If I type methods(print) I get a long list of methods.
For an object of class data.frame, print.data.frame will be called.
It's not always so simple though:
hw <- "hello world"
class(hw) # [1] "character"
There is no print.character method. How do I know which method is called when executing print(hw)?
Turn debugging on for print and then run your example:
> debug(print)
> print("hello")
debugging in: print("hello")
debug: UseMethod("print")
Browse[2]> <---------------------------- press Enter to step forward
debugging in: print.default("hello") <-- this is the method that gets called
debug: {
noOpt <- missing(digits) && missing(quote) && missing(na.print) &&
missing(print.gap) && missing(right) && missing(max) &&
missing(useSource) && missing(...)
.Internal(print.default(x, digits, quote, na.print, print.gap,
right, max, useSource, noOpt))
}
Have you read Hadley's Advanced R and the chapter on objects? It might not give you the whole answer, but fundamentally what you're experiencing is the difference between method dispatch in C and regular S3 behaviour.
[ isn't really an R function, it's a C function and the decision of what method to use is done in C. That doesn't mean you can't create an S3 method for [ (or sum, +, [<- and other .Primitive functions), but when you do it's more like you're making a wrapper/preprocess for the C function, which R will dispatch, before the ultimate decision is made by the C function based on classes defined separate from your regular (and extensible) R classes.
Or at least that's how I've understood it.
Related
I was wondering if it is possible to use Reference Classes to subclass a function in R. For instance, the following
> CustomFunction <- setRefClass("CustomFunction", contains = "function")
> foo <- CustomFunction()
> foo()
NULL
works OK (does not throw an error), but how can I customise the behaviour (i.e. other than returning NULL)? How can I define function arguments?
I also tried
> setMethod("(",
> signature(x = "CustomFunction"),
> function(...) {
> "Hello!" # A function that always returns "Hello!"
> }
> )
Error in genericForPrimitive(f) :
methods may not be defined for primitive function ‘(’ in this version of R
but that doesn't seem to work.
I was hoping that being able to subclass functions means that I could implement custom behaviour before and after function calls. E.g. to have functions that automatically logs the call expression each time it is called (for audit purposes), or to create functions that automatically throws an error if NULL is returned etc etc.
You don't need Reference Classes for this, you can just enclose the function of interest
logger <- function(f) {
force(f)
function(...) {
print("running function...")
f(...)
}
}
printhello <- function(name="Al") print(paste("hello", name))
printhello_logged <- logger(printhello)
printhello()
# [1] "hello Al"
printhello_logged("Zed")
# [1] "running function..."
# [1] "hello Zed"
If this is for auditing/testing type purposes, you might be interested in trace() which allows you to attach code to various parts of functions.
Is there an R function that lists all the functions in an R script file along with their arguments?
i.e. an output of the form:
func1(var1, var2)
func2(var4, var10)
.
.
.
func10(varA, varB)
Using [sys.]source has the very undesirable side-effect of executing the source inside the file. At the worst this has security problems, but even “benign” code may simply have unintended side-effects when executed. At best it just takes unnecessary time (and potentially a lot).
It’s actually unnecessary to execute the code, though: it is enough to parse it, and then do some syntactical analysis.
The actual code is trivial:
file_parsed = parse(filename)
functions = Filter(is_function, file_parsed)
function_names = unlist(Map(function_name, functions))
And there you go, function_names contains a vector of function names. Extending this to also list the function arguments is left as an exercise to the reader. Hint: there are two approaches. One is to eval the function definition (now that we know it’s a function definition, this is safe); the other is to “cheat” and just get the list of arguments to the function call.
The implementation of the functions used above is also not particularly hard. There’s probably even something already in R core packages (‘utils’ has a lot of stuff) but since I’m not very familiar with this, I’ve just written them myself:
is_function = function (expr) {
if (! is_assign(expr)) return(FALSE)
value = expr[[3L]]
is.call(value) && as.character(value[[1L]]) == 'function'
}
function_name = function (expr) {
as.character(expr[[2L]])
}
is_assign = function (expr) {
is.call(expr) && as.character(expr[[1L]]) %in% c('=', '<-', 'assign')
}
This correctly recognises function declarations of the forms
f = function (…) …
f <- function (…) …
assign('f', function (…) …)
It won’t work for more complex code, since assignments can be arbitrarily complex and in general are only resolvable by actually executing the code. However, the three forms above probably account for ≫ 99% of all named function definitions in practice.
UPDATE: Please refer to the answer by #Konrad Rudolph instead
You can create a new environment, source your file in that environment and then list the functions in it using lsf.str() e.g.
test.env <- new.env()
sys.source("myfile.R", envir = test.env)
lsf.str(envir=test.env)
rm(test.env)
or if you want to wrap it as a function:
listFunctions <- function(filename) {
temp.env <- new.env()
sys.source(filename, envir = temp.env)
functions <- lsf.str(envir=temp.env)
rm(temp.env)
return(functions)
}
In my continuing quest to avoid using parentheses for some simple commands, I wrote up the following operator to create a new graphics window. My question is: am I at risk of "breaking" anything in R, other than the obvious inability to execute the "not" function on my variable "newdev"?
# function to overload "!" for one purpose only
#this is adapted from the sos package code for "???", credited to Duncan Murdoch.
# Example of how to create a specialized unary operator that doesn't require
# parentheses for its argument. So far as I can tell,
#the only way to do this is to overload an existing function or
# operator which doesn't require parentheses. "?" and "!" meet this requirement.
`!` <- function (e1, e2) {
call <- match.call()
# match.call breaks out each callable function in argument list (which was "??foo" for the sos package "???",
# which allows topicExpr1 to become a list variable w/ callable function "!" (or "?" in sos)
original <- function() {
call[[1]]<-quote(base::`!`)
return(eval(call, parent.frame(2)))
}
# this does preclude my ever having an actual
# variable called "newdev" (or at least trying to create the actual NOT of it)
if(call[[2]] =='newdev') {
windows(4.5,4.5,restoreConsole=T)
}else{
return(original()) # do what "!" is supposed to do
}
}
I executed "!" = function(a){stop("'NOT' is used")} and executed the replications function, which uses the ! operator, and this worked fine. So it looks like it is safe to override "!".
Still you probably want to use classes, which you can do as follows:
# Create your object and set the class
A = 42
class(A) = c("my_class")
# override ! for my_class
"!.my_class" = function(v){
cat("Do wathever you want here. Argument =",v,"\n")
}
# Test ! on A
!A
with
makeActiveBinding
you can replace ls() by e.g LS w/o need of unary operators
In my R development I need to wrap function primitives in proto objects so that a number of arguments can be automatically passed to the functions when the $perform() method of the object is invoked. The function invocation internally happens via do.call(). All is well, except when the function attempts to access variables from the closure within which it is defined. In that case, the function cannot resolve the names.
Here is the smallest example I have found that reproduces the behavior:
library(proto)
make_command <- function(operation) {
proto(
func = operation,
perform = function(., ...) {
func <- with(., func) # unbinds proto method
do.call(func, list(), envir=environment(operation))
}
)
}
test_case <- function() {
result <- 100
make_command(function() result)$perform()
}
# Will generate error:
# Error in function () : object 'result' not found
test_case()
I have a reproducible testthat test that also outputs a lot of diagnostic output. The diagnostic output has me stumped. By looking up the parent environment chain, my diagnostic code, which lives inside the function, finds and prints the very same variable the function fails to find. See this gist..
How can the environment for do.call be set up correctly?
This was the final answer after an offline discussion with the poster:
make_command <- function(operation) {
proto(perform = function(.) operation())
}
I think the issue here is clearer and easier to explore if you:
Replace the anonymous function within make_command() with a named one.
Make that function open a browser() (instead of trying to get result). That way you can look around to see where you are and what's going on.
Try this, which should clarify the cause of your problem:
test_case <- function() {
result <- 100
myFun <- function() browser()
make_command(myFun)$perform()
}
test_case()
## Then from within the browser:
#
parent.env(environment())
# <environment: 0x0d8de854>
# attr(,"class")
# [1] "proto" "environment"
get("result", parent.env(environment()))
# Error in get("result", parent.env(environment())) :
# object 'result' not found
#
parent.frame()
# <environment: 0x0d8ddfc0>
get("result", parent.frame()) ## (This works, so points towards a solution.)
# [1] 100
Here's the problem. Although you think you're evaluating myFun(), whose environment is the evaluation frame of test_case(), your call to do.call(func, ...) is really evaluating func(), whose environment is the proto environment within which it was defined. After looking for and not finding result in its own frame, the call to func() follows the rules of lexical scoping, and next looks in the proto environment. Neither it nor its parent environment contains an object named result, resulting in the error message you received.
If this doesn't immediately make sense, you can keep poking around within the browser. Here are a few further calls you might find helpful:
environment(get("myFun", parent.frame()))
ls(environment(get("myFun", parent.frame())))
environment(get("func", parent.env(environment())))
ls(environment(get("func", parent.env(environment()))))
I was just wondering if there was a way to force a function to only accept certain data types, without having to check for it within the function; or, is this not possible because R's type-checking is done at runtime (as opposed to those programming languages, such as Java, where type-checking is done during compilation)?
For example, in Java, you have to specify a data type:
class t2 {
public int addone (int n) {
return n+1;
}
}
In R, a similar function might be
addone <- function(n)
{
return(n+1)
}
but if a vector is supplied, a vector will (obviously) be returned. If you only want a single integer to be accepted, then is the only way to do to have a condition within the function, along the lines of
addone <- function(n)
{
if(is.vector(n) && length(n)==1)
{
return(n+1)
} else
{
return ("You must enter a single integer")
}
}
Thanks,
Chris
This is entirely possible using S3 classes. Your example is somewhat contrived in the context or R, since I can't think of a practical reason why one would want to create a class of a single value. Nonetheless, this is possible. As an added bonus, I demonstrate how the function addone can be used to add the value of one to numeric vectors (trivial) and character vectors (so A turns to B, etc.):
Start by creating a generic S3 method for addone, utlising the S3 despatch mechanism UseMethod:
addone <- function(x){
UseMethod("addone", x)
}
Next, create the contrived class single, defined as the first element of whatever is passed to it:
as.single <- function(x){
ret <- unlist(x)[1]
class(ret) <- "single"
ret
}
Now create methods to handle the various classes. The default method will be called unless a specific class is defined:
addone.default <- function(x) x + 1
addone.character <- function(x)rawToChar(as.raw(as.numeric(charToRaw(x))+1))
addone.single <- function(x)x + 1
Finally, test it with some sample data:
addone(1:5)
[1] 2 3 4 5 6
addone(as.single(1:5))
[1] 2
attr(,"class")
[1] "single"
addone("abc")
[1] "bcd"
Some additional information:
Hadley's devtools wiki is a valuable source of information on all things, including the S3 object system.
The S3 method doesn't provide strict typing. It can quite easily be abused. For stricter object orientation, have a look at S4 classes, reference based classesor the proto package for Prototype object-based programming.
You could write a wrapper like the following:
check.types = function(classes, func) {
n = as.name
params = formals(func)
param.names = lapply(names(params), n)
handler = function() { }
formals(handler) = params
checks = lapply(seq_along(param.names), function(I) {
as.call(list(n('assert.class'), param.names[[I]], classes[[I]]))
})
body(handler) = as.call(c(
list(n('{')),
checks,
list(as.call(list(n('<-'), n('.func'), func))),
list(as.call(c(list(n('.func')), lapply(param.names, as.name))))
))
handler
}
assert.class = function(x, cls) {
stopifnot(cls %in% class(x))
}
And use it like
f = check.types(c('numeric', 'numeric'), function(x, y) {
x + y
})
> f(1, 2)
[1] 3
> f("1", "2")
Error: cls %in% class(x) is not TRUE
Made somewhat inconvenient by R not having decorators. This is kind of hacky
and it suffers from some serious problems:
You lose lazy evaluation, because you must evaluate an argument to determine
its type.
You still can't check the types until call time; real static type checking
lets you check the types even of a call that never actually happens.
Since R uses lazy evaluation, (2) might make type checking not very useful,
because the call might not actually occur until very late, or never.
The answer to (2) would be to add static type information. You could probably
do this by transforming expressions, but I don't think you want to go there.
I've found stopifnot() to be highly useful for these situations as well.
x <- function(n) {
stopifnot(is.vector(n) && length(n)==1)
print(n)
}
The reason it is so useful is because it provides a pretty clear error message to the user if the condition is false.