Click the Original, Code and Reconstruction tabs to read about the issues and how they were fixed.

Original


Source: Aperion Care (n.d.)


Objective

This data visualization is to show how different categories of inventions and advancements have affected modern life expectancy, and the number of lives saved by these inventions. The purpose of this visualization is to let their target audience know which types of inventions are saving the most lives so they can invest more resources in those inventions to increase modern life expectancy. This visualization includes the top fifty inventions that have had the greatest impact on modern life expectancy over the past two centuries.

The target audience for original data visualization can be people interested in public health and innovation, such as healthcare professionals, educators, researchers, and those individuals interested in understanding how inventions affect modern life expectancy.

The visualisation chosen had the following three main issues:

  • Some of the bars(in Billion) are too long to fit in the picture, which can be misleading. For example, by comparing the length of the bars, the Pasteurization seems to have a higher number of lives saved than toilets. But the fact is that the number of lives saved by Toilets is four times greater than Pasteurization. Therefore, the number of lives saved by each invention can’t be compared accurately in this bar chart.
  • The ‘NOW/FUTURE’ inventions should not be included in this visualization. One reason is that the units of measurement for ‘NOW/FUTURE’ and the past are different. The number of lives saved in the past is measured on a cumulative basis, while the number of lives saved in the ‘NOW/FUTURE’ is measured on a yearly basis. This can lead to misleading comparisons and make the data difficult to interpret. Another reason is that the number of lives saved for ‘NOW/FUTURE’ inventions are not presented in bars; they rely only on text to decode the information on the graph, which can be difficult to compare values and can be misleading as well. Therefore, ‘NOW/FUTURE’ inventions should be removed or they should be visualized using another bar chart.
  • Circular bar charts are generally not recommended to be used in data visualization, although they are attractive. The reason is that it may be difficult to compare values quickly and accurately. Therefore, it may mislead the viewer to draw incorrect conclusions. For example, it is difficult to compare “cervical cancer screening” and “anti-smoking efforts” by looking at this circular bar graph. In addition, the bars of this circular bar graph are represented by triangles, which unintentionally leads to issues with area distortion. For example, the area of the “Radiology” bar appears to be four times larger than the area of the “Higher Education” bar, which can also deceive the viewer. It would be better to use Cartesian coordinate system for comparing values accurately and efficiently.

Reference

Code

The following code was used to fix the issues identified in the original.

library(ggplot2)
willtolive <- read.csv("willtolive.csv")
willtolive <- willtolive[c(1:33),] # Remove "NOW/FUTURE" inventions
willtolive$NUMBER.OF.LIVES.SAVED <- as.integer(willtolive$NUMBER.OF.LIVES.SAVED)
willtolive$NUMBER.OF.LIVES.SAVED <- willtolive$NUMBER.OF.LIVES.SAVED/1000000 # Convert to Million
willtolive$CATEGORY <- as.factor(willtolive$CATEGORY)


p1 <- ggplot(data = willtolive, aes(x = reorder(TECHNOLOGY, YEAR.INVENTED), y = NUMBER.OF.LIVES.SAVED, fill = CATEGORY)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label=NUMBER.OF.LIVES.SAVED.MILLION), vjust=-0.3,  size=2) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.3, hjust=1), 
  plot.title = element_text(size = 18),
  text=element_text(family="Georgia"),
  title = element_text(face = "bold")) +
  scale_fill_brewer(palette = "Set1") +
  labs(x = "Technology invented over years", y = "Number of lives saved (millions)", title = "WILL TO LIVE", subtitle = "33 inventions and advancements in year 1850 - 2000 that drive modern life expectancy", caption = "Source: Aperion Care")

Data Reference

Reconstruction

The following plot fixes the main issues in the original.