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

Original


Source: Argon Data Visualization (2019).


Objective

Original Visualisation is used to show the emissions of greenhouse gases which are the reason behind the causes of greenhouse effect. Data from year 1990 to 2016 is used to produce the visualisation.

Generally, visualisation and the article related to visulaisation intend to show the countries that are heavily producing greenhouses gases, so that responsible communities for each country associated with United Nation can take further actions to minimize the effects in their respective regions.

The visualisation chosen had the following three main issues:

  • Use of Colors - Some of the representative colors for certain countries look similar and some of the lighter saturated colors tend to deceive the viewer. Consequently, visualization does not alllow viewers to observe the data for longer period with comparison.

  • Use of the stacked bar chart - Stacked bar chart always associates with certain issues regarding perception and here also it makes difficult to observe the exact propotion or the amount of gas. Some of the propotions are too small to be included in stacked bar chart.

  • Use of excessive categories - Too many countries as categories are in place as it makes less informative for viewers to even compare the the amount of gas produced by each region. splitting countries in to fewer groups such as continents would make it much easier for viewers to compare and contrast.

Reference

Code

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

library(readxl)
library(dplyr)
library(ggplot2)
library(gridExtra)
library(gtable)

Emission_Gas <- as.data.frame (read_excel("Greenhouse_Gas_Emissions.xlsx", sheet = "Sheet1"))

Emission_Gas$Continent <- Emission_Gas$Continent %>% as.factor()
Emission_Gas$Gas <-Emission_Gas$Gas %>% as.factor()


plot1 <- ggplot(data = Emission_Gas,aes(x=Gas,y=Amount,color=Gas))+geom_boxplot(outlier.size = 0,fill=NA)+
  geom_point(position = position_jitterdodge(dodge.width=0.3),alpha=0.3)+labs (title="Average Greenhouse Gas Emissions (1990 - 2016)",
  y="Emissions in Killotone",x="Type of Gas")+theme_bw()+theme(legend.position = "none")+ylim(0,120000)


plot2 <- ggplot(data = Emission_Gas,
             aes(x = Gas, y = Amount,colour = Continent)) + geom_point(position = "jitter") +
  scale_color_brewer(type = "seq", palette = "Dark2") +
  labs(title="Average Greenhouse Gas Emissions across continents (1990 - 2016)",
       y="Emissions in Killotone",x="Type of Gas")+ylim(0,120000)

Data Reference

Reconstruction

The following plot fixes the main issues in the original.