I have 8,000 data.frames inside my global environment (.GlobalEnv) in R, for example
head(ls(.GlobalEnv))
#[1] \"db1\" \"db2\" \"db3\"
You could use a combination of eapply and mget to put all data.frames that are present in the global environment in a list:
x <- eapply(.GlobalEnv, 'is.data.frame')
dflist <- mget(names(x[unlist(x)]), .GlobalEnv)
Then you can use for example lapply(dflist, ...) to run a regression on each of them.
A very concise alternative approach contributed by @RichardScriven in the comments is:
dflist <- Filter(is.data.frame, as.list(.GlobalEnv))