[Bioc-devel] R6 class v.s. S4 class
As the developer of all these three biothings_clients, we, of course, like to keep the same pattern for R, and R6 looks the closest to me. But it looks like, from R users' perspective, this is not a popular pattern to use Yes, there will probably be way more users of R wanting to use BioThings than BioThings users wanting to use R. Chunlei: That'true, so we think a native R client would be appreciated by R community. And also yes, it's pretty light on the code, but it's important to design it correctly. Then in R, we will have to create these generic methods (hope this is the right term): getBioThing(mything_client, ...) getBioThings As Herve points out, R users will expect queries to be vectorized implicitly. queryBioThings() or whatever should probably return a tabular structure describing the things. There is no need for distinguishing singular and plural. Chunlei: Good suggestion! We will see if we can wrap these two functions into just one. Maybe we can have just BTGet and BTQuery two functions, that seems much cleaner. queryBioThings queryManyBioThings BioThingsMetadata I personally still like the Python/JS pattern, as you can have client specific name like "getgene", "getgenes", instead of the generic getBioThing and getBioThings name. Plus that users can just call "gene_client" part as "gc" or whatever, it just has much less to type :-) in the code. In R S4 case, the function name has to be more verbose because they are global. There seems to be a misconception here. S4 has two types of classes, conventional value classes, and reference classes. The reference classes have the same syntax as the R6 classes. R6 is mostly a stripped down version of S4 reference classes. In this particular case, R is sufficiently flexible that it would be easy to support the reference class syntax on ordinary value classes. So you could use the reference class syntax, but I wouldn't recommend it, for the aforementioned reasons. Moreover, be careful about carrying over notions from Python and JS into R. R is unique in fundamental ways. Chunlei: Thanks for clarifying. As a non-regular R user, I understand (and also respect) more and more the significant difference between R and many other general-purpose languages now. The help from this group has been great! Does this sound good to the group? Any more suggestions? Chunlei
From: Michael Lawrence <lawrence.michael at gene.com<mailto:lawrence.michael at gene.com>>
Sent: Thursday, October 19, 2017 8:32 PM
To: Martin Morgan
Cc: Charles Plessy; bioc-devel at r-project.org<mailto:bioc-devel at r-project.org>; Chunlei Wu
Subject: Re: [Bioc-devel] R6 class v.s. S4 class
Sent: Thursday, October 19, 2017 8:32 PM
To: Martin Morgan
Cc: Charles Plessy; bioc-devel at r-project.org<mailto:bioc-devel at r-project.org>; Chunlei Wu
Subject: Re: [Bioc-devel] R6 class v.s. S4 class
API discoverability is a big problem in languages with a functional syntax. Namespaces are verbose, but they do provide for constrained autocompletion. Prefixing all symbols with an abbreviation like "bt_" seems too adhoc to me, but it is common practice. Explicitly querying for methods takes the user out of the flow.
One could imagine an IDE showing available methods in the tooltip of function symbols.
I guess an IDE could support autocompeting on "(object)" or "(object,", where <tab> would display generics with applicable methods and fill in the name in front of the "(". Not very intuitive though.
By simplifying our APIs we make discoverability less of an issue, because they are easily listed on cheat sheets and memorized.
I wonder if there are ideas to steal from Julia.
On Thu, Oct 19, 2017 at 7:36 PM, Martin Morgan <martin.morgan at roswellpark.org<mailto:martin.morgan at roswellpark.org>> wrote:
On 10/19/2017 09:24 PM, Charles Plessy wrote:
(Just sharing my thoughts as those days I am spending quite
some time preparing the upgrade of a Bioconductor package).
Le Fri, Oct 20, 2017 at 12:50:48AM +0000, Ryan Thompson a ?crit :
gene_client <- BioThingsClient("gene")
query("CDK2", client=gene_client)
In addition, since the piping operator (%>%) of dplyr and magrittr is
gaining traction, I would recommend to carefully consider which will be
the first argument of the function:
With the client as first argument, one can then write things like:
gene_client %>% query("CDK2") # similar to query(gene_client, "CDK2")
The Bioconductor convention would use S4 objects with CamelCase constructors.
geneClient = BioThingsGeneClient() ## or just GeneClient()
I agree with enabling the use of pipe, and think the generic + methods should have signature where the first argument is the client rather than the pattern against which the query occurs. There is to some extent an argument for name-mangling in the generic (other knowledgeable people disagree) so that one is free to implement contracts unique to the package in question, and avoid conflicts with other generics with identical names in different packages ( AnnotationDbi::select() / dplyr::select()).
setGeneric(
"btQuery",
function(x, query, ...) standardGeneric("btQuery")
)
setMethod(
"btQuery", "GeneClient",
function(x, query)
{
## implementation
})
btQuery(geneClient, "CDK2") ## maybe btquery(...)
Yes one could BioThings::query(), or semanticallyInformativeAlterntaiveToQuery(), but these seem cumbersome to me, and the first at least has rough edges (that of course should be fixed...), e.g.,
> methods(AnnotationHub::query)
Error in .S3methods(generic.function, class, parent.frame()) :
no function 'AnnotationHub::query' is visible
I think Michael is arguing for something like plain-old-functions (and the original examples and problems of multiplying methods seemed somehow to be plain old functions rather than S4 generics and methods?)
geneQuery <- function(x, query) ...
A down side is that one cannot discover programatically what one can do with a GeneClient object (if it were a method, one could ask for methods(class=class(geneClient))); as a developer one also needs to validate the incoming argument, which requires a certain but not unsurmountable discipline.
Michael didn't mention it, but these slides of his are relevant
https://bioconductor.org/help/course-materials/2017/BioC2017/DDay/BOF/usability.pdf
One other lesson from the annotation world is to think carefully about the structure of the return, in particular thinking about 1:1 versus 1:many mappings between vector-valued 'pattern='. While it's tempting to return say a character vector or named list, probably one wants these days to take the lessons of tidy data and return a data.frame-like (e.g., DataFrame(), but maybe that's not 'necessary'; nothing wrong with a tibble, but a data.table is not likely necessary or particularly advised [because of the novel syntax and reference semantics]) object where the first column is the query and the second and subsequent columns the result of the query; one wants to pay particular attention to dealing with 1:0 and 1:many mappings in ways that do not confuse users; some use cases (e.g., adding annotations to the rowData() of SummarizedExperiment) are really facilitated by a 1:1 mapping between query and response.
Martin
With the gene symbol as first argument:
"CDK2" %>% query(gene_client) # similar to query("CDK2", gene_client)
If gene symbols may come as output from other commands and the query
function is able to work smartly with a vector of gene symbols as input,
then the second pattern might be useful. Otherwise the first pattern
probably makes more sense.
See https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html for details.
(Note however that the piped and non-piped functions are not exactly
equivalent, and that piped commands can be harder to debug; therefore
it may be better to only use them in interactive sessions.)
Have a nice day,
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