 
  ggimage::geom_image(aes(y = party, x= 1, image = image), asp = 0.9, size = 0.12) + 
  facet_wrap(~year) + 
  bbplot::bbc_style() +
  scale_fill_manual(values = c("#f2542d", "#edf6f9", "#0e9594")) +
  theme(legend.position = "none") + 
  labs(title = "Favourability Polls for the Three Main Parties in Ireland", subtitle = "Data from Irish Polling Indicator (Louwerse & Müller, 2020)")
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Compare Irish census years with compareBars and csodata package in R

Packages we will need:

library(csodata)
library(janitor)
library(ggcharts)
library(compareBars)
library(tidyverse)

First, let’s download population data from the Irish census with the Central Statistics Office (CSO) API package, developed by Conor Crowley.

You can search for the data you want to analyse via R or you can go to the CSO website and browse around the site.

I prefer looking through the site because sometimes I stumble across a dataset I didn’t even think to look for!

Keep note of the code beside the red dot star symbol if you’re looking around for datasets.

Click here to check out the CRAN PDF for the CSO package.

You can search for keywords with cso_search_toc(). I want total population counts for the whole country.

cso_search_toc("total population")

We can download the variables we want by entering the code into the cso_get_data() function

irish_pop <- cso_get_data("EY007")
View(irish_pop)

The EY007 code downloads population census data in both 2011 and 2016 at every age.

It needs a little bit of tidying to get it ready for graphing.

irish_pop %<>%  
  clean_names()

First, we can be lazy and use the clean_names() function from the janitor package.

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Next we can get rid of the rows that we don’t want with select().

Then we use the pivot_longer() function to turn the data.frame from wide to long and to turn the x2011 and x2016 variables into one year variable.

irish_pop %>% 
  filter(at_each_year_of_age == "Population") %>% 
  filter(sex == 'Both sexes') %>% 
  filter(age_last_birthday != "All ages") %>% 
  select(!statistic) %>% 
  select(!sex) %>% 
  select(!at_each_year_of_age) -> irish_wide

irish_wide %>% 
  pivot_longer(!age_last_birthday,
    names_to = "year", 
    values_to = "pop_count",
    values_drop_na = TRUE) %>% 
    mutate(year = as.factor(year)) -> irish_long

No we can create our pyramid chart with the pyramid_chart() from the ggcharts package. The first argument is the age category for both the 2011 and 2016 data. The second is the actual population counts for each year. Last, enter the group variable that indicates the year.

irish_long %>%   
  pyramid_chart(age_last_birthday, pop_count, year)

One problem with the pyramid chart is that it is difficult to discern any differences between the two years without really really examining each year.

One way to more easily see the differences with the compareBars function

The compareBars package created by David Ranzolin can help to simplify comparative bar charts! It’s a super simple function to use that does a lot of visualisation leg work under the hood!

First we need to pivot the data.frame back to wide format and then input the age, and then the two groups – x2011 and x2016 – in the compareBars() function.

We can add more labels and colors to customise the graph also!

irish_long %>% 
  pivot_wider(names_from = year, values_from = pop_count) %>% 
  compareBars(age_last_birthday, x2011, x2016, orientation = "horizontal",
              xLabel = "Population",
              yLabel = "Year",
              titleLabel = "Irish Populations",
              subtitleLabel = "Comparing 2011 and 2016",
              fontFamily = "Arial",
              compareVarFill1 = "#FE6D73",
              compareVarFill2 = "#17C3B2") 

We can see that under the age of four-ish, 2011 had more at the time. And again, there were people in their twenties in 2011 compared to 2016.

However, there are more older people in 2016 than in 2011.

Similar to above it is a bit busy! So we can create groups for every five age years categories and examine the broader trends with fewer horizontal bars.

First we want to remove the word “years” from the age variable and convert it to a numeric class variable. We can easily do this with the parse_number() function from the readr package

irish_wide %<>% 
mutate(age_num = readr::parse_number(as.character(age_last_birthday))) 

Next we can group the age years together into five year categories, zero to 5 years, 6 to 10 years et cetera.

We use the cut() function to divide the numeric age_num variable into equal groups. We use the seq() function and input age 0 to 100, in increments of 5.

irish_wide$age_group = cut(irish_wide$age_num, seq(0, 100, 5))

Next, we can use group_by() to calculate the sum of each population number in each five year category.

And finally, we use the distinct() function to remove the duplicated rows (i.e. we only want to keep the first row that gives us the five year category’s population count for each category.

irish_wide %<>% 
  group_by(age_group) %>% 
  mutate(five_year_2011 = sum(x2011)) %>% 
  mutate(five_year_2016 = sum(x2016)) %>% 
  distinct(five_year_2011, five_year_2016, .keep_all = TRUE)

Next plot the bar chart with the five year categories

compareBars(irish_wide, age_group, five_year_2011, five_year_2016, orientation = "horizontal",
              xLabel = "Population",
              yLabel = "Year",
              titleLabel = "Irish Populations",
              subtitleLabel = "Comparing 2011 and 2016",
              fontFamily = "Arial",
              compareVarFill1 = "#FE6D73",
              compareVarFill2 = "#17C3B2") 

irish_wide2 %>% 
  select(age_group, five_year_2011, five_year_2016) %>% 
  pivot_longer(!age_group,
             names_to = "year", 
             values_to = "pop_count",
             values_drop_na = TRUE) %>% 
  mutate(year = as.factor(year)) -> irishlong2

irishlong2 %>%   
  pyramid_chart(age_group, pop_count, year)

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