Assignment 2

Deconstruct, Reconstruct Web Report

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

Original

*Source: https://howmuch.net/articles/the-world-economy-2017*

Objective

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

• To illustrate GDP by country and continent across the world for the year 2017.
• Audience: People interested in world economy, finance, economics.

The visualisation chosen had the following three main issues:

• Briefly explain issue 1:data is unclear, difficult to identify the statistics for all the countries listed
• Briefly explain issue 2:area of the tiles are irrelevant in terms of the objective/statistics, deceptive in nature
• Briefly explain issue 3:rounding is inaccurate when cross-checked with the original source

Reference

• Visualisation from website https://howmuch.net/articles/the-world-economy-2017

Code

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

 library(WDI)
 library(ggplot2)
 library(maps)
 library(rworldmap)
 library(tidyverse)
 library(plotly)
 library(ggfittext)
 library(magrittr)
 library(ggiraph)

 options(scipen = 999)
 dat = WDI(indicator='NY.GDP.MKTP.CD',extra=T ,start=2017, end=2017)
 dat$longitude <- as.numeric(levels(dat$longitude))[dat$longitude]
 dat$latitude <- as.numeric(levels(dat$latitude))[dat$latitude]
 dat$NY.GDP.MKTP.CD <- as.numeric(as.character(dat$NY.GDP.MKTP.CD))
 
 dat$NY.GDP.MKTP.CD <- dat$NY.GDP.MKTP.CD / 1000000000
 
 dat$NY.GDP.MKTP.CD  <- round(dat$NY.GDP.MKTP.CD ,2)
 
 dat_map <- joinCountryData2Map(dat, joinCode = "ISO2", nameJoinColumn = "iso2c")

## 214 codes from your data successfully matched countries in the map
## 50 codes from your data failed to match with a country code in the map
## 27 codes from the map weren't represented in your data

 dat_map_pol <- fortify(dat_map)
 dat_map_pol <- merge(dat_map_pol, dat_map@data, by.x="id", by.y="ADMIN", all.x=T)

• We got the data direectly from the world bank using the WDI package and after fixing the datatypes of all the columns we proceeded to round the GDP value to two decimals using the round function.
• We proceeded to create a datamap polygon using the fortify funvtion to specify the regions of the countries in the map, this polygon will be used to build the visualisation in the next section.

Data Reference

• World bank data website https://databank.worldbank.org/data/download/GDP.pdf

Reconstruction

The following plot fixes the main issues in the original.

• Data is self explanatory right now, we can hover over any country in the map to access the corresponding data.
• Area of the tiles are the areas of any countries, you can hover over the region to get the statistics for that particular country.
• We have taken the accurate data as per the world bank website so we are sure that statistics are correct.