Converting all data.frames in environment to data.tables

setDT operates on the name/symbol, while get returns the value of the object. You can construct the setDT expression and evaluate it:

library(data.table) 
df1 <- data.frame(A=1, B=2)
df2 <- data.frame(D=3)
for(x in ls()){
  if (is.data.frame(get(x))) {
    eval(substitute(setDT(x), list(x=as.name(x))))
  }
}
rm(x)
df1[, rn:=.I]

I would use a loop rather than lapply to avoid complications (eg, with the evaluating environment).

This should do the trick:

library(data.table) #Win R-3.5.1 x64 data.table_1.12.2
df1 <- data.frame(A=1, B=2)
df2 <- data.frame(D=3)
for (x in ls()) {
    if (is.data.frame(get(x))) {
        assign(x, as.data.table(get(x)))
    }
}
df1[, rn:=.I]

I guess (not sure though) that the for/lapply loop uses sort of an own environment which messes up with the by ref semantics of data.table.

A little late, but this seems like a great—and rare—use eapply() (along with list2env()). Of course, this is another option, certainly not asserting it is the idiomatic way.

library(data.table)
df1 <- data.frame(A=1, B=2)
df2 <- data.frame(D=3)

list2env(eapply(.GlobalEnv, function(x) {if(is.data.frame(x)) {setDT(x)} else {x}}), .GlobalEnv)

df1[, rn:=.I]
df1
   A B rn
1: 1 2  1

---

Some timings and memory usage:

set.seed(0L)
sz <- 1e7
df1 <- data.frame(A=rnorm(sz))
df2 <- data.frame(B=rnorm(sz))
df3 <- copy(df1)
df4 <- copy(df2)

microbenchmark::microbenchmark(unit="ms", times=1L,
    assign_mtd = {
        for (x in ls()) {
            if (is.data.frame(get(x))) {
                assign(x, as.data.table(get(x)))
            }
        }
    },
    eval_sub_mtd = {
        for(x in ls()){
            if (is.data.frame(get(x))) {
                eval(substitute(setDT(x), list(x=as.name(x))))
            }
        }
    },
    eapply_mtd = {
        list2env(eapply(.GlobalEnv, function(x) {
                if (is.data.frame(x)) setDT(x) else x
            }), .GlobalEnv)
    }
)

timings:

Unit: milliseconds
         expr        min         lq       mean     median         uq        max neval
   assign_mtd 115.922802 115.922802 115.922802 115.922802 115.922802 115.922802     1
 eval_sub_mtd   3.293358   3.293358   3.293358   3.293358   3.293358   3.293358     1
   eapply_mtd   1.913802   1.913802   1.913802   1.913802   1.913802   1.913802     1