Loading Libraries and Data Set

#Loading Libraries
library(dslabs)
library(tidyverse)
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library(plotly)
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library(highcharter)
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library(RColorBrewer)

#Loading Dataset
data(mice_weights)
## Warning in data(mice_weights): data set 'mice_weights' not found
#Renaming Dataset
data <- mice_weights

#Plotting Bone Density and Body Weight

#Plotting
plot1 <- ggplot(data, aes(x = bone_density, y = body_weight, color = diet)) +
  geom_point(alpha = 0.4) +
  scale_color_brewer(palette = "Set1", name = "Diet", labels=c('Chow', 'High Fat')) + #Renaming labels and adding color
  geom_smooth(method = "loess", span = 0.15) +
  labs(
    x = "Bone Density",
    y = "Body Weight in Grams",
    title = "Comparison of Body Weight and Bone Density based on Diet",
    caption = "Mice weights based on diets collected by Karen Svenson from Jackson Laboratories."
  ) + theme_classic() #Changing theme

plot1
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).

ggplotly(plot1)
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).

Essay Conclusion

I used the mice weights data set. This dataset shows the results of body weight, bone density, and percent fat for mice on a chow diet or a high fat diet. I created my scatterplot using ggplot. I used bone density for the x-axis and body weight for the y-axis. I thought it would be interesting to color by diet to see which diet produced the biggest mice. According to the graph, mice with high fat diets were the biggest. I put x and y axis labels and modified the labels for the Diet legend to make it more readable. Lastly, I used plotly to create interactivity. I tried to use highcharter but it looked wild because I could not properly arrange the data.