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
