Original


Source: https://howmuch.net/articles/global-gold-demand-2019


Objective

The objective of this visualisation is to show the demand of gold in different sectors.Even before corona upending markets, there were some serious conerns to the use of gold. Due to coronavirus pandemic, there is fluctuation in the market, so the investors are trying their best to limit the exposure to losses. So at this time where every industry are facing financial crises, investors have turned towards gold and as a result gold have posted their biggest weekly gain since 2008 and the demand for gold continue to surge among investors because gold is being used in technology sector from medicine to astronomy. The viusalisation has shown percentage of gold demand in different sectors like banking, jewelery etc. The main audience is general public, investors, bankers.

The visualisation chosen had the following main issues:

  • Choice of Chart: Unconventional Shattered circular plot is used to show the global gold demand. The area and angles in this shattered circular plot are inferior to position interms of accuracy .

  • Color issue: It is difficult to differentiate the categories as the same color is used. Colour needs to be used with caution in this type of chart because they reply on colours to differentiate between segments.

  • Area and Size : The relative size for the gold demand types depicted in this visualization is misleading. Ther value of gold demand in Technology is 7.5% and in investment(ETS and similar) is 9.21% but Technology appears to be bigger than investment(ETS and similar).

  • Visual Bombardment: The chart is bombarded with gold demand in tonnes as well as percentages but also with the images like of jewelery and banks etc, which are distracting the audience from the main objective in the data

Reference

Code

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

library(ggplot2)
library(lubridate)
library(plotly)
library(dplyr)
library(readxl)

GOLD2 <- read_excel("C:/Users/mafza/OneDrive/Desktop/GOLD2.xlsx")


Total_gold_Demand <- 4355
percentage <- round((GOLD2$Tonnes/Total_gold_Demand)*100,1)

p<-ggplot(data=GOLD2, aes(x=Demand, y=Tonnes)) +
   geom_bar(stat="identity",position="dodge",width =1 , color = "black",fill = "steelblue") +
   labs(title = "Global Gold Demand in 2019",
        y = "Tonnes (t) ",
        x = "Demand") + 
   theme_minimal() + 
   geom_text(aes(label=paste0(percentage,"%")),hjust =0.1, size = 2.3)
theme(axis.text.x = element_text(face="bold", size=20),
      axis.text.y = element_text(face="bold", size=20))
## List of 2
##  $ axis.text.x:List of 11
##   ..$ family       : NULL
##   ..$ face         : chr "bold"
##   ..$ colour       : NULL
##   ..$ size         : num 20
##   ..$ hjust        : NULL
##   ..$ vjust        : NULL
##   ..$ angle        : NULL
##   ..$ lineheight   : NULL
##   ..$ margin       : NULL
##   ..$ debug        : NULL
##   ..$ inherit.blank: logi FALSE
##   ..- attr(*, "class")= chr [1:2] "element_text" "element"
##  $ axis.text.y:List of 11
##   ..$ family       : NULL
##   ..$ face         : chr "bold"
##   ..$ colour       : NULL
##   ..$ size         : num 20
##   ..$ hjust        : NULL
##   ..$ vjust        : NULL
##   ..$ angle        : NULL
##   ..$ lineheight   : NULL
##   ..$ margin       : NULL
##   ..$ debug        : NULL
##   ..$ inherit.blank: logi FALSE
##   ..- attr(*, "class")= chr [1:2] "element_text" "element"
##  - attr(*, "class")= chr [1:2] "theme" "gg"
##  - attr(*, "complete")= logi FALSE
##  - attr(*, "validate")= logi TRUE

Data Reference

Reconstruction

The following plot fixes the main issues in the original.

So, i used a barchart to reconstruct the visual because trends from bar charts are accurate, easier and quicker to read. That will not only resolve the visual bombardment issue but also area size. I chose steelblue color for the bars as it has low saturation and its too bright for the audience.