A good way to show lots of data graphically I'm working on a project that involves 14 variables and 345,000 observations for housing data (things like year built, square footage, price sold, county of residence, etc). I'm concerned with trying to find good graphical techniques and R libraries that contain nice plotting techniques. 
I'm already seeing what in ggplot and lattice will work nicely, and I'm thinking of doing violin plots for some of my numerical variables.
What other packages would people recommend for displaying a large amount of either numerical or factor-typed variables in a clear, polished, and most importantly, succinct manner?
 A: I'd recommend taking a look at GGobi, which also has an R interface, at least for exploratory purposes.  It has a number of graphical displays especially useful for dealing with a large number observations and variables and for linking these together.  You might want to start by watching some of the videos under the "Watch a demo" section on the Learn GGobi page.  
Update
Links to Hadley Wickham's tools for GGobi, as suggested by chl in the comments:


*

*DescribeDisplay "R package that provides a way to recreate ggobi graphics in R"

*clusterfly "Explore clustering results in high dimensions"

*rggobi "R package that provides an easy interface with GGobi"

A: I feel you are actually asking two questions:  1) what types of visualizations to use and 2) what R package can produce them.
In the case of what type of graph to use, there are many, and it depends on your needs (e.g: types of variables - numeric, factor, geographic etc, and the type of connections you are interested to display):


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*If you have many numeric variables, you might want to use a scatter plot matrix (have a look here)

*If you have many factor variables, you might want to use a scatter plot matrix for factors (have a look here)

*You could also go with doing some Parallel coordinates there are several ways to do it in R.

*For a wider range of graphical facilities in R, have a look at the graphics task view.


Now regarding how to do it.  One problem with many data points is time till the plot is created.  ggplot2, iplots, ggobi are not very good for too many data points (at least from my experience).  In which case you might want to focus on R base graphics facilities, or sample your data and on that to use all the other tools.  Or you can hope that the people developing iplots extreme (or Acinonyx) would get to an advance release stage.
A: Mondrian provides interactive features and handles quite large data sets (it's in Java, though).
Paraview includes 2D/3D viz. features.
A: The best "graph" is so obvious nobody has mentioned it yet: make maps.  Housing data depend fundamentally on spatial location (according to the old saw about real estate), so the very first thing to be done is to make a clear detailed map of each variable.  To do this well with a third of a million points really requires an industrial-strength GIS, which can make short work of the process.  After that it makes sense to go on and make probability plots and boxplots to explore univariate distributions, and to plot scatterplot matrices and wandering schematic boxplots, etc, to explore dependencies--but the maps will immediately suggest what to explore, how to model the data relationships, and how to break up the data geographically into meaningful subsets.
A: I would like to bring to your attention, Parallel Coordinates: Visual Multidimensional Geometry and Its Applications, which contains the latest breakthroughs and applications in the field. 
The book was praised by Stephen Hawking among others. Surfaces are described (using duality) by their normal vectors at its points.  It contains applications to Air Traffic Control (Automatic Collision Avoidance -- 3 USA Patents), Multivariate Data Mining (on real datasets some with hundreds of variables), Multiobjective Optimization, Process Control, Intensive Care Smart Displays, Security, Network visualization and recently Big Data.
