Hoffman data visualization code

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summary(cars)

##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

Including Plots

packages <- c("ggplot2", "readr", "tidyverse", "dplyr", "ggpubr")
check_install_packages <- function(pkg){
  if (!require(pkg, character.only = TRUE)) {
    install.packages(pkg, dependencies = TRUE)
    library(pkg, character.only = TRUE)
  }
}
sapply(packages, check_install_packages)

## Loading required package: ggplot2

## Loading required package: readr

## Loading required package: tidyverse

## Warning: package 'lubridate' was built under R version 4.3.3

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ stringr   1.5.1
## ✔ forcats   1.0.0     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2

## ── 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
## Loading required package: ggpubr

## $ggplot2
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data("USArrests")
head(USArrests)

##            Murder Assault UrbanPop Rape
## Alabama      13.2     236       58 21.2
## Alaska       10.0     263       48 44.5
## Arizona       8.1     294       80 31.0
## Arkansas      8.8     190       50 19.5
## California    9.0     276       91 40.6
## Colorado      7.9     204       78 38.7

str(USArrests)

## 'data.frame':    50 obs. of  4 variables:
##  $ Murder  : num  13.2 10 8.1 8.8 9 7.9 3.3 5.9 15.4 17.4 ...
##  $ Assault : int  236 263 294 190 276 204 110 238 335 211 ...
##  $ UrbanPop: int  58 48 80 50 91 78 77 72 80 60 ...
##  $ Rape    : num  21.2 44.5 31 19.5 40.6 38.7 11.1 15.8 31.9 25.8 ...

ggplot(mtcars, aes(x = mpg, y = hp, color = factor(cyl))) +
  geom_point(shape = 8, size = 2.4) +  theme_minimal() + labs(title = "Effect of Horsepower on Fuel Efficiency",
       subtitle = "Categorized by Number of Cylinders",
       x = "Fuel Efficiency (MPG)",  # X-axis label
       y = "Horsepower") + theme(legend.position = "bottom")

ggplot(USArrests, aes(x = reorder(rownames(USArrests), Murder), y = Murder)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  labs(title = "Murder Arrests per 100,000 Residents by State",
       x = "State",
       y = "Murder Arrests (per 100,000 residents)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))