The Big Mac index

“The big mac index was invented by The Economist in 1986 as a lighthearted guide to whether currencies are at their “correct” level. It is based on the theory of purchasing-power parity (PPP), the notion that in the long run exchange rates should move towards the rate that would equalise the prices of an identical basket of goods and services (in this case, a burger) in any two countries.” (Source: https://www.economist.com/news/2020/07/15/the-big-mac-index)

Run the following code chunk first to load and clean the data

library(data.table)
library(ggplot2)
library(curl)
## Using libcurl 7.84.0 with Schannel
bigmac <- fread("https://raw.githubusercontent.com/dratnadiwakara/fin4820/master/bigmac.csv")
bigmac_USA <- bigmac[iso_a3=="USA", c("year","local_price")]
setnames(bigmac_USA,"local_price","US_price")

bigmac <- merge(bigmac,bigmac_USA,by="year")
bigmac[,bigmac_ex:= local_price/US_price]
bigmac[,diff:= (bigmac_ex-dollar_ex)/dollar_ex]
bigmac[,undervalued := ifelse(diff<0,1,0)]

head(bigmac)
##    year iso_a3 currency_code        name local_price dollar_ex dollar_price
## 1: 2000    ARG           ARS   Argentina        2.50      1.00     2.500000
## 2: 2000    AUS           AUD   Australia        2.59      1.68     1.541667
## 3: 2000    BRA           BRL      Brazil        2.95      1.79     1.648045
## 4: 2000    CAN           CAD      Canada        2.85      1.47     1.938776
## 5: 2000    CHE           CHF Switzerland        5.90      1.70     3.470588
## 6: 2000    CHL           CLP       Chile     1260.00    514.00     2.451362
##    gdp_dollar US_price   bigmac_ex         diff undervalued
## 1:         NA     2.51   0.9960159 -0.003984064           1
## 2:         NA     2.51   1.0318725 -0.385790173           1
## 3:         NA     2.51   1.1752988 -0.343408489           1
## 4:         NA     2.51   1.1354582 -0.227579478           1
## 5:         NA     2.51   2.3505976  0.382704476           0
## 6:         NA     2.51 501.9920319 -0.023361806           1

Data Dictionary: year: Year of observation iso_a3: Three-character ISO 3166-1 country code name: County name local_price: Price of a Big Mac in the local currency dollar_ex: Local currency units per dollar (Exchange rate) dollar_price: Price of a Big Mac in dollars gdp_dollar: GDP per person, in dollars

1. Plot the distribution of the price of a Big Mac in 2020 using a histogram

2. Plot how the price of a Big Mac changed overtime in the United States

3. Compare the price change of a Big Mac overtime in the United States, China, and Australia.

Following is not required. But good to try.

4. Create a scatter plot that shows the relationship between the gdp_dollar and dollar_price

5. Create a sorted bar graph showing the valuation difference (variable: diff) in 2020

6. Your own analysis

Bonus exercise

library(rgdal)
library(rgeos)

worldmap <- readOGR("https://raw.githubusercontent.com/dratnadiwakara/fin4820/master/countries/countries.geojson")
## OGR data source with driver: GeoJSON 
## Source: "https://raw.githubusercontent.com/dratnadiwakara/fin4820/master/countries/countries.geojson", layer: "countries"
## with 255 features
## It has 2 fields
worldmap <- data.table(fortify(worldmap,region="ISO_A3"))

worldmap <- merge(worldmap,bigmac[year==2020],by.x="id",by.y="iso_a3",all.x=T)

ggplot(data=worldmap,aes(x=long,y=lat,group=group,fill=dollar_price))+geom_polygon()+
  theme_minimal()+
  theme(legend.position = "bottom",legend.title =element_blank())+
  scale_fill_gradient(low = "yellow", high = "darkred")+
  labs(x="",y="",title="Big Mac Prices in 2020 ($)")