Re: [R] using "eval(parse(text)) " , gsub(pattern, replacement, x) , to process "code" within a loop/custom function

From: Emmanuel Charpentier <>
Date: Fri, 07 Dec 2007 00:00:21 +0100

Thomas Pujol a écrit :
> R-help users,
> Thanks in advance for any assistance ... I truly appreciate your expertise. I searched help and could not figure this out, and think you can probably offer some helpful tips. I apologize if I missed something, which I'm sure I probably did.
> I have data for many "samples". (e.g. 1950, 1951, 1952, etc.)
> For each "sample", I have many data-frames. (e.g. temp.1952, births.1952, gdp.1952, etc.)
> (Because the data is rather "large" (and for other reasons), I have chosen to store the data as individual files, as opposed to a list of data frames.)
> I wish to write a function that enables me to "run" any of many custom "functions/processes" on each sample of data.
> I currently accomplish this by using a custom function that uses:
> "eval(parse(t=text.i2)) ", and "gsub(pat, rep, x)" (this changes the "sample number" for each line of text I submit to "eval(parse(t=text.i2))" ).
> Is there a better/preferred/more flexible way to do this?

Beware : what follows is the advice of someone used to use RDBMS and SQL to work with data ; as anyone should know, everything is a nail to a man with a hammer. Caveat emptor...

Unless I misunderstand you, you are trying to treat piecewise a large dataset made of a large number of reasonably-sized independent chunks.

What you're trying to do seems to me a bit reinventing SAS macro language. What's the point ?

IMNSHO, "large" datasets that are used only piecewise are much better handled in a real database (RDBMS), queried at runtime via, for example, Brian Ripley's RODBC.

In your example, I'd create a table births with all your data + the relevant year. Out of the top of my mind :

# Do that ONCE in the lifetime of your data : a RDBMS is probably more # apt than R dataframes for this kind of management


sqlSave(channel, tablename="Births",


# ... ^W^Y ad nauseam ...

rm(births.1951, births.1952, ...) # get back breathing space

Beware : certain data types may be tricky to save ! I got bitten by Dates recently... See RODBC documentation, your DBMS documentation and the "R Data Import/Export guide"...

At analysis time, you may use the result of the relevant query exactly as one of your dataframes. instead of :
foo(... data=birth.1952, ...)
type :
foo(... data=sqlQuery(channel,"select * from \"Births\" where \"Year\"=1952;", ...) # Syntax illustrating talking to a "picky" DBMS...

Furthermore, the variable "Year" bears your "d" information. Problem (dis)solved.

You may loop (or even sapply()...) at will on d : for(year in 1952:1978) {
  query<-sprintf("select * from \"Births\" where \"Year\"=%d;",year)   foo(... data=sqlQuery(channel,query), ...)   ...

If you already use a DBMS with some connection to R (via RODBC or otherwise), use that. If not, sqlite is a very lightweight library that enables you to use a (very considerable) subset of SQL92 to manipulate your data.

I understand that some people of this list have undertaken the creation of a sqlite-based package dedicated to this kind of large data management.

HTH,                                         Emmanuel Charpentier mailing list PLEASE do read the posting guide and provide commented, minimal, self-contained, reproducible code. Received on Thu 06 Dec 2007 - 23:47:09 GMT

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