I'm not that much of an OOP guy, so could someone please explain this simple concept in layman terms.
When I call foo.child from the foo.parent function, it seems to pass the A=7 argument
down into the foo.child object and overrides or takes precedence over the A=3 default argument in foo.child as I would expect.
foo.parent <- function(A=7) foo.child(A)
foo.child <- function(A=3) 2+A
foo.parent(A=7)
#[1] 9
But when I instantiate it inside of foo.parent, the parameter A=7 does pass down or force the instantiated object to use A=7; instead it uses the child object's parameter of A=3
foo.child<-function(A=3) 2+A
foo.parent <- function(A=7){
foo.child(A=3)
}
foo.parent(A=7)
#[1] 5
Why does that happen? And what terminology would I use to describe the differences?
In your second example you do not give a value to A (At least not in such a way as you might thought). Try
foo.child<-function(A=3) 2+A
foo.parent<-function(A=7){
foo.child(A=A)
}
foo.parent(A=7)
foo.parent()
instead. So, you mix here two different As. The =sign within a function call defines, what happens if you do not give a value for that variable in the function call.
I think your problem is you don't quite understand how default arguments work. So
foo.child = function(A=1) 2+A
defines the function foo.child that has a default argument A=1. So,
foo.child()
gives 3. Now in this function
foo.parent = function(A=3){
foo.child(A=4)
}
you are always passing the value A=4 to the function foo.child, hence,
foo.parent(A=7)
# 6
Also, when you are trying to figure out what is happening, it's helpful to have different values of A
Related
I'm just getting my feet wet in R and was surprised to see that a function doesn't modify an object, at least it seems that's the default. For example, I wrote a function just to stick an asterisk on one label in a table; it works inside the function but the table itself is not changed. (I'm coming mainly from Ruby)
So, what is the normal, accepted way to use functions to change objects in R? How would I add an asterisk to the table title?
Replace the whole object: myTable = title.asterisk(myTable)
Use a work-around to call by reference (as described, for example, in Call by reference in R by TszKin Julian?
Use some structure other than a function? An object method?
The reason you're having trouble is the fact that you are passing the object into the local namespace of the function. This is one of the great / terrible things about R: it allows implicit variable declarations and then implements supercedence as the namespaces become deeper.
This is affecting you because a function creates a new namespace within the current namespace. The object 'myTable' was, I assume, originally created in the global namespace, but when it is passed into the function 'title.asterisk' a new function-local namespace now has an object with the same properties. This works like so:
title.asterisk <- function(myTable){ do some stuff to 'myTable' }
In this case, the function 'title.asterisk' does not make any changes to the global object 'myTable'. Instead, a local object is created with the same name, so the local object supercedes the global object. If we call the function title.asterisk(myTable) in this way, the function makes changes only to the local variable.
There are two direct ways to modify the global object (and many indirect ways).
Option 1: The first, as you mention, is to have the function return the object and overwrite the global object, like so:
title.asterisk <- function(myTable){
do some stuff to 'myTable'
return(myTable)
}
myTable <- title.asterisk(myTable)
This is okay, but you are still making your code a little difficult to understand, since there are really two different 'myTable' objects, one global and one local to the function. A lot of coders clear this up by adding a period '.' in front of variable arguments, like so:
title.asterisk <- function(.myTable){
do some stuff to '.myTable'
return(.myTable)
}
myTable <- title.asterisk(myTable)
Okay, now we have a visual cue that the two variables are different. This is good, because we don't want to rely on invisible things like namespace supercedence when we're trying to debug our code later. It just makes things harder than they have to be.
Option 2: You could just modify the object from within the function. This is the better option when you want to do destructive edits to an object and don't want memory inflation. If you are doing destructive edits, you don't need to save an original copy. Also, if your object is suitably large, you don't want to be copying it when you don't have to. To make edits to a global namespace object, simply don't pass it into or declare it from within the function.
title.asterisk <- function(){ do some stuff to 'myTable' }
Now we are making direct edits to the object 'myTable' from within the function. The fact that we aren't passing the object makes our function look to higher levels of namespace to try and resolve the variable name. Lo, and behold, it finds a 'myTable' object higher up! The code in the function makes the changes to the object.
A note to consider: I hate debugging. I mean I really hate debugging. This means a few things for me in R:
I wrap almost everything in a function. As I write my code, as soon as I get a piece working, I wrap it in a function and set it aside. I make heavy use of the '.' prefix for all my function arguments and use no prefix for anything that is native to the namespace it exists in.
I try not to modify global objects from within functions. I don't like where this leads. If an object needs to be modified, I modify it from within the function that declared it. This often means I have layers of functions calling functions, but it makes my work both modular and easy to understand.
I comment all of my code, explaining what each line or block is intended to do. It may seem a bit unrelated, but I find that these three things go together for me. Once you start wrapping coding in functions, you will find yourself wanting to reuse more of your old code. That's where good commenting comes in. For me, it's a necessary piece.
The two paradigms are replacing the whole object, as you indicate, or writing 'replacement' functions such as
`updt<-` <- function(x, ..., value) {
## x is the object to be manipulated, value the object to be assigned
x$lbl <- paste0(x$lbl, value)
x
}
with
> d <- data.frame(x=1:5, lbl=letters[1:5])
> d
x lbl
1 1 a
2 2 b
3 3 c
> updt(d) <- "*"
> d
x lbl
1 1 a*
2 2 b*
3 3 c*
This is the behavior of, for instance, $<- -- in-place update the element accessed by $. Here is a related question. One could think of replacement functions as syntactic sugar for
updt1 <- function(x, ..., value) {
x$lbl <- paste0(x$lbl, value)
x
}
d <- updt1(d, value="*")
but the label 'syntactic sugar' doesn't really do justice, in my mind, to the central paradigm that is involved. It is enabling convenient in-place updates, which is different from the copy-on-change illusion that R usually maintains, and it is really the 'R' way of updating objects (rather than using ?ReferenceClasses, for instance, which have more of the feel of other languages but will surprise R users expecting copy-on-change semantics).
For anybody in the future looking for a simple way (do not know if it is the more appropriate one) to get this solved:
Inside the function create the object to temporally save the modified version of the one you want to change. Use deparse(substitute()) to get the name of the variable that has been passed to the function argument and then use assign() to overwrite your object. You will need to use envir = parent.frame() inside assign() to let your object be defined in the environment outside the function.
(MyTable <- 1:10)
[1] 1 2 3 4 5 6 7 8 9 10
title.asterisk <- function(table) {
tmp.table <- paste0(table, "*")
name <- deparse(substitute(table))
assign(name, tmp.table, envir = parent.frame())
}
(title.asterisk(MyTable))
[1] "1*" "2*" "3*" "4*" "5*" "6*" "7*" "8*" "9*" "10*"
Using parentheses when defining an object is a little more efficient (and to me, better looking) than defining then printing.
I'm just getting my feet wet in R and was surprised to see that a function doesn't modify an object, at least it seems that's the default. For example, I wrote a function just to stick an asterisk on one label in a table; it works inside the function but the table itself is not changed. (I'm coming mainly from Ruby)
So, what is the normal, accepted way to use functions to change objects in R? How would I add an asterisk to the table title?
Replace the whole object: myTable = title.asterisk(myTable)
Use a work-around to call by reference (as described, for example, in Call by reference in R by TszKin Julian?
Use some structure other than a function? An object method?
The reason you're having trouble is the fact that you are passing the object into the local namespace of the function. This is one of the great / terrible things about R: it allows implicit variable declarations and then implements supercedence as the namespaces become deeper.
This is affecting you because a function creates a new namespace within the current namespace. The object 'myTable' was, I assume, originally created in the global namespace, but when it is passed into the function 'title.asterisk' a new function-local namespace now has an object with the same properties. This works like so:
title.asterisk <- function(myTable){ do some stuff to 'myTable' }
In this case, the function 'title.asterisk' does not make any changes to the global object 'myTable'. Instead, a local object is created with the same name, so the local object supercedes the global object. If we call the function title.asterisk(myTable) in this way, the function makes changes only to the local variable.
There are two direct ways to modify the global object (and many indirect ways).
Option 1: The first, as you mention, is to have the function return the object and overwrite the global object, like so:
title.asterisk <- function(myTable){
do some stuff to 'myTable'
return(myTable)
}
myTable <- title.asterisk(myTable)
This is okay, but you are still making your code a little difficult to understand, since there are really two different 'myTable' objects, one global and one local to the function. A lot of coders clear this up by adding a period '.' in front of variable arguments, like so:
title.asterisk <- function(.myTable){
do some stuff to '.myTable'
return(.myTable)
}
myTable <- title.asterisk(myTable)
Okay, now we have a visual cue that the two variables are different. This is good, because we don't want to rely on invisible things like namespace supercedence when we're trying to debug our code later. It just makes things harder than they have to be.
Option 2: You could just modify the object from within the function. This is the better option when you want to do destructive edits to an object and don't want memory inflation. If you are doing destructive edits, you don't need to save an original copy. Also, if your object is suitably large, you don't want to be copying it when you don't have to. To make edits to a global namespace object, simply don't pass it into or declare it from within the function.
title.asterisk <- function(){ do some stuff to 'myTable' }
Now we are making direct edits to the object 'myTable' from within the function. The fact that we aren't passing the object makes our function look to higher levels of namespace to try and resolve the variable name. Lo, and behold, it finds a 'myTable' object higher up! The code in the function makes the changes to the object.
A note to consider: I hate debugging. I mean I really hate debugging. This means a few things for me in R:
I wrap almost everything in a function. As I write my code, as soon as I get a piece working, I wrap it in a function and set it aside. I make heavy use of the '.' prefix for all my function arguments and use no prefix for anything that is native to the namespace it exists in.
I try not to modify global objects from within functions. I don't like where this leads. If an object needs to be modified, I modify it from within the function that declared it. This often means I have layers of functions calling functions, but it makes my work both modular and easy to understand.
I comment all of my code, explaining what each line or block is intended to do. It may seem a bit unrelated, but I find that these three things go together for me. Once you start wrapping coding in functions, you will find yourself wanting to reuse more of your old code. That's where good commenting comes in. For me, it's a necessary piece.
The two paradigms are replacing the whole object, as you indicate, or writing 'replacement' functions such as
`updt<-` <- function(x, ..., value) {
## x is the object to be manipulated, value the object to be assigned
x$lbl <- paste0(x$lbl, value)
x
}
with
> d <- data.frame(x=1:5, lbl=letters[1:5])
> d
x lbl
1 1 a
2 2 b
3 3 c
> updt(d) <- "*"
> d
x lbl
1 1 a*
2 2 b*
3 3 c*
This is the behavior of, for instance, $<- -- in-place update the element accessed by $. Here is a related question. One could think of replacement functions as syntactic sugar for
updt1 <- function(x, ..., value) {
x$lbl <- paste0(x$lbl, value)
x
}
d <- updt1(d, value="*")
but the label 'syntactic sugar' doesn't really do justice, in my mind, to the central paradigm that is involved. It is enabling convenient in-place updates, which is different from the copy-on-change illusion that R usually maintains, and it is really the 'R' way of updating objects (rather than using ?ReferenceClasses, for instance, which have more of the feel of other languages but will surprise R users expecting copy-on-change semantics).
For anybody in the future looking for a simple way (do not know if it is the more appropriate one) to get this solved:
Inside the function create the object to temporally save the modified version of the one you want to change. Use deparse(substitute()) to get the name of the variable that has been passed to the function argument and then use assign() to overwrite your object. You will need to use envir = parent.frame() inside assign() to let your object be defined in the environment outside the function.
(MyTable <- 1:10)
[1] 1 2 3 4 5 6 7 8 9 10
title.asterisk <- function(table) {
tmp.table <- paste0(table, "*")
name <- deparse(substitute(table))
assign(name, tmp.table, envir = parent.frame())
}
(title.asterisk(MyTable))
[1] "1*" "2*" "3*" "4*" "5*" "6*" "7*" "8*" "9*" "10*"
Using parentheses when defining an object is a little more efficient (and to me, better looking) than defining then printing.
Suppose, one forgets to set validity in an s4 class definition, how could one "append" this argument without rewriting the entire class.
Illustration:
setClass("test",slots = c(Taken="numeric",Data="data.frame"))
x<-new("test",Taken=123,Data=data.frame(GPA=0.02,Score=0.01))
Now, suppose I would like to check validity:
validity.test<-function(object){
if(!all(sapply(object#Data,is.numeric))){
print("Data must be all numeric")
} else print(TRUE)
}
I could just call validity.test(x).
However, how do I set it to test without rewriting test?!
We are working on the assumption that someone is new(like me) to s4 and is therefore likely to forget doing this. If the script is so many lines, this can easily get tiresome.
Thanks in advance!
There is a function to do exactly this; it is called setValidity. Just call it with the name of your class and the desired validity checking function.
Folks -
I'm going to keep my code here brief, as I think to those more familiar with R, it will be obvious. I am trying to use a function (not my own) that requires I feed it a list of named lists of parameters. I am having trouble naming the lists via a function I wrote to create each list element. Here is my function:
# for invoking grts
stratumdesign<- function(ns, points, oversamp) {
stratumname<-as.character(ns)
print("from function")
print(stratumname)
designlist<-list(ns=c(panel=points, seltype="Equal", over=oversamp))
return(designlist)
}
.. I have tried both having the function call have ns be the integer it is in the originating code, or be passed as a character. Neither work. What I'm illustrating here to myself w/in the function is that ns gets properly passed to the function, but the resulting list returned is always named "$ns" when I want it to be the value passed AS ns! What on Earth am I doing wrong, here?
Since this deserves an actual answer, not just a comment...
Try something more like this:
stratumdesign<- function(ns, points, oversamp) {
print("from function")
print(stratumname)
designlist<-list(c(panel=points, seltype="Equal", over=oversamp))
names(designlist) <- as.character(ns)
return(designlist)
}
I'd like to give a params argument to a function and then attach it so that I can use a instead of params$a everytime I refer to the list element a.
run.simulation<-function(model,params){
attach(params)
#
# Use elements of params as parameters in a simulation
detach(params)
}
Is there a problem with this? If I have defined a global variable named c and have also defined an element named c of the list "params" , whose value would be used after the attach command?
Noah has already pointed out that using attach is a bad idea, even though you see it in some examples and books. There is a way around. You can use "local attach" that's called with. In Noah's dummy example, this would look like
with(params, print(a))
which will yield identical result, but is tidier.
Another possibility is:
run.simulation <- function(model, params){
# Assume params is a list of parameters from
# "params <- list(name1=value1, name2=value2, etc.)"
for (v in 1:length(params)) assign(names(params)[v], params[[v]])
# Use elements of params as parameters in a simulation
}
Easiest way to solve scope problems like this is usually to try something simple out:
a = 1
params = c()
params$a = 2
myfun <- function(params) {
attach(params)
print(a)
detach(params)
}
myfun(params)
The following object(s) are masked _by_ .GlobalEnv:
a
# [1] 1
As you can see, R is picking up the global attribute a here.
It's almost always a good idea to avoid using attach and detach wherever possible -- scope ends up being tricky to handle (incidentally, it's also best to avoid naming variables c -- R will often figure out what you're referring to, but there are so many other letters out there, why risk it?). In addition, I find code using attach/detach almost impossible to decipher.
Jean-Luc's answer helped me immensely for a case that I had a data.frame Dat instead of the list as specified in the OP:
for (v in 1:ncol(Dat)) assign(names(Dat)[v], Dat[,v])