library(readr)
playfair_european_nations <- read_csv("playfair_european_nations.csv")Rows: 12 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Country
dbl (3): Area, Population, Taxation
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.View(playfair_european_nations)library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ purrr 1.0.2
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.0 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggplot2)
library(ggforce)
library(dichromat)
Through examining Playfair’s data visualization, it is clear that the graphic is ahead of its time. The diagram that he created contained many impressive elements, and represented a large amount of information. There were many elements that he did well, but at the same time, there were many areas for him to improve.
The visualization that he made displays nation size, population, and taxation. The larger bubbles represent larger land mass nations, and the red and yellow lines represent the population vs taxation relationship. The countries with a higher red line represent countries that have a higher population than taxation value. The countries where the yellow line is higher on the other hand represent countries where the taxation exceeds the population.
First, I think that the graph was very well put together. You can tell that a lot of time went into choosing each variable and how to depict them on the page. He wanted to examine the size of a nation and how it compared to the amount of taxation within that nation. Choosing to represent the land size of a nation with the size of the bubbles was very effective, because it immediately draws the viewers attention to the larger countries. Alongside this, I felt like the overall spacing of each data point was very well done. With a lot of information all packed into one graphic, I appreciated that he spaced out each data point so that there was no overlap and you can clearly see which elements belong to which country. Another element that I feel like he used very effectively was his use of color. The red line represents the population size of each country, while the yellow line represents the taxation. I appreciated how he represented these two quantitative variables in a very organized manner, and made the colors stick out from each other. This made it easier to interpret.
When it comes to the things that he did poorly, I think there were some areas that needed to be critiqued in order for the graphic to give off a better effect. To start with, I think that there may be too many elements going on all at once. While examining the graph, I get a good sense of either country size, population, or taxation. It is hard to get a good grasp of all these elements at the same time. Additionally, there were a few design elements that needed to be altered a bit. I think that the font size needed to be increased along the x-axis. The current font is a bit small, and tough to read. It does do a good job following the rustic theme that the graphic gives off, but as far as legibility goes, there are many better options. The numbers on the y-axis could also be increased in size to make for a better interpretation of what the values actually represent.
radius <- c(10, 4, 2.1, 2.2, 2, 2, 1.9, 1.8, 1.5, 1.3, 1.2, 1)
Radius <- (sqrt(playfair_european_nations$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 78.5axisbreak <- c(10, 26, 34, 40, 46, 52, 58, 63, 67, 71, 75, 78.5)Playfair <- ggplot(playfair_european_nations) +
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 = 90, 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")
Recreation <- ggplot(playfair_european_nations, aes(x=Population, y=Taxation)) +
geom_point(aes(col=Country, size=Area)) +
xlim(c(0, 35)) +
ylim(c(0, 30)) +
ggtitle("New and Improved Playfair Data Visualization") +
labs(subtitle="Population vs Taxation",
y="Taxation (Millions)",
x="Population (Millions)")
Recreation
Overall, I think that my recreation does a good job at keeping the main theme that Playfair wanted to portray through the use of his graphic, while at the same time simplifying it to make it easier to understand and pick out the data points of interest.
To start off, the first thing that I did was make the graph less cluttered by making the two quantitative variables (Taxation and Population) appear as the two axis titles and having there relationship shown through the use of a scatter plot. I think that portraying this relationship this way is better because it is more common nowadays, and most viewers will have seen graphs like this before, so it is easier to interpret what is actually going on. Additionally, I think that this type of graph does a better job at making it apparent where the outliers lie, since that was Playfair’s main goal. He wanted to view which countries were being over taxed as compared to their population. In his original graph, the nations where taxation exceeded population were represented through the use of the yellow lines being taller than the red lines and connecting them respectively. Another detail that I changed was the labeling of the countries. I didn’t like how Playfair’s visualization used a very small font in order to label the countries to the point where it wasn’t legible without zooming in on the data points. Instead, I decided to leave the labeling off the graphs itself, and use a legend on the side that color coded the countries. This made the graph less cluttered while still being able to identify which country was which. On top of that, I made the bubble size represent the land mass size similarly to Playfairs original graphic. Lastly, I added units to represent how large the population and taxation was. This didn’t necessarily add much to the relationship between the two variables, but rather added a bit of clarification for the viewer.
As far as additional improvements go, I think there is definitely still room to improve. I wish I could depict the land mass size better, since although the bubble size was used to represent the nation size, it is hard to distinguish between the small countries. I would also probably lable the countries where taxation exceeded population since those were the countries of interest.
Overall, I think that the data presented does support playfairs claim on taxation. He claimed that european nations were overburdened with taxes and it is clear to see, especially in countries such as Britain and Ireland, that the taxation value greatly exceeds the population value. He claimed that larger population countries should have a greater amount of taxes because of that, and the graph shows trends that prove this isn’t always the case.