prgm2.quarto

Develop a R programming to quickly explore agiven dataset including categorical analysis using the groups by command and visualize the findings using ggplot2 features.

Step 1: Load the 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 dataset.

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

Step 3 : Group by categorical variables.

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

Step 4: 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()