Aneesh

Develop an R program to quickly explore a given dataset,including categoral analysis using the group_by cpommand ,and visualize the findings using ggplopt2 features.

Step 1: Load necessary liberaries

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

#load dataset
data<- mtcars
#convert 'cyl' to a factor for categoral analysis
data$cyl<- as.factor(data$cyl)

step 3: Group by categoral variable

#summarize avg of 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()