The Big Mac index 🍔
Animated Graph No. 1
By Patrizia Maier
# get packages
library(rnaturalearth)
library(tmap)
library(tidyverse)
library(gifski)
library(extrafont)
# font_import() # only once
loadfonts(device = "win", quiet = TRUE) # every time# get data
tuesdata <- tidytuesdayR::tt_load('2020-12-22')
big_mac <- tuesdata$`big-mac`# inspect data
# lots of missing values in GDP_dollar, adj_price, XXX_adjusted --> correct or use other vars
# unique(big_mac$date) # from 2000 to 2020 --> use as time factor# get world geometry polygons
world <- ne_countries(returnclass='sf') %>%
select("iso_a3", "geometry")# join data
dat <- left_join(world,
big_mac %>%
group_by(date),
by="iso_a3")
dat$date <- as.factor(dat$date)
# create map
tmap_style("white")
animation <- tm_shape(dat) +
tm_fill(col="usd_raw", legend.show=TRUE, title="Rel. index to USD (raw)", palette="-RdYlGn", midpoint=0) +
tm_facets(by="date", free.coords=FALSE, drop.units=TRUE, drop.NA.facets=TRUE, nrow = 1, ncol = 1) +
tm_layout(main.title="How expensive is a Big Mac ...",
main.title.position="left",
main.title.size=3,
main.title.color="#003366",
title="... compared to buying it in the US?",
title.color="#003366",
title.size=2,
title.position=c("right","bottom"),
legend.position=c("left", "bottom"),
legend.title.size=1.75,
legend.text.size=1.25,
panel.label.size=1.5,
sepia.intensity=0.1,
fontfamily="Segoe UI Semibold") +
tm_shape(dat %>% filter(iso_a3=="USA")) +
tm_fill("#003366") +
tm_shape(dat) +
tm_borders(col="grey")# save as gif with time factor
tmap_animation(animation, filename="big_mac_anim.gif", delay=100, restart.delay=200,
width=1200, height=600)
Plot No. 1
By Andreas Neumann
Plot No. 2
By Long Nguyen (@long39ng)
library(tidyverse)
library(ggtext)
library(ggtextures)
theme_set(
theme_minimal(base_family = "Libre Franklin", base_size = 16) +
theme(axis.text.x = element_text(size = 10, colour = "#f07178"),
axis.text.y = element_text(face = "bold", colour = "#82aaff"),
panel.grid = element_blank(),
panel.grid.major.x = element_line(colour = "#373c53", size = .5),
plot.background = element_rect(fill = "#292d3e"),
plot.margin = margin(30, 30, 30, 30),
plot.title = element_markdown(size = rel(1.3),
family = "BioRhyme Expanded",
colour = "#c3e88d",
lineheight = 1.5),
plot.subtitle = element_markdown(colour = "#c792ea",
lineheight = 1.2,
margin = margin(b = 15)),
plot.title.position = "plot",
plot.caption = element_markdown(size = 10,
colour = "#c792ea",
lineheight = 1.2,
margin = margin(t = -20)))
)
big_mac <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-12-22/big-mac.csv')
glimpse(big_mac)
#> Rows: 1,386
#> Columns: 19
#> $ date <date> 2000-04-01, 2000-04-01, 2000-04-01, 2000-04-01, 2000-0…
#> $ iso_a3 <chr> "ARG", "AUS", "BRA", "CAN", "CHE", "CHL", "CHN", "CZE",…
#> $ currency_code <chr> "ARS", "AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK",…
#> $ name <chr> "Argentina", "Australia", "Brazil", "Canada", "Switzerl…
#> $ local_price <dbl> 2.50, 2.59, 2.95, 2.85, 5.90, 1260.00, 9.90, 54.37, 24.…
#> $ dollar_ex <dbl> 1.0000000, 1.6800000, 1.7900000, 1.4700000, 1.7000000, …
#> $ dollar_price <dbl> 2.500000, 1.541667, 1.648045, 1.938776, 3.470588, 2.451…
#> $ usd_raw <dbl> -0.00398, -0.38579, -0.34341, -0.22758, 0.38270, -0.023…
#> $ eur_raw <dbl> 0.05007, -0.35246, -0.30778, -0.18566, 0.45774, 0.02964…
#> $ gbp_raw <dbl> -0.16722, -0.48645, -0.45102, -0.35417, 0.15609, -0.183…
#> $ jpy_raw <dbl> -0.09864, -0.44416, -0.40581, -0.30099, 0.25130, -0.116…
#> $ cny_raw <dbl> 1.09091, 0.28939, 0.37836, 0.62152, 1.90267, 1.05023, 0…
#> $ gdp_dollar <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ adj_price <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ usd_adjusted <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ eur_adjusted <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ gbp_adjusted <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ jpy_adjusted <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ cny_adjusted <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
burger_img <- "https://twemoji.maxcdn.com/v/latest/72x72/1f354.png"
big_mac_capita <- big_mac %>%
slice_max(date) %>%
drop_na(gdp_dollar) %>%
mutate(n_burgers = gdp_dollar / dollar_price / 1000)
big_mac_capita %>%
ggplot(aes(x = fct_reorder(name, n_burgers), y = n_burgers)) +
geom_isotype_col(image = burger_img,
img_width = unit(1, "native"),
img_height = unit(.7, "native"),
ncol = NA, nrow = 1, hjust = 0, vjust = .5) +
scale_y_continuous(position = "right", labels = function(x) {
x <- paste0(x, "k")
x[x == "0k"] <- "0"
x
}) +
annotate(geom = "text", x = 36.5, y = 17.5, label = "}",
hjust = 0, vjust = .39, angle = 270,
family = "Libre Franklin", colour = "#c792ea", size = 7) +
annotate(geom = "text", x = 35.9, y = 17.5, label = "1000\nBig Macs",
hjust = .5, vjust = 1, lineheight = 1,
family = "Libre Franklin", colour = "#c792ea") +
coord_flip() +
labs(x = NULL, y = NULL,
title = toupper("Big Macs per capita"),
subtitle = "How many Big Macs could be bought at local price<br>
with the GDP per person in 2020",
caption = "Data: *The Economist*<br>Hamburger emoji: *Twemoji*")
Plot No. 3
By Sylvi Rzepka (@SylviRz)
library(ggplot2)
library(tidytuesdayR)
library(tidyverse)
library(tmap)
library(sf) # for worldmap data
library(dplyr)
library(rcartocolor)
# Read in as a dataframe
bigmac <- as.data.frame(tt_load('2020-12-22')$"big-mac")
class(bigmac)
str(bigmac)
summary(bigmac$date)
table(bigmac$name)
#loading world map data
data("World")
######data prep
#Extracting the Eurozone
eurozone<- bigmac %>%
filter(date=="2020-07-01") %>%
filter(iso_a3=="EUZ") %>%
dplyr::select(!iso_a3) %>%
#duplicating 19x one line for each euro zone country
slice(rep(1:n(), each=19))
#### iso_a3 for all eurozone countries
eurozone$iso_a3<-c("AUT", "BEL", "CYP", "EST", "FIN", "FRA", "DEU", "GRC",
"IRL", "ITA", "LUX", "LVA", "LTU", "MLT", "NLD",
"PRT", "SVK", "SVN", "ESP")
# binding eurozone to bigmac data
bigmac2<-rbind(bigmac, eurozone)
bigmac_prep <- bigmac2 %>%
#filtering for last date
filter(date=="2020-07-01")%>%
#dropping aggregated Euro zone line
filter(iso_a3!="EUZ") %>%
# keeping only a few vars, "..._adjusted" is the GPD adjusted over/undervaluation with respect to the currency
dplyr::select(iso_a3, usd_raw, gdp_dollar, usd_adjusted, eur_adjusted, cny_adjusted, jpy_adjusted)
#Joining the population to the shapefile data from world
bigmac_shapedata <- merge(World, bigmac_prep, by.x="iso_a3", by.y="iso_a3", all.x=TRUE)
head(bigmac_shapedata)
# Plotting
my_colors = carto_pal(7, "Fall")
map1<-tm_shape(bigmac_shapedata) +
tm_fill(title="Legend: \nUndervalued (<0) \nOvervalued (>0) \nlocal currency",
c("usd_adjusted", "eur_adjusted", "cny_adjusted", "jpy_adjusted"),
midpoint=0,
breaks=c(-.5, -.3, -.15, 0, 0.001, .15, .3, .5, .9),
palette=my_colors,
colorNA="grey95",
textNA = "No GDP data \nor McDonald's") + #c("usd_raw", "eur_raw", "cny_raw", "jpy_raw")
tm_facets(sync = TRUE, ncol = 2, nrow=2,) +
tm_layout(main.title="Adjusted Big Mac Index for different base currencies",
#main.subtitle="Shows how under- and overevaluation depends on the perspective",
title="DataViz by @SylviRz for #TidyTuesday \nData from The Economist",
title.size=1,
title.position=c("left","BOTTOM"),
fontfamily="Andale Mono",
legend.outside = TRUE,
panel.labels=c("USD base currency", "EUR base currency", "CNY base currency", "JPY base currency"),
panel.label.bg.color = my_colors[4],
frame=FALSE)
map1
tmap_save(
tm = map1,
filename = "bigmacindex_by_currency")
Plot No. 4
By Frie Preu
library(tidyverse)
library(countrycode)
library(hrbrthemes)
# load data
big_mac <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-12-22/big-mac.csv')
big_mac <- big_mac %>%
mutate(date = lubridate::ymd(date),
year = lubridate::year(date))
# add continent
big_mac$continent <- countrycode(big_mac$iso_a3, origin = 'iso3c', destination = 'continent')
big_mac <- big_mac %>%
mutate(continent = if_else(iso_a3 == "EUZ", "Europe", continent))
ggplot(big_mac, aes(x = date, group = iso_a3, color = continent, y = dollar_price))+
geom_point(size = 0.4)+
geom_line(alpha = 0.4)+
facet_wrap(~continent, ncol = 2)+
theme_ft_rc()+
labs(title = "Big Mac Index Over Time", y = "Big Mac Index")
