Dynamically Generate Reference Classes - r

I'm attempting to generate reference classes within an R package on the fly, and it's proving to be fairly difficult. Here are the approaches I've taken and problems I've run into:
I'm creating a package in which I hope to be able to dynamically read in a schema and automatically generate an associated reference class (think SOAP). Of course, this means I won't be able to define my reference classes before-hand in the package sources.
I initially attempted to create a new class using a simple:
myClass <- setRefClass("NewClassName", fields=list(fieldA="character"))
which, of course, works fine when executed interactively, but when included in the package sources, I get a locked binding error. From my reading, it looks like this occurs because when running interactively, the class information is being stored in the global environment, which is not locked, while my package's base environment is locked.
I then found a thread that suggested using something to the effect of:
myClass <- setRefClass("NewClassName", fields=list(fieldA="character"), where=globalenv())
This actually crashed R/Studio when I tried to build the package, so I don't have a log of the error it generated, unfortunately, but it certainly didn't work.
Next I tried creating a new environment within my package which I could use to store these reference classes. So I added a .classEnv <- new.env() line in my package sources (not inside of any function) and then attempted to use this class when creating a new reference class:
myClass <- setRefClass("NewClassName", fields=list(fieldA="character"), where=.classEnv)
This actually seemed to work OK, but generates the following warning:
> myClass <- setRefClass("NewClassName", where=.classEnv)
Warning message:
In getPackageName(where) :
Created a package name, ‘2013-04-23 10:19:14’, when none found
So, for some reason, methods::getPackageName() isn't able to pick up which package my new environment is in?
Is there a way to create my new environment differently so that getPackageName() can properly recognize the package? Can I add some feature which allows me to help getPackageName() detect the package? Will this even work if I can deal with the warning, or am I misusing reference classes by trying to create them dynamically?

To get the conversation going, I found that getpackageName stores the package name in a hidden .packageName variable in the specified environment.
So you can actually get around the warning with
assign(".packageName", "MyPkg", envir=.classEnv)
myClass <- setRefClass("NewClassName", fields=classFields, where=.classEnv)
which resolves the warning, but the documentation says not to trust the .packageName variable indefinitely, and I still feel like I'm hacking this in and may be misunderstanding something important about reference classes and their relationship to environments.
Full details from documentation:
Package names are normally installed during loading of the package, by the INSTALL script or by the library function. (Currently, the name is stored as the object .packageName but don't trust this for the future.)
Edit:
After reading a little further, the setPackageName method may be a more reliable way to set the package name for the environment. Per the docs:
setPackageName can be used to establish a package name in an environment that would otherwise not have one. This allows you to create classes and/or methods in an arbitrary environment, but it is usually preferable to create packages by the standard R programming tools (package.skeleton, etc.)
So it looks like one valid solution would be the following:
setPackageName("MyPkg", .classEnv)
myClass <- setRefClass("NewClassName", fields=classFields, where=.classEnv)
That eliminates the warning message and doesn't rely on anything that's documented as unstable. I'm still not clear why it's necessary, but...

Related

S3 - delayed method registration in R

I have an R-package ('mill') with a 'suggest' ('hlt') where I would like to register a new class for an S3 method in 'hlt'. Because the 'hlt' package is a suggest I followed the advise of Hadley and copied a function in 'mill' that registers my new mill::html.pdf_diff_df() function but not until after loading the 'hlt' package:
https://github.com/r-lib/vctrs/blob/master/R/register-s3.R
The problem I encounter is that when I load 'mill' and call the html() method on the new class ('pdf_diff_df') defined in the 'mill' package I just get the error:
html(df)
Error in html(df) : could not find function "html"
I would, of course, like to give the warning that one should load the 'hlt' package at that point, otherwise the user doesn't understand the problem. Any ideas? The source in 'mill' of this new function is here:
https://github.com/SVA-SE/mill/blob/136f372f88d794bb6149922c24dd9a4f731e4c7e/R/images.R#L195-L206
If I understand the situation correctly, hlt exports html(), but you don't want a fixed dependency on hlt, so you can't import html from hlt.
In that case, simply use a fully qualified call to it, i.e. hlt::html(...).
Since hlt is only in Suggests, this should only be called conditional on requireNamespace('hlt') returning TRUE.
Edited to add:
If you want a user to be able to call html(...), you have to export it from your package. This is hard, because it is exported from the hlt package, and if the two packages export different versions, you'll get warnings about one hiding the other.
As far as I know there isn't a nice solution to this, but really, it's not your problem. If a user hasn't attached hlt, they have no reason to expect html(...) to do anything. If they have, it will just work.
If you really want to help out the user without generating new messages, you could export your own function with a different name, something like
html2 <- function(...) {
if (requireNamespace("hlt"))
hlt::html(...)
else
warning("Suggested package 'hlt' must be installed for this to work.")
}

R Package Build/Install Error: "object not found" even though I have it in R/sysdata.rda

Similar Question
accessing sysdata.rda within package functions
Why This Similar Question Does Not Apply To Me
They were able to actually build it, and apparently it was a Github error for them (not related)
R VERSION
3.4.2 (I tried using 3.4.3 as well but the same problem occurred)
EDIT: I am on Windows 10
Context
I have fully read the following tutorial on R packages and how to include .Rda files in them. I have LazyData in my DESCRIPTION file set as true as well. I have tried both the data/ folder implementation and the R/sysdata.rda implementation using the function devtools::use_data() with the respective options of internal = FALSE and internal = TRUE.
However, when I try to build the package, or use devtools::install (which builds as well I assume), it fails and gives me the following error message:
Error in predict(finalModel, newInput) : object 'finalModel' not found
Where finalModel is stored within my .rda file.
Does anyone know any possible reasons why this might occur?
I also asked a coworker to install the package on his machine, but unfortunately he got the exact same error.
I made another test package as a 'sanity-check' by creating a simple linear model using the lm() function on datasets::swiss, and then made a test package with this newly created model as a .rda file. When I referenced this test model in a function within this test package, it eerily worked, despite the fact that (to the best of my knowledge) I used the exact same steps to create this new R package.
Also, I unfortunately cannot share the code for the package I am creating, but I am willing to share the code for the test package that uses the swiss dataset.
Thank you in advance.
EDIT: My .rda file I am putting in the package was created last year, if that has anything to do with it.
I just solved a similar issue of having object 'objectName' not found that arose during package management. In my case, the issue related to losing the context of variables when working with parallelization.
When using parallel::clusterExport(cl, varlist=c("function-name")), clusterExport looks at .GlobalEnv for variable definitions. This wouldn't come up during debugging, as I always the variables defined in .GlobalEnv. The solution was to state the environment explicitly: parallel::clusterExport(cl, varlist=c("function-name"), envir=environment()). This ensures that the parallel processes have context of the variables within the data/ folder and R/sysdata.rda.
Source
If you have more than one internal file, you must save them together:
usethis::use_data(file_1,
file_2,
file_3,
internal = TRUE,
overwrite = TRUE)

how to use utils::globalVariables

Following your recommendations (or trying to do it, at least), I have tried some options, but the problem remains, so there must be something I am missing.
I have included a more complete code
setwd("C:/naapp")
#' #import utils
#' #import devtools
I have tried with and without using suppressForeignCheck
if(getRversion() >= "2.15.1"){
utils::globalVariables(c("eleven"))
utils::suppressForeignCheck(c("eleven"))
}
myFunctionSum <- function(X){print(X+eleven)}
myFunctionMul <- function(X){print(X*eleven)}
myFunction11 <- function(X){
assign("eleven",11,envir=environment(myFunctionMul))
}
maybe I should use a particular environment?
package.skeleton(name = "myPack11", list=ls(),
path = "C:/naapp", force = TRUE,
code_files = character())
I remove the "man" directory from the directory myPack11,
otherwise I would get an error because the help files are empty.
I add the imports utils, and devtools to the descrption
Then I run check
devtools::check("myPack11")
And I still get this note
#checking R code for possible problems ... NOTE
#myFunctionMul: no visible binding for global variable 'eleven'
#myFunctionSum: no visible binding for global variable 'eleven'
#Undefined global functions or variables:eleven
I have tried also to make an enviroment, combining Tomas Kalibera's suggetion and an example I found in the Internet.
myEnvir <- new.env()
myEnvir$eleven <- 11
etc
In this case, I get the same note, but with "myEnvir", instead of "eleven"
First version of the question
I trying to use "globalVariables" from the package utils. I am building an interface in R and I am planning to submit to CRAN. This is my first time, so, sorry if the question is very basic.I have read the help and I have tried to find examples, but I still don't know how to use it.
I have made a little silly example to ilustrate my question, which is:
Where do I have to place this line exactly?:
if(getRversion() >= "2.15.1"){utils::globalVariables("eleven")}
My example has three functions. myFunction11 creates the global variable "eleven" and the other two functions manipulate it. In my real code, I cannot use arguments in the functions that are called by means of a button. Consider that this is just a silly example to learn how to use globalVariables (to avoid binding notes).
myFunction11 <- function(){
assign("eleven",11,envir=environment(myFunctionSum))
}
myFunctionSum <- function(X){
print(X+eleven)
}
myFunctionMul <- function(X){
print(X*eleven)
}
Thank you in advance
I thought that the file globals.R would be automatically generated when using globalsVariables. The problem was that I needed to create the package skeleton, then create the file globals.R, add it to the R directory in the package and check the package.
So, I needed to place this in a different file:
#' #import utils
utils::globalVariables(c("eleven"))
and save it
The documentation clearly says:
## In the same source file (to remind you that you did it) add:
if(getRversion() >= "2.15.1") utils::globalVariables(c(".obj1", "obj2"))
so put it in the same source file as your functions. It can go in any of your R source files, but the comment above recommends you put it close to your code. Looking at a bunch of github packages reveals another common pattern is to have a globals.R function with it in, but this is probably a bad idea. If you later remove the global from your package but neglect to update globals.R you could mask a problem. Putting it right close to the functions that use it will hopefully remind you when you edit those functions.
Make sure you put it outside any function definitions in the file, or it won't get seen.
You cannot modify bindings in a package namespace once the package is loaded (and namespace sealed, and bindings locked). The check tool helps you to spot violations of this restriction, so you find out about the problem when checking the package rather than while running it. globalVariables is just a call to silence check when looking for these violations, which is undesirable in almost all cases. If you really need mutable state in a package, you can create a new environment (using new.env) and bind it to an (unexported) "global" variable in your namespace. This binding will be locked, but this is ok, because in R you can change an environment in place (add/remove elements, effectively modifying the elements).
The best situation is however when you can keep all mutable state in user objects (passed in as arguments into functions, and their modified versions returned as output values of functions).

How to check for accidentally redefining a function name in R

I'm writing some fairly involved R code spread across multiple files and collected together into a package. A problem I've run into on occasion is that I will define a utility function in one file that has the same name as another utility function defined in another file. One of the two definitions gets replaced, leading to unintended behavior. Is there any sort of tool to check for this kind of accidental redefinition? Something that would check that no two top-level assignments foo <- ... in the package assign to the same name?
As pointed out in the comments, the right way to do this is to use packages. Packages give functions their own namespaces automatically, plus they make it very easy to reuse and share code. If you're using RStudio, you can create one with very little effort from the New Project menu.
However, if you can't use packages or namespaces for some reason, there's still a way to do what you want: you can lock a variable (including a function) so that it's not possible to overwrite it.
> pin <- 11
> lockBinding("pin", .GlobalEnv)
> pin <- 12
Error: cannot change value of locked binding for 'pin'
See Binding and Environment Locking for details.

R package namespace issue using data() -- data set not found

I've hit an issue trying to import a package (namely, 'robfilter') inside one of my own packages. One of its methods that I am trying to use, adore.filter, is failing at this line:
data(critvals)
With error 'data set 'critvals' not found'.
The function works fine if I load the library via require(robfilter). However, this means that in order to use my custom package which calls adore.filter, I will have to load my own package, and then load robfilter. Not a huge problem but slightly annoying.
I'm not sure if the problem is that there is an extra step I need to do in order to make critvals visible within my package, or if perhaps there is something the package author needed to do (and hasn't done) to add critvals to its package namespace; there is no sign of 'critvals' in the robfilter NAMESPACE file. I haven't encountered this issue before and don't really understand how the use of data() inside a package is supposed to work.
There are two solutions as far as I know:
Either ask the robfilter Maintainer to put the data needed by robfiler in the internal data file of robfilter. (R/sysdata.rda)
Or make your package Depends on robfilter
So it works if you put robfilter in the depends section of your description file. But in my case (both are my packages), I was trying to avoid the Depends solution as it loads the imported package and also any other package will need to depend ont its imported package... See my question is quite a duplicate of yours but not in the same context.

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