program1

1.Devlope an R program to quickly explore a given data task including categorical analysis using the group by command and visualize findings using ggplot2 features.

Step1: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)
library(ggplot2)

Step2: Assigning dataset

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

Step3: Group by categorical variable

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

step4: Visualising 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="number of cylinders",
         y="average MPG")+
  theme_minimal()