Visualization 1.

library(ggplot2)
library("ggthemes")
theme_set(theme_classic())

# Histogram on a Categorical variable
g <- ggplot(mpg, aes(class))
g + geom_bar(aes(fill=trans), width = 0.5) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Histogram on Categorical Variable", 
       subtitle="Manufacturer across Vehicle Classes")

Visualization 2.

mpg.plot <- ggplot(mpg)            
mpg.plot +                 ## Plot for mpg dataset
  geom_boxplot(aes(manufacturer, hwy)) +
  ## Use box plot to find distribution of Highway Fuel Efficiency by Manufacturer
  theme_classic() +        ## Use the classic theme
  coord_flip() +           ## Flip coordinate
  labs(y = "Highway Fuel Efficiency (mile/gallon)",
       ## Assign y-axis (horizontal) title
       x = "Vehicle Manufacturer")  ## Assign x-axis (vertical) title

Visualization 3.

ggplot(diamonds) +                ## Create plot for diamonds dataset
  geom_density(aes(price,         ## Find density of diamond price
                   fill = cut,    ## Legend is cut (quality of the cut). Use fill colors to differentiate
                   color = cut),  ## Legend is cut (quality of the cut). Use stroke colors to differentiate too
               alpha = 0.3,       ## Set transparency level of fill color
               size = 0.6) +      ## Set width of strokes
  labs(title = "Diamond Price Density",
       ## Assign plot title
       x = "Diamond Price (USD)", ## Assign x-axis title
       y = "Density") +           ## Assign y-axis title
  theme_economist()               ## Use the theme used by Economist magazine

Visualization 4.

ggplot(iris,                    ## Create plot for iris dataset
       aes(Sepal.Length, Petal.Length)) +
  ## X-axis is sepal length; y-axis is patel length
  geom_point() +                ## Use scatter plot
  geom_smooth(method = lm) +    ## Draw regression line
  theme_minimal() +             ## Use the "minimal" theme
  theme(panel.grid.major = element_line(size = 1),
        ## Set width of major grid line
        panel.grid.minor = element_line(size = 0.7)) +
  ## Set width of minor grid line
  labs(title = "Relationship between Petal and Sepal Length",
       ## Assign plot title
       x = "Iris Sepal Length", ## Assign x-axis title
       y = "Iris Petal Length") ## Assign y-axis title

Visualization 5.

ggplot(iris,                        ## Create plot for iris dataset
       aes(Sepal.Length,            ## Set x-axis: sepal length
           Petal.Length,            ## Set y-axis: petal length
           color = Species)) +      ## Set legend: species. Use colors to differentiate.
  geom_point() +                    ## Use scatter plot
  geom_smooth(method = lm, se = FALSE) +
  ## Draw regression line without confidence region
  theme_pander() +                  ## Use the Pander theme
  theme(text = element_text(family = "serif"),
        ## Use Times New Roman for all texts
        axis.ticks = element_line(color = "black",
                                  ## Set color of tick marks to be black
                                  size = 0.7),
        ## Set size of tick marks
        legend.position = "bottom", ## Move legend to the bottom of plot
        legend.title = element_text(face = "plain"),
        ## Legend title was italic. Set to plain.
        plot.title = element_text(size = 14,
                                  ## Set font size of plot title
                                  face = "plain")) +
  ## Set font of plot title to be plain
  labs(title = "Relationship between Petal and Sepal Length",
       ## Assign plot title
       subtitle = "Species level comparison",
       ## Assign plot subtitle
       x = "Iris Sepal Length",     ## Assign x-axis title
       y = "Iris Petal Length")     ## Assign y-axis title