William Playfair is said to be one of the first individuals to use graphical methods to represent statistical data and findings. The data explored comes from Playfair’s “Chart Representing the Extent, Population and Revenues of the Principal Nations in Europe.” His findings are documented in the Statistical Breviary. In his chart, he explores multiple variables; these being population, taxation, and the area of European countries. The population is measured by the number of inhabitants per million for each country. The taxation is measured by the amount of revenue in pounds of sterling per million for each country. These two variables are measured in in the same divided scale. Sterling is the currency that was used across Europe at this time, and it current times, it is the official currency of the United Kingdom. According to Playfair, size, population, and revenue are the three principal objects of power that can mathematically measured with precision and in turn statistically shown. Playfair hoped to compare the size of a country and the taxation of these nations to show that Europe was overburdened with taxes. Playfair shows through graphical representation that these European nations were in fact encumbered with taxation.

library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0      ✔ purrr   0.3.5 
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.4.1 
## ✔ readr   2.1.3      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
europe <- read.csv("playfair_european_nations.csv")
europe$Country <- fct_reorder(europe$Country, europe$Area, .desc=TRUE)
view(europe)
#install.packages("ggforce")
library(ggforce)
#install.packages("dichromat")
library(dichromat)

The red lines indicate population size for each country, while the yellow lines indicate taxation, and together it is evident that a number of European nations are overburdened with taxes. Although it is not unanimous that all countries are over taxed, there is evidence that multiple nations are. For example, Britain and Ireland, Spain, and Portugal have dotted lines that are drawn from right to left. Meaning, the yellow taxation line is larger than the population size, so it can be concluded these smaller circle nations have a small amount of land and are overburdened with taxes.

Playfair’s chart has multiple positive aspects that together show some nations are burdened with over taxation. His use of color was strategically sound. I found it helpful that he used red to indicate population and yellow to indicate taxation. These are two separate variables that are being compared, so I think choosing two starkly different colors was reasonable. Had he chosen two colors that were similar or a different hue of the same color per say, it would not be as significant of a comparison and may have been difficult to distinguish between the two. Additionally, he utilized color to indicate which countries were only powerful by land and which were powerful by maritime. Although this was not the main point in question for his graph, he was able to add in a detail about the nations by filling in the circles with either red or green, respectably.

knitr::include_graphics("playfair.png")
Playfair's Original Plot

Playfair’s Original Plot

Along with color, I think Playfair’s use of space was effective. The comparisons between the size of land for the countries were fairly easy to distinguish with the use of circles. For example, it was clear that the Russian Empire had the largest land area per square miles. Not only were the circles effective in their ways, the line segments used to indicate population and taxation also used space efficiently. The scales in millions accurately represented the data for each nation. Also, because the scale started at zero for each nation, population and taxation could be compared equally internally and externally for each nation.

radius <- c(10, 4, 2.1, 2.2, 2, 2, 1.9, 1.8, 1.5, 1.3, 1.2, 1)
Radius <- (sqrt(europe$Area/3.1459))/122.604
cent <- c(10,26,34,40,46,52,58,63,67,71,75,78.5)
x0 = cent
xlabel <- c("Russian Empire", "Turkish Empire","Sweden",
            "Emperor’s Dominions", "France", "Denmark",
            "German Empire", "Spain", "Britain & Ireland",
            "Prussia", "Countries Under the \nDominion of France",
            "Portugal")
cent
##  [1] 10.0 26.0 34.0 40.0 46.0 52.0 58.0 63.0 67.0 71.0 75.0 78.5
axisbreak <- c(10, 26, 34, 40, 46, 52, 58, 63, 67, 71, 75, 78.5)
Playfair <- ggplot(europe) +
  geom_circle(aes(x0 = cent , y0 = 0, r = Radius,)) +
  geom_segment(aes(x=cent-Radius, xend=cent-Radius, y=0, yend=Population),
               size = 1, color = "red") +
  geom_segment(aes(x=cent+Radius, xend=cent+Radius, y=0, yend=Taxation),
               size = 1, color= "yellow")+
  geom_segment(aes(x=cent-Radius, xend=cent+Radius, 
                   y=Population, yend=Taxation), linetype="longdash") +
  coord_fixed() +
  ggtitle("Recreation of William Playfair's \nTaxation and Population Graph")+
  theme(axis.text.x = element_text(angle = 45, face = "italic", hjust = 1, size = 7)) +
  scale_x_continuous(breaks = axisbreak, label = xlabel)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
Playfair + labs(x = "Country", y = "Population and Taxation \nComparison")

However, there are some flaws. Personally, I believe the axis are difficult to interpret. The Y-axis is difficult to distinguish, perhaps Playfair could have enlarged his numbers on the axis so they were more readable. At first glace, it is difficult to realize the data is represented in millions. For those with poor eyesight, I could for see issues with being able to interpret the graph. Furthermore, on the X-axis, it may have been more beneficial to enlarge the country names as well as it is difficult to read them from a far. Lastly, even though the circles represent the square miles for each nation in a substantial way, the labeling of the actual quantitative data was poorly executed. It is especially difficult to interpret the size of Portugal, perhaps Playfair could have added a legend with size ranges in accordance to the size of the circles. Despite the few flaws Playfair’s graph contained, he was still able to accurately represent the comparison between size, revenue, and population. Especially because he was able to show that certain European nations were in fact overburdened with taxes, which was the ultimate goal.

options(scipen = 9999)
gg <- ggplot(europe, aes(x=Population, y=Taxation)) + 
  geom_point(aes(col=Country, size=Area)) + 
  xlim(c(0, 32)) + 
  ylim(c(0, 32)) + 
  ggtitle("Improved Playfair Graph of Population and 
Taxation Compared to Area of the Country") +
  labs(subtitle="Population vs Taxation", 
       y="Taxation (Millions)", 
       x="Population (Millions)")

library(RColorBrewer)
library(ggrepel)

gg + scale_color_brewer(palette = "Paired") + 
  scale_size(range = c(2,17)) +
   geom_text_repel(aes(label = Country, size = 30))

After interpreting Playfair’s graph, I created a data visualization that also showed some European nations were being overtaxed in comparison to their population. As seen with the light purple point, the population of Britain and Ireland is relatively small at approximately fourteen million, but it has the highest taxation with a revenue of approximately 27 million pounds of sterling. Additionally, Spain also has a small population of approximately nine million, but their taxation is the third largest with a revenue of approximately fourteen million. In both cases, these relatively lightly populated nations are taxed higher than those with a heavier population. Playfair was right to believe some European nations were getting overwhelmed with taxes.

I decided to graph a scatter plot to show the comparison of the two numeric variables, population and taxation to indicate the overwhelmingness of taxation for certain nations. I wanted to be able to show the countries as a point in comparison to the others. As seen on the graph, it is significant to note that Britain and Ireland have almost half the population than the Russian Empire, but their taxation is approximately four times the Russian Empire. The scatter plot made it so the comparison would be easily seen. Furthermore, in order to represent the categorical variable, the countries, I decided to use colors and labels. I thought this would make it easily distinguishable which point was which country. Lastly, in order to graph the last variable, the area, I manipulated the size of the points to coincide with the area of the specific nation.

Additionally, I used different colors to indicate the countries. Because the nations are considered different categories, I decided to make the countries different colors rather than a gradient or different values. Also, it would be easier to deduce which country was where on the plot because they are all of a different hue. I labeled each point in addition to color because there are twelve different countries and I thought it would be convenient to also have the country labeled so there was little to no confusion. For spacial organization, I decided a scatter plot would allow for a large amount of negative space among the twelve points. In light of this, the eye would focus on the colored points with ease.

However, just as Playfair’s graph had flaws, so does mine. I wanted to make the areas of the points significant, so I enlarged the range. Unfortunately, this made it so it was harder to tell the exact location of the point on the plane. The Russian Empire is large, therefore it is hard to tell where the exact point would be for population and taxation. Despite this, the graph still represents the research question at hand. I was able to visually show that certain European countries have a smaller population than they do taxation.