Module 7 Assignment

 

For this assignment, I chose to analyze the distribution of the mtcars dataset, which is readily available in R. The mtcars dataset contains information about various characteristics of different car models, such as miles per gallon (mpg), number of cylinders (cyl), and horsepower (hp), among others.

To start, I imported the mtcars dataset into RStudio using the following code:

R
```
data(mtcars)
```
Next, I performed a distribution analysis on three variables from the mtcars dataset: mpg, cyl, and hp. I used histograms to visualize the distribution of each variable individually and then created a grid of histograms to compare their distributions side by side.

Here's the R code to create the grid of histograms:

R
```
# Load necessary libraries
library(gridExtra)

# Create histograms for each variable
mpg_hist <- hist(mtcars$mpg, main = "Distribution of MPG", xlab = "MPG", col = "lightblue")
cyl_hist <- hist(mtcars$cyl, main = "Distribution of Cylinders", xlab = "Cylinders", col = "lightgreen")
hp_hist <- hist(mtcars$hp, main = "Distribution of Horsepower", xlab = "Horsepower", col = "lightpink")

# Arrange histograms in a grid
grid.arrange(mpg_hist, cyl_hist, hp_hist, ncol = 3)
```

After running this code, I generated a grid of histograms displaying the distribution of mpg, cyl, and hp in the mtcars dataset. This visualization allows for easy comparison between the distributions of these variables.

In my opinion, Stephen Few's recommendations for visualizing distributions are helpful in this context. By using histograms, we can effectively display the frequency distribution of each variable and compare them visually. The grid layout enhances the comparison by presenting the histograms side by side, making it easier to identify patterns and differences between the variables.

Overall, I found Few's recommendations to be practical and effective for visualizing and comparing distributions in R. They provide a clear and concise way to analyze the data and draw insights from it.

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