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Develop and R program to quickly wxplore agiven datset including categorical ananlysis usingthe group by command and visualize the findings using ggplot2 features

Step1:Load the necessary libraries

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)

Step2:Load the dataset

#load dataset
data <- mtcars

#convert 'cyl' to a factor for categorical analysis
data$cyl <- as.factor(data$cyl)

Step3:Group by categorical variables

# summary avarge mpg by cylinder category
summary_data <- data %>%
  group_by(cyl)%>%
  summarise(avg_mpg = mean(mpg), .group='drop')
#display summary
print(summary_data)

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

Step4:Visualising the findings

#create a bar plot using ggplot
ggplot(summary_data, aes(x=cyl,y=avg_mpg, fill=cyl)) +
  geom_bar(stat="identity") +
  labs(title="Average mpg by the cylinder count",
       x="Number of Cylinders",
       y="Average mpg") +
  theme_minimal()

Step5: