Burgernomics: R codes and datasets

“Data! Data! Data!” he cried impatiently. “I can’t make bricks without clay.”

Sherlock Holmes.
The Adventure of the Copper Beeches, Sir Arthur Conan Doyle (1892).

In my previous post, I tried to explain in the simplest way Burgernomics. Here I reproduce the R code and the graphs used in the Big Mac Index made by the Economist.

For the July 2020 update, there are two source files (Comma-separated values format):

R Script for the results

# Generate data for the Big Mac Index

setwd("C:/Users/EconJamel/R/big-mac-data-2020-07")

library('tidyverse')
library('data.table')
library(dplyr)
library(magrittr)
library(ggplot2)

big_mac_countries = c('ARG', 'AUS', 'BRA', 'GBR', 'CAN', 'CHL', 'CHN', 'CZE', 'DNK',
                      'EGY', 'HKG', 'HUN', 'IDN', 'ISR', 'JPN', 'MYS', 'MEX', 'NZL',
                      'NOR', 'PER', 'PHL', 'POL', 'RUS', 'SAU', 'SGP', 'ZAF', 'KOR',
                      'SWE', 'CHE', 'TWN', 'THA', 'TUR', 'ARE', 'USA', 'COL', 'CRI',
                      'PAK', 'LKA', 'UKR', 'URY', 'IND', 'VNM', 'GTM', 'HND', #VNZ 
                      'NIC', 'AZE', 'BHR', 'HRV', 'JOR', 'KWT', 'LBN', 'MDA', 'OMN',
                      'QAT', 'ROU', 'EUZ')
base_currencies = c('USD', 'EUR', 'GBP', 'JPY', 'CNY')

#NB: Venezuela removed.

# Data importation.

big_mac_data = fread('./source-data/big-mac-source-data.csv', na.strings = '#N/A',
                     # sort by date and then by country name, for easy reading;
                     # index on currency_code for faster joining
                     key = 'date,name', index = 'currency_code') %>%
  # remove lines where the local price is missing
  .[!is.na(local_price)]
tail(big_mac_data)

latest_date = big_mac_data$date %>% max
latest_date

# Raw index.

big_mac_data[, dollar_price := local_price / dollar_ex]
tail(big_mac_data)

big_mac_index = big_mac_data[
    !is.na(dollar_price) & iso_a3 %in% big_mac_countries
    ,.(date, iso_a3, currency_code, name, local_price, dollar_ex, dollar_price)]

for(currency in base_currencies) {
  big_mac_index[
    ,
    (currency) := dollar_price / .SD[currency_code == currency]$dollar_price - 1,
    by=date
  ]
}
big_mac_index[, (base_currencies) := lapply(.SD, round, 3L), .SDcols=base_currencies]
tail(big_mac_index)

to_plot = big_mac_index[date == latest_date]
to_plot$name = factor(to_plot$name, levels=to_plot$name[order(to_plot$USD)])
ggplot(to_plot[, over := USD > 0], aes(x=name, y=USD, color=over)) +
  geom_hline(yintercept = 0) +
  geom_linerange(aes(ymin=0, ymax=USD)) +
  geom_point() +
  coord_flip()

fwrite(big_mac_index, './output-data/big-mac-raw-index.csv')

# GDP-adjusted index.

big_mac_gdp_data = big_mac_data[GDP_dollar > 0]

regression_countries = c('ARG', 'AUS', 'BRA', 'GBR', 'CAN', 'CHL', 'CHN', 'CZE', 'DNK',
                         'EGY', 'EUZ', 'HKG', 'HUN', 'IDN', 'ISR', 'JPN', 'MYS', 'MEX',
                         'NZL', 'NOR', 'PER', 'PHL', 'POL', 'RUS', 'SAU', 'SGP', 'ZAF',
                         'KOR', 'SWE', 'CHE', 'TWN', 'THA', 'TUR', 'USA', 'COL', 'PAK',
                         'IND', 'AUT', 'BEL', 'NLD', 'FIN', 'FRA', 'DEU', 'IRL', 'ITA',
                         'PRT', 'ESP', 'GRC', 'EST')
big_mac_gdp_data = big_mac_gdp_data[iso_a3 %in% regression_countries]

head(big_mac_gdp_data)

ggplot(big_mac_gdp_data, aes(x=GDP_dollar, y=dollar_price)) +
  facet_wrap(~date) +
  geom_smooth(method = lm, color='tomato') +
  geom_point(alpha=0.5)

big_mac_gdp_data[,adj_price := lm(dollar_price ~ GDP_dollar)$fitted.values, by=date]
tail(big_mac_gdp_data)

ggplot(big_mac_gdp_data, aes(x=GDP_dollar, y=dollar_price)) +
  facet_wrap(~date) +
  geom_smooth(method = lm, color='tomato') +
  geom_linerange(aes(ymin=dollar_price, ymax=adj_price), color='royalblue', alpha=0.3) +
  geom_point(alpha=0.1) +
  geom_point(aes(y=adj_price), color='royalblue', alpha=0.5)

big_mac_adj_index = big_mac_gdp_data[
  !is.na(dollar_price) & iso_a3 %in% big_mac_countries
  ,.(date, iso_a3, currency_code, name, local_price, dollar_ex, dollar_price, GDP_dollar, adj_price)]

for(currency in base_currencies) {
  big_mac_adj_index[
    ,
    (currency) := (dollar_price / adj_price) /
      .SD[currency_code == currency, dollar_price / adj_price] - 1,
    by=date
  ]
}
big_mac_adj_index[, (base_currencies) := lapply(.SD, round, 3L), .SDcols=base_currencies]
tail(big_mac_index)

to_plot = big_mac_adj_index[date == latest_date]
to_plot$name = factor(to_plot$name, levels=to_plot$name[order(to_plot$USD)])
ggplot(to_plot[, over := USD > 0], aes(x=name, y=USD, color=over)) +
  geom_hline(yintercept = 0) +
  geom_linerange(aes(ymin=0, ymax=USD)) +
  geom_point() +
  coord_flip()

fwrite(big_mac_adj_index, './output-data/big-mac-adjusted-index.csv')

big_mac_full_index = merge(big_mac_index, big_mac_adj_index,
  by=c('date', 'iso_a3', 'currency_code', 'name', 'local_price', 'dollar_ex', 'dollar_price'),
  suffixes=c('_raw', '_adjusted'),
  all.x=TRUE
  )

fwrite(big_mac_full_index, './output-data/big-mac-full-index.csv')

R Script for exporting to Excel

# This script generates the Excel file for download
install.packages("devtools")
devtools::install_github("kassambara/r2excel")
library('r2excel')
library('magrittr')
library('data.table')

setwd("C:/Users/EconJamel/R/big-mac-data-2020-07")

data = fread('./output-data/big-mac-full-index.csv') %>%
    .[, .(
        Country = name,
        iso_a3,
        currency_code,
        local_price,
        dollar_ex,
        dollar_price,
        dollar_ppp = dollar_ex * dollar_price / .SD[currency_code == 'USD']$dollar_price,
        GDP_dollar,
        dollar_valuation = USD_raw * 100,
        euro_valuation = EUR_raw * 100,
        sterling_valuation = GBP_raw * 100,
        yen_valuation = JPY_raw * 100,
        yuan_valuation = CNY_raw * 100,
        dollar_adj_valuation = USD_adjusted * 100,
        euro_adj_valuation = EUR_adjusted * 100,
        sterling_adj_valuation = GBP_adjusted * 100,
        yen_adj_valuation = JPY_adjusted * 100,
        yuan_adj_valuation = CNY_adjusted * 100
    ), by=date]

dates = data$date %>% unique

wb = createWorkbook(type='xls')

for(sheetDate in sort(dates, decreasing = T)) {
    dateStr = sheetDate %>% strftime(format='%b%Y')
    sheet = createSheet(wb, sheetName = dateStr)
    xlsx.addTable(wb, sheet, data[date == sheetDate, -1], row.names=FALSE, startCol=1)
}

saveWorkbook(wb, paste0('./output-data/big-mac-',max(dates),'.xls'))

I get the following file that gather all the previous results: