Hand Data Visualization Code

# List any packages you need to use here
packages <- c("ggplot2", "readr", "tidyverse", "dplyr", "ggpubr")

#Check to see if any of your listed packages need installed
check_install_packages <- function(pkg){
  if (!require(pkg, character.only = TRUE)) {
    install.packages(pkg, dependencies = TRUE)
    library(pkg, character.only = TRUE)
  }
}

# Download the packages and read in the libraries if necessary
sapply(packages, check_install_packages)

## $ggplot2
## NULL
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## $readr
## NULL
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## $tidyverse
## NULL
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## $dplyr
## NULL
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## $ggpubr
## NULL

data("USArrests")
view(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

?USArrests

USArrests data questions: 1: What are the variables available? murder, assault, urbanpop, and rape

2: How is each variable defined or calculated? muder, assault, and rape are numeric values calculated for one per 100,000 arrests urbanpop is defined as the percent urban population

3: Is each one numerical or categorical? each is numerical

GGplot Graphic Code

#General format is going to be calling a ggplot, followed by the dataframe name (mtcars), followed by defining the X and Y variables of the graphic.
data("mtcars")  
ggplot(mtcars, aes(x = mpg, y=hp)) +
    #You then indicate the type of graph to make (in this case, a dotplot using points).
    geom_point() +
  #changing dots to size 2.4 and star shaped
  geom_point(size = 2.4, shape = 8) +
  #change theme to minimal 
  theme_minimal() +
  #create cyl column into a color gradient 
  aes(colour = cyl) +
  #change the legend position
  theme(legend.position = "bottom") +
  #labeling the graph
  labs(title = "Effect of Horsepower on Fuel Efficiency", subtitle = "Categorized by Number of Cylinders", x = "Horsepower", y = "Fuel Efficiency (MPG)")

  library(ggplot2)

#using a different demo data set to create another graph 
library(ggplot2)
data(Irisi)
#create plot
ggplot(iris, aes(x = Species, y = Petal.Length, fill = Species)) + geom_boxplot() +
  #change theme
  theme_classic() +
  #labels
  labs(title = "Patel Length of Iris by Species") + 
  #custom legend position
  theme(legend.position = c(0.2,0.8))