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#PLOT 1 
ggplot(mtcars, aes(x = wt, y = mpg)) +
    geom_point(color = "black", fill = "white", size = 7, shape = 21, stroke = 1.5) +  
    labs(title = "Plot 1: MPG vs Weight", 
         x = "Weight represented in per 1000 lbs", 
         y = "Miles per Gallon abbreviated to: (mpg)") +
    theme_classic() +
    theme(
        axis.text = element_text(size = 15, color = "lightgray"),
        plot.title = element_text(size = 8, face = "bold")
    ) +
    scale_x_continuous(breaks = seq(1, 6, by = 1), labels = seq(1000, 6000, by = 1000))

#PLOT 2
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point(color = "hotpink", size = 3) +
  labs(title = "Plot 2: MPG vs Weight", x = "Weight (1000 lbs)", y = "Miles per Gallon (mpg)") +
  theme_minimal() +
  theme(
    panel.grid.major = element_line(color = "gray", size = 1.5),   
    panel.grid.minor = element_line(color = "gray", size = 1)  
  )
## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

#PLOT 3
ggplot(mtcars, aes(x = wt, y = mpg)) +
    geom_point(aes(color = factor(cyl), shape = factor(cyl)), size = 4, alpha = 0.5) +
    scale_color_brewer(palette = "Set1") +
    labs(title = "Plot 3: MPG vs Weight", 
         x = "Weight (1000 lbs)", 
         y = "Miles per Gallon (mpg)", 
         color = "Cylinders", 
         shape = "Cylinders") +
    theme_dark() +
    theme(
        panel.grid.major = element_line(color = "black", size = 0.4),    
        panel.grid.minor = element_line(color = "black", size = 0.4),  
        axis.text = element_text(size = 7)                             
    )

#PLOT 4 (BEST PLOT)
plot4 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
    geom_line(aes(group = cyl, 
                  text = paste("Cylinders:", cyl, 
                               "<br>Weight:", wt, 
                               "<br>MPG:", mpg)), 
              color = "orange", linetype = "dashed") +
    geom_point(aes(color = factor(cyl),  
                   text = paste("Cylinders:", cyl, 
                                "<br>Weight:", wt, 
                                "<br>MPG:", mpg)), 
               size = 3) +
    scale_color_manual(values = c("#B2182B", "#2166AC", "#4D9221")) + 
    labs(title = "Plot 4: MPG vs Weight", 
         x = "Weight (1000 lbs)", 
         y = "Miles per Gallon (mpg)", 
         color = "Cylinders") +  
    theme_bw() +
    theme(
        axis.text = element_text(size = 11) 
    )
## Warning in geom_line(aes(group = cyl, text = paste("Cylinders:", cyl,
## "<br>Weight:", : Ignoring unknown aesthetics: text
## Warning in geom_point(aes(color = factor(cyl), text = paste("Cylinders:", :
## Ignoring unknown aesthetics: text
ggplotly(plot4, tooltip = "text")
#In general, I think the best plot is “Plot 4”. This plot is clean, concise, 
#and merely easy to interpret while simultaneously presenting all the critical information conveyed by the data. The axis/titles are short & simple. There is
#a legend which color codes the Cylinder types to show in the data. I made the 
#points to a fitting size, as well as color coding them to muted but noticeable
#colors. With the “background” of the visualization I purposefully kept it as minimalist as possible, as I didn’t want background color and gridlines to draw attention from the interpretation of the data. I even included a dashed-trendline 
#to show the general path/trend of the data. Additionally, I included hover-texts 
#to make the visualization more interactive. I think this feature really emphasizes user convenience, as the viewer can just hover over a specific point to find the 
#MPG and Weight. 

#With my other graphs, they are just too straining on the eyes. Specifically
#“Plot 3,” this plot is taking away from the actual interpretation of the data. 
#The dark gray background color and point transparency make the data 10 times
#more difficult to view. The size of the axis numerics are also incredibly small. 
#Both “Plot  2 & 1” follow this same theme. The gridlines on “Plot 2” are 
#obnoxiously loud making the visualization unappealing to even look at. 

#Overall there are multiple different aesthetic properties demonstrated: 
#Point Color, Point Shape, Point Size, Point Transparency, Point Borders, 
#Gridline Color, Gridline Size, Title Font Size, Size of Numerical Axis 
#Values, Background Theme, Color Pallet/Color Mapping, Legend Inclusion,
#Trendline Inclusion (Color changed aswell), and Hover Texts.