Program_01

Step 1: Load necessary libaries.

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(dplyr)
library(ggplot2)

Step 2: Load the dataset.

data <- mtcars 
data$cyl <- as.factor(data$cyl)

Step 3:

summary_data <- data %>%
  group_by(cyl) %>%
  summarise(avg_mpg = mean(mpg), .groups = 'drop')
print(summary_data)

# A tibble: 3 × 2
  cyl   avg_mpg
  <fct>   <dbl>
1 4        26.7
2 6        19.7
3 8        15.1

Step 4: Visualizing the findings.

ggplot(summary_data, aes(x = cyl, y=avg_mpg, fill = cyl)) + geom_bar(stat = "identity")+ labs(title = "Average MPG by cyclinder Count", 
          x= "Number of Cyclinders", 
          y= "Average MPG" ) + theme_minimal()