[datatable-help] merge/join/match

[Gabor Grothendieck]

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
<ggrothendieck at gmail.com> 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
> <aragorn168b at gmail.com> 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
>> <eduard.antonyan at gmail.com> 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
>> <aragorn168b at gmail.com> 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
>> <ggrothendieck at gmail.com> 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
>> <aragorn168b at gmail.com> 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
>> <eduard.antonyan at gmail.com> 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
>> <eduard.antonyan at gmail.com>
>> 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
>> <ggrothendieck at gmail.com> 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
>> <eduard.antonyan at gmail.com> 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
>> <eduard.antonyan at gmail.com>
>> 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
>> <ggrothendieck at gmail.com> 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
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>>
>>
>> --
>> Statistics & Software Consulting
>> GKX Group, GKX Associates Inc.
>> tel: 1-877-GKX-GROUP
>> email: ggrothendieck at gmail.com
>>
>>
>>
>>
>> --
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>> GKX Group, GKX Associates Inc.
>> tel: 1-877-GKX-GROUP
>> email: ggrothendieck at gmail.com
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>> --
>> 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