Module # 7 assignment

Issaiah Jennings 

Module # 7 assignment

In this post, I tested visual distribution analysis using the mtcars dataset in R, following the recommendation from our textbook to use a grid layout for comparing variables. The mtcars dataset is built into R, so you can easily access it by typing data(mtcars).

I created scatter plots to compare variables like miles per gallon (mpg), weight (wt), and horsepower (hp). By organizing these plots into a grid, I could quickly see the relationships between the variables. Here's the R code I used to generate the plots:

# Load the necessary libraries

install.packages("gridExtra")

library(ggplot2)

library(gridExtra)

# Load the mtcars dataset

data(mtcars)

# Create scatter plots

plot1 <- ggplot(mtcars, aes(x=mpg, y=wt)) + geom_point() + ggtitle("MPG vs Weight")

plot2 <- ggplot(mtcars, aes(x=mpg, y=hp)) + geom_point() + ggtitle("MPG vs Horsepower")

plot3 <- ggplot(mtcars, aes(x=wt, y=hp)) + geom_point() + ggtitle("Weight vs Horsepower")

# Combine plots into a grid

grid.arrange(plot1, plot2, plot3, ncol=2)

The grid layout makes it easier to compare the variables side by side, which aligns with Few’s recommendations for useful visual analytics. I found it helpful for quickly spotting patterns and relationships in the data. Overall, this approach definitely enhances data analysis by improving clarity and comparison.