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Objectives The objectives of this problem set is to gain experience working with the ggplot2 package for data visualization. To do this I have provided a series of graphics, all created using the ggplot2 package. Your objective for this assignment will be write the code necessary to exactly recreate the provided graphics.

When completed submit a link to your file on rpubs.com. Be sure to include echo = TRUE for each graphic so that I can see the visualization and the code required to create it.

Vis 1 This graphic is a traditional stacked bar chart. This graphic works on the mpg dataset, which is built into the ggplot2 library. This means that you can access it simply by ggplot(mpg, ….). There is one modification above default in this graphic, I renamed the legend for more clarity.

require(datasets)  ## Load base R datasets. This program uses mpg, diamonds, and iris
require(ggplot2)   ## Load ggplot2 package to draw below plots
## Loading required package: ggplot2
require(ggthemes)  ## Load ggthemes package to use extra themes for Vis 3 (theme_economist()) and Vis 5 (theme_pander())
## Loading required package: ggthemes
mpg.plot <- ggplot(mpg)                       ## Create plot for mpg dataset
mpg.plot +
   geom_bar(aes(class,                        ## Use bar chart. X-asix is class
                 fill = trans)) +             ## Use stacked bar chart. Legend is trans (transmission)
   scale_fill_discrete(name = "Transmission") ## Assign legend title

Vis 2 This boxplot is also built using the mpg dataset. Notice the changes in axis labels, and an altered theme_XXXX

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

Vis 3 This graphic is built with another dataset diamonds a dataset also built into the ggplot2 package. For this one I used an additional package called library(ggthemes) check it out to reproduce this view.

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

Vis 4 For this plot we are changing vis idioms to a scatter plot framework. Additionally, I am using ggplot2 package to fit a linear model to the data all within the plot framework. Three are edited labels and theme modifications as well.

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

Vis 5 Finally, in this vis I extend on the last example, by plotting the same data but using an additional channel to communicate species level differences. Again I fit a linear model to the data but this time one for each species, and add additional theme and labeling modicitations.

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