[R] subtract a list of vectors from a list of data.frames in an elegant way

[Eric Fail]

Dear R experts,

I've realized that it might not be possible to define a negative SELCET statement in a SQL call so now I'm looking for the smoothest way to generate a list of what I would like from my large database by first pulling all the names with a query like this "SELECT top 1 * FROM your_table" (thank you Bart Joosen for the idea) and then subtract the variables I am not allow to pull manually ending up with a 'positive' definition of what I want, something I can use in a SQL SELCT statement (see my email on this list from yesterday for more on that). 

When I query the database for the variable names I get something similar to 'DBquery' in my working example below, but considerable longer with over 2400 hundred variables. As I only need to remove two or three variables I would like to define a lookup table (like the list 'lookup' in my example) and subtract that from my data base query. Now to my question. Is there a way I can subtract one list from another? Like setoff or alike? 

I would like to end up with a list like the one shown in my example called 'result.' In short, I would like to subtract 'lookup' from 'DBquery' and end up with 'result,' please note that 'result' is a list fo vecktors and not a list of dataframes. In my real life example DBquery is considerable longer so defining that by hand would make a really really long syntax.

Hope someone know some smart function that I can use to solve my problem in an elegant way.

Thanks for reading.

Erick

###### begin R code ######

DBquery     <- list(tableA=data.frame(id = numeric(0), atwin = numeric(0), atrout = numeric(0)),
                    tableB=data.frame(id = numeric(0), mq   = numeric(0), z = numeric(0), m = numeric(0)),
                    tableC=data.frame(V1 = numeric(0), mfn   = numeric(0), iiff = numeric(0)),
                    tableD=data.frame(id    = numeric(0), msf   = numeric(0), oom  = numeric(0)))

lookup     <- list(tableA=   c('atwin', 'atrout'), 
                   tableB=    c('m', 'z'),
                   tableC=    'ALL')

### ...

result     <- list(tableA= c('id'),
                    tableB= c('id', 'mq'),
                    tableC= c('V1', 'mfn', 'iiff'))