The 1980s were a prolific time for the UNGA with voting, with arms control being the largest share of votes. And it has stablised in the decades since.
And if we compare civic versus politically-oriented aid, we can see that more money goes towards projects that have political or electoral aims rather than civic or civil society education goals
tokens %>%
group_by(year) %>%
top_n(n = 20,
wt = n) %>%
mutate(word = case_when(word == "party" ~ "political",
word == "parties" ~ "political",
word == "election" ~ "political",
word == "electoral" ~ "political",
word == "civil" ~ "civic",
word == "civic" ~ "civic",
word == "social" ~ "civic",
word == "education" ~ "civic",
word == "society" ~ "civic",
TRUE ~ as.character(word))) %>%
filter(word == "political" | word == "civic") %>%
ggplot(aes(x = year, y = n, group = word)) +
geom_line(aes(color = word ), size = 2.5,alpha = 0.6) +
geom_point(aes(color = word ), fill = "white",
shape = 21, size = 3, stroke = 2) +
bbplot::bbc_style() +
scale_x_discrete(limits = c(2001:2019)) +
theme(axis.text.x= element_text(size = 15,
angle = 45)) +
scale_color_discrete(name = "Aid type", labels = c("Civic grants", "Political grants"))
According to Urquhart (1995) in his article, “Selecting the World’s CEO”,
From the outset, the U.N. secretary general has been an important part of the institution, not only as its chief executive, but as both symbol and guardian of the original vision of the organization. There, however, specific agreement has ended. The United Nations, like any important organization, needs strong and independent leadership, but it is an inter- governmental organization, and govern ments have no intention of giving up control of it. While the secretary-general can be extraordinarily useful in times of crisis, the office inevitably embodies something more than international coop eration, sometimes even an unwelcome hint of supranationalism. Thus, the atti- tude of governments toward the United Nations’ chief and only elected official is and has been necessarily ambivalent.
(Urquhart, 1995: 21)
So who are these World CEOs? We’ll examine more in this dataset.
The table we scrape is a bit of a hot mess in this state …. but we can fix it
We can first use the clean_names() function from the janitor package
A quick way to clean up the table and keep only the rows with the names of the Secretaries-General is to use the distinct() function. Last we filter out the rows and select out the columns we don’t want.
Already we can see a much cleaner table. However, the next problem is that the names and their years of birth / death are in one cell.
Also the dates in office are combined together.
So we can use the separate() function from tidyr to make new variables for each piece of information.
First we will separate the name of the Secretary-General from their date of birth and death.
We supply the two new variable names to the into = argument.
We then use the regex code pattern [()] to indicate where we want to separate the character string into two separate columns. In this instance the regex pattern is for what is after the round brackets (
I want to remove the original cluttered varaible so remove = TRUE
sg_clean %<>%
separate(
col = secretary_general_born_died,
into = c("sec_gen", "born_died"),
sep = '[()]',
remove = TRUE)
We can repeat this step to create a separate born and died variable. This time the separator symbol is a hyphen And so we do not need regex pattern; we can just indicate a hyphen.
sg_clean %<>%
separate(
col = born_died,
into = c("born", "died"),
sep = '–',
remove = TRUE)
And I want to ignore the “present” variable, so I extract the numbers with the parse_number() function, converting things from characters to numbers
sg_clean %<>%
mutate(born = parse_number(born))
Last, we repeat with the dates in office. This is also easily seperated by indicating the hyphen.
sg_clean %<>%
separate(
col = dates_in_office,
into = c("start_office", "end_office"),
sep = '–',
remove = TRUE)
We convert the word “present” to the actual present date
For this blog post, we will look at UN peacekeeping missions and compare across regions.
Despite the criticisms about some operations, the empirical record for UN peacekeeping records has been robust in the academic literature
“In short, peacekeeping intervenes in the most difficult cases, dramatically increases the chances that peace will last, and does so by altering the incentives of the peacekept, by alleviating their fear and mistrust of each other, by preventing and controlling accidents and misbehavior by hard-line factions, and by encouraging political inclusion” (Goldstone, 2008: 178).
The data on the current and previous PKOs (peacekeeping operations) will come from the Wikipedia page. But the variables do not really lend themselves to analysis as they are.
Once we have the url, we scrape all the tables on the Wikipedia page in a few lines
We then bind the completed and current mission data.frames
rbind(pko_complete, pko_current) -> pko
Then we clean the variable names with the function from the janitor package.
pko_df <- pko %>%
janitor::clean_names()
Next we’ll want to create some new variables.
We can make a new row for each country that is receiving a peacekeeping mission. We can paste all the countries together and then use the separate function from the tidyr package to create new variables.
pko_countries %>%
ggplot(mapping = aes(x = decade,
y = duration,
fill = decade)) +
geom_boxplot(alpha = 0.4) +
geom_jitter(aes(color = decade),
size = 6, alpha = 0.8, width = 0.15) +
coord_flip() +
geom_curve(aes(x = "1950s", y = 60, xend = "1940s", yend = 72),
arrow = arrow(length = unit(0.1, "inch")), size = 0.8, color = "black",
curvature = -0.4) +
annotate("text", label = "First Mission to Kashmir",
x = "1950s", y = 49, size = 8, color = "black") +
geom_curve(aes(x = "1990s", y = 46, xend = "1990s", yend = 32),
arrow = arrow(length = unit(0.1, "inch")), size = 0.8, color = "black",curvature = 0.3) +
annotate("text", label = "Most Missions after the Cold War",
x = "1990s", y = 60, size = 8, color = "black") +
bbplot::bbc_style() + ggtitle("Duration of Peacekeeping Missions")
Years
Following the end of the Cold War, there were renewed calls for the UN to become the agency for achieving world peace, and the agency’s peacekeeping dramatically increased, authorizing more missions between 1991 and 1994 than in the previous 45 years combined.
We can use a waffle plot to see which decade had the most operation missions. Waffle plots are often seen as more clear than pie charts.
To get the data ready for a waffle chart, we just need to count the number of peacekeeping missions (i.e. the number of rows) in each decade. Then we fill the groups (i.e. decade) and enter the n variable we created as the value.
In this post, we are going to scrape NATO accession data from Wikipedia and turn it into panel data. This means turning a list of every NATO country and their accession date into a time-series, cross-sectional dataset with information about whether or not a country is a member of NATO in any given year.
This is helpful for political science analysis because simply a dummy variable indicating whether or not a country is in NATO would lose information about the date they joined. The UK joined NATO in 1948 but North Macedonia only joined in 2020. A simple binary variable would not tell us this if we added it to our panel data.
We will first scrape a table from the Wikipedia page on NATO member states with a few functions form the rvest pacakage.
Our dataset now has 60 observations. We see Albania joined in 2009 and is still a member in 2020, for example.
Next we will use the complete() function from the tidyr package to fill all the dates in between 1948 until 2020 in the dataset. This will increase our dataset to 2,160 observations and a row for each country each year.
Nect we will group the dataset by country and fill the nato_member status variable down until the most recent year.
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:
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) %>%
arrange(desc(n))
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.
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.
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
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:
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.
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.
President 4370
Prime Minister 2945
Other Type 685
Chairperson 520
King 470
Premier 169
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)
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.
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.
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.
Autocratic countries can be in the following main categories: absolute monarchy, military rule, party-based rule, personalist rule, and oligarchy.
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)
library(democracyData)
library(tidyverse)
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)
library(democracyData)
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()
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:
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):
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()
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:
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 coolors.co website!
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!
References
Cheibub, J. A., Gandhi, J., & Vreeland, J. R. (2010). . Public choice, 143(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.
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.
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)
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
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
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.
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
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
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).
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 ~
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.
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
stargazer(my_df_summary,
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)
Statistic
N
Mean
Median
St. Dev.
Corruption index
179
0.477
0.519
0.304
Civil society strength
179
0.670
0.805
0.287
Rule of Law score
173
7.451
7.000
4.745
Physical Integerity Score
179
0.696
0.807
0.284
GDP growth
163
0.019
0.020
0.032
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() +
geom_bar(aes(as.factor(region),
fill = as.factor(regime)),
color = "white", size = 2.5) -> my_barplot
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.
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.
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.
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.
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:
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.
R Functions and Packages for Political Science Analysis 
