Activity #3

plot(cars)

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install.packages("tidyverse", dependencies = TRUE)

library(tidyverse)

library(ggplot2) #loading ggplot2 for creating plots 

data_mtcars <- mtcars #loading the mtcards dataset (built into R)

head(data_mtcars) #view the first rows to undertand the data 

#Converting 'am' (transmission type) and 'cyl' (number of cylinders) to factors for categorial plotting 

data_mtcars$am <- as.factor(data_mtcars$am)
data_mtcars$cyl <- as.factor(data_mtcars$cyl)

#Creating a scatter plot of car weight vs. miles per gallon, colored by cylinder count 
ggplot(data_mtcars, aes(x = wt, y = mpg, color = cyl)) +
  geom_point() +  # command adds points to the plot
  labs(
    title = "Weights vs Miles Per Gallon",
    x = "Weight (1000 lbs)",
    y = "Miles Per Gallon"
  )

#Creating a line graph of ordered mpg by the row number 
data_mtcars_line <- data_mtcars %>% mutate(index = row_number()) #adding index column so we can plot it 

ggplot(data_mtcars_line, aes(x = index, y = mpg)) +
  geom_line() + #adding a line to the plot 
  labs(title = "Miles Per Gallon by Index", x = "Index", y = "Miles Per Gallon") #adding plot labels

# Create a horizontal bar chart of the average horsepower grouped by cylinder count
hp_by_cyl <- data_mtcars %>% group_by(cyl) %>% summarize(avg_hp = mean(hp)) # Calculate average horsepower for each cylinder group

ggplot(hp_by_cyl, aes(y = cyl, x = avg_hp)) +
  geom_bar(stat = 'identity') + # Create bars based on the calculated averages
  labs(title = "Average HP by Cylinder Count", y = "Cylinder Count", x = "Average Horsepower") # Add plot labels

#Create a stacked bar chart of average mpg, disp, hp, and wt, grouped by cyl.
bar_data_mtcars <- data_mtcars %>% group_by(cyl) %>% summarize(mpg = mean(mpg), disp = mean(disp), hp = mean(hp), wt = mean(wt)) %>% pivot_longer(cols = c("mpg", "disp", "hp", "wt"), names_to = "Measurement", values_to = "Average") #Calculate average values for each measurement, and pivot the data into a long format.

ggplot(bar_data_mtcars, aes(x = cyl, fill = Measurement, y = Average)) +
  geom_bar(stat = "identity") + #Create bars based on the calculated averages
  labs(title = "Average Measurements by Cylinder Count", x = "Cylinder Count", y = "Average Measurement") #add plot labels