Here's a solution using data.table's :=
operator, building on Andrie and Ramnath's answers.
require(data.table) # v1.6.6
require(gdata) # v2.8.2
set.seed(1)
dt1 = create_dt(2e5, 200, 0.1)
dim(dt1)
[1] 200000 200 # more columns than Ramnath's answer which had 5 not 200
f_andrie = function(dt) remove_na(dt)
f_gdata = function(dt, un = 0) gdata::NAToUnknown(dt, un)
f_dowle = function(dt) { # see EDIT later for more elegant solution
na.replace = function(v,value=0) { v[is.na(v)] = value; v }
for (i in names(dt))
eval(parse(text=paste("dt[,",i,":=na.replace(",i,")]")))
}
system.time(a_gdata = f_gdata(dt1))
user system elapsed
18.805 12.301 134.985
system.time(a_andrie = f_andrie(dt1))
Error: cannot allocate vector of size 305.2 Mb
Timing stopped at: 14.541 7.764 68.285
system.time(f_dowle(dt1))
user system elapsed
7.452 4.144 19.590 # EDIT has faster than this
identical(a_gdata, dt1)
[1] TRUE
Note that f_dowle updated dt1 by reference. If a local copy is required then an explicit call to the copy
function is needed to make a local copy of the whole dataset. data.table's setkey
, key<-
and :=
do not copy-on-write.
Next, let's see where f_dowle is spending its time.
Rprof()
f_dowle(dt1)
Rprof(NULL)
summaryRprof()
$by.self
self.time self.pct total.time total.pct
"na.replace" 5.10 49.71 6.62 64.52
"[.data.table" 2.48 24.17 9.86 96.10
"is.na" 1.52 14.81 1.52 14.81
"gc" 0.22 2.14 0.22 2.14
"unique" 0.14 1.36 0.16 1.56
... snip ...
There, I would focus on na.replace
and is.na
, where there are a few vector copies and vector scans. Those can fairly easily be eliminated by writing a small na.replace C function that updates NA
by reference in the vector. That would at least halve the 20 seconds I think. Does such a function exist in any R package?
The reason f_andrie
fails may be because it copies the whole of dt1
, or creates a logical matrix as big as the whole of dt1
, a few times. The other 2 methods work on one column at a time (although I only briefly looked at NAToUnknown
).
EDIT (more elegant solution as requested by Ramnath in comments) :
f_dowle2 = function(DT) {
for (i in names(DT))
DT[is.na(get(i)), (i):=0]
}
system.time(f_dowle2(dt1))
user system elapsed
6.468 0.760 7.250 # faster, too
identical(a_gdata, dt1)
[1] TRUE
I wish I did it that way to start with!
EDIT2 (over 1 year later, now)
There is also set()
. This can be faster if there are a lot of column being looped through, as it avoids the (small) overhead of calling [,:=,]
in a loop. set
is a loopable :=
. See ?set
.
f_dowle3 = function(DT) {
# either of the following for loops
# by name :
for (j in names(DT))
set(DT,which(is.na(DT[[j]])),j,0)
# or by number (slightly faster than by name) :
for (j in seq_len(ncol(DT)))
set(DT,which(is.na(DT[[j]])),j,0)
}
data.table
to adata.frame
? Adata.table
is adata.frame
. Any data.frame operation will just work. – Narcisdata.table
by specifying column number. soDT[,3]
will not give the third column. i think this makes the solution proposed in the link unviable here. i am sure there is an elegant approach using somedata.table
wizardry! – Phalansteriandata.table
solution. – PhalansterianDT[, 3, with=FALSE]
returns the third column. – Narciswith
argument. – Phalansterianmydf[is.na(mydf) == TRUE]
does the job on data frames, whilemydt[is.na(mydt) == TRUE]
gives me something strange even if i usewith=FALSE
– Phalansterian