Just chiming in to say something similar to colwise from plyr would be quite nice. You could just carry around a vector of variable names, then do something DT[ ,colwise( f, var_names), by=by_names ].
On 15 July 2011 14:06, Dennis Murphy <[email protected]> wrote: > On Fri, Jul 15, 2011 at 8:23 AM, Steve Lianoglou > <[email protected]> wrote: > > Hi Dennis, > > > > I didn't see your post before I sent my latest reply. > > > > Nice detective work! > > Thanks, Steve. I just followed my nose and the docs, which I have > conveniently kept in a small binder for such occasions :) Like JV, I > don't use data.table every day, so some of its idiosyncracies get > cobwebbed in the hard drive over time. The wiki entries helped a lot. > > > > > For what it's worth, from what I understand your > > "punchline"/kewpie-prize solution is so much faster because it avoids > > building the .SD data.table within each group. > > That was my deduction from having read the first entry in the wiki. I > still can't believe I got that thing to work :) > > > > > I'll let Matthew leave a more detailed comment, since he's (obviously) > > much more intimately familiar w/ the inner voodoo of data.table. But > > as a last comment -- if the speed differences are so drastic because > > of the cost of creating the .SD data.table, maybe we should think > > about taking some "inspiration" from plyr and define a similar > > `colwise` function -- which would operate across each "column" of > > supposedly-build .SD object applying a function to each of them w/o > > actually building an .SD object itself. > > Your clairvoyance skills are clearly operating today :) More > seriously, this is what I would consider an 'obvious' "big-data" > problem - I could easily see situations arising in finance and genomic > applications where a fairly large subset of variables of the same > type, but not necessarily all of them, need to be summarized in a > particular way. The colwise() functions would be problematic as well > in the scenario described in my eariler post, but I haven't tried > ddply() to verify that assertion so I could be mistaken. > > It would be *really* helpful to have a convenient, fast mechanism in > data.table that allows one to substitute a (possibly large) vector of > variable names into a function. Alas, I don't have any bright ideas > about how to program it. Fortunately, there are some nice functions in > R to select variable subsets efficiently in data frames (e.g., the > grep() family of functions, regular expressions, %in% and so on), but > I don't know how that would translate easily to data.table() since the > internals are so different. > > Looking forward to the team's take on this... > > Dennis > > > > > -steve > > > > On Fri, Jul 15, 2011 at 10:34 AM, Dennis Murphy <[email protected]> > wrote: > >> Hi: > >> > >> <A bunch snipped because I get the archives in digest form> > >> > >> Re Prof. Voelkel's recent posts: > >> > >> (1) Quoting does not work well in data.table; this is mentioned in > >> several of the FAQs. Apropos to this discussion, some of the relevant > >> ones include 1.2, 1.6 and 2.1; there may be others :) > >> > >> (2) Steve's response seems to be the right way to go (although see > >> below), but I thought I'd up the stakes a little and assume that Prof. > >> Voelkel has a large number of variables, only a subset of which he may > >> want summarized in a particular go. To that end, I created the > >> following toy data frame cum data.table; this is as much for my own > >> edification as anyone else's (which explains the eventual length of > >> this post...I got curious :) > >> > >> This goes against the advice given in the first example of the > >> data.table wiki, but if you have, say, 100 variables to select out of > >> a possible 1000, it doesn't make sense to list them individually as > >> recommended on the wiki. (But see below...) > >> > >> library('data.table') > >> set.seed(1043) > >> m <- matrix(rpois(240, 10), nrow = 6) > >> colnames(m) <- paste('A', 1:40, sep = '') > >> m <- as.data.frame(m) > >> dt2 <- data.table(x = rep(1:3, 2), y = rep(1:3, each = 2), m, key = 'x') > >> dim(dt2) > >> # [1] 6 42 ...so far, so good > >> > >> # Subset of variables for which sums are desired > >> vars <- paste('A', c(1, 4, 10, 15, 31), sep = '') > >> > >> # One approach: use the select = argument of subset() to restrict > >> # the variables under consideration: > >> dt2[, lapply(subset(.SD, select = vars), sum), by = 'x'] > >> x A1 A4 A10 A15 A31 > >> [1,] 1 18 21 22 22 24 > >> [2,] 2 20 13 27 23 21 > >> [3,] 3 22 15 16 23 15 > >> > >> # Use the with = FALSE construct of data.table to do the same: > >> dt2[, lapply(.SD[, vars, with = FALSE], sum), by = 'x, y'] > >> x y A1 A4 A10 A15 A31 > >> [1,] 1 1 11 13 12 11 16 > >> [2,] 1 2 7 8 10 11 8 > >> [3,] 2 1 10 4 16 7 11 > >> [4,] 2 3 10 9 11 16 10 > >> [5,] 3 2 11 8 7 11 7 > >> [6,] 3 3 11 7 9 12 8 > >> > >> # For this example, it is the same (apart from the key variables) as > >> dt2[, vars, with = FALSE] > >> > >> Not bad for this small example, but what happens in a much larger data > table? > >> > >> To find out, I created a 10000 x 1000 matrix that I converted into a > >> data table, added two grouping variables of 100 levels each and then > >> tried both approaches above again. Performance isn't bad when > >> summarizing over one variable, but there is a definite hit when two > >> variables are summarized. [It makes some sense since one is grouping > >> over 10000 level combinations rather than 100, but once again, keep > >> reading.] Curiously, it makes no difference if there is one key > >> variable or two, which made me wonder what the preferred approach is > >> in this circumstance. > >> > >> m <- matrix(rpois(10000000, 10), nrow = 10000) > >> m <- as.data.table(m) > >> m <- transform(m, x = rep(1:100, each = 100), y = rep(1:100, 100)) > >> setkey(m, 'x') > >> dim(m) > >> # [1] 10000 1002 > >> > >> # Randomly select 150 variables from the 1000 > >> vars <- paste('A', sample(1:1000, 150, replace = FALSE), sep = '') > >> length(vars) > >> # [1] 150 > >> key(m) > >> # [1] "x" > >>> system.time(m[, lapply(subset(.SD, select = vars), sum), by = 'x']) > >> user system elapsed > >> 0.75 0.00 0.75 > >>> system.time(m[, lapply(.SD[, vars, with = FALSE], sum), by = 'x']) > >> user system elapsed > >> 0.64 0.00 0.64 > >>> system.time(m[, lapply(subset(.SD, select = vars), sum), by = 'x, y']) > >> user system elapsed > >> 53.65 0.00 53.85 > >>> system.time(m[, lapply(.SD[, vars, with = FALSE], sum), by = 'x, y']) > >> user system elapsed > >> 44.21 0.01 44.35 > >> > >> m2 <- data.table(m, key = 'x, y') > >> rm(m) > >> key(m2) > >> # [1] "x" "y" > >>> system.time(m2[, lapply(subset(.SD, select = vars), sum), by = 'x, y']) > >> user system elapsed > >> 53.54 0.00 53.73 > >>> system.time(m2[, lapply(.SD[, vars, with = FALSE], sum), by = 'x, y']) > >> user system elapsed > >> 44.30 0.04 44.60 > >> > >> The first question in the wiki > >> (http://rwiki.sciviews.org/doku.php?id=packages:cran:data.table) says > >> to use the columns directly rather than to rely on .SD. I wanted to > >> know how to pass new names to the summaries instead of overwriting the > >> original variable names. For the fun of it, I tried the following: > >> > >> select <- sample(1:1000, 150, replace = FALSE) > >> vars <- paste('A', select, sep = '') > >> outvars <- paste('S', select, sep = '') > >> > >> # Create a long expression of the form 'list(..., Sn = sum(An), ...)', > >> # n a subscript from 1 to 150. > >> expr <- paste('list(', paste(outvars, paste('sum(', vars, ')', sep = > >> ''), sep = '=', collapse = ','), > >> ')', sep = '') > >> u <- m2[, eval(parse(text = expr)), by = 'x'] > >>> dim(u) > >> # [1] 100 151 seems reasonable... > >> > >> This seemed to run rather fast, so I decided to time it: > >> > >>> system.time(m2[, eval(parse(text = expr)), by = 'x']) > >> user system elapsed > >> 0.03 0.00 0.03 > >>> system.time(m2[, eval(parse(text = expr)), by = 'x, y']) > >> user system elapsed > >> 1.05 0.00 1.04 > >> > >> I've got to admit, this is not the approach I would have taken > >> normally, is certainly not intuitively obvious to me and flouts the > >> usual advice to avoid the eval(parse(text = )) mantra, but the data > >> don't lie :) Please tell me there's a more code-efficient way to do > >> this (the new variable names included), because my 'solution' was a > >> complete kludge and accidental kewpie prize. > >> > >> Cheers, > >> Dennis > >> > >>> Message: 1 > >>> Date: Thu, 14 Jul 2011 16:36:11 -0400 > >>> From: Joseph Voelkel <[email protected]> > >>> Subject: [datatable-help] Skipping some Vi names > >>> To: "[email protected]" > >>> <[email protected]> > >>> Message-ID: > >>> < > [email protected]> > >>> Content-Type: text/plain; charset="us-ascii" > >>> > >>> I don't use data.table too much (though I probably should use it > more...). > >>> > >>> I was surprised at the results below. It appears that the name V1 gets > assigned to the first result, but then the keys ("in the background") are > assigned the next set of Vi names, creating a gap in the names depending on > the number of keys. I would like to see the Vi names appear in their > natural, sequential, order. Not a show stopper, but it's annoying. (I have > over 40 Vi's and it'd be good to have them numbered more rationally.) > Thanks. > >>> > >>>> > dt<-data.table(x=c(1,2,3,1,2,3),y=c(1,1,2,2,3,3),A1=1:6,A2=7:12,A3=13:18,key="x") > >>>> dt[,list("sum(A1),sum(A2),sum(A3)"),by="x"] > >>> x V1 V3 V4 > >>> [1,] 1 5 17 29 > >>> [2,] 2 7 19 31 > >>> [3,] 3 9 21 33 > >>>> key(dt)<-c("x","y") > >>>> dt[,list("sum(A1),sum(A2),sum(A3)"),by="x,y"] > >>> x y V1 V4 V5 > >>> [1,] 1 1 1 7 13 > >>> [2,] 1 2 4 10 16 > >>> [3,] 2 1 2 8 14 > >>> [4,] 2 3 5 11 17 > >>> [5,] 3 2 3 9 15 > >>> [6,] 3 3 6 12 18 > >>> > >>> > >>> > >>> Joseph G. Voelkel, Ph.D. > >>> Professor, Center for Quality and Applied Statistics > >>> Kate Gleason College of Engineering > >>> Rochester Institute of Technology > >>> V 585-475-2231 > >>> F 585-475-5959 > >>> [email protected] > >>> > >> _______________________________________________ > >> datatable-help mailing list > >> [email protected] > >> > https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/datatable-help > >> > > > > > > > > -- > > Steve Lianoglou > > Graduate Student: Computational Systems Biology > > | Memorial Sloan-Kettering Cancer Center > > | Weill Medical College of Cornell University > > Contact Info: http://cbio.mskcc.org/~lianos/contact > > > _______________________________________________ > datatable-help mailing list > [email protected] > https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/datatable-help >
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