Original


Source: Global resources stock check (2012)


Objective

This data visualisation (BBC 2012) is to demonstrate how much time left before we run out of mineral or energy resources, and ecosystems in result of climate change and human acitivities. With technology being so popular in our daily life, we heavily rely on energy and minerals, such as antimony, lead, indium, and rare earths used in renewable energy solutions, zinc, gold, and copper used in mobile phones, transportation, and piping, and so on. Coral reefs are the most biodiverse ecosystems in the ocean with ecological and economical significance globally (UNESCO 2017).

The visualisation aims to call for awareness and attention from the government, investors, and those who care about our earth planet by emphosizing the fact that the finite resources would not last long. Therefore, to attract further investment in conservation, operations and recovery techniques, and better consumption habits in order to increase sustainability. The visualisation was produced based on secondary data across multiple sources, and published in 2012.

The original visualisation chosen had the following three main issues:

  • Issue 1: Data Integrity - the original data source was not cited properly, and lack of clarification on how calculation were derived from secondary data. Furthermore, some data sources were not reliable and do not exist in webpage anymore.

  • Issue 2: Visual Perception - the visual design is distracting and unnecessarily complex, which makes confusing and difficult for accurate reading.

  • Issue 3: Visual Bombardment - the visualisation is overwhelming despite that it was based on a simple data set, and the inclusion of distinct categories makes difficult to understand, for example, the “climate tipping points” and “rainforest” are unlike to “energy” category.

Reference

Code

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

library(readxl)
library(tidyr)
library(tidyverse)
library(dplyr)
library(ggplot2)

data <- read_excel("datanotes.xlsx", 
    sheet = "final new data", range = "A1:C17")
    
as.data.frame(data)
##                        Mineral Years_remaining    Category
## 1              All coral reefs        79.00000   Ecosystem
## 2                         Coal       139.00000 Fossil Fuel
## 3                          Oil        54.00000 Fossil Fuel
## 4                          Gas        49.00000 Fossil Fuel
## 5                     Titanium        90.22061    Minerals
## 6                    Aluminium        82.30453    Minerals
## 7                     Tantalum        78.87324    Minerals
## 8                       Cobalt        50.00000    Minerals
## 9  Phosphorus (phosphate rock)        42.49668    Minerals
## 10                      Copper        35.15152    Minerals
## 11                        Zinc        20.24291    Minerals
## 12                      Silver        19.41748    Minerals
## 13                        Lead        19.29825    Minerals
## 14                        Gold        16.30769    Minerals
## 15                     Indium*        13.68613    Minerals
## 16                    Antimony        12.06349    Minerals
data$Years_remaining <- as.integer(data$Years_remaining)

data <- mutate(data, Gone_by = Years_remaining + 2021)

data$Category <- factor(data$Category,
                        levels = c("Ecosystem", "Fossil Fuel", "Minerals"),
                        ordered = TRUE)

data %>% 
  ggplot(aes(Gone_by, y=reorder(Mineral, desc(Gone_by)), fill = Category))+
  geom_bar(stat = "identity", position = "dodge", width = 0.7, alpha = 0.5)+
  geom_point(aes(colour = Category), size = 10, position = position_nudge(x = 2), shape = 1)+
  geom_text(aes(label = Years_remaining), hjust = -0.4, size = 3)+
  facet_grid(Category~., scales = "free", space = "free", shrink = TRUE)+
  coord_cartesian(xlim = c(2021, 2165), expand = TRUE)+
  theme_classic()+
  labs(caption = "* based on NREL 2015 estimation; \n Numbers in circles indicate 'number of years' remaining for the supplies; \n Calculations based on reserves-to-production ratio - the length of time that those known reserves would last if production were to continue at that rate.", y = "Resources Category", x = "Remaining Supplies by Year (based on 2021 estimation)")+
  ggtitle("Stock Check \n Estimated Remaining Supplies of Non-renewable Resources")+
  theme(plot.title = element_text(hjust = 0.5),
        axis.text = element_text(size = 10),
        axis.title = element_text(size = 12),
        panel.grid.major.x = element_line(colour = "grey"))

Data Reference

Reconstruction

The following plot fixes the main issues in the original.

Source: US Geological Survey (2021); BP (2021); UNESCO(2017); AGU (2020); Lokanc et al (2015); Sale (2011).