For small function, it is trivial to just write conditional statement based on the argument value. For example, I have a function that extracts variable label from an ex-STATA dataframe. There are two options for output-type, environment and df.
f_extract_stata_label <- function(df, output="environment") {
if (output=="env") {
lab_env <- new.env()
for (i in seq_along(names(df))) {
lab_env[[names(df)[i]]] <- attr(df, "var.labels")[i]
}
return(lab_env)
} else if (output=="df") {
lab_df <- data.frame(var.name = names(d_tmp),
var.label = attr(d_tmp, "var.labels"))
return(lab_df)
}
}
However, I suspect that this is not good R idiom. First, how the function depends on output is not clear -- the reader has to read half way through the code to find out. Second, adding options to output in the future makes the function very hard to read.
So how should I rewrite this function?
R uses this kind of pattern in its core stats libraries where "label" strings make sense. These are functions where R's dispatch system is not that useful. That said, what you want is still dispatch-like.
You could refactor it to use a switch that calls a function dedicated to a specific output type. Two things happen then. First, the extra function call makes it clear what context you're in when using the traceback. Second, it makes the functions smaller and easier to read.
I would question whether you really want to use a dispatch function though, and why separate direct functions are not appropriate.
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$lbl, value)
x
}
d <- updt1(d, value="*")
but the label 'syntactic sugar' doesn't really do justice, in my mind, to the central paradigm that is involved. It is enabling convenient in-place updates, which is different from the copy-on-change illusion that R usually maintains, and it is really the 'R' way of updating objects (rather than using ?ReferenceClasses, for instance, which have more of the feel of other languages but will surprise R users expecting copy-on-change semantics).
For anybody in the future looking for a simple way (do not know if it is the more appropriate one) to get this solved:
Inside the function create the object to temporally save the modified version of the one you want to change. Use deparse(substitute()) to get the name of the variable that has been passed to the function argument and then use assign() to overwrite your object. You will need to use envir = parent.frame() inside assign() to let your object be defined in the environment outside the function.
(MyTable <- 1:10)
[1] 1 2 3 4 5 6 7 8 9 10
title.asterisk <- function(table) {
tmp.table <- paste0(table, "*")
name <- deparse(substitute(table))
assign(name, tmp.table, envir = parent.frame())
}
(title.asterisk(MyTable))
[1] "1*" "2*" "3*" "4*" "5*" "6*" "7*" "8*" "9*" "10*"
Using parentheses when defining an object is a little more efficient (and to me, better looking) than defining then printing.
I have a command with six lines that I want to use several times. Therfore, I want to assign a name to this command and use it as a procedure instead of writing the whole command lines over and over.
In this case it is a <-rbind() command, but the issue is also more general.
modelcoeff<-rbind(modelcoeff,c(as.character((summary(mymodel)$terms[[2]])[[3]]),
as.character((((((summary(mymodel)$terms[[2]])[[2]])[[3]])[[3]])[[2]])[[3]]),
summary(mymodel)$coefficients[2,1],
summary(mymodel)$coefficients[2,4],
summary(mymodel)$coefficients[2,2],
summary(mymodel)$r.squared*100))
I would like to call something like rbindmodelcoeff and execute these command lines. How can I achieve this?
I tried to write a function, but it didn't seem to be the right approach.
A literal wrapping of your code into a function:
rbindmodelcoeff <- function(modelcoeff, mymodel) {
rbind(modelcoeff,
c(as.character((summary(mymodel)$terms[[2]])[[3]]),
as.character((((((summary(mymodel)$terms[[2]])[[2]])[[3]])[[3]])[[2]])[[3]]),
summary(mymodel)$coefficients[2,1],
summary(mymodel)$coefficients[2,4],
summary(mymodel)$coefficients[2,2],
summary(mymodel)$r.squared*100))
}
However, there are a couple changes I recommend:
call summary(mymodel) once, then re-use the results
you are using as.character on some of the objects but not all within the enclosing c(.), so everything is being converted to a character; to see what I mean, try c(as.character(1), 2); we can use a list instead to preserve string-vs-number
rbindmodelcoeff <- function(modelcoeff, mymodel) {
summ <- summary(mymodel)
rbind(modelcoeff,
list(as.character((summ$terms[[2]])[[3]]),
as.character((((((summ$terms[[2]])[[2]])[[3]])[[3]])[[2]])[[3]]),
summ$coefficients[2,1],
summ$coefficients[2,4],
summ$coefficients[2,2],
summ$r.squared*100))
}
But there are still some problems with this. I can't get it to work at the moment since I don't know the model parameters you're using, so as.character((summ$terms[[2]])[[3]]) for me will fail. With that, I'm always hesitant to hard-code so many brackets without a firm understanding of what is being used. It's out of scope for this question (which is being converting your basic code into a function), but you might want to find out how to generalize that portion a bit.
I'm trying to package some code I use for data analysis so that other workers can use it. Currently, I'm stuck trying to write a simple function that imports data from a specific file type generated by a datalogger and trims it for use by other functions. Here's the code:
import<-function(filename,type="campbell",nprobes){
if (filename==TRUE){
if (type=="campbell"){
message("File import type is from Campbell CR1000")
flux.data<<-read.table(filename,sep=",",header=T,skip=1)
flux.data<<-flux.data[,-c(1,2)];flux.data<<-flux.data[-c(1,2),]
if (nprobes=="missing"){
nprobes<-32
}
flux.data<<-flux.data[,c(1:nprobes)]
flux.data.names<<-colnames(flux.data) #Saves column names
}
}
}
Ideally, the result would be a dataframe/matrix flux.data and a concomittant vector/list of the preserved column headers flux.data.names. The code runs and the function executes without errors, but the outputs aren't preserved. I usually use <<- to get around the function enclosure but its not working in this case - any suggestions?
I think the real problem is that I don't quite understand how enclosures work, despite a lot of reading... should I be using environment to assign environments within the function?
User joran answered my question in the comments above:
The critical issue was just in how the function was written: the conditional at the start (if filename==TRUE) was intended to see if filename was specified, and instead was checking to see if it literally equaled TRUE. The result was the conditional never being met, and no function output. Here's what fixed it:
import<-function(filename,type="campbell",nprobes){
if (exists(filename){
if (type=="campbell"){
#etc....
Another cool thing he pointed out was that I didn't need the <<- operator to utilize the function output and instead could write return(flux.data). This is a much more flexible approach, and helped me understand function enclosures a lot better.
I had changed the for loop into sapply function ,but failed .
I want to know why ?
list.files("c:/",pattern="mp3$",recursive=TRUE,full.names=TRUE)->z
c()->w
left<-basename(z)
for (i in 1:length(z)){
if (is.element(basename(z[i]),left))
{
append(w,values=z[i])->w;
setdiff(left,basename(z[i]))->left
}}
print(w)
list.files("c:/",pattern="mp3$",recursive=TRUE,full.names=TRUE)->z
c()->w
left<-basename(z)
sapply(z,function(y){
if (is.element(basename(y),left))
{ append(w,values=y)->w;
setdiff(left,basename(y))->left
}})
print(w)
my rule of selecting music is that if the basename(music) is the same ,then save only one full.name of music ,so unique can not be used directly.there are two concepts full.name and basename in file path which can confuse people here.
The problem you have here is that you want your function to have two side-effects. By side-effect, I mean modify objects that are outside its scope:w and left.
Curently, w and left are only modified within the function's scope, then they are eventually lost as the function call ends.
Instead, you want to modify w and left outside the function's environment. For that you can use <<- instead of <-:
sapply(z, function(y) {
if (is.element(basename(y),left)) {
w <<- append(w, values = y)
left <<- setdiff(left, basename(y))
}
})
Note that I have been saying "you want", "you can", but this is not what "you should" do. Functions with side-effects are really considered bad programming. Try reading about it.
Also, it is good to reserve the *apply tools to functions that can run their inputs independently. Here instead, you have an algorithm where the outcome of an iteration depends on the outcome of the previous ones. These are cases where you're better off using a for loop, unless you can rethink the algorithm in a framework that better suits *apply or can make use of functions that can handle such dependent situations: filter, unique, rle, etc.
For example, using unique, your code can be rewritten as:
base.names <- basename(z)
left <- unique(base.names)
w <- z[match(left, base.names)]
It also has the advantage that it is not building an object recursively, another no-no of your current code.