Make Wes Anderson themed graphs with wesanderson package in R

Well this is just delightful!

install.packages("wesanderson")
library(wesanderson)

After you install the wesanderson package, you can

  1. create a ggplot2 graph object
  2. choose the Wes Anderson color scheme you want to use and create a palette object
  3. add the graph object and and the palette object and behold your beautiful data
Wes Anderson Trailer GIF - Find & Share on GIPHY

I want to examine the breakdown of how each head of state was appointed to rule the country and the type of regime. First I’ll examine the break down in the 18th century.

To generate a vector of colors, the wes_palette() function requires:

wes_palette(name, n, type = c("discrete", "continuous"))
  • name: Name of desired palette
  • n: Number of colors desired (i.e. how many categories. In my case, there are four regime types so n = 4).
  • type: Either “continuous” or “discrete”. Use continuous if you want to automatically interpolate between colors.
Wes Anderson Trailer GIF - Find & Share on GIPHY

eighteenth_century <- data_1880s %>%
filter(!is.na(regime)) %>%
filter(!is.na(appointment)) %>%
ggplot(aes(appointment)) + geom_bar(aes(fill = factor(regime)), position = position_stack(reverse = TRUE)) + theme(legend.position = "top", text = element_text(size=15), axis.text.x = element_text(angle = -30, vjust = 1, hjust = 0))

Both the regime variable and the appointment variable are discrete categories so we can use the geom_bar() function. When adding the palette to the barplot object, we can use the scale_fill_manual() function.

eighteenth_century + scale_fill_manual(values = wes_palette("Darjeeling1", n = 4)

Now to compare the breakdown with countries in the 21st century (2000 to present)

The names of all the palettes you can enter into the wes_anderson() function

Wes Anderson GIF - Find & Share on GIPHY

Include country labels to a regression plot with ggplot2 package in R

Sometimes the best way to examine the relationship between our variables of interest is to plot it out and give it a good looking over. For me, it’s most helpful to see where different countries are in relation to each other and to see any interesting outliers.

For this, I can use the geom_text() function from the ggplot2 package.

I will look at the relationship between economic globalization and social globalization in OECD countries in the year 2000.

The KOF Globalisation Index, introduced by Dreher (2006) measures globalization along the economicsocial and political dimension for most countries in the world

First, as always, we install and load the necessary package. This time, it is the ggplot2 package

install.packages("ggplot2")
library(ggplot2)

Next add the following code:

fin <- ggplot(oecd2000, aes(economic_globalization, social_globalization)) 
        + ggtitle("Relationship between Globalization Index Scores among OECD countries in 2000")
        + scale_x_continuous("Economic Globalization Index")
        + scale_y_continuous("Social Globalization Index") 
        + geom_smooth(method = "lm") 
        + geom_point(aes(colour = polity_score), size = 2) + labs(color = "Polity Score")
        + geom_text(hjust = 0, nudge_x = 0.5, size = 4, aes(label = country)) 

fin 

In the aes() function, we enter the two variables we want to plot.

Then I use the next three lines to add titles to axes and graph

I use the geom_smooth() function with the “lm” method to add a best fitting regression line through the points on the plot. Click here to learn more about adding a regression line to a plot.

I add a legend to examine where countries with different democracy scores (taken from the Polity Index) are located on the globalization plane. Click here to learn about adding legends.

The last line is the geom_text() function that I use to specify that I want to label each observation (i.e. each OECD country) with its name, rather than the default dataset number.

Some geom_text() commands to use:

  • nudge_x (or nudge_y) slightly “nudge” the labels from their corresponding points to help minimise messy overlapping.
  • hjust and vjust move the text label “left”, “center”, “right”, “bottom”, “middle” or “top” of the point.

Yes, yes! There is a package that uses the color palettes of Wes Anderson movies to make graphs look just beautiful. Click here to use different Wes Anderson aesthetic themed graphs!

zissou_colors <- wes_palette("Zissou1", 100, type = "continuous")

fin + scale_color_gradientn(colours = zissou_colors)

Which outputs:

Interestingly, it seems that at the very bottom left hand corner of the plot (which shows the countries that are both low in economic globalization and low in social globalization), we have two OECD countries that score high on democracy – Japan and South Korea- right next to two countries that score the lowest in the OECD on democracy, Turkey and Mexico.

So it could be interesting to further examine why these countries from opposite ends of the democracy spectrum have similar pattern of low globalization. It puts a spanner in the proverbial works with my working theory that countries higher in democracy are more likely to be more globalized! What is special about these two high democracy countries that gives them such low scores on globalization.