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

Packages we will need:


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

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

Click here to read more about this super handy package:

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

pacl <- redownload_pacl()

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

pacl %>% 
  count(npost) %>% 

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

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

# ... with 145 more rows

155 groups is a bit difficult to meaningfully compare.

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

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

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

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

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

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

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

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

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

First, we can quickly look at fct_lump_min().

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

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

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

Next we look at fct_lump_lowfreq().

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

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

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

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

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

fh <- download_fh()

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

Visualise DemocracyData with graphs and maps

Packages we will need:


In this post, we will look at easy ways to graph data from the democracyData package.

The two datasets we will look at are the Anckar-Fredriksson dataset of political regimes and Freedom House Scores.

Regarding democracies, Anckar and Fredriksson (2018) distinguish between republics and monarchies. Republics can be presidential, semi-presidential, or parliamentary systems.

Within the category of monarchies, almost all systems are parliamentary, but a few countries are conferred to the category semi-monarchies.

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Autocratic countries can be in the following main categories: absolute monarchy, military rule, party-based rule, personalist rule, and oligarchy.

anckar <- democracyData::redownload_anckar()
fh <- download_fh()

We will see which regime types have been free or not since 1970.

We join the fh dataset to the anckar dataset with inner_join(). Luckily, both the datasets have the cown and year variables with which we can merge.

Then we sumamrise the mean Freedom House level for each regime type.

anckar %>% 
  inner_join(fh, by = c("cown", "year")) %>% 
  filter(! %>%
  group_by(regimebroadcat, year) %>% 
  summarise(mean_fh = mean(fh_total_reversed, na.rm = TRUE)) -> anckar_sum

We want to place a label for each regime line in the graph, so create a small dataframe with regime score information only about the first year.

anckar_start <- anckar_sum %>%
  group_by(regimebroadcat) %>% 
  filter(year == 1972) %>% 

And we pick some more jewel toned colours for the graph and put them in a vector.

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

And we graph it out

anckar_sum %>%
  ggplot(aes(x = year, y = mean_fh, groups = as.factor(regimebroadcat))) + 
  geom_point(aes(color = regimebroadcat), alpha = 0.7, size = 2) + 
  geom_line(aes(color = regimebroadcat), alpha = 0.7, size = 2) +
  ggrepel::geom_label_repel(data = anckar_start, hjust = 1.5,
            aes(x = year,
                y = mean_fh,
                color = regimebroadcat,
                label = regimebroadcat),
            alpha = 0.7,
            show.legend = FALSE, 
            size = 9) + 
  scale_color_manual(values = my_palette) +
  expand_limits(x = 1965) +  
  ggthemes::theme_pander() + 
  theme(legend.position = "none",
        axis.text = element_text(size = 30, colour ="grey40")) 

We can also use map data that comes with the tidyverse() package.

To merge the countries easily, I add a cown variable to this data.frame

world_map <- map_data("world")

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

I want to only look at regimes types in the final year in the dataset – which is 2018 – so we filter only one year before we merge with the map data.frame.

The geom_polygon() part is where we indiciate the variable we want to plot. In our case it is the regime category

anckar %>% 
 filter(year == max(year)) %>%
  inner_join(world_map, by = c("cown")) %>%
  mutate(regimebroadcat = ifelse(region == "Libya", 'Military rule', regimebroadcat)) %>% 
  ggplot(aes(x = long, y = lat, group = group)) + 
  geom_polygon(aes(fill = regimebroadcat), color = "white", size = 1) 
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We can next look at the PIPE dataset and see which countries have been uninterrupted republics over time.

pipe <- democracyData::redownload_pipe()

We graph out the max_republic_age variable with geom_bar()

pipe %>% 
  mutate(iso_lower = tolower(countrycode::countrycode(PIPE_cowcodes, "cown", "iso2c"))) %>% 
  mutate(country_name = countrycode::countrycode(PIPE_cowcodes, "cown", "")) %>% 
  filter(year == max(year)) %>% 
  filter(max_republic_age > 100) %>% 
  ggplot(aes(x = reorder(country_name, max_republic_age), y = max_republic_age)) + 
  geom_bar(stat = "identity", width = 0.7, aes(fill = as.factor(europe))) +
  ggflags::geom_flag(aes(y = max_republic_age, x = country_name, 
                         country = iso_lower), size = 15) + 
  coord_flip() +  ggthemes::theme_pander() -> pipe_plot

And fix up some aesthetics:

pipe_plot + 
  theme(axis.text = element_text(size = 30),
        legend.text = element_text(size = 30),
        legend.title = element_blank(),
        axis.title = element_blank(),
        legend.position = "bottom") + 
  labs(y= "", x = "") + 
scale_fill_manual(values =  c("#d62828", "#457b9d"),
 labels = c("Former British Settler Colony", "European Country")) 

I added the header and footer in

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Download democracy data with democracyData package in R

Packages we will need:

library(magrittr)       # for pipes
library(ggstream)       # proportion plots
library(ggthemes)       # nice ggplot themes
library(forcats)        # reorder factor variables
library(ggflags)        # add flags
library(peacesciencer)  # more great polisci data
library(countrycode)    # add ISO codes to countries

This blog will highlight some quick datasets that we can download with this nifty package.

To install the democracyData package, it is best to do this via the github of Xavier Marquez:

remotes::install_github("xmarquez/democracyData", force = TRUE)

We can download the dataset from the Democracy and Dictatorship Revisited paper by Cheibub Gandhi and Vreeland (2010) with the redownload_pacl() function. It’s all very simple!

pacl <- redownload_pacl()
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This gives us over 80 variables, with information on things such as regime type, geographical data, the name and age of the leaders, and various democracy variables.

We are going to focus on the different regimes across the years.

The six-fold regime classification Cheibub et al (2010) present is rooted in the dichotomous classification of regimes as democracy and dictatorship introduced in Przeworski et al. (2000). They classify according to various metrics, primarily by examining the way in which governments are removed from power and what constitutes the “inner sanctum” of power for a given regime. Dictatorships can be distinguished according to the characteristics of these inner sanctums. Monarchs rely on family and kin networks along with consultative councils; military rulers confine key potential rivals from the armed forces within juntas; and, civilian dictators usually create a smaller body within a regime party—a political bureau—to coopt potential rivals. Democracies highlight their category, depending on how the power of a given leadership ends

We can change the regime variable from numbers to a factor variables, describing the type of regime that the codebook indicates:

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)) 

Before we make the graph, we can give traffic light hex colours to the types of democracy. This goes from green (full democracy) to more oranges / reds (autocracies):

regime_palette <- c("Military dictatorships" = "#f94144", 
                    "Civilian autocracies" = "#f3722c", 
                    "Royal dictatorships" =  "#f8961e", 
                    "Mixed democracies" = "#f9c74f", 
                    "Presidential democracies" = "#90be6d", 
                    "Parliamentary democracies" = "#43aa8b")

We will use count() to count the number of countries in each regime type and create a variable n

pacl %>% 
  mutate(regime_name = as.factor(regime_name)) %>% 
  mutate(regime_name = fct_relevel(regime_name, 
 levels = c("Parliamentary democracies", 
           "Presidential democracies",
           "Mixed democracies",
           "Royal dictatorships",
           "Civilian autocracies",
           "Military dictatorships"))) %>% 
  group_by(year, un_continent_name) %>% 
  filter(! %>% 
  count(regime_name) %>% 
  ungroup() %>%  
  filter(un_continent_name != "") %>%
  filter(un_continent_name != "Oceania") -> pacl_count
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We have all the variables we need.

We can now graph the count variables across different regions.

pacl_count %>% 
  ggplot(aes(x = year, y = n, 
             groups = regime_name, 
             fill = regime_name)) +
  ggstream::geom_stream(type = "proportion") + 
  facet_wrap(~un_continent_name) + 
  scale_fill_manual(values = regime_palette) + 
  ggthemes::theme_fivethirtyeight() + 
  theme(legend.title = element_blank(),
        text = element_text(size = 30)) 

I added the title and source header / footer section on to finish the graph.

Of course, the Cheibub et al (2010) dataset is not the only one that covers types of regimes.

Curtis Bell in 2016 developed the Rulers, Elections, and Irregular Governance Dataset (REIGN) dataset.

This describes political conditions in every country (including tenures and personal characteristics of world leaders, the types of political institutions and political regimes in effect, election outcomes and election announcements, and irregular events like coups)

Again, to download this dataset with the democracyData package, it is very simple:

reign <- download_reign()
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I want to compare North and South Korea since their independence from Japan and see the changes in regimes and democracy scores over the years.

Next, we can easily download Freedom House or Polity 5 scores.

The Freedom House Scores default dataset ranges from 1972 to 2020, covering around 195 countries (depending on the year)

fh <- download_fh()

Alternatively, we can look at Polity Scores. This default dataset countains around 190 ish countries (again depending on the year and the number of countries in existance at that time) and covers a far longer range of years; from 1880 to 2018.

polityiv <- redownload_polityIV()

Alternatively, to download democracy scores, we can also use the peacesciencer dataset. Click here to read more about this package:

democracy_scores <- peacesciencer::create_stateyears() %>% 
  add_gwcode_to_cow() %>%

With inner_join() we can merge these two datasets together:

reign %>% 
  select(ccode = cown, everything()) %>% 
  inner_join(democracy_scores, by = c("year", "ccode")) -> reign_demo

We next choose the years and countries for our plot.

Also, for the geom_flag() we will need the country name to be lower case ISO code. Click here to read more about the ggflags package.

reign_demo %>% 
    filter(year > 1945) %>% 
    mutate(gwf_regimetype = str_to_title(gwf_regimetype)) %>% 
    mutate(iso2c_lower = tolower(countrycode::countrycode(reign_country, "", "iso2c"))) %>% 
filter(reign_country == "Korea North" | reign_country == "Korea South") -> korea_reign

We may to use specific hex colours for our graphs. I always prefer these deeper colours, rather than the pastel defaults that ggplot uses. I take them from website!

korea_palette <- c("Military" = "#5f0f40",
                   "Party-Personal" = "#9a031e",
                   "Personal" = "#fb8b24",
                   "Presidential" = "#2a9d8f",
                   "Parliamentary" = "#1e6091")

We will add a flag to the start of the graph, so we create a mini dataset that only has the democracy scores for the first year in the dataset.

  korea_start <- korea_reign %>%
    group_by(reign_country) %>% 
    slice(which.min(year)) %>% 

Next we plot the graph

korea_reign %>% 
 ggplot(aes(x = year, y = v2x_polyarchy, groups = reign_country))  +
    geom_line(aes(color = gwf_regimetype), 
         size = 7, alpha = 0.7, show.legend = FALSE) +
    geom_point(aes(color = gwf_regimetype), size = 7, alpha = 0.7) +
    ggflags::geom_flag(data = korea_start, 
       aes(y = v2x_polyarchy, x = 1945, country = iso2c_lower), 
           size = 20) -> korea_plot

And then work on the aesthetics of the plot:

korea_plot + ggthemes::theme_fivethirtyeight() + 
    ggtitle("Electoral democracy on Korean Peninsula") +
    labs(subtitle = "Sources: Teorell et al. (2019) and Curtis (2016)") +
    xlab("Year") + 
    ylab("Democracy Scores") + 
    theme(plot.title = element_text(face = "bold"),
      axis.ticks = element_blank(), = element_blank(),
      legend.title = element_blank(),
      legend.text = element_text(size = 40),
      text = element_text(size = 30)) +
    scale_color_manual(values = korea_palette) + 
    scale_x_continuous(breaks = round(seq(min(korea_reign$year), max(korea_reign$year), by = 5),1))

While North Korea has been consistently ruled by the Kim dynasty, South Korea has gone through various types of government and varying levels of democracy!


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

Przeworski, A., Alvarez, R. M., Alvarez, M. E., Cheibub, J. A., Limongi, F., & Neto, F. P. L. (2000). Democracy and development: Political institutions and well-being in the world, 1950-1990 (No. 3). Cambridge University Press.

Scrape and graph election polling data from Wikipedia

Packages we will need:


With the Korean Presidential elections coming up, I wanted to graph the polling data since the beginning of this year.

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The data we can use is all collated together on Wikipedia.

Click here to read more about using the rvest package for scraping data from websites and click here to read the CRAN PDF for the package.

poll_html <- read_html("")

poll_tables <- poll_html %>% html_table(header = TRUE, fill = TRUE)

There are 22 tables on the page in total.

I count on the page that the polling data is the 16th table on the page, so extract index [[16]] from the list

feb_poll <- poll_tables[[16]]

It is a bit messy, so we will need to do a bit of data cleaning before we can graph.

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First the names of many variables are missing or on row 2 / 3 of the table, due to pictures and split cells in Wikipedia.

 [1] "Polling firm / Client" "Polling firm / Client" "Fieldwork  date"       "Sample  size" "Margin of  error"     
 [6] ""       ""      ""     ""      ""                     
[11] ""  "Others/Undecided"   "Lead"   

The clean_names() function from the janitor package does a lot of the brute force variable name cleaning!

feb_poll %<>% clean_names()

We now have variable names rather than empty column names, at least.

 [1] "polling_firm_client" "polling_firm_client_2" "fieldwork_date"        "sample_size"  "margin_of_error"      
 [6] "x"  "x_2"  "x_3"  "x_4"  "x_5"                  
[11] "x_6"  "others_undecided"   "lead"

We can choose the variables we want and rename the x variables with the names of each candidate, according to Wikipedia.

feb_poll %<>% 
         Lee = x, 
         Yoon = x_2,
         Shim = x_3,
         Ahn = x_4, 
         Kim = x_5, 
         Heo = x_6,

We then delete the rows that contain text not related to the poll number values.

feb_poll = feb_poll[-25,]
feb_poll = feb_poll[-81,]
feb_poll = feb_poll[-1,]

I want to clean up the fieldwork_date variable and convert it from character to Date class.

First I found that very handy function on Stack Overflow that extracts the last n characters from a string variable.

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

If we look at the table, some of the surveys started in Feb but ended in March. We want to extract the final section (i.e. the March section) and use that.

So we use grepl() to find rows that have both Feb AND March, and just extract the March section. If it only has one of those months, we leave it as it is.

feb_poll %<>% 
  mutate(clean_date = ifelse(grepl("Feb", fieldwork_date) & grepl("Mar", fieldwork_date), substrRight(fieldwork_date, 5), fieldwork_date))

Next want to extract the three letter date from this variables and create a new month variable

feb_poll %<>%
  mutate(month = substrRight(clean_date, 3)) 

Following that, we use the parse_number() function from tidyr package to extract the first number found in the string and create a day_number varible (with integer class now)

 feb_poll %<>%
   mutate(day_number = parse_number(clean_date))   

We want to take these two variables we created and combine them together with the unite() function from tidyr again! We want to delete the variables after we unite them. But often I want to keep the original variables, so usually I change the argument remove to FALSE.

We indicate we want to have nothing separating the vales with the sep = "" argument

 feb_poll %<>%
     unite("date", day_number:month, sep = "", remove = TRUE)

And we convert this new date into Date class with as.Date() function.

Here is a handy cheat sheet to help choose the appropriate % key so the format recognises the dates. I will never memorise these values, so I always need to refer to this site.

We have days as numbers (1, 2, 3) and abbreviated 3 character month (Jan, Feb, Mar), so we choose %d and %b

feb_poll %<>%
  mutate(dates_format = as.Date(date, "%d%b")) %>% 
  select(dates_format, Lee:others_undecided) 

Next, we will use the pivot_longer() function to combine all the poll number values into one column. This will make it far easier to plot later.

feb_poll %<>%
  pivot_longer(!dates_format, names_to = "candidate", values_to = "favour") 

After than, we need to clean the actual numbers, remove the percentage signs and convert from character to number class. We use the str_extract() and the regex code to extract the number and not keep the percentage sign.

feb_poll %<>%
    mutate(candidate = as.factor(candidate),
 favour_percent = str_extract(favour, "\\d+\\.*\\d*")) %>% 
   mutate(favour_percent = as.integer(favour_percent)) 

Some of the different polls took place on the same day. So we will take the average poll favourability value for each candidate on each day with the group_by() function

feb_poll %<>%
  group_by(dates_format, candidate) %>% 
  mutate(favour_percent_mean = mean(favour_percent, na.rm = TRUE)) %>% 
  ungroup() %>% 
  select(candidate, dates_format, favour_percent_mean) 

And this is how the cleaned up data should look!

We repeat for the 17th and 16th tables, which contain data going back to the beginning of January 2022

early_feb_poll <- poll_tables[[17]]
early_feb_poll = early_feb_poll[-37,]
early_feb_poll = early_feb_poll[-1,]

We repeat the steps from above with early Feb in one chunk

early_feb_poll %<>%
  clean_names() %>% 
  mutate(month = substrRight(fieldwork_date, 3))  %>% 
  mutate(day_number = parse_number(fieldwork_date)) %>%
  unite("date", day_number:month, sep = "", remove = FALSE) %>% 
  mutate(dates_format = as.Date(date, "%d%b")) %>% 
         Lee = lee_jae_myung, 
         Yoon = yoon_seok_youl,
         Shim = sim_sang_jung,
         Ahn = ahn_cheol_soo, 
         Kim = kim_dong_yeon, 
         Heo = huh_kyung_young,
         others_undecided) %>% 
  pivot_longer(!dates_format, names_to = "candidate", values_to = "favour") %>% 
  mutate(candidate = as.factor(candidate),
         favour_percent = str_extract(favour, "\\d+\\.*\\d*")) %>% 
  mutate(favour_percent = as.integer(favour_percent)) %>% 
  group_by(dates_format, candidate) %>% 
  mutate(favour_percent_mean = mean(favour_percent, na.rm = TRUE)) %>% 
  ungroup() %>% 
  select(candidate, dates_format, favour_percent_mean)

And we use rbind() to combine the two data.frames

polls <- rbind(feb_poll, early_feb_poll)

Next we repeat with January data:

jan_poll <- poll_tables[[18]]

jan_poll = jan_poll[-34,]
jan_poll = jan_poll[-1,]

jan_poll %<>% 
  clean_names() %>% 
  mutate(month = substrRight(fieldwork_date, 3))  %>% 
  mutate(day_number = parse_number(fieldwork_date)) %>%   # drops any non-numeric characters before or after the first number. 
  unite("date", day_number:month, sep = "", remove = FALSE) %>% 
  mutate(dates_format = as.Date(date, "%d%b")) %>% 
         Lee = lee_jae_myung, 
         Yoon = yoon_seok_youl,
         Shim = sim_sang_jung,
         Ahn = ahn_cheol_soo, 
         Kim = kim_dong_yeon, 
         Heo = huh_kyung_young,
         others_undecided) %>% 
  pivot_longer(!dates_format, names_to = "candidate", values_to = "favour") %>% 
  mutate(candidate = as.factor(candidate),
         favour_percent = str_extract(favour, "\\d+\\.*\\d*")) %>% 
  mutate(favour_percent = as.integer(favour_percent)) %>% 
  group_by(dates_format, candidate) %>% 
  mutate(favour_percent_mean = mean(favour_percent, na.rm = TRUE)) %>% 
  ungroup() %>% 
  select(candidate, dates_format, favour_percent_mean)

And bind to our combined data.frame:

polls <- rbind(polls, jan_poll)

We can create variables to help us filter different groups of candidates. If we want to only look at the largest candidates, we can makes an important variable and then filter

We can lump the candidates that do not have data from every poll (i.e. the smaller candidate) and add them into the “other_undecided” category with the fct_lump_min() function from the forcats package

polls %>% 
  mutate(important = ifelse(candidate %in% c("Ahn", "Yoon", "Lee", "Shim"), 1, 0)) %>% 
  mutate(few_candidate = fct_lump_min(candidate, min = 110, other_level = "others_undecided")) %>% 
  group_by(few_candidate, dates_format) %>% 
  filter(important == 1) -> poll_data

I want to only look at the main two candidates from the main parties that have been polling in the 40% range – Lee and Yoon – as well as the data for Ahn (who recently dropped out and endorsed Yoon).

poll_data %>% 
  filter(candidate %in% c("Lee", "Yoon", "Ahn")) -> lee_yoon_data

We take the official party hex colors for the graph and create a vector to use later with the scale_color_manual() function below:

party_palette <- c(
  "Ahn" = "#df550a",
  "Lee" = "#00a0e2",
  "Yoon" = "#e7001f")

And we plot the variables.

lee_yoon_data %>% 
  ggplot(aes(x = dates_format, y = favour_percent_mean,
             groups = candidate, color = candidate)) + 
  geom_line( size = 2, alpha = 0.8) +
  geom_point(fill = "#5e6472", shape = 21, size = 4, stroke = 3) + 
  labs(title = "Polling data for Korean Presidential Election", subtitle = "Source: various polling companies, via Wikipedia") -> poll_graph

The bulk of aesthetics for changing the graph appearance in the theme()

poll_graph + theme(panel.border = element_blank(),
        legend.position = "bottom",        
        text = element_text(size = 15, color = "white"),
        plot.title = element_text(size = 40),
        legend.title = element_blank(),
        legend.text = element_text(size = 50, color = "white"),
        axis.text.y = element_text(size = 20), 
        axis.text.x = element_text(size = 20),
        legend.background = element_rect(fill = "#5e6472"),
        axis.title = element_blank(),
        axis.text = element_text(color = "white", size = 20),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        legend.key = element_rect(fill = "#5e6472"),
        plot.background = element_rect(fill = "#5e6472"),
        panel.background = element_rect(fill = "#5e6472")) +
  scale_color_manual(values = party_palette) 

Last, with the annotate() functions, we can also add an annotation arrow and text to add some more information about Ahn Cheol-su the candidate dropping out.

  annotate("text", x = as.Date("2022-02-11"), y = 13, label = "Ahn dropped out just as the polling blackout began", size = 10, color = "white") +
  annotate(geom = "curve", x = as.Date("2022-02-25"), y = 13, xend = as.Date("2022-03-01"), yend = 10, 
    curvature = -.3, arrow = arrow(length = unit(2, "mm")), size = 1, color = "white")

We will just have to wait until next Wednesday / Thursday to see who is the winner ~

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Exploratory Data Analysis and Descriptive Statistics for Political Science Research in R

Packages we will use:

library(tidyverse)      # of course
library(ggridges)       # density plots
library(GGally)         # correlation matrics
library(stargazer)      # tables
library(knitr)          # more tables stuff
library(kableExtra)     # more and more tables
library(ggrepel)        # spread out labels
library(ggstream)       # streamplots
library(bbplot)         # pretty themes
library(ggthemes)       # more pretty themes
library(ggside)         # stack plots side by side
library(forcats)        # reorder factor levels

Before jumping into any inferentional statistical analysis, it is helpful for us to get to know our data. For me, that always means plotting and visualising the data and looking at the spread, the mean, distribution and outliers in the dataset.

Before we plot anything, a simple package that creates tables in the stargazer package. We can examine descriptive statistics of the variables in one table.

Click here to read this practically exhaustive cheat sheet for the stargazer package by Jake Russ. I refer to it at least once a week.

I want to summarise a few of the stats, so I write into the summary.stat() argument the number of observations, the mean, median and standard deviation.

The kbl() and kable_classic() will change the look of the table in R (or if you want to copy and paste the code into latex with the type = "latex" argument).

In HTML, they do not appear.

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To find out more about the knitr kable tables, click here to read the cheatsheet by Hao Zhu.

Choose the variables you want, put them into a data.frame and feed them into the stargazer() function

          covariate.labels = c("Corruption index",
                               "Civil society strength", 
                               'Rule of Law score',
                               "Physical Integerity Score",
                               "GDP growth"),
          summary.stat = c("n", "mean", "median", "sd"), 
          type = "html") %>% 
  kbl() %>% 
  kable_classic(full_width = F, html_font = "Times", font_size = 25)
StatisticNMeanMedianSt. Dev.
Corruption index1790.4770.5190.304
Civil society strength1790.6700.8050.287
Rule of Law score1737.4517.0004.745
Physical Integerity Score1790.6960.8070.284
GDP growth1630.0190.0200.032

Next, we can create a barchart to look at the different levels of variables across categories. We can look at the different regime types (from complete autocracy to liberal democracy) across the six geographical regions in 2018 with the geom_bar().

my_df %>% 
  filter(year == 2018) %>%
  ggplot() +
               fill = as.factor(regime)),
           color = "white", size = 2.5) -> my_barplot

And we can add more theme changes

my_barplot + bbplot::bbc_style() + 
  theme(legend.key.size = unit(2.5, 'cm'),
        legend.text = element_text(size = 15),
        text = element_text(size = 15)) +
  scale_fill_manual(values = c("#9a031e","#00a896","#e36414","#0f4c5c")) + 
  scale_color_manual(values = c("#9a031e","#00a896","#e36414","#0f4c5c")) 

This type of graph also tells us that Sub-Saharan Africa has the highest number of countries and the Middle East and North African (MENA) has the fewest countries.

However, if we want to look at each group and their absolute percentages, we change one line: we add geom_bar(position = "fill"). For example we can see more clearly that over 50% of Post-Soviet countries are democracies ( orange = electoral and blue = liberal democracy) as of 2018.

We can also check out the density plot of democracy levels (as a numeric level) across the six regions in 2018.

With these types of graphs, we can examine characteristics of the variables, such as whether there is a large spread or normal distribution of democracy across each region.

my_df %>% 
  filter(year == 2018) %>%
  ggplot(aes(x = democracy_score, y = region, fill = regime)) +
  geom_density_ridges(color = "white", size = 2, alpha = 0.9, scale = 2) -> my_density_plot

And change the graph theme:

my_density_plot + bbplot::bbc_style() + 
  theme(legend.key.size = unit(2.5, 'cm')) +
  scale_fill_manual(values = c("#9a031e","#00a896","#e36414","#0f4c5c")) + 
  scale_color_manual(values = c("#9a031e","#00a896","#e36414","#0f4c5c")) 

Click here to read more about the ggridges package and click here to read their CRAN PDF.

Next, we can also check out Pearson’s correlations of some of the variables in our dataset. We can make these plots with the GGally package.

The ggpairs() argument shows a scatterplot, a density plot and correlation matrix.

my_df %>%
  filter(year == 2018) %>%
         gdp_growth) %>% 
  ggpairs(columns = 2:5, 
          ggplot2::aes(colour = as.factor(regime), 
          alpha = 0.9)) + 
  bbplot::bbc_style() +
  scale_fill_manual(values = c("#9a031e","#00a896","#e36414","#0f4c5c")) + 
  scale_color_manual(values = c("#9a031e","#00a896","#e36414","#0f4c5c"))

Click here to read more about the GGally package and click here to read their CRAN PDF.

We can use the ggside package to stack graphs together into one plot.

There are a few arguments to add when we choose where we want to place each graph.

For example, geom_xsideboxplot(aes(y = freedom_house), orientation = "y") places a boxplot for the three Freedom House democracy levels on the top of the graph, running across the x axis. If we wanted the boxplot along the y axis we would write geom_ysideboxplot(). We add orientation = "y" to indicate the direction of the boxplots.

Next we indiciate how big we want each graph to be in the panel with theme(ggside.panel.scale = .5) argument. This makes the scatterplot take up half and the boxplot the other half. If we write .3, the scatterplot takes up 70% and the boxplot takes up the remainning 30%. Last we indicade scale_xsidey_discrete() so the graph doesn’t think it is a continuous variable.

We add Darjeeling Limited color palette from the Wes Anderson movie.

Click here to learn about adding Wes Anderson theme colour palettes to graphs and plots.

my_df %>%
 filter(year == 2018) %>% 
 filter(! %>% 
  mutate(freedom_house = ifelse(fh_number == 1, "Free", 
         ifelse(fh_number == 2, "Partly Free", "Not Free"))) %>%
  mutate(freedom_house = forcats::fct_relevel(freedom_house, "Not Free", "Partly Free", "Free")) %>% 
ggplot(aes(x = freedom_from_torture, y = corruption_level, colour = as.factor(freedom_house))) + 
  geom_point(size = 4.5, alpha = 0.9) +
  geom_smooth(method = "lm", color ="#1d3557", alpha = 0.4) +  
  geom_xsideboxplot(aes(y = freedom_house), orientation = "y", size = 2) +
  theme(ggside.panel.scale = .3) +
  scale_xsidey_discrete() +
  bbplot::bbc_style() + 
  facet_wrap(~region) + 
  scale_color_manual(values= wes_palette("Darjeeling1", n = 3))

The next plot will look how variables change over time.

We can check out if there are changes in the volume and proportion of a variable across time with the geom_stream(type = "ridge") from the ggstream package.

In this instance, we will compare urban populations across regions from 1800s to today.

my_df %>% 
  group_by(region, year) %>% 
  summarise(mean_urbanization = mean(urban_population_percentage, na.rm = TRUE)) %>% 
  ggplot(aes(x = year, y = mean_urbanization, fill = region)) +
  geom_stream(type = "ridge") -> my_streamplot

And add the theme changes

  my_streamplot + ggthemes::theme_pander() + 
legend.title = element_blank(),
        legend.position = "bottom",
        legend.text = element_text(size = 25),
        axis.text.x = element_text(size = 25),
        axis.title.y = element_blank(),
        axis.title.x = element_blank()) +
  scale_fill_manual(values = c("#001219",

Click here to read more about the ggstream package and click here to read their CRAN PDF.

We can also look at interquartile ranges and spread across variables.

We will look at the urbanization rate across the different regions. The variable is calculated as the ratio of urban population to total country population.

Before, we will create a hex color vector so we are not copying and pasting the colours too many times.

my_palette <- c("#1d3557",

We use the facet_wrap(~year) so we can separate the three years and compare them.

my_df %>% 
  filter(year == 1980 | year == 1990 | year == 2000)  %>% 
  ggplot(mapping = aes(x = region, 
                       y = urban_population_percentage, 
                       fill = region)) +
  geom_jitter(aes(color = region),
              size = 3, alpha = 0.5, width = 0.15) +
  geom_boxplot(alpha = 0.5) + facet_wrap(~year) + 
  scale_fill_manual(values = my_palette) +
  scale_color_manual(values = my_palette) + 
  coord_flip() + 

If we want to look more closely at one year and print out the country names for the countries that are outliers in the graph, we can run the following function and find the outliers int he dataset for the year 1990:

is_outlier <- function(x) {
  return(x < quantile(x, 0.25) - 1.5 * IQR(x) | x > quantile(x, 0.75) + 1.5 * IQR(x))

We can then choose one year and create a binary variable with the function

my_df_90 <- my_df %>% 
  filter(year == 1990) %>% 

my_df_90$my_outliers <- is_outlier(my_df_90$urban_population_percentage)

And we plot the graph:

my_df_90 %>% 
  ggplot(mapping = aes(x = region, y = urban_population_percentage, fill = region)) +
  geom_jitter(aes(color = region), size = 3, alpha = 0.5, width = 0.15) +
  geom_boxplot(alpha = 0.5) +
  geom_text_repel(data = my_df_90[which(my_df_90$my_outliers == TRUE),],
            aes(label = country_name), size = 5) + 
  scale_fill_manual(values = my_palette) +
  scale_color_manual(values = my_palette) + 
  coord_flip() + 

In the next blog post, we will look at t-tests, ANOVAs (and their non-parametric alternatives) to see if the difference in means / medians is statistically significant and meaningful for the underlying population.

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Graphing Pew survey responses with ggplot in R

Packages we will need:


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:


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("")

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

eu_members <- eu_tables[[3]]

eu_members %<>% janitor::clean_names()  %>% 

eu_members$iso3 <- countrycode::countrycode(eu_members$geo, "", "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") )

Bump charts for ranking with ggbump package in R


Click here for Part 1 and here for Part 2 of the series on Eurostat data – explains how to download and visualise the Eurostat data

In this blog, we will look at government expenditure of the European Union!

Part 1 will go into detail about downloading Eurostat data with their package.

govt <- get_eurostat("gov_10a_main", fix_duplicated = TRUE)

Some quick data cleaning and then we can look at the variables in the dataset.

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

The numbers and letters are a bit incomprehensible. We can go to the Eurostat data browser site. It ascts as a codebook for all the variables we downloaded:

I want to take the EU accession data from Wikipedia. Check out the Part 1 blog post to scrape the data.

govt$iso3 <- countrycode(govt$geo, "iso2c", "iso3c")

govt_df <- merge(govt, eu_members, by.x = "iso3", by.y = "iso_3166_1_alpha_3", all.x = TRUE)

We will look at general government spending of the countries from the 2004 accession.

Also we will choose data is government expenditure as a percentage of GDP.

govt_df %<>%
  filter(sector == "S13") %>%      # General government spending
  filter(accession == 2004) %>%    # For countries that joined 2004
  filter(unit == "PC_GDP") %>%     # Spending as percentage of GDP
  filter(na_item == "TE")          # Total expenditure

A little more data cleaning! To use the ggflags package, the ISO 2 character code needs to be in lower case.

Also we will use some regex to remove the strings in the square brackets from the dataset.

govt_df$iso2_lower <- tolower(govt_df$iso_3166_1_alpha_2)

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

To put the flags at the start of the graph and names of the countries at the end of the lines, create mini dataframes with only information for the last year and first year:

last_time <- govt_df %>%
  group_by(geo) %>% 
  slice(which.max(year)) %>% 

first_time <- govt_df %>%
  group_by(geo) %>% 
  slice(which.min(year)) %>% 

I choose some nice hex colours from the coolors website. They need # in the strings to be acknowledged as hex colours by ggplot

add_hashtag <- function(my_vec){
  hash_vec <-  paste0('#', my_vec)

pal <- c("0affc2","ffb8d1","05e6dc","00ccf5","ff7700",

pal_hash <- add_hashtag(pal)

Now we can plot:

govt_df %>% 
  filter(geo != "CY" | geo != "MT") %>% 
  filter( year < 2020) %>% 
  ggplot(aes(x = year,
             y = values, group = name)) + 
  geom_text_repel(data = last_time, aes(label = name_clean, 
                                        color = name), 
                  size = 6, hjust = -3) +
  geom_point(aes(color = name)) + 
  geom_line(aes(color = name), size = 3, alpha = 0.8) +
  ggflags::geom_flag(data = first_time,
                     aes(x = year,
                         y = values,
                         country = iso2_lower),
                     size = 8) +
   scale_color_manual(values = pal_hash) +
  xlim(1994, 2021) + 
   ggthemes::theme_fivethirtyeight() +
  theme(panel.background = element_rect(fill = "#284b63"),
        legend.position = "none",
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20),
        panel.grid.major.y = element_line(color = "#495057",
                                          size = 0.5,
                                          linetype = 2),
        panel.grid.minor.y = element_line(color = "#495057",
                                          size = 0.5,
                                          linetype = 2)) +
  guides(colour = guide_legend(override.aes = list(size=10)))

Sometimes a simple line graph doesn’t easily show us the ranking of the countries over time.

The last graph was a bit cluttered, so we can choose the top average highest government expenditures to compare

govt_rank %>% 
  distinct(geo, mean_rank) %>% 
  top_n(6, mean_rank) %>%
  pull(geo) -> top_rank

We can look at a bump chart that ranks the different positions over time

govt_df %>% 
  filter(geo %in%  top_rank) %>% 
  group_by(year) %>%
  mutate(rank_budget = rank(-values, ties.method = "min")) %>%
  ungroup() %>% 
  group_by(geo) %>% 
  mutate(mean_rank = mean(values)) %>% 
  ungroup()  %>% 
  select(geo, iso2_lower, year, fifth_year, rank_budget, mean_rank) -> govt_rank

We recreate the last and first dataframes for the flags with the new govt_rank dataset.

last_time <- govt_rank %>%
  filter(geo %in% top_rank ) %>% 
  group_by(geo) %>% 
  slice(which.max(year)) %>% 

first_time <- govt_rank %>%
  filter(geo %in% top_rank ) %>% 
  group_by(geo) %>% 
  slice(which.min(year)) %>% 

All left to do is code the bump plot to compare the ranking of highest government expenditure as a percentage of GDP

govt_rank %>% 
  ggplot(aes(x = year, y = rank_budget, 
             group = country,
             color = country, fill = country)) +
  geom_point() +
            size = 3, alpha = 0.8,
            lineend = "round") + 
  geom_flag(data = last_time %>%
              filter(year == max(year)),
            aes(country = iso2_lower ),
            size = 20,
            color = "black") +
  geom_flag(data = first_time %>%
              filter(year == max(year)),
            aes(country = iso2_lower),
            size = 20,
            color = "black") -> govt_bump

Last we change the theme aesthetics of the bump plot

govt_bump + theme(panel.background = element_rect(fill = "#284b63"),
      legend.position = "bottom",
      axis.text.x = element_text(size = 20),
      axis.text.y = element_text(size = 20),
      axis.line = element_line(color='black'),
      axis.title.x = element_blank(), 
      axis.title.y = element_blank(), 
      legend.title = element_blank(),
      legend.text = element_text(size = 20),
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank()) + 
  guides(colour = guide_legend(override.aes = list(size=10))) + 
  scale_y_reverse(breaks = 1:100)

I added the title and moved the legend with, rather than attempt it with ggplots! I feel bad for cheating a bit.

Download EU data with Eurostat package in R: Part 1 (with pyramid graphs)


Eurostat is the statistical office of the EU. It publishes statistics and indicators that enable comparisons between countries and regions.

With the eurostat package, we can visualise some data from the EU and compare countries. In this blog, we will create a pyramid graph and a Statista-style bar chart.

First, we use the get_eurostat_toc() function to see what data we can download. We only want to look at datasets.

available_data <- get_eurostat_toc()

available_datasets <- available_data %>% 
  filter(type == "dataset")

A simple dataset that we can download looks at populations. We can browse through the available datasets and choose the code id. We feed this into the get_eurostat() dataset.

demo <- get_eurostat(id = "demo_pjan", 
                     type = "label")


Some quick data cleaning. First changing the date to a numeric variable. Next, extracting the number from the age variable to create a numeric variable.

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

demo$age_number <- as.numeric(gsub("([0-9]+).*$", "\\1", demo$age))

Next we filter out the data we don’t need. For this graph, we only want the total columns and two years to compare.

demo %>%
  filter(age != "Total") %>%
  filter(age != "Unknown") %>% 
  filter(sex == "Total") %>% 
  filter(year == 1960 | year == 2019 ) %>% 
  select(geo, iso3, values, age_number) -> demo_two_years

I want to compare the populations of the founding EU countries (in 1957) and those that joined in 2004. I’ll take the data from Wikipedia, using the rvest package. Click here to learn how to scrape data from the Internet.

eu_site <- read_html("")

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

eu_members <- eu_tables[[3]]

eu_members %<>% janitor::clean_names()  %>% 

Some quick data cleaning to get rid of the square bracket footnotes from the Wikipedia table data.

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

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

We merge the two datasets, on the same variable. In this case, I will use the ISO3C country codes (from the countrycode package). Using the names of each country is always tricky (I’m looking at you, Czechia / Czech Republic).

demo_two_years$iso3 <- countrycode::countrycode(demo_two_years$geo, ", "iso3c")

my_pyramid <- merge(demo_two_years, eu_members, by.x = "iso3", by.y = "iso_3166_1_alpha_3", all.x = TRUE)

We will use the pyramid_chart() function from the ggcharts package. Click to read more about this function.

The function takes the age group (we go from 1 to 99 years of age), the number of people in that age group and we add year to compare the ages in 1960 versus in 2019.

The first graph looks at the countries that founded the EU in 1957.

my_pyramid %>%  
  filter(! %>%  
  filter(accession == 1957 ) %>% 
  arrange(age_number) %>% 
  group_by(year, age_number) %>% 
  summarise(mean_age = mean(values, na.rm = TRUE)) %>% 
  ungroup() %>% 
  pyramid_chart(age_number, mean_age, year,
                bar_colors = c("#9a031e", "#0f4c5c")) 
Source: Eurostat

The second graph is the same, but only looks at the those which joined in 2004.

my_pyramid %>%  
  filter(! %>%  
  filter(accession == 2004 ) %>% 
  arrange(age_number) %>% 
  group_by(year, age_number) %>% 
  summarise(mean_age = mean(values, na.rm = TRUE)) %>% 
  ungroup() %>% 
  pyramid_chart(age_number, mean_age, year,
                bar_colors = c("#9a031e", "#0f4c5c")) 

Next we will use the Eurostat data on languages in the EU and compare countries in a bar chart.

I want to try and make this graph approximate the style of Statista graphs. It is far from identical but I like the clean layout that the Statista website uses.

Similar to above, we add the code to the get_eurostat() function and claen the data like above.

lang <- get_eurostat(id = "edat_aes_l22", 
                     type = "label")

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

lang$iso2 <- tolower(countrycode(lang$geo, "", "iso2c"))

lang %>% 
  mutate(geo = ifelse(geo == "Germany (until 1990 former territory of the FRG)", "Germany", 
                      ifelse(geo == "European Union - 28 countries (2013-2020)", "EU", geo))) %>% 
  filter(n_lang == "3 languages or more") %>% 
  filter(year == 2016) %>% 
  filter(age == "From 25 to 34 years") %>% 
  filter(! %>% 
  group_by(geo, year) %>% 
  mutate(mean_age = mean(values, na.rm = TRUE)) %>% 
  arrange(mean_age) -> lang_clean

Next we will create bar chart with the stat = "identity" argument.

We need to make sure our ISO2 country code variable is in lower case so that we can add flags to our graph with the ggflags package. Click here to read more about this package

lang_clean %>%
  ggplot(aes(x = reorder(geo, mean_age), y = mean_age)) + 
  geom_bar(stat = "identity", width = 0.7, color = "#0a85e5", fill = "#0a85e5") + 
  ggflags::geom_flag(aes(x = geo, y = -1, country = iso2), size = 8) +
  geom_text(aes(label= values), position = position_dodge(width = 0.9), hjust = -0.5, size = 5, color = "#000500") + 
  labs(title = "Percentage of people that speak 3 or more languages",
       subtitle = ("(% of overall population)"),
       caption = "         Source: Eurostat ") +
  xlab("") + 
  ylab("") -> lang_plot 

To try approximate the Statista graphs, we add many arguments to the theme() function for the ggplot graph!

lang_plot + coord_flip() + 
  expand_limits(y = 65) + 
  ggthemes::theme_pander() + 
  theme(plot.background = element_rect(color = "#f5f9fc"),
        panel.grid = element_line(colour = "#f5f9fc"),
        # axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.text.y = element_text(color = "#000500", size = 16),
        # axis.title.y = element_blank(),
        axis.ticks.x = element_blank(),
        text = element_text(family = "Gadugi"),
        plot.title = element_text(size = 28, color = "#000500"),
        plot.subtitle = element_text(size = 20, color = "#484e4c"),
        plot.caption = element_text(size = 20, color = "#484e4c") )

Next, click here to read Part 2 about visualizing Eurostat data with maps

Compare Irish census years with compareBars and csodata package in R

Packages we will need:


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")

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 %<>%  

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 %>% 
    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) %>% 
             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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