R: modify any function applied to a S4 class - r

I've been developing a S4 class which is essentially a data.frame with a little bit of extra information. For the purposes of this question, the "extra" features of this class are irrelevant. What matters is that the class contains a data.frame object stored in one of it's slots. (I put the data.frame in a slot, instead of naming it a superclass, because I find that S4 classes which contain data.frames simplify the data.frames to lists for some reason).
Here's a basic example:
setClass('tmp_class', slots = c(df = 'data.frame'))
test_object <- new('tmp_class', df = data.frame(Num = 1:10, Let = letters[1:10]))
Now what I'd like to do is make it so that essentially any function applied to an object of this class is applied to the data.frame in slot #df. It's easy to write methods for specific functions to do this, like:
setMethod('dim', signature = c(x = 'tmp_class'), function(x) dim(x#df))
But I'm limited to only the functions I can think of, and any function invented by a user wouldn't work.
It is a simple matter to write a sort of wrapper/closure to modify a function to work on my class, like this:
tmp_classize <- function(func){
function(tmp, ...){ func(tmp#df, ...) }
}
So, rather than writing methods for, say, colnames() or ncol(), I could just run:
tmp_classize(colnames)(test_object)
or
tmp_classize(ncol)(test_object)
But what I'd like to do is somehow evoke my "tmp_classize" function on any function applied to my class, automatically. I can't figure out how to do it. I was thinking that if could somehow call a "universal method" with an input signature of class "tmp_class", and then use sys.function() to grab the actual function being called, maybe I could make something work, but A) there are recursion problems B) I don't know how to call such a "universal" method. It seems to me that the solution, if it exists at all, might necessitate non-standard evaluation, which I'd rather avoid, but might use if necessary.
Thanks!
P.S. I realize this undertaking may be unwise/poor programming technique, and I may never actually implement it in a package. Still I'm curious to know if it is possible.
P.P.S. I'd also be interested in the same idea applied to S3 classes!

In principal what you could do is make a classUnion for your class and data.frame and write methods for your class that deal with all of the ways to read and write to data.frames such as $, [, dim(), <- and many more. Then when other functions seek to use your new class as data.frame there will be methods for this to work. This is somewhat explained in John Chambers "Software for Data Analysis" starting on page 375. That said this system may be very difficult to implement.
A simpler system may be to just add an extra attribute to your data.frame with the extra info you need. For example:
x<-data.frame(a=1:3,b=4:6)
attr(x,"Info")<-"Extra info I need"
attributes(x)$Info
[1] "Extra info I need"
This is not as elegant as a S4 class but will do everything a data.frame does. I suspect that someone who is familiar with S3 classes could improve on this idea quite a bit.

The simplest solution is to have your class contain data.frame instead of having it as one of the slots. For example here is a data.frame with a timestamp:
setclass(
"timestampedDF",
slots=c(timestamp="POSIXt"),
contains="data.frame"
)
Now all functions which work for a data.frame (such as head) will automatically work for timestampedDF objects. If you need to get at the "data frame part", then that is held in a hidden slot object#.Data.

Related

Is attributes() a function in R?

Help files call attributes() a function. Its syntax looks like a function call. Even class(attributes) calls it a function.
But I see I can assign something to attributes(myobject), which seems unusual. For example, I cannot assign anything to log(myobject).
So what is the proper name for "functions" like attributes()? Are there any other examples of it? How do you tell them apart from regular functions? (Other than trying supposedfunction(x)<-0, that is.)
Finally, I guess attributes() implementation overrides the assignment operator, in order to become a destination for assignments. Am I right? Is there any usable guide on how to do it?
Very good observation Indeed. It's an example of replacement function, if you see closely and type apropos('attributes') in your R console, It will return
"attributes"
"attributes<-"
along with other outputs.
So, basically the place where you are able to assign on the left sign of assignment operator, you are not calling attributes, you are actually calling attributes<- , There are many functions in R like that for example: names(), colnames(), length() etc. In your example log doesn't have any replacement counterpart hence it doesn't work the way you anticipated.
Definiton(from advanced R book link given below):
Replacement functions act like they modify their arguments in place,
and have the special name xxx<-. They typically have two arguments (x
and value), although they can have more, and they must return the
modified object
If you want to see the list of these functions you can do :
apropos('<-$') and you can check out similar functions, which has similar kind of properties.
You can read about it here and here
I am hopeful that this solves your problem.

Are there obvious reasons to choose between methods and functions in R?

I've written a function to return a class I built that contains some calculations for the data passed to the function.
Once the new object is returned, I intend to print out some of the data in a little "report" and then map the lines contained in the sf slot colored by an attribute the original function calculated.
carbon_class <- setClass("carbon_class", slots = c(total_carbon = "numeric", carbon_by_type = "data.frame", trips = "sf"), contains = c("data.frame", "sf"))
I was going to define two methods for the class to create the report and map, mainly to practice object oriented programming in R, but as I'm reading about it, I'm having a hard time coming up with a reason to use a method instead of just another function.
Are there obvious use cases for each? I'm reading through Hadley's Advanced R and it talks about how to use the S3 and S4 classes/methods, but not why.
Thanks
edit: is it for using side effects because technically functions should only return a value without any side effects while its more acceptable for methods to do other things in addition to what they return?

R: Finding class inheritance structure within an R package

You can find all the objects in a package with
objs <- mget(ls("package:base"), inherits = TRUE)
You can select the functions from these with
funs <- objs[is.function(objs)]
You can get a complete list of the dependencies of the listed functions in a package by applying codetools::findGlobals(), miniCRAN::makeDepGraph, pkgnet::CreatePackageReport (or others) to the function list. All of these functions either graph the resulting dependencies or return an object easily plotable with, e.g., igraph or DependenciesGraph.
Is there an comparable set of commands to find all the classes created by a package and the inheritance structure of those classes? I know that for most packages the resulting web of class inheritance would be relatively simple, but I think that in a few cases, such as ggplot2 and the survey package, the resulting web of class inheritance could be quite helpful.
I have found a package, classGraph, that creates directed acyclic graphs for S4 class structures, but I am more interested in the much more common S3 structures.
This seems brute-force and sloppy, but I suppose if I had a list of all the class attributes used by objects in the base packages, and all the class attributes of objects in a package, then any of the latter which is not among the former would be new classes created by the package or inherited from another non-base package.
This is slightly tricky since I am not aware of any formal definition of a S3 class. For R objects the S3 classes are governed by a very simple character vector of class names stored in the class attribute. Method dispatch is then done by matching element(s) of that attribute with a function name.
You could essentially do:
x <- 1:5
class(x) <- "MyMadeUpClass"
x
# [1] 1 2 3 4 5
# attr(,"class")
# [1] "MyMadeUpClass"
Does the above really define a class in the intuitive formal understanding of the term ?
You can create a print method for objects of this class like (silly example incoming):
print.MyMadeUpClass <- function(x, ...) {
print(sprintf("Pretty vector: %s", paste(x, collapse = ",")))
}
x
# [1] "Pretty vector: 1,2,3,4,5"
The important distinction here is that methods in S3
"belong to" (generic) functions, not classes
are chosen based on classes of the arguments provided to the function call
Point I am trying to make is that S3 does not really have a formally defined inheritance (which I assume is what you are looking for), with contrast to S4 which implements this via the contains concept, so I am not really sure what would you like to see as a result.
Very good read on the topic Object-Oriented Programming, Functional
Programming and R by John M. Chambers: https://arxiv.org/pdf/1409.3531.pdf
Edit (after question edit) - the sloop package:
From S3 perspective I think it makes a lot of sense to examine the structure of generics and methods. A found the sloop package to be a very useful tool for this: https://github.com/r-lib/sloop.

Dispatch of constructor in R (S3)

Is it ok from architectural point of view to dispatch the constructor in R (S3 system)?
I have a constructor for class returns and I want to dispatch it in a way like: returns.zoo, returns.data.frame etc.
Just my opinion, but I think there is (unwritten) convention to use as prefix in this case. For example: as.data.frame coerces various objects to a data frame.
Same with as.matrix, as.Date and as.list ...
Often a "non-as" function calls the generic as function (e.g. data.frame function calls as.data.frame).
There is also a good practice to implement a function with is prefix.
For example: is.data.frame, is.list.
But sometimes this is not so. For example formula is a generic "coercer"
and as.formula is not. And there are a lot of packages with combined practice. For example igraph includes as.igraph generic but uses from_data_frame to create object from a data frame.
So I guess as.returns.zoo will look aligned with existing practice but
returns.zoo is not wrong either.

Trying to understand R structure: what does a dot in function names signify?

I am trying to learn how to use R. I can use it to do basic things like reading in data and running a t-test. However, I am struggling to understand the way R is structured (I am have a very mediocre java background).
What I don't understand is the way the functions are classified.
For example in is.na(someVector), is is a class? Or for read.csv, is csv a method of the read class?
I need an easier way to learn the functions than simply memorizing them randomly. I like the idea of things belonging to other things. To me it seems like this gives a language a tree structure which makes learning more efficient.
Thank you
Sorry if this is an obvious question I am genuinely confused and have been reading/watching quite a few tutorials.
Your confusion is entirely understandable, since R mixes two conventions of using (1) . as a general-purpose word separator (as in is.na(), which.min(), update.formula(), data.frame() ...) and (2) . as an indicator of an S3 method, method.class (i.e. foo.bar() would be the "foo" method for objects with class attribute "bar"). This makes functions like summary.data.frame() (i.e., the summary method for objects with class data.frame) especially confusing.
As #thelatemail points out above, there are some other sets of functions that repeat the same prefix for a variety of different options (as in read.table(), read.delim(), read.fwf() ...), but these are entirely conventional, not specified anywhere in the formal language definition.
dotfuns <- apropos("[a-z]\\.[a-z]")
dotstart <- gsub("\\.[a-zA-Z]+","",dotfuns)
head(dotstart)
tt <- table(dotstart)
head(rev(sort(tt)),10)
## as is print Sys file summary dev format all sys
## 118 51 32 18 17 16 16 15 14 13
(Some of these are actually S3 generics, some are not. For example, Sys.*(), dev.*(), and file.*() are not.)
Historically _ was used as a shortcut for the assignment operator <- (before = was available as a synonym), so it wasn't available as a word separator. I don't know offhand why camelCase wasn't adopted instead.
Confusingly, methods("is") returns is.na() among many others, but it is effectively just searching for functions whose names start with "is."; it warns that "function 'is' appears not to be generic"
Rasmus Bååth's presentation on naming conventions is informative and entertaining (if a little bit depressing).
extra credit: are there any dot-separated S3 method names, i.e. cases where a function name of the form x.y.z represents the x.y method for objects with class attribute z ?
answer (from Hadley Wickham in comments): as.data.frame.data.frame() wins. as.data.frame is an S3 generic (unlike, say, as.numeric), and as.data.frame.data.frame is its method for data.frame objects. Its purpose (from ?as.data.frame):
If a data frame is supplied, all classes preceding ‘"data.frame"’
are stripped, and the row names are changed if that argument is
supplied.

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