I went online and found the logos for the three main parties (sorry, Labour) and saved them in the working directory I have for my RStudio. That way I can call the file with the prefix “~/**.png” rather than find the exact location they are saved on the computer.
Now we are ready to plot out the density plots for each party with the geom_density_ridges() function from the ggridges package.
We will add a few arguments into this function.
We add an alpha = 0.8 to make each density plot a little transparent and we can see the plots behind.
The scale = 2 argument pushes all three plots togheter so they are slightly overlapping. If scale =1, they would be totally separate and 3 would have them overlapping far more.
The rel_min_height = 0.01 argument removes the trailing tails from the plots that are under 0.01 density. This is again for aesthetics and just makes the plot look slightly less busy for relatively normally distributed densities
The geom_image takes the images and we place them at the beginning of the x axis beside the labels for each party.
Last, we use the bbplot package BBC style ggplot theme, which I really like as it makes the overall graph look streamlined with large font defaults.
ggplot(aes(x = opinion_poll, y = as.factor(party))) +
geom_density_ridges(aes(fill = party),
alpha = 0.8,
scale = 2,
rel_min_height = 0.01) +
ggimage::geom_image(aes(y = party, x= 1, image = image), asp = 0.9, size = 0.12) +
scale_fill_manual(values = c("#f2542d", "#edf6f9", "#0e9594")) +
theme(legend.position = "none") +
labs(title = "Favourability Polls for the Three Main Parties in Ireland", subtitle = "Data from Irish Polling Indicator (Louwerse & Müller, 2020)")
When we plot the graph, we need a few geom arguments.
Along the x axis we have all the countries, and reorder them from most trusting of their goverments to least trusting.
We will color the points with one of the four geographic regions.
We use geom_jitter() rather than geom_point() for the different yearly trust values to make the graph a little more interesting.
I also make the sizes scaled to the year in the aes() argument. Again, I did this more to look interesting, rather than to convey too much information about the different values for trust across each country. But smaller circles are earlier years and grow larger for each susequent year.
The geom_hline() plots a vertical line to indicate the average trust level for all countries.
We then use the geom_segment() to horizontally connect the country’s individual average (the yend argument) to the total average (the y arguement). We can then easily see which countries are above or below the total average. The x and xend argument, we supply the country_name variable twice.
Next we use the geom_flag(), which comes from the ggflags package. In order to use this package, we need the ISO 2 character code for each country in lower case!
Click here to read more about the ggflags package.
We can see that countries in southern Europe are less trusting of their governments than in other regions. Western countries seem to occupy the higher parts of the graph, with France being the least trusting of their government in the West.
There is a large variation in Northern countries. However, if we look at the countries, we can see that the Scandinavian countries are more trusting and the Baltic countries are among the least trusting. This shows they are more similar in their trust levels to other Post-Soviet countries.
Next we can look into see if there is a relationship between democracy scores and level of trust in the goverment with a geom_point() scatterplot
The geom_smooth() argument plots a linear regression OLS line, with a standard error bar around.
We want the labels for the country to not overlap so we use the geom_label_repel() from the ggrepel package. We don’t want an a in the legend, so we add show.legend = FALSE to the arguments
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.
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:
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.