MATH2270 Assignment 2

Student Details

Student Name Mohammad Mohammad (s3650497)

Link to the video

https://drive.google.com/open?id=1FaRmAhzU7LxdYAU8dPvxbbVpEDxI3uXp

Introduction

The dataset is sourced from https://data.world/worldbank/gdp-ranking and it is about the the GDP worth in US Dollars of countries based on the rankings The dataset will be used to create a visualisation that will check what countries have higher GDP in US Dollars as compared to countries with a lower GDP.

Loading Packages

library(readr)
library(reshape)
library(ggplot2)
library(rworldmap)

## Loading required package: sp

## ### Welcome to rworldmap ###

## For a short introduction type :   vignette('rworldmap')

Data & Proprecessing

getwd()

## [1] "/Users/mohammadrazzak/Documents/University/RMIT/dataviz/Assignment 2"

setwd("/Users/mohammadrazzak/Documents/University/RMIT/dataviz/Assignment 2")

library(readr)
GDP<- read.csv ("GDP.csv", header=T, sep=",") [-c(3,6:10)]

View(GDP)
names(GDP)

## [1] "X"             "Ranking"       "Economy"       "X.millions.of"

colnames(GDP) <- c("Country_Code", "Ranking", "Economy_Type", "Millions_of_US_Dollars")
names(GDP)

## [1] "Country_Code"           "Ranking"               
## [3] "Economy_Type"           "Millions_of_US_Dollars"

head(GDP)

sum(is.na(GDP))

## [1] 37

colSums(is.na(GDP))

##           Country_Code                Ranking           Economy_Type 
##                      0                     37                      0 
## Millions_of_US_Dollars 
##                      0

GDP_NA <- na.omit(GDP)
View(GDP_NA)
GDP_NA["Continents"] <-  c("North America","Asia", "Asia","Europe", "Europe","Europe","Asia","Europe","South America",
                             "North America","Asia", "Oceania","Europe", "Europe","North America", "Asia","Europe","Asia",
                             "Europe","Asia","South America","Europe","Africa","Europe","Europe","Asia","Asia","Europe",
                             "Europe","South America","Asia","Africa","Africa","Asia","Asia", "Asia","Europe","Asia",
                             "South America","Asia","Asia","South America","Europe","Europe","Europe","Europe","Asia","Asia",
                             "South America","Asia","Europe","Europe","Oceania","Asia","Asia","Africa","Europe","Asia",
                             "North America","Africa","South America","Africa","Europe","Europe","Africa","Asia","North America",
                             "Asia","North America","Asia","Asia","North America","Africa","Africa","Europe","Europe","South America",
                             "Asia","North America", "North America","Europe", "Europe","Asia","Asia","Africa", "Africa", "Europe", "Europe",
                             "Africa","Asia","Asia","Europe", "Asia","Africa","South America","Asia","Africa","Africa", "Africa",
                             "South America","South America","Europe", "Africa","North America","Europe","Africa","Asia","North America","Europe",
                             "Asia","Asia","Oceania","Europe","Europe", "Asia","Africa", "Africa", "Africa",
                             "North America","Europe","Africa","Africa", "Africa", "North America", "Asia", "Asia","Asia", "Africa","Africa",
                             "Europe", "Africa","Africa","Asia","Europe","Africa", "Europe","Africa","Africa","North America","North America",
                             "Africa", "Africa", "Africa","Asia", "Africa","Europe","Africa", "Asia","Africa","Europe", "Europe","Africa",
                             "North America", "Africa","South America", "Africa", "North America","Oceania", "Africa", "Africa","Europe",
                             "Europe",  "South America",  "Asia","Africa", "Europe","North America","Africa","Africa", "Asia", "North America",
                             "Africa", "Africa","Africa","Africa", "North America","Asia", "North America", "Oceania", "Africa","North America",
                             "North America", "Africa","Oceania","Oceania", "North America","Africa","North America","Oceania", "Africa",
                             "Oceania","Oceania","Oceania","Oceania","Oceania")


GDP_NA$Economy <- ifelse(GDP_NA$Ranking >=50, "Unstable Economy", "Stable Economy")
View(GDP_NA)
library(reshape)
mdata <- melt(GDP_NA)

## Using Country_Code, Economy_Type, Millions_of_US_Dollars, Continents, Economy as id variables

colnames(mdata)[colnames(mdata)=="value"] <- "GDP Ranking"
colnames(mdata)

## [1] "Country_Code"           "Economy_Type"          
## [3] "Millions_of_US_Dollars" "Continents"            
## [5] "Economy"                "variable"              
## [7] "GDP Ranking"

Visualisation

mapped_data <- joinCountryData2Map(mdata, joinCode = "ISO3", nameJoinColumn = "Country_Code")

## 188 codes from your data successfully matched countries in the map
## 7 codes from your data failed to match with a country code in the map
## 55 codes from the map weren't represented in your data

par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")
mapCountryData(mapped_data, nameColumnToPlot = "GDP Ranking")

##The visualisation.