qatarcars – qatarcars

Fuel economy, size, performance, and price data for cars in Qatar

Description

Includes prices and other specifications taken from YallaMotors Qatar between January to August 2025

Usage

qatarcars

Format

A tibble with 105 rows and 15 variables:

origin: a factor denoting car country of origin
make: a factor denoting car make
model: a factor denoting car model
length: a number denoting car length (meters)
width: a number denoting car width (meters)
height: a number denoting car height (meters)
seating: a number denoting number of seats in car
trunk: a number denoting volume of trunk (liters)
economy: a number denoting car fuel economy (L/100km)
horsepower: a number denoting car horsepower
price: a number denoting car price (2025 Qatari riyals (QAR))
mass: a number denoting car mass (kilograms)
performance: a number denoting car performance (Time 0-100 km/h (seconds))
type: a factor denoting car type
enginetype: a factor denoting car engine type

Source

https://github.com/profmusgrave/qatarcars

https://open.substack.com/pub/musgrave/p/introducing-the-qatar-cars-dataset

Yalla Motors Qatar

Examples

library("qatarcars")

str(qatarcars)

tibble [105 × 15] (S3: tbl_df/tbl/data.frame)
 $ origin     : Factor w/ 8 levels "Germany","Italy",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ make       : Factor w/ 31 levels "Audi","Bentley",..: 4 4 1 1 1 22 22 22 22 22 ...
 $ model      : Factor w/ 105 levels "2 Series Coupe",..: 3 96 80 81 7 57 41 33 43 44 ...
 $ length     : num [1:105] 4.71 4.5 5.01 4.54 4.46 ...
  ..- attr(*, "label")= chr "Length (m)"
 $ width      : num [1:105] 1.83 1.84 1.69 1.85 1.96 ...
  ..- attr(*, "label")= chr "Width (m)"
 $ height     : num [1:105] 1.44 1.64 2 1.41 1.42 ...
  ..- attr(*, "label")= chr "Height (m)"
 $ seating    : num [1:105] 5 5 5 5 5 4 5 5 5 5 ...
  ..- attr(*, "label")= chr "Number of Seats"
 $ trunk      : num [1:105] 59 505 605 321 425 500 480 610 435 565 ...
  ..- attr(*, "label")= chr "Trunk Capacity (L)"
 $ economy    : num [1:105] 7.6 6.6 12.1 8.7 6.5 13.3 13.1 NA 5.6 7.5 ...
  ..- attr(*, "label")= chr "Fuel Economy (L/100km)"
 $ horsepower : num [1:105] 386 313 600 400 180 612 585 333 163 221 ...
  ..- attr(*, "label")= chr "Horsepower"
 $ price      : num [1:105] 164257 264000 630000 310000 165000 ...
  ..- attr(*, "label")= chr "Price (QAR)"
 $ mass       : num [1:105] 1653 1701 2490 1565 1325 ...
  ..- attr(*, "label")= chr "Mass (kg)"
 $ performance: num [1:105] 4.3 5.4 3.6 3.8 6.7 4.1 4.3 5.6 6.8 9 ...
  ..- attr(*, "label")= chr "Time 0-100 km/h (s)"
 $ type       : Factor w/ 4 levels "Coupe","Hatchback",..: 4 3 3 4 4 4 3 3 3 3 ...
  ..- attr(*, "label")= chr "Type"
 $ enginetype : Factor w/ 3 levels "Electric","Hybrid",..: 3 3 3 3 3 3 3 1 3 2 ...
  ..- attr(*, "label")= chr "Engine Type"

head(qatarcars)

# A tibble: 6 × 15
  origin make  model length width height seating trunk economy horsepower  price
  <fct>  <fct> <fct>  <dbl> <dbl>  <dbl>   <dbl> <dbl>   <dbl>      <dbl>  <dbl>
1 Germa… BMW   3 Se…   4.71  1.83   1.44       5    59     7.6        386 1.64e5
2 Germa… BMW   X1      4.50  1.84   1.64       5   505     6.6        313 2.64e5
3 Germa… Audi  RS Q8   5.01  1.69   2.00       5   605    12.1        600 6.30e5
4 Germa… Audi  RS3     4.54  1.85   1.41       5   321     8.7        400 3.10e5
5 Germa… Audi  A3      4.46  1.96   1.42       5   425     6.5        180 1.65e5
6 Germa… Merc… Mayb…   5.47  1.92   1.51       4   500    13.3        612 1.28e6
# ℹ 4 more variables: mass <dbl>, performance <dbl>, type <fct>,
#   enginetype <fct>

summary(qatarcars)

         origin         make                    model        length     
 Japan      :29   Toyota  :10   2 Series Coupe     : 1   Min.   :3.595  
 Germany    :20   Kia     : 6   3                  : 1   1st Qu.:4.495  
 PR China   :18   BMW     : 5   3 Series Sedan     : 1   Median :4.681  
 UK         :11   Mercedes: 5   720S               : 1   Mean   :4.664  
 South Korea:10   MG      : 5   8 Series Gran Coupe: 1   3rd Qu.:4.850  
 USA        : 9   Hyundai : 4   991 Carrera S      : 1   Max.   :5.469  
 (Other)    : 8   (Other) :70   (Other)            :99                  
     width           height         seating         trunk       
 Min.   :1.595   Min.   :1.122   Min.   :2.00   Min.   :   0.0  
 1st Qu.:1.818   1st Qu.:1.455   1st Qu.:5.00   1st Qu.: 284.0  
 Median :1.875   Median :1.540   Median :5.00   Median : 448.0  
 Mean   :1.914   Mean   :1.574   Mean   :5.01   Mean   : 437.6  
 3rd Qu.:1.979   3rd Qu.:1.685   3rd Qu.:5.00   3rd Qu.: 542.0  
 Max.   :4.633   Max.   :1.998   Max.   :8.00   Max.   :1233.0  
                                                                
    economy         horsepower         price               mass     
 Min.   : 1.200   Min.   :  76.0   Min.   :   35000   Min.   : 945  
 1st Qu.: 6.500   1st Qu.: 154.0   1st Qu.:   91000   1st Qu.:1428  
 Median : 7.600   Median : 248.0   Median :  164000   Median :1701  
 Mean   : 8.698   Mean   : 317.8   Mean   :  796573   Mean   :1776  
 3rd Qu.:10.650   3rd Qu.: 380.0   3rd Qu.:  310000   3rd Qu.:2055  
 Max.   :22.500   Max.   :1973.0   Max.   :33000000   Max.   :2746  
 NA's   :10                                                         
  performance            type       enginetype
 Min.   : 2.400   Coupe    :14   Electric:10  
 1st Qu.: 4.800   Hatchback: 8   Hybrid  :14  
 Median : 6.900   SUV      :55   Petrol  :81  
 Mean   : 7.101   Sedan    :28                
 3rd Qu.: 8.800                               
 Max.   :14.500

table(qatarcars$origin)


    Germany       Italy       Japan    PR China South Korea      Sweden 
         20           5          29          18          10           3 
         UK         USA 
         11           9

aggregate(price ~ enginetype, qatarcars, mean)

  enginetype     price
1   Electric 1044377.0
2     Hybrid  372256.9
3     Petrol  839318.7

barplot(table(factor(
  qatarcars$seating,
  levels = min(qatarcars$seating):max(qatarcars$seating)
)))

plot(economy ~ mass, qatarcars)

plot(price ~ performance, qatarcars, log = "y")

if (require("dplyr")) {
  glimpse(qatarcars)
}

Rows: 105
Columns: 15
$ origin      <fct> Germany, Germany, Germany, Germany, Germany, Germany, Germ…
$ make        <fct> BMW, BMW, Audi, Audi, Audi, Mercedes, Mercedes, Mercedes, …
$ model       <fct> 3 Series Sedan, X1, RS Q8, RS3, A3, Maybach, G-Wagon, EQS,…
$ 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        <fct> Sedan, SUV, SUV, Sedan, Sedan, Sedan, SUV, SUV, SUV, SUV, …
$ enginetype  <fct> Petrol, Petrol, Petrol, Petrol, Petrol, Petrol, Petrol, El…

if (require("dplyr")) {
  qatarcars |> 
    count(origin)
}

# A tibble: 8 × 2
  origin          n
  <fct>       <int>
1 Germany        20
2 Italy           5
3 Japan          29
4 PR China       18
5 South Korea    10
6 Sweden          3
7 UK             11
8 USA             9

if (require("dplyr")) {
  qatarcars |> 
    group_by(enginetype) |> 
    summarize(avg_price = mean(price))
}

# A tibble: 3 × 2
  enginetype avg_price
  <fct>          <dbl>
1 Electric    1044377 
2 Hybrid       372257.
3 Petrol       839319.

if (require("ggplot2")) {
  ggplot(qatarcars, aes(x = seating)) + 
    geom_bar()
}

if (require("ggplot2")) {
  ggplot(qatarcars, aes(x = mass, y = economy)) + 
    geom_point()
}

if (require("ggplot2")) {
  ggplot(qatarcars, aes(x = performance, y = price)) +
    geom_point() + 
    scale_y_log10()
}