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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Make a timeline graph with dates in ggplot2

We will use the geom_segment layer from ggplot2 to make a timeline graph!

This layer takes

  • x and xend for the start of the segment lines
  • y and yend inputs for the end of the segment lines

For our timeline, the x will be the start of each Irish Taoiseach’s term.

The xend will be the end of their term, when they get kicked out of office.

Taoisigh (plural of Taoiseach) are Irish prime ministers and are in charge of the executive branch when their party is in change.

For Ireland, that means that basically every Taoiseach has been the leader of one of the two main parties – Fianna Fail or Fine Gael.

Not very exciting.

Also they have all been men.

This is also not very exciting.

We have a bit more to go with increasing the diversity in Ireland’s top job.

The y argument is the Taoiseach number in office. Although there have been fifteen men that have held the office of Taoiseach, this does not mean that they only held office for one time only.

Ireland has a parliamentary system so when a party loses an election, the former Taoiseach can become the leader of the opposition and hope in the future they can become Taoiseach again. Some men have been Taoiseach two or three times in non-consecutive terms.

When we are adding the labels with the geom_text() layer, I created an order variable which indicates the first time each man took the office of Taoiseach.

This is so I only have the name of each man only once in the graph. If we don’t do this step, if a man held office more than once, their name appears every time on the graph and the plot becomes a crowded mess.

I add the ifelse statement so that the first name appears after the segment line and therefore text does not take up too much room on the left edge of the graph.

Last we use the scale_color_manual() function with nice hex colors for each of the political parties.

time_line <- df %>% 
 ggplot(aes(x = as.Date(start), y = number, color = party_factor)) +
 geom_segment(aes(xend = as.Date(end), yend = number, color =  party_factor), size = 6) +
 geom_text(aes(label = order, hjust = ifelse(taoiseach_number < 2, -0.7, 1.1)), size = 8, show.legend = FALSE) +
 scale_color_manual(values = c("Fine Gael" = "#004266", "Fianna Fáil" = "#FCB322", "Cumann na nGaedheal" = "#D62828"))

I increase the limits of the graph to accommodate the name labels. Most of the time, these extra bits of code in ggplot2 depend on the type of data you have and what fits on the graph plane nicely!

So this stages is often only finished after trial-and-error.

I add a snazzy theme_fivethirtyeight() theme from ggthemes package.

Last, with the theme() function, we can remove most of the elements of the graph to make the graph cleaner.

time_line <- time_line + 
  expand_limits(x = as.Date("1915-01-01")) +
  theme_fivethirtyeight() +
  theme(legend.position = "top",
        legend.title = element_blank(),
        legend.direction = "vertical",
        axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        text = element_text(size = 20)) +
  labs(title = "Taoiseach Terms in Ireland",
 subtitle = "From 1922 to 2021") 

We can also create the pie chart to see which party has held power longest in Ireland.

With dplyr we can subtract the start date from the end date and add all the Taoiseach durations (in days) together with the cumsum() argument.

We then choose the highest duration value for each party with the slice(which.max()) functions.

I was lazy and I just re-wrote the values in a new data.frame and called it counts.

df %>%
  group_by(party_factor) %>% 
  dplyr::summarise(max_count = cumsum(duration_number)) %>%  
  slice(which.max(max_count)) %>% 
  select(party_factor, max_count) %>% 
  arrange(desc(max_count))

counts <- data.frame(group = c("Cumann na nGaedheal", "Fine Gael" ,"Fianna Fáil"), 
                     value = c(3381, 10143, 22539))

Create proportion values for our pie-chart graph. To do this divide value by the sum of the values and multiply by 100.

data <- counts %>% 
  arrange(desc(group)) %>%
  dplyr::mutate(prop = value / sum(value) * 100) 

Change the numeric variables to factors.

data$duration <- as.factor(data$value)
data$party_factor <- as.factor(data$group)

We use the coord_polar() to create the piechart. To learn more, check out the r-graph-gallery page about creating pie-charts:

pie_chart <- ggplot(data, aes(x = ", y = prop, fill = group)) + geom_bar(stat = "identity", width = 1, color = "white") + coord_polar("y", start = 0) +

theme(legend.position = "none") + scale_fill_manual(values = c("Fine Gael" = "#004266", "Fianna Fáil" = "#FCB322", "Cumann na nGaedheal" = "#D62828")) +
 labs(title = "Which party held the office of Taoiseach longest?", subtitle = "From 1922 to 2021")

We can tidy up the plot and get rid of theme elements we don’t want with theme_void()

pie_chart <- pie_chart + theme_void() + theme(legend.title = element_blank(), legend.position = none, text = element_text(size = 40))

I want to add both graphs together so I can save the pie chart with a transparent background with the ggsave() function. I also make sure the lines are not jagged with the type = "cairo" from with Cairo package.

ggsave(pie_chart, file="pie_chart.png", type="cairo", bg = "transparent", width = 50, height = 50, units = "cm")

And we can use canva.com to add them together and create a single chart

And viola!

Examining speeches from the UN Security Council Part 1

Let’s look at how many speeches took place at the UN Security Council every year from 1995 until 2019.

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I want to only look at countries, not organisations. So a quick way to do that is to add a variable to indicate whether the speaker variable has an ISO code.

Only countries have ISO codes, so I can use this variable to filter away all the organisations that made speeches

library(countrycode)

speech$iso2 <- countrycode(speech$country, "country.name", "iso2c")

library(bbplot)

speech %>% 
  dplyr::filter(!is.na(iso2)) %>% 
  group_by(year) %>% 
  count() %>% 
  ggplot(aes(x = year, y = n)) + 
  geom_line(size = 1.2, alpha = 0.4) +
  geom_label(aes(label = n)) +
  bbplot::bbc_style() +
  theme(plot.title = element_text(hjust = 0.5)) +
  labs(title = "Number of speeches given by countries at UNSC")

We can see there has been a relatively consistent upward trend in the number of speeches that countries are given at the UN SC. Time will tell what impact COVID will have on these trends.

There was a particularly sharp increase in speeches in 2015.

We can look and see who was talking, and in the next post, we can examine what they were talking about in 2015 with some simple text analytic packages and functions.

First, we will filter only the year 2015 and count the number of observations per group (i.e. the number of speeches per country this year).

To add flags to the graph, add the iso2 code to the dataset (and it must be in lower case).

Click here to read more about adding circular flags to graphs and maps

speech %>% 
  dplyr::filter(year == 2015) %>% 
  group_by(country) %>% 
  dplyr::summarise(speech_count = n()) -> speech_2015

speech_2015$iso2_lower <- tolower(speech_2015$iso2)

We can clean up the names and create a variable that indicates whether the country is one of the five Security Council Permanent Members, a Temporary Member elected or a Non-,ember.

I also clean up the names to make the country’s names in the dataset smaller. For example, “United Kingdom Of Great Britain And Northern Ireland”, will be very cluttered in the graph compared to just “UK” so it will be easier to plot.

library(ggflags)
library(ggthemes)

speech_2015 %>% 
# To avoid the graph being too busy, we only look at countries that gave over 20 speeches
  dplyr::filter(speech_count > 20) %>% 

# Clean up some names so the graph is not too crowded
  dplyr::mutate(country = ifelse(country == "United Kingdom Of Great Britain And Northern Ireland", "UK", country)) %>%
  dplyr::mutate(country = ifelse(country == "Russian Federation", "Russia", country)) %>%
  dplyr::mutate(country = ifelse(country == "United States Of America", "USA", country)) %>%
  dplyr::mutate(country = ifelse(country == "Republic Of Korea", "South Korea", country)) %>%
  dplyr::mutate(country = ifelse(country == "Venezuela (Bolivarian Republic Of)", "Venezuela", country)) %>% 
  dplyr::mutate(country = ifelse(country == "Islamic Republic Of Iran", "Iran", country)) %>% 
  dplyr::mutate(country = ifelse(country == "Syrian Arab Republic", "Syria", country)) %>% 
 
# Create a Member status variable:
# China, France, Russia, the United Kingdom, and the United States are UNSC Permanent Members
  dplyr::mutate(Member = ifelse(country == "UK", "Permanent", 
  ifelse(country == "USA", "Permanent",
  ifelse(country == "China", "Permanent",
  ifelse(country == "Russia", "Permanent",
  ifelse(country == "France", "Permanent",

# Non-permanent members in their first year (elected October 2014)
  ifelse(country == "Angola", "Temporary (Elected 2014)",
  ifelse(country == "Malaysia", "Temporary (Elected 2014)",              
  ifelse(country == "Venezuela", "Temporary (Elected 2014)",       
  ifelse(country == "New Zealand", "Temporary (Elected 2014)",
  ifelse(country == "Spain", "Temporary (Elected 2014)",                 

# Non-permanent members in their second year (elected October 2013)        
  ifelse(country == "Chad", "Temporary (Elected 2013)",                                                               
  ifelse(country == "Nigeria", "Temporary (Elected 2013)",
  ifelse(country == "Jordan", "Temporary (Elected 2013)",
  ifelse(country == "Chile", "Temporary (Elected 2013)",
  ifelse(country == "Lithuania", "Temporary (Elected 2013)", 
 
# Non members that will join UNSC next year (elected October 2015)          
  ifelse(country == "Egypt", "Non-Member (Elected 2015)",                                                               
  ifelse(country == "Sengal", "Non-Member (Elected 2015)",
  ifelse(country == "Uruguay", "Non-Member (Elected 2015)",
  ifelse(country == "Japan", "Non-Member (Elected 2015)",
  ifelse(country == "Ukraine", "Non-Member (Elected 2015)", 

# Everyone else is a regular non-member           
               "Non-Member"))))))))))))))))))))) -> speech_2015

When we have over a dozen nested ifelse() statements, we will need to check that we have all our corresponding closing brackets.

Next choose some colours for each Memberships status. I always take my hex values from https://coolors.co/

membership_palette <- c("Permanent" = "#e63946", "Non-Member" = "#2a9d8f", "Non-Member (Elected 2015)" = "#a8dadc", "Temporary (Elected 2013)" = "#457b9d","Temporary (Elected 2014)" = "#1d3557")
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And all that is left to do is create the bar chart.

With geom_bar(), we can indicate stat = "identity" because we are giving the plot the y values and ggplot does not need to do the automatic aggregation on its own.

To make sure the bars are descending from most speeches to fewest speeches, we use the reorder() function. The second argument is the variable according to which we want to order the bars. So for us, we give the speech_count integer variable to order our country bars with x = reorder(country, speech_count).

We can change the bar from vertical to horizontal with coordflip().

I add flags with geom_flag() and feed the lower case ISO code to the country = iso2_lower argument.

I add the bbc_style() again because I like the font, size and sparse lines on the plot.

We can move the title of the plot into the centre with plot.title = element_text(hjust = 0.5))

Finally, we can supply the membership_palette vector to the values = argument in the scale_fill_manual() function to specify the colours we want.

speech_2015 %>%  ggplot(aes(x = reorder(country, speech_count), y = speech_count)) + 
  geom_bar(stat = "identity", aes(fill = as.factor(Member))) +
  coord_flip() +
  ggflags::geom_flag(mapping = aes(y = -15, x = country, country = iso2_lower), size = 10) +
  geom_label(mapping = aes( label = speech_count), size = 8) +
  theme(legend.position = "top") + 
  labs(title = "UNSC speeches given in 2015", y = "Number of speeches", x = "") +
  bbplot::bbc_style() +
  theme(text = element_text(size = 20),
  plot.title = element_text(hjust = 0.5)) +
  scale_fill_manual(values =  membership_palette)

In the next post, we will look at the texts themselves. Here is a quick preview.

library(tidytext)

speech_tokens <- speech %>%
  unnest_tokens(word, text) %>% 

  anti_join(stop_words)

We count the number of tokens (i.e. words) for each country in each year. With the distinct() function we take only one observation per year per country. This reduces the number of rows from 16601520 in speech_tokesn to 3142 rows in speech_words_count :

speech_words_count <- speech_tokens %>%
  group_by(year, country) %>%
  mutate(word_count = n_distinct(word)) %>%
  select(country, year, word_count, permanent, iso2_lower) %>%
  distinct() 

Subset the data.frame to only plot the five Permanent Members. Now we only have 125 rows (25 years of total annual word counts for 5 countries!)

permanent_words_summary <- speech_words_count %>% 
  filter(permanent == 1) 

Choose some nice hex colors for my five countries:

five_pal <- c("#ffbc42","#d81159","#8f2d56","#218380","#73d2de")

It is a bit convoluted to put the flags ONLY at the start and end of the lines. We need to subset the dataset two times with the geom_flag() sections. First, we subset the data.frame to year == 1995 and the flags appear at the start of the word_count on the y axis. Then we subset to year == 2019 and do the same

ggplot(data = permanent_word_summary) +
  geom_line(aes(x = year, y = word_count, group = as.factor(country), color = as.factor(country)), 
size = 2) +
  ggflags::geom_flag(data = subset(permanent_word_summary, year == 1995), aes(x = 1995, y = word_count,  country = iso2_lower), size = 9) +
  ggflags::geom_flag(data = subset(permanent_word_summary, 
year == 2019), 
aes(x = 2019, 
y = word_count, 
country = iso2_lower), 
size = 12) + 
  bbplot::bbc_style() +
 theme(legend.position = "right") + labs(title = "Number of words spoken by Permanent Five in the UN Security Council") + 
  scale_color_manual(values = five_pal)

We can see that China has been the least chattiest country if we are measuring chatty with number of words spoken. Translation considerations must also be taken into account. We can see here again at around the 2015 mark, there was a discernible increase in the number of words spoken by most of the countries!

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Create a correlation matrix with GGally package in R

We can create very informative correlation matrix graphs with one function.

Packages we will need:

library(GGally)
library(bbplot) #for pretty themes

First, choose some nice hex colors.

my_palette <- c("#005D8F", "#F2A202")
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Next, we can go create a dichotomous factor variable and divide the continuous “freedom from torture scale” variable into either above the median or below the median score. It’s a crude measurement but it serves to highlight trends.

Blue means the country enjoys high freedom from torture. Yellow means the county suffers from low freedom from torture and people are more likely to be tortured by their government.

Then we feed our variables into the ggpairs() function from the GGally package.

I use the columnLabels to label the graphs with their full names and the mapping argument to choose my own color palette.

I add the bbc_style() format to the corr_matrix object because I like the font and size of this theme. And voila, we have our basic correlation matrix (Figure 1).

corr_matrix <- vdem90 %>% 
  dplyr::mutate(
    freedom_torture = ifelse(torture >= 0.65, "High", "Low"),
    freedom_torture = as.factor(freedom_t))
  dplyr::select(freedom_torture, civil_lib, class_eq) %>% 
  ggpairs(columnLabels = c('Freedom from Torture', 'Civil Liberties', 'Class Equality'), 
    mapping = ggplot2::aes(colour = freedom_torture)) +
  scale_fill_manual(values = my_palette) +
  scale_color_manual(values = my_palette)

corr_matrix + bbplot::bbc_style()
Figure 1.
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First off, in Figure 2 we can see the centre plots in the diagonal are the distribution plots of each variable in the matrix

Figure 2.

In Figure 3, we can look at the box plot for the ‘civil liberties index’ score for both high (blue) and low (yellow) ‘freedom from torture’ categories.

The median civil liberties score for countries in the high ‘freedom from torture’ countries is far higher than in countries with low ‘freedom from torture’ (i.e. citizens in these countries are more likely to suffer from state torture). The spread / variance is also far great in states with more torture.

Figure 3.

In Figur 4, we can focus below the diagonal and see the scatterplot between the two continuous variables – civil liberties index score and class equality index scores.

We see that there is a positive relationship between civil liberties and class equality. It looks like a slightly U shaped, quadratic relationship but a clear relationship trend is not very clear with the countries with higher torture prevalence (yellow) showing more randomness than the countries with high freedom from torture scores (blue).

Saying that, however, there are a few errant blue points as outliers to the trend in the plot.

The correlation score is also provided between the two categorical variables and the correlation score between civil liberties and class equality scores is 0.52.

Examining at the scatterplot, if we looked only at countries with high freedom from torture, this correlation score could be higher!

Figure 4.

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