This report provides an interactive data analysis of car performance characteristics using the mtcars dataset in R. The analysis aims to give insights into the relationship between miles per gallon, horsepower, car weight, and other factors. All visualizations are created using Plotly for interactivity, allowing you to hover over data points for more detail.
The mtcars dataset includes information about various
car models:
| Variable | Description |
|---|---|
| mpg | Miles per gallon |
| cyl | Number of cylinders |
| hp | Horsepower |
| wt | Weight (in 1000 lbs) |
| am | Transmission type (0 = Automatic, 1 = Manual) |
The goal of this report is to explore the relationships and distributions of these variables.
| 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 |
The first chart shows a scatter plot of mpg (miles per gallon) versus hp (horsepower), with color representing the number of cylinders.
fig <- plot_ly(data = mtcars, x = ~hp, y = ~mpg, color = ~factor(cyl), type = 'scatter', mode = 'markers') %>%
layout(title = "Miles per Gallon vs Horsepower",
xaxis = list(title = "Horsepower"),
yaxis = list(title = "Miles per Gallon (MPG)"),
colorway = c("#636EFA", "#EF553B", "#00CC96"))
figInsight: This scatter plot helps in understanding the trade-off between fuel efficiency and engine power. Generally, higher horsepower tends to correlate with lower miles per gallon, especially in cars with more cylinders.
The bar plot below shows the average miles per gallon for each cylinder type.
avg_mpg <- mtcars %>%
group_by(cyl) %>%
summarise(avg_mpg = mean(mpg))
fig <- plot_ly(data = avg_mpg, x = ~factor(cyl), y = ~avg_mpg, type = 'bar') %>%
layout(title = "Average MPG by Cylinder Count",
xaxis = list(title = "Number of Cylinders"),
yaxis = list(title = "Average MPG"))
figInsight: Cars with fewer cylinders generally achieve higher miles per gallon, indicating better fuel efficiency. This can be useful when choosing vehicles for fuel efficiency.
The histogram below visualizes the distribution of car weights in the dataset.
fig <- plot_ly(data = mtcars, x = ~wt, type = 'histogram') %>%
layout(title = "Distribution of Car Weights",
xaxis = list(title = "Weight (1000 lbs)"),
yaxis = list(title = "Frequency"))
figInsight: The histogram shows that most cars in this dataset have weights clustered in a specific range. Knowing the common weight range can help in evaluating and comparing car models based on size and potential fuel usage.
The box plot below compares the distribution of horsepower across automatic and manual transmission types.
fig <- plot_ly(data = mtcars, y = ~hp, x = ~factor(am), type = 'box') %>%
layout(title = "Horsepower Distribution by Transmission Type",
xaxis = list(title = "Transmission Type (0 = Automatic, 1 = Manual)"),
yaxis = list(title = "Horsepower"))
figInsight: Cars with manual transmissions generally show a wider range of horsepower. This insight could help in assessing the performance capabilities of cars with different transmission types.
This pie chart illustrates the proportion of cars with different gear counts.
gear_count <- as.data.frame(table(mtcars$gear))
fig <- plot_ly(data = gear_count, labels = ~Var1, values = ~Freq, type = 'pie') %>%
layout(title = "Proportion of Cars by Gear Count")
figInsight: This pie chart indicates which gear counts are most common among the cars in the dataset. This information can be useful for understanding the typical transmission configurations available.
This report provides insights into car performance metrics in relation to engine size, fuel efficiency, weight, and transmission type. Key findings include:
Each interactive visualization provides an opportunity to dive deeper into the data by exploring individual data points.