pro1

DEVELOP AN R PROGRAM TO QUICKLY EXPLORE A GIVEN DATA SET INCLUDING CATAGORICAL ANALYSIS USING THE GROUP BY COMMANDS AND VISUALIZE THE FINDS USING GGPLOT2 FEATURES

Step1: Load required library

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

Step3: Group by categorical varibles

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

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

step4: Visualizing the findings

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