Cars in Qatar: Prices and Performance by Engine Type

Author

Chandana Sai Poluru

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library(tidyverse)

Warning: package 'tidyverse' was built under R version 4.5.2

Warning: package 'ggplot2' was built under R version 4.5.2

Warning: package 'dplyr' was built under R version 4.5.1

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   4.0.0     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

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

Rows: 105 Columns: 15
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (5): origin, make, model, type, enginetype
dbl (10): length, width, height, seating, trunk, economy, horsepower, price,...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

glimpse(qatarcars)

Rows: 105
Columns: 15
$ origin      <chr> "Germany", "Germany", "Germany", "Germany", "Germany", "Ge…
$ make        <chr> "BMW", "BMW", "Audi", "Audi", "Audi", "Mercedes", "Mercede…
$ model       <chr> "3 Series Sedan", "X1", "RS Q8", "RS3", "A3", "Maybach", "…
$ length      <dbl> 4.713, 4.505, 5.012, 4.542, 4.456, 5.469, 4.613, 5.216, 4.…
$ width       <dbl> 1.827, 1.845, 1.694, 1.851, 1.960, 1.921, 1.984, 1.926, 1.…
$ height      <dbl> 1.440, 1.642, 1.998, 1.412, 1.416, 1.510, 1.969, 1.512, 1.…
$ seating     <dbl> 5, 5, 5, 5, 5, 4, 5, 5, 5, 5, 4, 5, 5, 5, 5, 5, 5, 2, 2, 5…
$ trunk       <dbl> 59, 505, 605, 321, 425, 500, 480, 610, 435, 565, 132, 627,…
$ economy     <dbl> 7.6, 6.6, 12.1, 8.7, 6.5, 13.3, 13.1, NA, 5.6, 7.5, 11.2, …
$ horsepower  <dbl> 386, 313, 600, 400, 180, 612, 585, 333, 163, 221, 473, 453…
$ price       <dbl> 164257, 264000, 630000, 310000, 165000, 1281000, 1011500, …
$ mass        <dbl> 1653, 1701, 2490, 1565, 1325, 2376, 2588, 2495, 1565, 1656…
$ performance <dbl> 4.3, 5.4, 3.6, 3.8, 6.7, 4.1, 4.3, 5.6, 6.8, 9.0, 3.3, 4.4…
$ type        <chr> "Sedan", "SUV", "SUV", "Sedan", "Sedan", "Sedan", "SUV", "…
$ enginetype  <chr> "Petrol", "Petrol", "Petrol", "Petrol", "Petrol", "Petrol"…

qatarcars %>%
  group_by(enginetype) %>%
  summarise(
    avg_price = mean(price, na.rm = TRUE),
    avg_performance = mean(performance, na.rm = TRUE) )

# A tibble: 3 × 3
  enginetype avg_price avg_performance
  <chr>          <dbl>           <dbl>
1 Electric    1044377             5.4 
2 Hybrid       372257.            6.09
3 Petrol       839319.            7.49

summary_data <- qatarcars %>%
  group_by(enginetype) %>%
  summarise(
    avg_price = mean(price, na.rm = TRUE),
    avg_performance = mean(performance, na.rm = TRUE) ) %>%
  pivot_longer(
    cols = c(avg_price, avg_performance),
    names_to = "metric",
    values_to = "value" )
summary_data

# A tibble: 6 × 3
  enginetype metric               value
  <chr>      <chr>                <dbl>
1 Electric   avg_price       1044377   
2 Electric   avg_performance       5.4 
3 Hybrid     avg_price        372257.  
4 Hybrid     avg_performance       6.09
5 Petrol     avg_price        839319.  
6 Petrol     avg_performance       7.49

ggplot(summary_data, aes(x = enginetype, y = value, fill = metric)) +
  geom_col(position = "dodge") +
  labs(
    title = "Car Prices and Performance by Engine Type in Qatar",
    x = "Engine Type",
    y = "Value",
    fill = "Metric" ) +
  theme_minimal()

ggplot(
  summary_data %>% filter(metric == "avg_price"),
  aes(x = enginetype, y = value, fill = enginetype) ) +
  geom_col() +
  labs(
    title = "Average Car Price by Engine Type in Qatar",
    x = "Engine Type",
    y = "Average Price (QAR)" ) +
  theme_minimal()

ggplot(
  summary_data %>% filter(metric == "avg_performance"),
  aes(x = enginetype, y = value, fill = enginetype)
) +
  geom_col() +
  labs(
    title = "Average Car Performance by Engine Type in Qatar",
    x = "Engine Type",
    y = "Average Performance"
  ) +
  theme_minimal()

Interpretation of Price and Performance by Engine Type

The results reveal a clear and systematic relationship between engine type, price, and performance in the Qatari automobile market. Electric vehicles emerge as the most expensive category, with an average price exceeding one million Qatari Riyals, yet they also exhibit the strongest performance, achieving the fastest average acceleration times. Hybrid vehicles, in contrast, represent the most affordable option among the three engine types and display moderate performance, positioning them as a cost-efficient compromise between price and speed. Petrol vehicles fall between electric and hybrid cars in terms of price but exhibit the slowest average acceleration, indicating comparatively weaker performance. Taken together, these patterns suggest that higher prices in the Qatari car market are generally associated with superior performance, particularly among electric vehicles, while hybrid cars prioritize affordability over speed.