Check model assumptions with easystats package in R

Packages we will need:

install.packages("easystats", repos = "https://easystats.r-universe.dev")
library(easystats)
easystats::install_suggested()

Easystats is a collection of R packages, which aims to provide a unifying and consistent framework to tame, discipline, and harness the scary R statistics and their pesky models, according to their github repo.

Click here to browse the github and here to go to the specific perfomance package CRAN PDF

First run your regression. I will try to explain variance is Civil Society Organization participation (CSOs) with the independent variables in my model with Varieties of Democracy data in 1990.

cso_model <- lm(cso_part ~ education_level + mortality_rate + democracy,data = vdem_90)
Dependent variable:
cso_part
education_level-0.017**
(0.007)
mortality_rate-0.00001
(0.00004)
democracy0.913***
(0.064)
Constant0.288***
(0.054)
Observations134
R20.690
Adjusted R20.682
Residual Std. Error0.154 (df = 130)
F Statistic96.243*** (df = 3; 130)
Note:*p<0.1; **p<0.05; ***p<0.01
Excited Season 2 GIF by The Office - Find & Share on GIPHY

Then we check the assumptions:

performance::check_model(cso_model)

Graphing Pew survey responses with ggplot in R

Packages we will need:

library(tidyverse)
library(forcats)
library(ggthemes)

We are going to look at a few questions from the 2019 US Pew survey on relations with foreign countries.

Data can be found by following this link:

We are going to make bar charts to plot out responses to the question asked to American participaints: Should the US cooperate more or less with some key countries? The countries asked were China, Russia, Germany, France, Japan and the UK.

Before we dive in, we can find some nice hex colors for the bar chart. There are four possible responses that the participants could give: cooperate more, cooperate less, cooperate the same as before and refuse to answer / don’t know.

pal <- c("Cooperate more" = "#0a9396",
         "Same as before" = "#ee9b00",
         "Don't know" = "#005f73",
         "Cooperate less" ="#ae2012")

We first select the questions we want from the full survey and pivot the dataframe to long form with pivot_longer(). This way we have a single column with all the different survey responses. that we can manipulate more easily with dplyr functions.

Then we summarise the data to count all the survey reponses for each of the four countries and then calculate the frequency of each response as a percentage of all answers.

Then we mutate the variables so that we can add flags. The geom_flag() function from the ggflags packages only recognises ISO2 country codes in lower cases.

After that we change the factors level for the four responses so they from positive to negative views of cooperation

pew %>% 
  select(id = case_id, Q2a:Q2f) %>% 
  pivot_longer(!id, names_to = "survey_question", values_to = "response")  %>% 
  group_by(survey_question, response) %>% 
  summarise(n = n()) %>%
  mutate(freq = n / sum(n)) %>% 
  ungroup() %>% 
  mutate(response_factor = as.factor(response)) %>% 
  mutate(country_question = ifelse(survey_question == "Q2a", "fr",
ifelse(survey_question == "Q2b", "gb",
ifelse(survey_question == "Q2c", "ru",
ifelse(survey_question == "Q2d", "cn",
ifelse(survey_question == "Q2e", "de",
ifelse(survey_question == "Q2f", "jp", survey_question))))))) %>% 
  mutate(response_string = ifelse(response_factor == 1, "Cooperate more",
ifelse(response_factor == 2, "Cooperate less",
ifelse(response_factor == 3, "Same as before",
ifelse(response_factor == 9, "Don't know", response_factor))))) %>% 
  mutate(response_string = fct_relevel(response_string, c("Cooperate less","Same as before","Cooperate more", "Don't know"))) -> pew_clean

We next use ggplot to plot out the responses.

We use the position = "stack" to make all the responses “stack” onto each other for each country. We use stat = "identity" because we are not counting each reponses. Rather we are using the freq variable we calculated above.

pew_clean %>%
  ggplot() +
  geom_bar(aes(x = forcats::fct_reorder(country_question, freq), y = freq, fill = response_string), color = "#e5e5e5", size = 3, position = "stack", stat = "identity") +
  geom_flag(aes(x = country_question, y = -0.05 , country = country_question), color = "black", size = 20) -> pew_graph

And last we change the appearance of the plot with the theme function

pew_graph + 
coord_flip() + 
  scale_fill_manual(values = pal) +
  ggthemes::theme_fivethirtyeight() + 
  ggtitle("Should the US cooperate more or less with the following country?") +
  theme(legend.title = element_blank(),
        legend.position = "top",
        legend.key.size = unit(2, "cm"),
        text = element_text(size = 25),
        legend.text = element_text(size = 20),
        axis.text = element_blank())

Lollipop plots with ggplot2 in R

Packages we will need:

library(tidyverse)
library(rvest)
library(ggflags)
library(countrycode)
library(ggpubr)

We will plot out a lollipop plot to compare EU countries on their level of income inequality, measured by the Gini coefficient.

A Gini coefficient of zero expresses perfect equality, where all values are the same (e.g. where everyone has the same income). A Gini coefficient of one (or 100%) expresses maximal inequality among values (e.g. for a large number of people where only one person has all the income or consumption and all others have none, the Gini coefficient will be nearly one).

To start, we will take data on the EU from Wikipedia. With rvest package, scrape the table about the EU countries from this Wikipedia page.

Click here to read more about the rvest pacakge

With the gsub() function, we can clean up the different variables with some regex. Namely delete the footnotes / square brackets and change the variable classes.

eu_site <- read_html("https://en.wikipedia.org/wiki/Member_state_of_the_European_Union")

eu_tables <- eu_site %>% html_table(header = TRUE, fill = TRUE)

eu_members <- eu_tables[[3]]

eu_members %<>% janitor::clean_names()  %>% 
  filter(!is.na(accession))

eu_members$iso3 <- countrycode::countrycode(eu_members$geo, "country.name", "iso3c")

eu_members$accession <- as.numeric(gsub("([0-9]+).*$", "\\1",eu_members$accession))

eu_members$name_clean <- gsub("\\[.*?\\]", "", eu_members$name)

eu_members$gini_clean <- gsub("\\[.*?\\]", "", eu_members$gini)

Next some data cleaning and grouping the year member groups into different decades. This indicates what year each country joined the EU. If we see clustering of colours on any particular end of the Gini scale, this may indicate that there is a relationship between the length of time that a country was part of the EU and their domestic income inequality level. Are the founding members of the EU more equal than the new countries? Or conversely are the newer countries that joined from former Soviet countries in the 2000s more equal. We can visualise this with the following mutations:

eu_members %>%
  filter(name_clean != "Totals/Averages") %>% 
  mutate(gini_numeric = as.numeric(gini_clean)) %>% 
  mutate(accession_decades = ifelse(accession < 1960, "1957", ifelse(accession > 1960 & accession < 1990, "1960s-1980s", ifelse(accession == 1995, "1990s", ifelse(accession > 2003, "2000s", accession))))) -> eu_clean 

To create the lollipop plot, we will use the geom_segment() functions. This requires an x and xend argument as the country names (with the fct_reorder() function to make sure the countries print out in descending order) and a y and yend argument with the gini number.

All the countries in the EU have a gini score between mid 20s to mid 30s, so I will start the y axis at 20.

We can add the flag for each country when we turn the ISO2 character code to lower case and give it to the country argument.

Click here to read more about the ggflags package

eu_clean %>% 
ggplot(aes(x= name_clean, y= gini_numeric, color = accession_decades)) +
  geom_segment(aes(x = forcats::fct_reorder(name_clean, -gini_numeric), 
                   xend = name_clean, y = 20, yend = gini_numeric, color = accession_decades), size = 4, alpha = 0.8) +
  geom_point(aes(color = accession_decades), size= 10) +
  geom_flag(aes(y = 20, x = name_clean, country = tolower(iso_3166_1_alpha_2)), size = 10) +
  ggtitle("Gini Coefficients of the EU countries") -> eu_plot

Last we add various theme changes to alter the appearance of the graph

eu_plot + 
coord_flip() +
ylim(20, 40) +
  theme(panel.border = element_blank(),
        legend.title = element_blank(),
        axis.title = element_blank(),
        axis.text = element_text(color = "white"),
        text= element_text(size = 35, color = "white"),
        legend.text = element_text(size = 20),
        legend.key = element_rect(colour = "#001219", fill = "#001219"),
        legend.key.width = unit(3, 'cm'),
        legend.position = "bottom",
        panel.grid.major.y = element_line(linetype="dashed"),
        plot.background = element_rect(fill = "#001219"),
        panel.background = element_rect(fill = "#001219"),
        legend.background = element_rect(fill = "#001219") )

We can see there does not seem to be a clear pattern between the year a country joins the EU and their level of domestic income inequality, according to the Gini score.

Of course, the Gini coefficient is not a perfect measurement, so take it with a grain of salt.

Another option for the lolliplot plot comes from the ggpubr package. It does not take the familiar aesthetic arguments like you can do with ggplot2 but it is very quick and the defaults look good!

eu_clean %>% 
  ggdotchart( x = "name_clean", y = "gini_numeric",
              color = "accession_decades",
              sorting = "descending",                      
              rotate = TRUE,                                
              dot.size = 10,   
              y.text.col = TRUE,
              ggtheme = theme_pubr()) + 
  ggtitle("Gini Coefficients of the EU countries") + 
  theme(panel.border = element_blank(),
        legend.title = element_blank(),
        axis.title = element_blank(),
        axis.text = element_text(color = "white"),
        text= element_text(size = 35, color = "white"),
        legend.text = element_text(size = 20),
        legend.key = element_rect(colour = "#001219", fill = "#001219"),
        legend.key.width = unit(3, 'cm'),
        legend.position = "bottom",
        panel.grid.major.y = element_line(linetype="dashed"),
        plot.background = element_rect(fill = "#001219"),
        panel.background = element_rect(fill = "#001219"),
        legend.background = element_rect(fill = "#001219") )

Replicating Eurostat graphs in R

Packages we will need:

library(eurostat)
library(tidyverse)
library(maggritr)
library(ggthemes)
library(forcats)

In this blog, we will try to replicate this graph from Eurostat!

It compares all European countries on their Digitical Intensity Index scores in 2020. This measures the use of different digital technologies by enterprises.

The higher the score, the higher the digital intensity of the enterprise, ranging from very low to very high. 

For more information on the index, I took the above information from this site: https://ec.europa.eu/eurostat/web/products-eurostat-news/-/ddn-20211029-1

First, we will download the digital index from Eurostat with the get_eurostat() function.

Click here to learn more about downloading data on EU from the Eurostat package.

Next some data cleaning. To copy the graph, we will aggregate the different levels into very low, low, high and very high categories with the grepl() function in some ifelse() statements.

The variable names look a bit odd with lots of blank space because I wanted to space out the legend in the graph to replicate the Eurostat graph above.

dig <- get_eurostat("isoc_e_dii", type = "label")

dig %<>% 
   mutate(dii_level = ifelse(grepl("very low", indic_is), "Very low        " , ifelse(grepl("with low", indic_is), "Low        ", ifelse(grepl("with high", indic_is), "High        ", ifelse(grepl("very high", indic_is), "Very high        ", indic_is)))))

Next I fliter out the year I want and aggregate all industry groups (from the sizen_r2 variable) in each country to calculate a single DII score for each country.

dig %>% 
  select(sizen_r2, geo, values, dii_level, year) %>%  
  filter(year == 2020) %>% 
  group_by(dii_level, geo) %>% 
  summarise(total_values = sum(values, na.rm = TRUE)) %>% 
  ungroup() -> my_dig

I use a hex finder website imagecolorpicker.com to find the same hex colors from the Eurostat graph and assign them to our version.

dii_pal <- c("Very low        " = "#f0aa4f",
             "Low        " = "#fee229",
             "Very high        " = "#154293", 
             "High        " = "#7fa1d4")

We can make sure the factors are in the very low to very high order (rather than alphabetically) with the ordered() function

my_dig$dii_level <- ordered(my_dig$dii_level, levels = c("Very Low        ", "Low        ", "High        ","Very high        "))

Next we filter out the geo rows we don’t want to add to the the graph.

Also we can change the name of Germany to remove its longer title.

my_dig %>% 
  filter(geo != "Euro area (EA11-1999, EA12-2001, EA13-2007, EA15-2008, EA16-2009, EA17-2011, EA18-2014, EA19-2015)") %>% 
  filter(geo != "United Kingdom") %>% 
  filter(geo != "European Union - 27 countries (from 2020)") %>% 
  filter(geo != "European Union - 28 countries (2013-2020)") %>% 
  mutate(geo = ifelse(geo == "Germany (until 1990 former territory of the FRG)", "Germany", geo)) -> my_dig 

And also, to have the same order of countries that are in the graph, we can add them as ordered factors.

my_dig$country <- factor(my_dig$geo, levels = c("Finland", "Denmark", "Malta", "Netherlands", "Belgium", "Sweden", "Estonia", "Slovenia", "Croatia", "Italy", "Ireland","Spain", "Luxembourg", "Austria", "Czechia", "France", "Germany", "Portugal", "Poland", "Cyprus", "Slovakia", "Hungary", "Lithuania", "Latvia", "Greece", "Romania", "Bulgaria", "Norway"), ordered = FALSE)

Now to plot the graph:

my_dig %>% 
  filter(!is.na(country)) %>% 
  group_by(country, dii_level) %>% 
  ggplot(aes(y = country, 
             x = total_values,
             fill = forcats::fct_rev(dii_level))) +
  geom_col(position = "fill", width = 0.7) + 
  scale_fill_manual(values = dii_pal) + 
  ggthemes::theme_pander() +
  coord_flip() +
  labs(title = "EU's Digital Intensity Index (DII) in 2020",
       subtitle = ("(% of enterprises with at least 10 persons employed)"),
       caption = "ec.europa/eurostat") +
  xlab("") + 
  ylab("") + 
  theme(text = element_text(family = "Verdana", color = "#154293"),
        axis.line.x = element_line(color = "black", size = 1.5),
        axis.text.x = element_text(angle = 90, size = 20, color = "#154293", hjust = 1),
        axis.text.y = element_text(color = "#808080", size = 13, face = "bold"),
        legend.position = "top", 
        legend.title = element_blank(),
        legend.text = element_text(color = "#808080", size = 20, face = "bold"),
        plot.title = element_text(size = 42, color = "#154293"),
        plot.subtitle = element_text(size = 25, color = "#154293"),
        plot.caption = element_text(size = 20, color = "#154293"),
        panel.background = element_rect(color = "#f2f2f2"))

It is not identical and I had to move the black line up and the Norway model more to the right with Paint on my computer! So a bit of cheating!

Click to read Part 1, Part 2 and Part 3 of the blog series on visualising Eurostat data

For information on the index discussed in this blog post: https://ec.europa.eu/eurostat/web/products-eurostat-news/-/ddn-20211029-1

Visualize EU data with Eurostat package in R: Part 2 (with maps)

In this post, we will map prison populations as a percentage of total populations in Europe with Eurostat data.

library(eurostat)
library(tidyverse)
library(sf)
library(rnaturalearth)
library(ggthemes)
library(countrycode)
library(ggflags)
library(viridis)
library(rvest)

Click here to read Part 1 about downloading Eurostat data.


prison_pop <- get_eurostat("crim_pris_pop", type = "label")

prison_pop$iso3 <- countrycode::countrycode(prison_pop$geo, "country.name", "iso3c")

prison_pop$year <- as.numeric(format(prison_pop$time, format = "%Y"))

Next we will download map data with the rnaturalearth package. Click here to read more about using this package.

We only want to zoom in on continental EU (and not include islands and territories that EU countries have around the world) so I use the coordinates for a cropped European map from this R-Bloggers post.

map <- rnaturalearth::ne_countries(scale = "medium", returnclass = "sf")

europe_map <- sf::st_crop(map, xmin = -20, xmax = 45,
                          ymin = 30, ymax = 73)

prison_map <- merge(prison_pop, europe_map, by.x = "iso3", by.y = "adm0_a3", all.x = TRUE)

We will look at data from 2000.

prison_map %>% 
  filter(year == 2000) -> map_2000

To add flags to our map, we will need ISO codes in lower case and longitude / latitude.

prison_map$iso2c <- tolower(countrycode(prison_map$geo, "country.name", "iso2c"))

coord <- read_html("https://developers.google.com/public-data/docs/canonical/countries_csv")

coord_tables <- coord %>% html_table(header = TRUE, fill = TRUE)

coord <- coord_tables[[1]]

prison_map <- merge(prison_map, coord, by.x= "iso_a2", by.y = "country", all.y = TRUE)

Nex we will plot it out!

We will focus only on European countries and we will change the variable from total prison populations to prison pop as a percentage of total population. Finally we multiply by 1000 to change the variable to per 1000 people and not have the figures come out with many demical places.

prison_map %>% 
  filter(continent == "Europe") %>% 
  mutate(prison_pc = (values / pop_est)*1000) %>% 
  ggplot() +
  geom_sf(aes(fill = prison_pc, geometry = geometry), 
          position = "identity") + 
  labs(fill='Prison population')  +
  ggflags::geom_flag(aes(x = longitude, 
                         y = latitude+0.5, 
                         country = iso2_lower), 
                     size = 9) +  
  scale_fill_viridis_c(option = "mako", direction = -1) +
  ggthemes::theme_map() -> prison_map

Next we change how it looks, including changing the background of the map to a light blue colour and the legend.

prison_map + 
  theme(legend.title = element_text(size = 20),
        legend.text = element_text(size = 14), 
         legend.position = "bottom",
        legend.background = element_rect(fill = "lightblue",
                                         colour = "lightblue"),
        panel.background = element_rect(fill = "lightblue",
                                        colour = "lightblue"))

I will admit that I did not create the full map in ggplot. I added the final titles and block colours with canva.com because it was just easier! I always find fonts very tricky in R so it is nice to have dozens of different fonts in Canva and I can play around with colours and font sizes without needing to reload the plot each time.

Building a dataset for political science analysis in R, PART 2

Packages we will need

library(tidyverse)
library(peacesciencer)
library(countrycode)
library(bbplot)

The main workhorse of this blog is the peacesciencer package by Stephen Miller!

The package will create both dyad datasets and state datasets with all sovereign countries.

Thank you Mr Miller!

There are heaps of options and variables to add.

Go to the page to read about them all in detail.

Here is a short list from the package description of all the key variables that can be quickly added:

We create the dyad dataset with the create_dyadyears() function. A dyad-year dataset focuses on information about the relationship between two countries (such as whether the two countries are at war, how much they trade together, whether they are geographically contiguous et cetera).

In the literature, the study of interstate conflict has adopted a heavy focus on dyads as a unit of analysis.

Alternatively, if we want just state-year data like in the previous blog post, we use the function create_stateyears()

We can add the variables with type D to the create_dyadyears() function and we can add the variables with type S to the create_stateyears() !

Focusing on the create_dyadyears() function, the arguments we can include are directed and mry.

The directed argument indicates whether we want directed or non-directed dyad relationship.

In a directed analysis, data include two observations (i.e. two rows) per dyad per year (such as one for USA – Russia and another row for Russia – USA), but in a nondirected analysis, we include only one observation (one row) per dyad per year.

The mry argument indicates whether they want to extend the data to the most recently concluded calendar year – i.e. 2020 – or not (i.e. until the data was last available).

dyad_df <- create_dyadyears(directed = FALSE, mry = TRUE) %>%
  add_atop_alliance() %>%  
  add_nmc() %>%
  add_cow_trade() %>% 
  add_creg_fractionalization() 

I added dyadic variables for the

You can follow these links to check out the codebooks if you want more information about descriptions about each variable and how the data were collected!

The code comes with the COW code but I like adding the actual names also!

dyad_df$country_1 <- countrycode(dyad_df$ccode1, "cown", "country.name")

With this dataframe, we can plot the CINC data of the top three superpowers, just looking at any variable that has a 1 at the end and only looking at the corresponding country_1!

According to our pals over at le Wikipedia, the Composite Index of National Capability (CINC) is a statistical measure of national power created by J. David Singer for the Correlates of War project in 1963. It uses an average of percentages of world totals in six different components (such as coal consumption, military expenditure and population). The components represent demographic, economic, and military strength

First, let’s choose some nice hex colors

pal <- c("China" = "#DE2910",
         "United States" = "#3C3B6E", 
         "Russia" = "#FFD900")

And then create the plot

dyad_df %>% 
 filter(country_1 == "Russia" | 
          country_1 == "United States" | 
          country_1 == "China") %>% 
  ggplot(aes(x = year, y = cinc1, group = as.factor(country_1))) +
  geom_line(aes(color = country_1)) +
  geom_line(aes(color = country_1), size = 2, alpha = 0.8) + 
  scale_color_manual(values =  pal) +
  bbplot::bbc_style()

In PART 3, we will merge together our data with our variables from PART 1, look at some descriptive statistics and run some panel data regression analysis with our different variables!

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.

GIF by The Good Place - Find & Share on GIPHY

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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Add Correlates of War codes with countrycode package in R

One problem with merging two datasets by country is that the same countries can have different names. Take for example, America. It can be entered into a dataset as any of the following:

  • USA
  • U.S.A.
  • America
  • United States of America
  • United States
  • US
  • U.S.

This can create a big problem because datasets will merge incorrectly if they think that US and America are different countries.

Correlates of War (COW) is a project founded by Peter Singer, and catalogues of all inter-state war since 1963. This project uses a unique code for each country.

For example, America is 2.

When merging two datasets, there is a helpful R package that can convert the various names for a country into the COW code:

install.packages("countrycode")
library(countrycode)

To read more about the countrycode package in the CRAN PDF, click here.

First create a new name for the variable I want to make; I’ll call it COWcode in the dataset.

Then use the countrycode() function. First type in the brackets the name of the original variable that contains the list of countries in the dataset. Then finally add "country.name", "cown". This turns the word name for each country into the numeric COW code.

dataset$COWcode <- countrycode(dataset$countryname, "country.name", "cown")

If you want to turn into a country name, swap the "country.name" and "cown"

dataset$countryname <- countrycode(dataset$COWcode, "country.name", "cown")

Now the dataset is ready to merge more easily with my other dataset on the identical country variable type!

There are many other types of codes that you can add to your dataset.

A very popular one is the ISO-2 and ISO-3 codes. For example, if you want to add flags to your graph, you will need a two digit code for each country (for example, Ireland is IE).

To see the list of all the COW codes, click here.

To check out the COW database website, click here.

Alternative codes than the country.name and the cown options include:

• ccTLD: IANA country code top-level domain
• country.name: country name (English)
• country.name.de: country name (German)
• cowc: Correlates of War character
• cown: Correlates of War numeric
• dhs: Demographic and Health Surveys Program
• ecb: European Central Bank
• eurostat: Eurostat
• fao: Food and Agriculture Organization of the United Nations numerical code
• fips: FIPS 10-4 (Federal Information Processing Standard)
• gaul: Global Administrative Unit Layers
• genc2c: GENC 2-letter code
• genc3c: GENC 3-letter code
• genc3n: GENC numeric code
• gwc: Gleditsch & Ward character
• gwn: Gleditsch & Ward numeric
• imf: International Monetary Fund
• ioc: International Olympic Committee
• iso2c: ISO-2 character
• iso3c: ISO-3 character
• iso3n: ISO-3 numeric
• p4n: Polity IV numeric country code
• p4c: Polity IV character country code
• un: United Nations M49 numeric codes
4 codelist
• unicode.symbol: Region subtag (often displayed as emoji flag)
• unpd: United Nations Procurement Division
• vdem: Varieties of Democracy (V-Dem version 8, April 2018)
• wb: World Bank (very similar but not identical to iso3c)
• wvs: World Values Survey numeric code