Original


Source:ELEMENTS by visualcapitalist (2022).


Objective

The world is facing one of the most challenging periods in history, record-breaking inflations, a worldwide economic and energy crisis leading to social crisis due to the continuous emergence of pandemics, the Ukraine-Russian war, climate change and tensions between world giants have impacted the monetary policies of governments worldwide.

The article by Govind Bhutada (2022) aims to provide insight into one aspect of the crisis by analysing total fossil fuel subsidies by governments worldwide. Fossil fuel subsidies are any steps governments take to lower the cost of fossil fuel energy production and consumption (Oil Change International 2022).

Fuel subsidies keep the prices low but come at a high cost and sizable fiscal cost and encourage pollution(IMF 2022) since studying fossil fuel subsidies is critical to manage monetary policy.

Bhutada (2022) visualises the fossil fuel subsidies from 2010-2020 using the data published by the International Energy Agency (2022) to examine the cost of using fossil fuels through various energy sources. Data visualisation aims to deliver the message to the public and audiences keen on the world’s energy, political and economic situations and climate change.

The visualisation chosen had the following three main issues:

  • The three dimension bar chart visualisation has created two horizontal lines making it difficult to judge the height leading to false hierarchies.

  • Bar graphs with no scales can be misleading, and stacked graphs make it very hard to compare energy sources.

  • Visualisation has failed to meet the objective, which allows the audience to compare the fossil fuel subsidies across energy sources. Critical analysis of viewers is hindered by background graphics overwhelming the viewer. Subsidise for coal can go unnoticed by the viwer and data labels are misleading.

Reference

Code

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

#Loading the packages
library(ggplot2)
library(ggthemes)
library(tidyr)
library(readxl)
library(dplyr)
library(RColorBrewer)

#Importing data in to R 
energy <- read_excel("/Users/thisna/Desktop/RMIT/subsidise.xlsx",
  sheet="total",
  col_names=TRUE,
  range="B2:D46")

#data preprocessing

energy$Year <- as.character(energy$Year)

energy$Value <- round(digits = 1,(energy$Value/1000))


#correcting the plot

plot1 <- ggplot(energy, aes(fill=Fuel, y=Value, x=Year)) + 
    geom_bar(stat="identity",position="dodge") +  
  theme_classic() +
  theme (
    axis.text.x=element_text(color="Blue",angle=45,hjust=1,size=16),
        axis.text.y=element_text(color="Blue",size=14,hjust=0.5),
        plot.title = element_text(color="Black", size=16, face="bold"),
        axis.title.x = element_text(color="black", size=12, face="bold"),
        axis.title.y = element_text(color="black", size=12, face="bold")) +
labs(title = "Global Fossil Fuel Subsidies from 2010-2020",
     caption = "Source:Internationla Energy Agecy(2022)",
       y = "CONSUMPTION SUBSIDIES (ROUNDED TO NEAREST DECIMAL IN USD BILLIONS)",
       x = "YEAR") +
  scale_fill_brewer(palette="BrBG") +
  geom_text(aes(label=Value), position=position_dodge(width=0.99), vjust=0.25, hjust=0.5, size=4)
#Second plot

table1 <- data.frame(Year=c("2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020"),
                    Total=c(462.9,514.0,585.5,553.6,491.9,351.5,301.9,357.6,471.7,312.2,181.5))

plot2 <- ggplot(table1,aes(x = Year, y = Total))+
  geom_bar(stat="identity",fill="darkgoldenrod4") +
  theme_classic() +
  theme (
    axis.text.x=element_text(angle=45,hjust=1),
    axis.text.y=element_text(color="Black",size=14,hjust=0.5),
     plot.title = element_text(color="Black", size=14, face="bold"),
    axis.title.x = element_text(color="black", size=12, face="bold"),
    axis.title.y = element_text(color="black", size=12, face="bold")) +
  labs(title = "TOTAL GLOBAL FOSSIL FUEL SUBSIDISE FROM 2010-2020",
       y = "TOTAL FOSSIL FUEL SUBSIDISE 
       (Rounded to fist decimal in USD Billions)",
       x = "YEARS",
      caption = "Source:International Energy Agecy(2022)") +
  geom_text(aes(label=round(Total ,2)), vjust = -0.5,size = 3)

Data Reference

Reconstruction

The following plot fixes the main issues in the original.

First plot fixes the comparison across different enerrgy sources and enable audience to comapre the values.

Second plot fixes the obective of visulaising total fossil fuel subsidise worlwide without using stacked bars which mislead the viwers.