This is a template file. The example included is not considered a good example to follow for Assignment 2. Remove this warning prior to submitting.

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

Original


Source: The World’s Biggest Economies Over Time. howmuch.net. (2020)


Objective

Explain the objective of the original data visualisation and the targetted audience.

The visualisation chosen had the following three main issues:

  • The first issue is about data integrity. The data use in this visualisation is significantly different to the data I found from Worldbank. for example, the data for china is only 14 Trillian in 2019 which is only half of the 2020 projection figures. In 2019, US is about double the size of China’s GDP. Those figures are misleading

  • Briefly explain issue 2 The visualization do not reflect the real size of the GDP. The figure didn’t stand out and the figure also too small to read.

  • Briefly explain issue 3 The colour is too light and fade, and there are also too many yellow color in the visualization. This is not very catching and sometimes looks quite confusing.

Reference

Code

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

library(ggplot2)
library(dplyr)
library(readxl)
library(ggpubr)
library(tinytex)
library(tidyverse)


GDP <- read_excel("C:/Users/wei_s/Desktop/GDP.xlsx")


GDP$`Country` <- as_factor(GDP$`Country`)
n1980 <- GDP%>%select(1,2)%>%top_n(10, y1980)
n1990 <- GDP%>%select(1,3)%>%top_n(10, y1990)
n2000 <- GDP%>%select(1,4)%>%top_n(10, y2000)
n2010 <- GDP%>%select(1,5)%>%top_n(10, y2010)
n2019 <- GDP%>%select(1,6)%>%top_n(10, y2019)

Data Reference

World Development Indicators | DataBank. Databank.worldbank.org. (2020). Retrieved 21 September 2020, from https://databank.worldbank.org/reports.aspx?source=2&series=NY.GDP.MKTP.CD&country=.

Reconstruction

The following plot fixes the main issues in the original.