ggplot2 Continued

• Go to top menu bar of RStudio -> File -> New File -> R Script
• Click on “Lec02 Exercise” in Moodle
• Copy contents and paste into R Script

• aes mappings of data variables to aesthetics we can perceive on a graphic
• geom geometric objects
• stat statistical transformations to summarise data
• facet how to break up plots into subsets
• coord coordinate system for x/y values: typically cartesian (can be polar)
• scale both convert data units to physical units the computer can display AND draw a legend/axes.
• Extras titles, axes labels, themes

• Help files. Ex ?geom_line()
• Online help files: http://docs.ggplot2.org/current/

• Ultimate Tech Support

• ggplot2 book is on Moodle. To learn more, I suggest reading

  • Chapter 1,
  • Chapter 3, but it will be hard to grasp the first time
  • Chapter 4
  • Revisit Chapter 3. Chapters 5-10 go into specific details.

• The code for all examples in the book: http://ggplot2.org/book/

• layers: mechanism by which additional data elements are added to a plot
• position adjustments: minor tweeks to the position of elements
• groups: splitting data into groups within the same plot (not via facets)

We build plots by adding layers. Each layer consists of:

1. data and aesthetic mappings (the base)
2. geometric object
3. statistical transformation (with default values)
4. position adjustment (with default values)

Without all four elements specified, nothing will plot!

Notes:

• Make the base as general as possible
• Every geom has a default stat & position value if they aren't specified
• Layer settings override plot defaults
• You can save plots as variables and add to variables, to reduce duplication

In ggplot_2.R we have examples of

• setting groups: Examples of groups.
• many different types of geoms: Examples of geoms.
• geom_histogram() and position adjustments: Titanic Survival Data

In 1973, the UC Berkeley was sued for bias against women who had applied for admission to graduate schools. n=4526 applicants:

Source: local data frame [4 x 3]
Groups: Admit

     Admit Gender Freq
1 Admitted   Male 1198
2 Admitted Female  557
3 Rejected   Male 1493
4 Rejected Female 1278

However, there was another variable researchers could consider: department applied to.

Source: local data frame [24 x 4]

      Admit Gender Dept Freq
1  Admitted   Male    A  512
2  Rejected   Male    A  313
3  Admitted Female    A   89
4  Rejected Female    A   19
5  Admitted   Male    B  353
6  Rejected   Male    B  207
7  Admitted Female    B   17
8  Rejected Female    B    8
9  Admitted   Male    C  120
10 Rejected   Male    C  205
..      ...    ...  ...  ...

Investigate:

1. Construct a statistical graphic showing how male vs female acceptance varied by department. Bonus: Using google, make one of the axes reflect percentage of applicants.
2. Construct a statistical graphic showing the “competitiveness” of the department as measured by acceptance rate.