• Help the researcher: Visualization is "hypothesis generating". It is a tool for discovery and research.
• Communicate better:
  • Comparatively few people look at tables
  • But graphs catch attention easily
• Why is communication important?
  • Academia is partly about marketing results.
  • Industry is definitely about marketing results.
  • Marketing is about the effective use of rhetorical devices

• Beautiful graphs are "eye-catching"
• Ugly graphs are boring -people stop listening/reading
• Some people have done substantive work on this:
  • The visual display of quantitative information, Edward Tufte
  • Visualizing data and The elements of graphing data, William S. Cleveland
  • The grammar of graphics, Leland Wilkinson
• ggplot2 is a package based on Wilkinson's idea that generate beautiful graphs for you.

• Gender Heterogeneity (i.e. inequality) in labor market outcomes can be measured either in terms of wages or employment gaps
  • Wage gap: difference between average wage of men and women average wage.
  • Employment gap: difference between employment of men and women average wage
• We will consider data from Spain

#See what the data looks like
genderGaps

##   dates wageGap employmentGap
## 1  2002    20.2          26.9
## 2  2006    17.9          21.8
## 3  2007    18.1          20.7
## 4  2008    16.1          19.1
## 5  2009    16.7          16.7
## 6  2010    16.2          15.2
## 7  2011    17.9          13.7
## 8  2012    19.3          12.6
## 9  2013    19.3          11.3

• It's hard to see any pattern!
• That's why we resort to visualization

There seems to be a pattern we may explore.

ggplot2 generates beautiful graphs for us.

On ggplot

• ggplot2 documentation (Hadley Wickham): [http://ggplot2.org] (well written)
• R Graphics Cookbook 1st Edition by Winston Chang
• Sean Anderson's tutorial (from which I borrowed some of the examples)

On visualization generally

• An introduction to Visualizing Data by joel laumans
• Chris Adolph's (Washington University) course