program1

Program 1

Develop an R program to quickly explore a given dataset, including categorical analysis using the group_by command, and visualize the findings using ggplot2 features.

Step 1: Load 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)

Step 2: Load the dataset

# Load dataset 
data <- mtcars  
# Convert 'cyl' to a factor for categorical analysis 
data$cyl <- as.factor(data$cyl)

Step 3: Group by categorical variables

# Summarize average mpg by cylinder category 
summary_data <- data %>%   
  group_by(cyl) %>%   
  summarise(avg_mpg = mean(mpg), .groups = 'drop')  

# Display summary 
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

# Create a bar plot using ggplot2
ggplot(summary_data, aes(x = cyl, y = avg_mpg, fill = cyl)) +
  geom_bar(stat = "identity") +
  labs(title = "Average MPG by Cylinder Count",
       x = "Number of Cylinders",
       y = "Average MPG") +
  theme_minimal()