explore mtcars

Explore dataset

mtcars %>% explore_tbl()

mtcars %>% describe()
#>    variable type na na_pct unique   min   mean    max
#> 1       mpg  dbl  0      0     25 10.40  20.09  33.90
#> 2       cyl  dbl  0      0      3  4.00   6.19   8.00
#> 3      disp  dbl  0      0     27 71.10 230.72 472.00
#> 4        hp  dbl  0      0     22 52.00 146.69 335.00
#> 5      drat  dbl  0      0     22  2.76   3.60   4.93
#> 6        wt  dbl  0      0     29  1.51   3.22   5.42
#> 7      qsec  dbl  0      0     30 14.50  17.85  22.90
#> 8        vs  dbl  0      0      2  0.00   0.44   1.00
#> 9        am  dbl  0      0      2  0.00   0.41   1.00
#> 10     gear  dbl  0      0      3  3.00   3.69   5.00
#> 11     carb  dbl  0      0      6  1.00   2.81   8.00

The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

• mpg = Miles/(US) gallon
• cyl = Number of cylinders
• disp = Displacement (cu.in.)
• hp = Gross horsepower
• drat = Rear axle ratio
• wt = Weight (lb/1000)
• qsec = 1/4 mile time
• vs = V/S
• am = Transmission (0 = automatic, 1 = manual)
• gear = Number of forward gears
• carb Number of carburetors

Explore variables

mtcars %>% 
  select(mpg, cyl, disp, hp) %>% 
  explore_all()

mtcars %>% 
  select(drat, wt, qsec, vs) %>% 
  explore_all()

mtcars %>% 
  select(am, gear, carb) %>% 
  explore_all()

Number of gears?

Is there a difference between cars with 3,4 and 5 gears?

mtcars %>% 
  explore(gear)

Most of the cars in the dataset have 3 or 4 gears. 15.6% have 5 gears.

Now check relation between some of the variables and gear:

mtcars %>% 
  select(gear, mpg, hp, cyl, am) %>% 
  explore_all(target = gear)

We see that 100% of cars with am = 0 (automatic) have 3 gears. All cars with am = 1 (manual) have 5 gears.

High miles per gallon?

Let’s define an interesting target: Cars that have mpg (miles per gallon) > 25

We copy the data and create a new target variable

data <- mtcars %>% 
  mutate(highmpg = if_else(mpg > 25, 1, 0, 0)) %>% 
  select(-mpg)

data %>% explore(highmpg)

So, about 19% of all cars have mpg > 25. What else is special about them?

data %>% 
  select(highmpg, cyl, disp, hp) %>% 
  explore_all(target = highmpg)

data %>% 
  select(highmpg, drat, wt, qsec, vs) %>% 
  explore_all(target = highmpg)

data %>% 
  select(highmpg, am, gear, carb) %>% 
  explore_all(target = highmpg)

There are some strong differences between cars with / without “high mpg”.

Now let’s grow a decision tree:

data %>% 
  explain_tree(target = highmpg)

Growing a decision tree, shows that there seems to be a very strong correlation between wt (weight) and “high mpg”. Cars with a low weight are much more likely to have “high mpg”.

Let’s take a closer look to wt:

data %>% explore(wt, target = highmpg)

data %>% explore(wt, target = highmpg, split = FALSE)

wt (weight) is a good predictor for high mpg.

mtcars %>% explore(wt, mpg)

There is a strong correlation between wt and mpg.

If you want to have high miles per gallon (mpg), buy a car with low weight (wt)!

Horsepower?

Is there a relation between horsepower and other variables like number of cylinder?

Let’s build a decision tree with horsepower as target:

mtcars %>% 
  explain_tree(target = hp, minsplit=15)

mtcars %>% 
  select(hp, cyl, mpg) %>% 
  explore_all(target = hp)

Cars with 8 cylinders have higher horsepower.

Cars with low miles per gallon (mgp) have higher horsepower!