P1

Develop and R program to quickly explore a given data set, 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)
library(ggplot2)

Step 2: Load the data set

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

[1] "factor"

Step 3: Group by categorical variables

summary_data <- data %>%
group_by(cyl) %>%
  summarize(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

Step 4: 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="Number of Cylinders", 
      y="Average MPG") + theme_minimal()