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
When we save our plots and graphs in R, we can use the ggsave() function and specify the type, size and look of the file.
We are going to look two features in particular: anti-aliasing lines with the Cairo package and creating transparent backgrounds.
Make your graph background transparent
First, let’s create a pie chart with a transparent background. The pie chart will show which party has held the top spot in Irish politics for the longest.
After we prepare and clean our data of Irish Taoisigh start and end dates in office and create a doughnut chart (see bottom of blog for doughnut graph code), we save it to our working directorywith ggsave().
If we want to add our doughnut chart to a power point but we don’t want it to be a white background, we can ask ggsave to save the chart as transparent and then we can add it to our powerpoint or report!
To do this, we specify bg argument to "transparent"
When we save our graph in R with ggsave(), we can specify in the type argument that we want type = cairo.
I make a quick graph that looks at the trends in migration and GDP from 1960s to 2018 in Ireland. I made the lines extra large to demonstrate the difference between aliased and anti-aliased lines in the graphs.
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.