Thanks, Dennis! (Dennis is a former student of mine who clearly knows data.table much better than I do.)
Joe -----Original Message----- From: Dennis Murphy [mailto:[email protected]] Sent: Friday, July 15, 2011 10:34 AM To: [email protected]; Joseph Voelkel Subject: Re: datatable-help Digest, Vol 17, Issue 10 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
