Program-1

Author

Anshul

Quarto

Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org.

Running Code

When you click the Render button a document will be generated that includes both content and the output of embedded code. You can embed code like this:

1 + 1

[1] 2

You can add options to executable code like this

[1] 4

The echo: false option disables the printing of code (only output is displayed).

Develop an R program to quickly explore a given dataset, 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 dataset

# load dataset
data <- mtcars

#convert 'cyl' to a factor for categorical analysis
data$cyl <- as.factor(data$cyl)

STEP 3 : Group by categorical variables

#summarize average 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

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()