TidyTuesday Week 49: Qars-tar

This week we are exploring data about cars in Qatar! One of the most common example datasets in R is mtcars, which contains data on a bunch of cars from 1974 (!). Some of the car companies in there don’t even exist anymore, like Datsun. The mpg dataset that comes with {ggplot2} was designed to be an improvement on mtcars and includes vehicles from 1999 and 2008. However, both mpg and mtcars are highly US-centric—most people in the world don’t think in gallons and miles and feet and inches—and neither dataset includes details about electric cars, which are increasingly common today.

Box Plots of Car prices in Qatar

1. R code

# | echo: true
# | eval: false
# | warning: false
# | message: false

if(!require(tidyverse)){install.packages("tidyverse"); library(tidyverse)}
if(!require(scales)){install.packages("scales"); library(scales)}
if(!require(patchwork)){install.packages("patchwork"); library(patchwork)}

options(scipen=999)

qatarcars <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-12-09/qatarcars.csv')


df <- qatarcars |> 
  select(origin, price) |> 
# Convert price to USD 
mutate(price_usd = price * 0.27) |> 
  mutate(origin = case_when(
    origin == "PR China" ~ "China", TRUE ~ origin
  ))

plot_list <- list()

countries <- c("Italy", "RoW")

i = 1

for(i in 1:length(countries)) {
  
    if(countries[i] == "Italy") {
      
      df_i <- df |> 
        filter(origin  == "Italy" | origin == "UK")
      
      plot <- ggplot(df_i, aes(x = origin, y = price, fill = origin)) +
        geom_boxplot(width=.30) +
      theme(
      panel.grid.major = element_blank(),
      panel.background = element_blank(),
      panel.grid.minor = element_blank(),
      plot.title = element_text(size = 12),
      legend.title = element_text(size = 12),
      axis.title.x = element_blank(), 
       axis.line = element_line(linewidth = 0.2)
    ) +
    labs(
      title = paste0("Italy and UK"),
      y = "Price",
      fill = "Country of Origin"
    ) +
    scale_x_discrete(labels = ~ str_wrap(., width = 12)) +
  scale_y_continuous(labels = label_dollar(prefix = "QR"))
      
      plot_list[[i]] <- plot
      
    }
  
  else{
  
      
      df_i <- df |> 
        filter(origin  != "Italy" & origin != "UK")
  
    plot <- ggplot(df_i, aes(x = origin, y = price, fill = origin)) +
    geom_boxplot() +
      theme(
      panel.grid.major = element_blank(),
      panel.background = element_blank(),
      panel.grid.minor = element_blank(),
      plot.title = element_text(size = 12),
      legend.title = element_text(size = 12),
      axis.title.x = element_blank(),
      axis.line = element_line(linewidth = 0.2)
    ) +
    labs(
      title = paste(unique(df$origin[df$origin != "Italy" & df$origin != "UK"]), collapse = ", "),
      y = "Price",
      fill = "Country of Origin"
    ) +
    scale_x_discrete(labels = ~ str_wrap(., width = 12)) +
  scale_y_continuous(labels = label_dollar(prefix = "QR"))
      
      plot_list[[i]] <- plot
      
    }

}

plots <- plot_list[[2]] + plot_list[[1]]


plots <- plots +
  plot_annotation(
    title = "Prices of cars in Qatar",
    caption = "Italy and UK split out due to significant differences in Pricing",
    theme = theme(
      plot.title = element_text(
        face = "bold",
        size = 14,     # optional
        hjust = 0.5    # optional centering
      )
    )
  )