program1a

Quarto

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Running Code

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[1] 2

You can add options to executable code like this

[1] 4

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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.

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)

2. Load the Dataset

# load dataset
data<-mtcars
# convert"cyl" to a factor for data$cyls
data$cyl <- as.factor(data$cyl)

3. Group by categorical variable

   # summarize average mpg by cylinder catogery
   summary_data <- data %>%
     group_by(cyl) %>%
     summarize(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

4. Visualizing the Findings

::: {.cell}

```{.r .cell-code}
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()
```

::: {.cell-output-display}
![](PROGRAM1_files/figure-html/unnamed-chunk-6-1.png){width=672}
:::
:::