Here is a short list from the package description of all the key variables that can be quickly added:
We create the dyad dataset with the create_dyadyears() function. A dyad-year dataset focuses on information about the relationship between two countries (such as whether the two countries are at war, how much they trade together, whether they are geographically contiguous et cetera).
In the literature, the study of interstate conflict has adopted a heavy focus on dyads as a unit of analysis.
Alternatively, if we want just state-year data like in the previous blog post, we use the function create_stateyears()
We can add the variables with type D to the create_dyadyears() function and we can add the variables with type S to the create_stateyears() !
Focusing on the create_dyadyears() function, the arguments we can include are directed and mry.
The directed argument indicates whether we want directed or non-directed dyad relationship.
In a directed analysis, data include two observations (i.e. two rows) per dyad per year (such as one for USA – Russia and another row for Russia – USA), but in a nondirected analysis, we include only one observation (one row) per dyad per year.
The mry argument indicates whether they want to extend the data to the most recently concluded calendar year – i.e. 2020 – or not (i.e. until the data was last available).
With this dataframe, we can plot the CINC data of the top three superpowers, just looking at any variable that has a 1 at the end and only looking at the corresponding country_1!
According to our pals over at le Wikipedia, the Composite Index of National Capability (CINC) is a statistical measure of national power created by J. David Singer for the Correlates of War project in 1963. It uses an average of percentages of world totals in six different components (such as coal consumption, military expenditure and population). The components represent demographic, economic, and military strength
Next, we can go create a dichotomous factor variable and divide the continuous “freedom from torture scale” variable into either above the median or below the median score. It’s a crude measurement but it serves to highlight trends.
Blue means the country enjoys high freedom from torture. Yellow means the county suffers from low freedom from torture and people are more likely to be tortured by their government.
Then we feed our variables into the ggpairs() function from the GGally package.
I use the columnLabels to label the graphs with their full names and the mapping argument to choose my own color palette.
I add the bbc_style() format to the corr_matrix object because I like the font and size of this theme. And voila, we have our basic correlation matrix (Figure 1).
First off, in Figure 2 we can see the centre plots in the diagonal are the distribution plots of each variable in the matrix
In Figure 3, we can look at the box plot for the ‘civil liberties index’ score for both high (blue) and low (yellow) ‘freedom from torture’ categories.
The median civil liberties score for countries in the high ‘freedom from torture’ countries is far higher than in countries with low ‘freedom from torture’ (i.e. citizens in these countries are more likely to suffer from state torture). The spread / variance is also far great in states with more torture.
In Figur 4, we can focus below the diagonal and see the scatterplot between the two continuous variables – civil liberties index score and class equality index scores.
We see that there is a positive relationship between civil liberties and class equality. It looks like a slightly U shaped, quadratic relationship but a clear relationship trend is not very clear with the countries with higher torture prevalence (yellow) showing more randomness than the countries with high freedom from torture scores (blue).
Saying that, however, there are a few errant blue points as outliers to the trend in the plot.
The correlation score is also provided between the two categorical variables and the correlation score between civil liberties and class equality scores is 0.52.
Examining at the scatterplot, if we looked only at countries with high freedom from torture, this correlation score could be higher!
The graph lists countries in descending order according to the percentage of sampled participants that indicated they had low trust levels in politicians.
The respondents in Croatia, Bulgaria and Spain have the most distrust towards politicians.
For this example, I want to compare different analyses to see what impact different weights have on the coefficient estimates and standard errors in the regression analyses:
with no weights (dEfIniTelYy not recommended by ESS)
with post-stratification weights only (not recommended by ESS) and
with the combined post-strat AND population weight (the recommended weighting strategy according to ESS)
First we create two special svydesign objects, with the survey package. To create this, we need to add a squiggly ~ symbol in front of the variables (Google tells me it is called a tilde).
The ids argument takes the cluster ID for each participant.
psu is a numeric variable that indicates the primary sampling unit within which the respondent was selected to take part in the survey. For example in Ireland, this refers to the particular electoral division of each participant.
The strata argument takes the numeric variable that codes which stratum each individual is in, according to the type of sample design each country used.
The first svydesign object uses only post-stratification weights: pspwght
Finally we need to specify the nest argument as TRUE. I don’t know why but it throws an error message if we don’t …
With the stargazer package, we can compare the models side-by-side:
stargazer(simple_glm, post_strat_glm, full_weight_glm, type = "text")
We can see that the standard errors in brackets were increased for most of the variables in model (3) with both weights when compared to the first model with no weights.
The biggest change is the rural-urban scale variable. With no weights, it is positive correlated with trust in politicians. That is to say, the more urban a location the respondent lives, the more likely the are to trust politicians. However, after we apply both weights, it becomes negative correlated with trust. It is in fact the more rural the location in which the respondent lives, the more trusting they are of politicians.
Additionally, age becomes statistically significant, after we apply weights.
Of course, this model is probably incorrect as I have assumed that all these variables have a simple linear relationship with trust levels. If I really wanted to build a robust demographic model, I would have to consult the existing academic literature and test to see if any of these variables are related to trust levels in a non-linear way. For example, it could be that there is a polynomial relationship between age and trust levels, for example. This model is purely for illustrative purposes only!
Plus, when I examine the R2 score for my models, it is very low; this model of demographic variables accounts for around 6% of variance in level of trust in politicians. Again, I would have to consult the body of research to find other explanatory variables that can account for more variance in my dependent variable of interest!
We can look at the R2 and VIF score of GLM with the summ() function from the jtools package. The summ() function can take a svyglm object. Click here to read more about various functions in the jtools package.
We will look at the Soft Power rankings from Portland Communications. According to Wikipedia, In politics (and particularly in international politics), soft power is the ability to attract and co-opt, rather than coerce or bribe other countries to view your country’s policies and actions favourably. In other words, soft power involves shaping the preferences of others through appeal and attraction.
A defining feature of soft power is that it is non-coercive; the currency of soft power includes culture, political values, and foreign policies.
Joseph Nye’s primary definition, soft power is in fact:
“the ability to get what you want through attraction rather than coercion or payments. When you can get others to want what you want, you do not have to spend as much on sticks and carrots to move them in your direction. Hard power, the ability to coerce, grows out of a country’s military and economic might. Soft power arises from the attractiveness of a country’s culture, political ideals and policies. When our policies are seen as legitimate in the eyes of others, our soft power is enhanced”
(Nye, 2004: 256).
Every year, Portland Communication ranks the top countries in the world regarding their soft power. In 2019, the winner was la France!
We will also add circular flags to the graphs with the ggflags package. The geom_flag() requires the ISO two letter code as input to the argument … but it will only accept them in lower case. So first we need to make the country code variable suitable:
Here I run a simple scatterplot and compare Post-Soviet states and see whether there has been a major change in class equality between 1991 after the fall of the Soviet Empire and today. Is there a relationship between class equality and demolcratisation? Is there a difference in the countries that are now in EU compared to the Post-Soviet states that are not?
library(ggrepel) # to stop text labels overlapping
library(gridExtra) # to place two plots side-by-side
library(ggbubr) # to modify the gridExtra titles
region_liberties_91 <- vdem %>%
dplyr::filter(year == 1991) %>%
dplyr::filter(regions == 'Post-Soviet') %>%
ggplot(aes(x = democracy, y = class_equality, color = EU_member)) +
geom_point(aes(size = population)) +
scale_alpha_continuous(range = c(0.1, 1))
plot_91 <- region_liberties_91 +
labs(subtitle = "1991") +
ylim(-2.5, 3.5) +
xlim(0, 1) +
geom_text_repel(aes(label = country_name), show.legend = FALSE, size = 7) +
region_liberties_18 <- vdem %>%
dplyr::filter(year == 2018) %>%
dplyr::filter(regions == 'Post-Soviet') %>%
ggplot(aes(x = democracy_score, y = class_equality, color = EU_member)) +
geom_point(aes(size = population)) +
scale_alpha_continuous(range = c(0.1, 1))
plot_18 <- region_liberties_15 +
labs(subtitle = "2015") +
ylim(-2.5, 3.5) +
xlim(0, 1) +
geom_text_repel(aes(label = country_name), show.legend = FALSE, size = 7) +
scale_size(guide = "none")
my_title = text_grob("Relationship between democracy and class equality in Post-Soviet states", size = 22, face = "bold")
my_y = text_grob("Democracy Score", size = 20, face = "bold")
my_x = text_grob("Class Equality Score", size = 20, face = "bold", rot = 90)
grid.arrange(plot_1, plot_2, ncol=2, top = my_title, bottom = my_y, left = my_x)
The BBC cookbook vignette offers the full function. So we can tweak it any way we want.
For example, if I want to change the default axis labels, I can make my own slightly adapted my_bbplot() function
The survey package was created by Thomas Lumley, a professor from Auckland. The srvyr package is a wrapper packages that allows us to use survey functions with tidyverse.
Why do we need to add weights to the data when we analyse surveys?
When we import our survey data file, R will assume the data are independent of each other and will analyse this survey data as if it were collected using simple random sampling.
However, the reality is that almost no surveys use a simple random sample to collect data (the one exception being Iceland in ESS!)
Rather, survey institutions choose complex sampling designs to reduce the time and costs of ultimately getting responses from the public.
Their choice of sampling design can lead to different estimates and the standard errors of the sample they collect.
For example, the sampling weight may affect the sample estimate, and choice of stratification and/or clustering may mean (most likely underestimated) standard errors.
As a result, our analysis of the survey responses will be wrong and not representative to the population we want to understand. The most problematic result is that we would arrive at statistical significance, when in reality there is no significant relationship between our variables of interest.
Therefore it is essential we don’t skip this step of correcting to account for weighting / stratification / clustering and we can make our sample estimates and confidence intervals more reliable.
This table comes from round 8 of the ESS, carried out in 2016. Each of the 23 countries has an institution in charge of carrying out their own survey, but they must do so in a way that meets the ESS standard for scientifically sound survey design (See Table 1).
Sampling weights aim to capture and correct for the differing probabilities that a given individual will be selected and complete the ESS interview.
For example, the population of Lithuania is far smaller than the UK. So the probability of being selected to participate is higher for a random Lithuanian person than it is for a random British person.
Additionally, within each country, if the survey institution chooses households as a sampling element, rather than persons, this will mean that individuals living alone will have a higher probability of being chosen than people in households with many people.
Click here to read in detail the sampling process in each country from round 1 in 2002. For example, if we take my country – Ireland – we can see the many steps involved in the country’s three-stage probability sampling design.
The Primary Sampling Unit (PSU) is electoral districts. The institute then takes addresses from the Irish Electoral Register. From each electoral district, around 20 addresses are chosen (based on how spread out they are from each other). This is the second stage of clustering. Finally, one person is randomly chosen in each house to answer the survey, chosen as the person who will have the next birthday (third cluster stage).
Click here for more information about Design Effects (DEFF) and click here to read how ESS calculates design effects.
DEFF p refers to the design effect due to unequal selection probabilities (e.g. a person is more likely to be chosen to participate if they live alone)
DEFF c refers to the design effect due to clustering
According to Gabler et al. (1999), if we multiply these together, we get the overall design effect. The Irish design that was chosen means that the data’s variance is 1.6 times as large as you would expect with simple random sampling design. This 1.6 design effects figure can then help to decide the optimal sample size for the number of survey participants needed to ensure more accurate standard errors.
So, we can use the functions from the survey package to account for these different probabilities of selection and correct for the biases they can cause to our analysis.
In this example, we will look at demographic variables that are related to levels of trust in politicians. But there are hundreds of variables to choose from in the ESS data.
Click here for a list of all the variables in the European Social Survey and in which rounds they were asked. Not all questions are asked every year and there are a bunch of country-specific questions.
We can look at the last few columns in the data.frame for some of Ireland respondents (since we’ve already looked at the sampling design method above).
The dweight is the design weight and it is essentially the inverse of the probability that person would be included in the survey.
The pspwght is the post-stratification weight and it takes into account the probability of an individual being sampled to answer the survey AND ALSO other factors such as non-response error and sampling error. This post-stratificiation weight can be considered a more sophisticated weight as it contains more additional information about the realities survey design.
The pweight is the population size weight and it is the same for everyone in the Irish population.
When we are considering the appropriate weights, we must know the type of analysis we are carrying out. Different types of analyses require different combinations of weights. According to the ESS weighting documentation:
when analysing data for one country alone – we only need the design weight or the poststratification weight.
when comparing data from two or more countries but without reference to statistics that combine data from more than one country – we only need the design weight or the poststratification weight
when comparing data of two or more countries and with reference to the average (or combined total) of those countries – we need BOTH design or post-stratification weight AND population size weights together.
when combining different countries to describe a group of countries or a region, such as “EU accession countries” or “EU member states” = we need BOTH design or post-stratification weights AND population size weights.
ESS warn that their survey design was not created to make statistically accurate region-level analysis, so they say to carry out this type of analysis with an abundance of caution about the results.
ESS has a table in their documentation that summarises the types of weights that are suitable for different types of analysis:
Since we are comparing the countries, the optimal weight is a combination of post-stratification weights AND population weights together.
Click here to read Part 2 and run the regression on the ESS data with the survey package weighting design
Below is the code I use to graph the differences in mean level of trust in politicians across the different countries.
library(ggimage) # to add flags
library(countrycode) # to add ISO country codes
# r_agg is the aggregated mean of political trust for each countries' respondents.
dplyr::mutate(country, EU_member = ifelse(country == "BE" | country == "BG" | country == "CZ" | country == "DK" | country == "DE" | country == "EE" | country == "IE" | country == "EL" | country == "ES" | country == "FR" | country == "HR" | country == "IT" | country == "CY" | country == "LV" | country == "LT" | country == "LU" | country == "HU" | country == "MT" | country == "NL" | country == "AT" | country == "AT" | country == "PL" | country == "PT" | country == "RO" | country == "SI" | country == "SK" | country == "FI" | country == "SE","EU member", "Non EU member")) -> r_agg
filter(EU_member == "EU member") %>%
dplyr::summarize(eu_average = mean(mean_trust_pol))
r_agg$country_name <- countrycode(r_agg$country, "iso2c", "country.name")
#eu_average <- r_agg %>%
# summarise_if(is.numeric, mean, na.rm = TRUE)
eu_avg <- data.frame(country = "EU average",
mean_trust_pol = 3.55,
EU_member = "EU average",
country_name = "EU average")
r_agg <- rbind(r_agg, eu_avg)
my_palette <- c("EU average" = "#ef476f",
"Non EU member" = "#06d6a0",
"EU member" = "#118ab2")
r_agg <- r_agg %>%
dplyr::mutate(ordered_country = fct_reorder(country, mean_trust_pol))
r_graph <- r_agg %>%
ggplot(aes(x = ordered_country, y = mean_trust_pol, group = country, fill = EU_member)) +
ggimage::geom_flag(aes(y = -0.4, image = country), size = 0.04) +
geom_text(aes(y = -0.15 , label = mean_trust_pol)) +
scale_fill_manual(values = my_palette) + coord_flip()
In this post, we can compare countries on the left – right political spectrum and graph the trends.
In the European Social Survey, they ask respondents to indicate where they place themselves on the political spectrum with this question: “In politics people sometimes talk of ‘left’ and ‘right’. Where would you place yourself on this scale, where 0 means the left and 10 means the right?”
lrscale_graph <- round_df %>%
dplyr::filter(country == "IE" | country == "GB" | country == "FR" | country == "DE") %>%
ggplot(aes(x= round, y = mean_lr, group = country)) +
geom_line(aes(color = factor(country)), size = 1.5, alpha = 0.5) +
ggimage::geom_flag(aes(image = country), size = 0.04) +
scale_color_manual(values = my_palette) +
scale_x_discrete(name = "Year", limits=c("2002","2004","2006","2008","2010","2012","2014","2016","2018")) +
labs(title = "Where would you place yourself on this scale,\n where 0 means the left and 10 means the right?",
subtitle = "Source: European Social Survey, 2002 - 2018",
x = "Year",
y = "Left - Right Spectrum")
lrscale_graph + guides(color=guide_legend(title="Country")) + theme_economist()
The European Social Survey (ESS) measure attitudes in thirty-ish countries (depending on the year) across the European continent. It has been conducted every two years since 2001.
The survey consists of a core module and two or more ‘rotating’ modules, on social and public trust; political interest and participation; socio-political orientations; media use; moral, political and social values; social exclusion, national, ethnic and religious allegiances; well-being, health and security; demographics and socio-economics.
So lots of fun data for political scientists to look at.
The very first thing you need to do before you can download any of the data is set your email address.
Don’t forget the email address goes in as a string in “quotations marks”.
Show what countries are in the survey with the show_countries() function.
It’s important to know that country names are case sensitive and you can only use the name printed out by show_countries(). For example, you need to write “Russian Federation” to access Russian survey data; if you write “Russia”…
Using these country names, we can download specific rounds or waves (i.e survey years) with import_country. We have the option to choose the two most recent rounds, 8th (from 2016) and 9th round (from 2018).
ire_data <- import_all_cntrounds("Ireland")
The resulting data comes in the form of nine lists, one for each round
These rounds correspond to the following years:
ESS Round 9 – 2018
ESS Round 8 – 2016
ESS Round 7 – 2014
ESS Round 6 – 2012
ESS Round 5 – 2010
ESS Round 4 – 2008
ESS Round 3 – 2006
ESS Round 2 – 2004
ESS Round 1 – 2002
I want to compare the first round and most recent round to see if Irish people’s views have changed since 2002. In 2002, Ireland was in the middle of an economic boom that we called the “Celtic Tiger”. People did mad things like buy panini presses and second house in Bulgaria to resell. Then the 2008 financial crash hit the country very hard.
Irish people during the Celtic Tiger:
Irish people after the Celtic Tiger crash:
Ireland in 2018 was a very different place. So it will be interesting to see if these social changes translated into attitude changes.
First, we use the import_country() function to download data from ESS. Specify the country and rounds you want to download.
The resulting ire object is a list, so we’ll need to extract the two data.frames from the list:
ire_1 <- ire[]
ire_9 <- ire[]
The exact same questions are not asked every year in ESS; there are rotating modules, sometimes questions are added or dropped. So to merge round 1 and round 9, first we find the common columns with the intersect() function.
All the variables in the dataset are a special class called “haven_labelled“. So we must convert them to numeric variables with a quick function. We exclude the first variable because we want to keep country name as a string character variable.
We can look at the distribution of our variables and count how many missing values there are with the skim() function from the skimr package
We can run a quick t-test to compare the mean attitudes to immigrants on the statement: “Immigrants make country worse or better place to live” across the two survey rounds.
Lower scores indicate an attitude that immigrants undermine Ireland’ quality of life and higher scores indicate agreement that they enrich it!
t.test(att_df$imm_qual_life ~ att_df$round)
In future blog, I will look at converting the raw output of R into publishable tables.
The results of the independent-sample t-test show that if we compare Ireland in 2002 and Ireland in 2018, there has been a statistically significant increase in positive attitudes towards immigrants and belief that Ireland’s quality of life is more enriched by their presence in the country.
As I am currently an immigrant in a foreign country myself, I am glad to come from a country that sees the benefits of immigrants!
If we load the ggpubr package, we can graphically look at the difference in mean attitude scores.
box1 <- ggpubr::ggboxplot(att_df, x = "round", y = "imm_qual_life", color = "round", palette = c("#d11141", "#00aedb"),
ylab = "Attitude", xlab = "Round")
box1 + stat_compare_means(method = "t.test")
It’s not the most glamorous graph but it conveys the shift in Ireland to more positive attitudes to immigration!
I suspect that a country’s economic growth correlates with attitudes to immigration.
The geom_rect() function graphs the coloured rectangles on the plot. I take colours from this color-hex website; the green rectangle for times of economic growth and red for times of recession. Makes sure the geom-rect() comes before the geom_line().
And we can see that there is a relationship between attitudes to immigrants in Ireland and Irish GDP growth. When GDP is growing, Irish people see that immigrants improve quality of life in Ireland and vice versa. The red section of the graph corresponds to the financial crisis.
We can now add the the geom_flag() function to the graph. The y = -50 prevents the flags overlapping with the bars and places them beside their name label. The image argument takes the iso2 variable.
Quick tip: with the reorder argument, if we wanted descending order (rather than ascending order of ODA amounts, we would put a minus sign in front of the oda_per_capita in the reorder() function for the x axis value.
oda_bar <- oda %>%
ggplot(aes(x = reorder(donor, oda_per_capita), y = oda_per_capita, fill = continent)) +
geom_flag(y = -50, aes(image = iso2)) +
geom_bar(stat = "identity") +
labs(title = "ODA donor spending ",
subtitle = "Source: OECD's Development Assistance Committee, 2019 ",
x = "Donor Country",
y = "ODA per capita")
The fill argument categorises the continents of the ODA donors. Sometimes I take my hex colors from https://www.color-hex.com/ website.
We can all agree that Wikipedia is often our go-to site when we want to get information quick. When we’re doing IR or Poli Sci reesarch, Wikipedia will most likely have the most up-to-date data compared to other databases on the web that can quickly become out of date.
So in R, we can scrape a table from Wikipedia and turn into a database with the rvest package .
First, we copy and paste the Wikipedia page we want to scrape into the read_html() function as a string:
Next we save all the tables on the Wikipedia page as a list. Turn the header = TRUE.
nato_tables <- nato_members %>% html_table(header = TRUE, fill = TRUE)
The table that I want is the third table on the page, so use [[two brackets]] to access the third list.
nato_exp <- nato_tables[]
The dataset is not perfect, but it is handy to have access to data this up-to-date. It comes from the most recent NATO report, published in 2019.
Some problems we will have to fix.
The first row is a messy replication of the header / more information across two cells in Wikipedia.
The headers are long and convoluted.
There are a few values in as N/A in the dataset, which R thinks is a string.
All the numbers have commas, so R thinks all the numeric values are all strings.
There are a few NA values that I would not want to impute because they are probably zero. Iceland has no armed forces and manages only a small coast guard. North Macedonia joined NATO in March 2020, so it doesn’t have all the data completely.
So first, let’s do some quick data cleaning:
Clean the variable names to remove symbols and adds underscores with a function from the janitor package