I included in my package the following scripts;
foo <- function(X){ Y <-substitute(X);cat(Y)}
fooo <-function(){
foo(aaa)
}
Then R cmd check said the undesired note:
checking R code for possible problems ... NOTE fooo: no visible binding for global variable 'aaa'
How to remove this note or I have to change the definition of foo()?
Details
The foo iscat() without double quote "".
Surrounding character by double quote bother me, and I wanted to remove it by making foo()
> foo(aaa)
aaa
which is same as cat("aaa").
But, R CMD check thinks the pseudo object aaa does not exist.
I want to remove this note. In past, such note did not appear. But today it appears.
Use globalVariables:
globalVariables('foo')
It’s … not a great solution — in particular, you have to do this for every name you use in this way.
It’s therefore better to limit NSE in package code to the absolute necessary. Pure convenience functions such as yours need to be weighed carefully against this.
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$lbl, value)
x
}
d <- updt1(d, value="*")
but the label 'syntactic sugar' doesn't really do justice, in my mind, to the central paradigm that is involved. It is enabling convenient in-place updates, which is different from the copy-on-change illusion that R usually maintains, and it is really the 'R' way of updating objects (rather than using ?ReferenceClasses, for instance, which have more of the feel of other languages but will surprise R users expecting copy-on-change semantics).
For anybody in the future looking for a simple way (do not know if it is the more appropriate one) to get this solved:
Inside the function create the object to temporally save the modified version of the one you want to change. Use deparse(substitute()) to get the name of the variable that has been passed to the function argument and then use assign() to overwrite your object. You will need to use envir = parent.frame() inside assign() to let your object be defined in the environment outside the function.
(MyTable <- 1:10)
[1] 1 2 3 4 5 6 7 8 9 10
title.asterisk <- function(table) {
tmp.table <- paste0(table, "*")
name <- deparse(substitute(table))
assign(name, tmp.table, envir = parent.frame())
}
(title.asterisk(MyTable))
[1] "1*" "2*" "3*" "4*" "5*" "6*" "7*" "8*" "9*" "10*"
Using parentheses when defining an object is a little more efficient (and to me, better looking) than defining then printing.
I have a command with six lines that I want to use several times. Therfore, I want to assign a name to this command and use it as a procedure instead of writing the whole command lines over and over.
In this case it is a <-rbind() command, but the issue is also more general.
modelcoeff<-rbind(modelcoeff,c(as.character((summary(mymodel)$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 was going through swirl() again as a refresher, and I've noticed that the author of swirl says the command ?matrix is the correct form to calling for a help screen. But, when I run ?matrix(), it still works? Is there a difference between having and not having a pair of parenthesis?
It's not specific to the swirl environment (about which I was entirely unaware until 5 minutes ago) That is standard for R. The help page for the ? shortcut says:
Arguments
topic
Usually, a name or character string specifying the topic for which help is sought.
Alternatively, a function call to ask for documentation on a corresponding S4 method: see the section on S4 method documentation. The calls pkg::topic and pkg:::topic are treated specially, and look for help on topic in package pkg.
It something like the second option that is being invoked with the command:
?matrix()
Since ?? is actually a different shortcut one needs to use this code to bring up that page, just as one needs to use quoted strings for help with for, if, next or any of the other reserved words in R:
?'?' # See ?Reserved
This is not based on a "fuzzy logic" search in hte help system. Using help instead of ? gets a different response:
> help("str()")
No documentation for ‘str()’ in specified packages and libraries:
you could try ‘??str()’
You can see the full code for the ? function by typing ? at the command line, but I am just showing how it starts the language level processing of the expressions given to it:
`?`
function (e1, e2)
{
if (missing(e2)) {
type <- NULL
topicExpr <- substitute(e1)
}
#further output omitted
By running matrix and in general any_function you get the source code of it.
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 am a bit of an R novice, and I am stuck with what seems like a simple problem, yet touches pretty deep questions about how and when things get evaluated in R.
I am using Rserve quite a bit; the typical syntax to get things evaluated remotely is a bit of a pain to type repeatedly:
RSeval(connection, quote(try(command)))
So I would like a function r which does the same thing with just the call:
r(command)
My first, naive, bound to fail attempt involved:
r <- function(command) {
RSeval(c, quote(try(command)))
}
You've guessed it: this sends, literally, try(command) to my confused Rserve daemon. I want command to be partially evaluated, if that makes any sense -- i.e. replaced by what I typed as an argument, but without evaluating it locally.
I looked for solutions to this, browsed throught the documentation for quote, substitute, eval, call, etc.. but I was not able to find something that worked. Either command gets evaluated locally, or not at all.
This is not a big problem, I can type the whole damn quote(try()) thing all the time; but at this point I am mostly curious as to how to get this to work!
EDIT:
More explanations as to what I want to do.
In the text above, command is meant to be a call do a function, ideally -- i.e., not a character string. Something like a <- 3 or assign("a", 3) rather than "a<-3" or quote(a<-3).
I believe that this is part of what makes this tricky. It seems really hard to tell R not to evaluate this locally, but only send it literally. Basically I would like my function to be a bit like quote(), which does not evaluate its argument.
Some explanation about my intentions. I wish to use Rserve frequently to pass commands to a remote R daemon. The commands would be my own (or my colleagues) and the daemon protected by firewall and authentication (and would not be run as root) -- so there is no worry of malicious commands being passed.
To be perfectly honest, this is not a big issue, and I would be pretty happy to always use the RSeval(c, quote(try())). At this point I see this more like an interesting inquiry into the subleties of R :-)
You probably want to use the substitute command, it can give you the argument unevaluated that you can build into the call.
I'm not sure if I understood you correctly - would eval(parse(text = command)) do the trick? Notice that command is a character, so you can easily pass it as a function argument. If I'm getting the point...
Anyway, evaluating user-specified commands is potentially malicious, therefore not recommended. You should either install AppArmor and tweak it (which is not an easy one), or drop the whole evaluation thing...