One further comment on nomatch=0 weirdness. It seems that the value of nomatch= is the row index of the row of X to return if a row in Y matches no row in X here: X[Y,,nomatch=?] In ordinary R indexing using an index value of 0 means drop the corresponding component and NA means return an NA. nomatch=1 would presumably return the first row of X for non-matching rows of Y but, in fact, nomatch= seems to be restricted to 0 and NA as any other value generates an error message to this effect. Likely it was decided that values other than 0 and NA would be too bizarre and most likely represent user error. If any.y= were used then it would naturally be logical and this artificial distinction (i.e .between 0/NA on one hand and everything else on the other hand) would not have to be made.
On Fri, May 3, 2013 at 6:41 PM, Gabor Grothendieck <[email protected]> wrote: > In thinking about this a bit more I can see the argument for leaving > the default at nomatch=NA. Consider these examples of indexing: > >> letters[27] > [1] NA >> BOD[7,] > Time demand > NA NA NA > > nomatch=NA seems more compatible with these examples than nomatch=0. > > (At the same time this does not mean we could not also change the > argument name from nomatch= to all.y= and add the other merge > arguments (all.x=, by.x=, by.y=, by=) as well since it remains the > case that R's merge() seems closer than R's match() to this > functionality regardless of the default.) > > > On Fri, May 3, 2013 at 4:42 PM, Gabor Grothendieck > <[email protected]> wrote: >> One can view data.table's generalization of indexing as the >> realization that all indexing can conceptually be viewed as merging >> where indexing with numeric values corresponds to merging with the >> data.table's row numbers and indexing with logical values, L, is >> equivalent to merging with which(L) so there are really not two types: >> indexing and merging but just one type: merging that covers them all. >> >> >> On Fri, May 3, 2013 at 1:01 PM, Arunkumar Srinivasan >> <[email protected]> wrote: >>> I am wondering if performing X[Y] as a "merge" in correspondence with R's >>> base "merge", if the basic idea of "i" becomes confusing. That is, when "i" >>> is not a data.table in X[i] it indexes by rows. When `i` is a data.table, >>> instead of the current definition which is in par with the subletting >>> operation that use `i` (here data.table) as an index to subset X and then >>> JOIN both X and Y, we say, here X and Y are data.tables and we perform a >>> merge. I think this becomes confusing regarding the purpose of `i`. >>> >>> Remember that the main purpose of having the X[Y] is to have the flexibility >>> of using `j` to to filter/subset only the desired columns. So, for example >>> if you want to get 1 column of Y out of 100 columns when joining, you do: >>> X[Y, list(cols_of_x, one_col_of_y)] and here, it doesn't go with the >>> traditional definition of merge. >>> >>> As much as I like the idea of having consistent syntax, I also love the >>> feature of X[Y, j]. So I'm confused as to how to deal with this. >>> >>> Arun >>> >>> On Friday, May 3, 2013 at 6:54 PM, Gabor Grothendieck wrote: >>> >>> I think that from the viewpoint of compatibility and convenience it >>> would be best to implement all.x and all.y and not rely on swapping X >>> and Y. SQLite did something like this (they implemented left join but >>> not right join based on the idea that all you have to do is swap join >>> arguments) but the problem with it is that it adds a layer of mental >>> specification effort if the actual problem is better stated in the >>> unsupported orientation. >>> >>> On Fri, May 3, 2013 at 12:49 PM, Eduard Antonyan >>> <[email protected]> wrote: >>> >>> Arun, it only needs the addition of smth like X[Y, keep.all = TRUE], all of >>> the other merge options already exist as either X[Y] or Y[X] with or without >>> nomatch = 0/NA. >>> >>> >>> On Fri, May 3, 2013 at 11:45 AM, Arunkumar Srinivasan >>> <[email protected]> wrote: >>> >>> >>> Gabor, >>> >>> Very true. I suppose your request is that the x[i] where `i` is a >>> data.table should have the same set of options like R's base `merge` >>> function, like, by.y=TRUE, by.x=TRUE or all=TRUE. I like the idea by itself. >>> However, I am not able to think of a way to do this. I mean, I find the >>> syntax X[Y, by.x=TRUE] weird / not making sense. That is, to me even though >>> >>> X[Y] is equal to Y[X, by.y=TRUE] (or) X[Y, by.x=TRUE] (ignoring the >>> reordered columns) the latter 2 don't seem to make sense/is redundant (maybe >>> it's because I am used to this syntax). >>> >>> Arun >>> >>> On Friday, May 3, 2013 at 5:57 PM, Gabor Grothendieck wrote: >>> >>> In my last post it should have read: >>> >>> That X[Y] is not the same as Y[X] is analogous to the fact that >>> merge(X, Y, all.y=TRUE) is not the same as merge(Y, X, all.y=TRUE) >>> >>> On Fri, May 3, 2013 at 11:55 AM, Gabor Grothendieck >>> <[email protected]> wrote: >>> >>> Assuming same-named keys, then these are all the same except possibly >>> for row and column order: >>> >>> X[Y,,nomatch=0] >>> Y[X,,nomatch=0] >>> merge(X, Y) >>> merge(Y, X) >>> >>> That X[Y] is not the same as Y[X] is analogous to the fact that >>> merge(X, Y, all.x=TRUE) is not the same as merge(Y, X, all.x=TRUE) >>> >>> On Fri, May 3, 2013 at 11:46 AM, Arunkumar Srinivasan >>> <[email protected]> wrote: >>> >>> Gabor, >>> >>> X[Y] and Y[X] are not necessarily the same operations (meaning, they don't >>> *have* to give the same output). However, merge(X,Y) and merge(Y,X) *have* >>> to provide the same output (except for the column order and names). In >>> that >>> sense, a join is a bit different from a merge, no? >>> >>> Arun >>> >>> On Friday, May 3, 2013 at 5:36 PM, Gabor Grothendieck wrote: >>> >>> Yes, except that is not really what happens since match() only matches >>> one row whereas with mult="all", the default, all rows are matched >>> which is not really matching in the sense of match(). The current >>> naming confuses matching with joining and its really the latter that >>> is being done. >>> >>> Regarding the existence of merge the advantage of [ is that it will >>> automatically only take the columns needed so merge is not really >>> equivalent to [ in all respects. Furthermore having to use different >>> constructs for different types of merge seems awkward. >>> >>> >>> On Fri, May 3, 2013 at 11:27 AM, Eduard Antonyan >>> <[email protected]> wrote: >>> >>> Btw the way I think about the "nomatch" name is as follows - normally X[Y] >>> tries to match rows of Y with rows of X, and then "nomatch" tells it what >>> to >>> do when there is *no match*. >>> >>> >>> On Fri, May 3, 2013 at 10:23 AM, Eduard Antonyan >>> <[email protected]> >>> wrote: >>> >>> >>> To clarify - that behavior is already implemented in merge (more >>> specifically merge.data.table). I don't really have a view on having it in >>> X[Y] as well - I don't like all.x and all.y as the names, since there are >>> no >>> params named 'x' and 'y' in [.data.table (as opposed to merge), but some >>> param that would do a full outer join could certainly be added. >>> >>> >>> On Fri, May 3, 2013 at 10:09 AM, Gabor Grothendieck >>> <[email protected]> wrote: >>> >>> >>> Yes, sorry. Its nomatch= which presumably derives from the parameter >>> of the same name in the match() function. If the idea of the nomatch= >>> name was to leverage off existing argument names in R then I would >>> prefer all.y= to be consistent with merge() in place of nomatch= since >>> we are really merging/joining rather than just matching. That would >>> also allow extension to all types of join by adding all.an x= argument >>> too. >>> >>> On Fri, May 3, 2013 at 10:59 AM, Eduard Antonyan >>> <[email protected]> wrote: >>> >>> I would prefer nomatch=0 as a default though, simply because that's >>> what I >>> do most of the time :) >>> >>> >>> On Fri, May 3, 2013 at 9:57 AM, Eduard Antonyan >>> <[email protected]> >>> wrote: >>> >>> >>> A correction - the param is called "nomatch", not "match". >>> >>> This use case seems like smth a user shouldn't really do - in an ideal >>> world you should have them both keyed by the same-name column. >>> >>> As is, my view on it is that data.table is correcting the user mistake >>> of >>> naming the column in Y - y, instead of x, and so the output makes >>> sense and >>> I don't see the need of complicating the behavior by adding more cases >>> one >>> has to go through to figure out what the output columns would be. >>> Similar to >>> asking for X[J(c("b", "c", "d"))] - you wouldn't want an anonymous >>> column >>> there, would you? >>> >>> >>> >>> On Fri, May 3, 2013 at 6:18 AM, Gabor Grothendieck >>> <[email protected]> wrote: >>> >>> >>> I am moving this discussion which started with mdowle to the list. >>> >>> Consider this example slightly modified from the data.table FAQ: >>> >>> X = data.table(x=c("a","a","b","b","b","c","c"), foo=1:7, key="x") >>> Y = data.table(y=c("b","c","d"), bar=c(4,2,3)) >>> out <- X[Y]; out >>> >>> x foo bar >>> 1: b 3 4 >>> 2: b 4 4 >>> 3: b 5 4 >>> 4: c 6 2 >>> 5: c 7 2 >>> 6: d NA 3 >>> >>> Note that the first column of the output is labelled x even though >>> the >>> data to produce it comes from y, e.g. "d" in out$x is not in X$x but >>> does appear in Y$y so clearly the data is coming from y as opposed to >>> x . In terms of SQL the above would be written: >>> >>> select Y.y as x, ... >>> >>> and the need to renamne the first column of out suggests that there >>> may be a deeper problem here. >>> >>> Here are some ideas to address this (they would require changes to >>> data.table): >>> >>> - the default of X[Y,, match=NA] would be changed to a default of >>> X[Y,,match=0] so that it corresponds to the defaults in R's merge and >>> in SQL joins. >>> >>> - the column name of the first column in the example above would be >>> changed to y if match=0 but be left at x if match=NA. In the case >>> that match=0 (the proposed new default) x and y are equal so the >>> first >>> column can be validly labelled as x but in the case that match=NA >>> they >>> are not so y would be used as the column name. >>> >>> - the name match= does seem a bit misleading since R's match only >>> matches one item in the target whereas in data.table match matches >>> many if mult="all" and that is the default. Perhaps some thought >>> should be given to a name change here? >>> >>> The above would seem to correspond more closely to R's merge and SQL >>> join defaults. Any use cases or other comments? >>> >>> -- >>> Statistics & Software Consulting >>> GKX Group, GKX Associates Inc. >>> tel: 1-877-GKX-GROUP >>> email: ggrothendieck at gmail.com >>> _______________________________________________ >>> datatable-help mailing list >>> [email protected] >>> >>> >>> >>> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/datatable-help >>> >>> >>> >>> >>> -- >>> Statistics & Software Consulting >>> GKX Group, GKX Associates Inc. >>> tel: 1-877-GKX-GROUP >>> email: ggrothendieck at gmail.com >>> >>> >>> >>> >>> -- >>> Statistics & Software Consulting >>> GKX Group, GKX Associates Inc. >>> tel: 1-877-GKX-GROUP >>> email: ggrothendieck at gmail.com >>> _______________________________________________ >>> datatable-help mailing list >>> [email protected] >>> >>> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/datatable-help >>> >>> >>> >>> >>> -- >>> Statistics & Software Consulting >>> GKX Group, GKX Associates Inc. >>> tel: 1-877-GKX-GROUP >>> email: ggrothendieck at gmail.com >>> >>> >>> >>> >>> -- >>> Statistics & Software Consulting >>> GKX Group, GKX Associates Inc. >>> tel: 1-877-GKX-GROUP >>> email: ggrothendieck at gmail.com >>> >>> >>> >>> >>> -- >>> Statistics & Software Consulting >>> GKX Group, GKX Associates Inc. >>> tel: 1-877-GKX-GROUP >>> email: ggrothendieck at gmail.com >>> >>> >> >> >> >> -- >> Statistics & Software Consulting >> GKX Group, GKX Associates Inc. >> tel: 1-877-GKX-GROUP >> email: ggrothendieck at gmail.com > > > > -- > Statistics & Software Consulting > GKX Group, GKX Associates Inc. > tel: 1-877-GKX-GROUP > email: ggrothendieck at gmail.com -- Statistics & Software Consulting GKX Group, GKX Associates Inc. tel: 1-877-GKX-GROUP email: ggrothendieck at gmail.com _______________________________________________ datatable-help mailing list [email protected] https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/datatable-help
