Data Exploration

We will use the built-in mtcars dataset, which contains 32 observations on 11 variables related to automobile design and performance. Specifically, we are exploring the relationship between Horsepower (hp) and Miles per Gallon (mpg), colored by the number of Cylinders (cyl).

# Display structure and head of the dataset
str(mtcars)
## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...
head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

##Interactive Plotly Visualization This slide features the main interactive element: a scatter plot created with Plotly. This plot allows the user to hover over points to see details, zoom in on specific areas, and toggle visibility of data points by cylinder count.

# Convert 'cyl' to a factor for distinct colors in the plot
mtcars$cyl <- as.factor(mtcars$cyl)

# Create the interactive Plotly graph
p <- ggplot(mtcars, aes(x = hp, y = mpg, color = cyl, 
                        text = paste("Car: ", rownames(mtcars), 
                                     "<br>HP:", hp, 
                                     "<br>MPG:", mpg))) +
  geom_point(size = 3) +
  labs(title = "HP vs. MPG, Grouped by Cylinders", 
       x = "Gross Horsepower (hp)", 
       y = "Miles per Gallon (mpg)", 
       color = "Cylinders") +
  theme_minimal()

# Convert the ggplot object to an interactive plotly object
ggplotly(p, tooltip = "text")

Analysis Insight As expected, there is a strong inverse relationship between Horsepower and MPG. Cars with fewer cylinders (4-cyl, shown in red) typically achieve much higher MPG. Hover your mouse over the points to see the car model names and exact values