PDC Database : Frequency vs. Donation size in the 42nd LD to date

The political piece for this code is here. This R (3.1.0) code helped me dig into the PDC database to understand campaign financing for the 42nd LD in WA. The first example gives a jpeg_create() I used for automated printing, but I had some trouble applying it for all my charts configured par(mfrow) . Hadley Wickham's Advanced R text on data structures gave me the stringsAsFactors = FALSE setting for read.csv.  I have an intuition that could be a useful flag. QQplot seems to portray certain types of data that would otherwise be complicated to understand very well. One such data is comparing the frequency vs. donation size of out both in and out of state donations in a specific political district.

05.20.2014 Whatcom County Voter General Election Database: The Precincts....

The political piece for this code is here.  I received quite a bit of practice in the skill of sending multivariate graphs to file for this piece. Election databases nearly scream for raster and GIS files.  Something to learn next....

Boomers vs. Millenials ...

Political post for this code is here.

Hadley Wickham has said that one of the problems with R is that it isn't very "programmerly".  In working through R loops in complex functions, I have discovered this. Here is what I want to do:

for (i in 1:5)  {VDB_M <- as.data.frame(table(VDB_N%i%$BirthDate)) ... }

where %i% is the set of variables I want to loop through. If there is a method for doing that, I can not find it yet.  Clearly a Python or Powershell wrapper might be the answer.   But it is a big language and maybe applying functions in R for the data analyst needs more research on my part.

05.20.2014 Whatcom County Voter General Election Database: What Happened...

Corresponding political piece here.

Whatcom County Voter Database 05.20.2014

The corresponding political piece for this article is here.
The active voter Whatcom County database as of 05.20.2014 is  (row) 36  *  (column) 126283 =  (ncell) 4546188. On an 8 GB I-5 laptop on running 64 bit R 3.1 this isn't much of a problem. There are a number of different approaches/packaged I have found to using R for large data: 

• data.table
• RPostgres
• plyr
• sqldf

For my laptop, ncell < 5M is handled well enough by  the base stats and graphics packages, although I really like the plyr count function.  Both RPostgres and data.table manipulate the code a faster at some added complexity and increased functionality.  Being able to keep multiple voter databases in Postgres, perform complex SQL queries from the RPostrgres DBI interface and display the results/graphs in R is probably the best bet. I will discuss this in another post.  For the graphs above, I rely heavily on an awkward and nearly unreadable subsetting  and concatenation as in this line:

Radiation Sampling with Visual Comparisons : Part II

Radiation Sampling with Visual Comparisons : Part I