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What is the difference between rm() and rm(list=ls())?
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Closed 4 years ago.
While looking at some R code written by someone else, I find near the top of the document rm(list=ls()). I tried looking up what the rm function does and as far as I can tell it deletes specified variables from the workspace, so that if you had x=3 somewhere, then after running rm(x) the variable x would behave as if it had never been assigned (so for instance would throw an error if you tried to print it).
But then the rest of the code is strange if I have this right, because it seems to be removing the empty list. So it's not doing anything at all, right? Is there a reason to have this code here?
I think the parameter list for the rm function is confusing, at least it was for me when I started using R. The list parameter is not actually supposed to be a list but rather a character vector. The ls-function does return a character vector of all the named objects visible in the environment where it is called and it has a default value that gets used if no other value is offered for its envir argument. If you do this at the console, then the default environment is the global environment. So this will clear all the "visible" objects (but not the ones defined in other namespaces or environments like the ones that exist in the base, graphics, stats or other loaded package-namespaces.
So now go look at ?ls and ?rm to better understand their capabilities. In particular new useRs should get their heads clear on the differences between R names, i.e. symbols, versus the character representations of them. The ls function is really reaching into the language level of R's implementation and returning a non-language-level value, whereas rm typically takes a language level input ... unless (as in this case) it is offered a value to its "list"-argument, ... which is not an R list. Clear? Maybe, hopefully, a bit more so.
It clears all objects from the workspace.
Related
I'm working in R, and I'd like to define some variables that I (or one of my collaborators) cannot change. In C++ I'd do this:
const std::string path( "/projects/current" );
How do I do this in the R programming language?
Edit for clarity: I know that I can define strings like this in R:
path = "/projects/current"
What I really want is a language construct that guarantees that nobody can ever change the value associated with the variable named "path."
Edit to respond to comments:
It's technically true that const is a compile-time guarantee, but it would be valid in my mind that the R interpreter would throw stop execution with an error message. For example, look what happens when you try to assign values to a numeric constant:
> 7 = 3
Error in 7 = 3 : invalid (do_set) left-hand side to assignment
So what I really want is a language feature that allows you to assign values once and only once, and there should be some kind of error when you try to assign a new value to a variabled declared as const. I don't care if the error occurs at run-time, especially if there's no compilation phase. This might not technically be const by the Wikipedia definition, but it's very close. It also looks like this is not possible in the R programming language.
See lockBinding:
a <- 1
lockBinding("a", globalenv())
a <- 2
Error: cannot change value of locked binding for 'a'
Since you are planning to distribute your code to others, you could (should?) consider to create a package. Create within that package a NAMESPACE. There you can define variables that will have a constant value. At least to the functions that your package uses. Have a look at Tierney (2003) Name Space Management for R
I'm pretty sure that this isn't possible in R. If you're worried about accidentally re-writing the value then the easiest thing to do would be to put all of your constants into a list structure then you know when you're using those values. Something like:
my.consts<-list(pi=3.14159,e=2.718,c=3e8)
Then when you need to access them you have an aide memoir to know what not to do and also it pushes them out of your normal namespace.
Another place to ask would be R development mailing list. Hope this helps.
(Edited for new idea:) The bindenv functions provide an
experimental interface for adjustments to environments and bindings within environments. They allow for locking environments as well as individual bindings, and for linking a variable to a function.
This seems like the sort of thing that could give a false sense of security (like a const pointer to a non-const variable) but it might help.
(Edited for focus:) const is a compile-time guarantee, not a lock-down on bits in memory. Since R doesn't have a compile phase where it looks at all the code at once (it is built for interactive use), there's no way to check that future instructions won't violate any guarantee. If there's a right way to do this, the folks at the R-help list will know. My suggested workaround: fake your own compilation. Write a script to preprocess your R code that will manually substitute the corresponding literal for each appearance of your "constant" variables.
(Original:) What benefit are you hoping to get from having a variable that acts like a C "const"?
Since R has exclusively call-by-value semantics (unless you do some munging with environments), there isn't any reason to worry about clobbering your variables by calling functions on them. Adopting some sort of naming conventions or using some OOP structure is probably the right solution if you're worried about you and your collaborators accidentally using variables with the same names.
The feature you're looking for may exist, but I doubt it given the origin of R as a interactive environment where you'd want to be able to undo your actions.
R doesn't have a language constant feature. The list idea above is good; I personally use a naming convention like ALL_CAPS.
I took the answer below from this website
The simplest sort of R expression is just a constant value, typically a numeric value (a number) or a character value (a piece of text). For example, if we need to specify a number of seconds corresponding to 10 minutes, we specify a number.
> 600
[1] 600
If we need to specify the name of a file that we want to read data from, we specify the name as a character value. Character values must be surrounded by either double-quotes or single-quotes.
> "http://www.census.gov/ipc/www/popclockworld.html"
[1] "http://www.census.gov/ipc/www/popclockworld.html"
I am still not very good at using R’s standard debugging tools, and I often find that neither the error nor the traceback tell me enough to figure out what is going on. I would like to change R’s default behavior on error to provide more information.
Specifically, I would always like the call, including the formals, the expression assigned to each formal (the default expression if the default is the expression assigned), and the value of each of the argument expressions as evaluated in place, all returned in a format that makes it unambiguous which expression has been matched to which formal and which values go with which expression. Since the values might be large or of unexpected or evanescent type, I’d like them to be returned in a format, such as a str(), that makes intelligent choices about truncation and correctly identifies promises and other object types that tend to evaluate themselves into something else before you see them.
And finally, I’d like all these things, together with the return value of each call, for every function on the call stack from the error back to (and including) some piece of code that I wrote. It seems to me that the natural structure would be a single R object, a list of lists, one list per call (perhaps tidied, broom-like, into a tibble with some list columns) that I could single-step through in the obvious way.
I apologize if I have described some standard R debugging tool that I just haven't learned how to use properly yet. Is this even possible? If it is, could it be implemented via R's available error handlers, or would it need some package-scale coding project?
I would most prefer a solution that changes the default error response to this, but if that is impracticable, I'd accept a solution that requires that I rerun a code chunk with a wrapper or something similar.
I'm a long-time programmer in C, assembler, etc. I have just begun an intro to R (not by choice). On page 2, I encounter rm(list=ls()) which works to remove all objects. Since the = seems to be assigning the output of ls() to something named list, you would think that rm(list=ls()) would do the same thing, since the name shouldn't matter, or even more, why not just rm(ls())? Neither of those work. Further, the book says that ls() "lists all of the objects...", but the output of ls() isn't a list, it is a vector of character strings, apparently. So, apparently rm() takes something formally of type list, rather than simply a vector of character strings. Fine. But if I separately execute list=ls(), that creates a vector named list, not a list. So list=ls() followed by rm(list) is not the same as rm(list=ls()), apparently, in violation of what ought to be transitivity of operations or something.
So, apparently list=ls() inside of the rm() does not assign anything to something named list, apparently it produces something of type list to be fed into rm(). But that makes no sense, to write list=ls() if the goal is to produce something of type list from a vector (the output of ls()).
Should I just fall on my sword and realize that I am not going to be able to tolerate learning this language? I'm just too grumpy? Or, is the rest of R going to make perfect sense and I happened to stumble on some kind of ad-hoc adaptation of calling rm that was cooked up?
This post (Lazy evaluation in R – is assign affected?) covers some common ground but I am not sure it answers my question.
I stopped using assign when I discovered the apply family quite a while back, albeit, purely for reasons of elegance in situations such as this:
names.foo <- letters
values.foo <- LETTERS
for (i in 1:length(names.foo))
assign(names.foo[i], paste("This is: ", values.foo[i]))
which can be replaced by:
foo <- lapply(X=values.foo, FUN=function (k) paste("This is :", k))
names(foo) <- names.foo
This is also the reason this (http://cran.r-project.org/doc/FAQ/R-FAQ.html#How-can-I-turn-a-string-into-a-variable_003f) R-faq says this should be avoided.
Now, I know that assign is generally frowned upon. But are there other reasons I don't know? I suspect it may mess with the scoping or lazy evaluation but I am not sure? Example code that demonstrates such problems will be great.
Actually those two operations are quite different. The first gives you 26 different objects while the second gives you only one. The second object will be a lot easier to use in analyses. So I guess I would say you have already demonstrated the major downside of assign, namely the necessity of then needing always to use get for corralling or gathering up all the similarly named individual objects that are now "loose" in the global environment. Try imagining how you would serially do anything with those 26 separate objects. A simple lapply(foo, func) will suffice for the second strategy.
That FAQ citation really only says that using assignment and then assigning names is easier, but did not imply it was "bad". I happen to read it as "less functional" since you are not actually returning a value that gets assigned. The effect looks to be a side-effect (and in this case the assign strategy results in 26 separate side-effects). The use of assign seems to be adopted by people that are coming from languages that have global variables as a way of avoiding picking up the "True R Way", i.e. functional programming with data-objects. They really should be learning to use lists rather than littering their workspace with individually-named items.
There is another assignment paradigm that can be used:
foo <- setNames( paste0(letters,1:26), LETTERS)
That creates a named atomic vector rather than a named list, but the access to values in the vector is still done with names given to [.
As the source of fortune(236) I thought I would add a couple examples (also see fortune(174)).
First, a quiz. Consider the following code:
x <- 1
y <- some.function.that.uses.assign(rnorm(100))
After running the above 2 lines of code, what is the value of x?
The assign function is used to commit "Action at a distance" (see http://en.wikipedia.org/wiki/Action_at_a_distance_(computer_programming) or google for it). This is often the source of hard to find bugs.
I think the biggest problem with assign is that it tends to lead people down paths of thinking that take them away from better options. A simple example is the 2 sets of code in the question. The lapply solution is more elegant and should be promoted, but the mere fact that people learn about the assign function leads people to the loop option. Then they decide that they need to do the same operation on each object created in the loop (which would be just another simple lapply or sapply if the elegant solution were used) and resort to an even more complicated loop involving both get and apply along with ugly calls to paste. Then those enamored with assign try to do something like:
curname <- paste('myvector[', i, ']')
assign(curname, i)
And that does not do quite what they expected which leads to either complaining about R (which is as fair as complaining that my next door neighbor's house is too far away because I chose to walk the long way around the block) or even worse, delve into using eval and parse to get their constructed string to "work" (which then leads to fortune(106) and fortune(181)).
I'd like to point out that assign is meant to be used with environments.
From that point of view, the "bad" thing in the example above is using a not quite appropriate data structure (the base environment instead of a list or data.frame, vector, ...).
Side note: also for environments, the $ and $<- operators work, so in many cases the explicit assign and get isn't necessary there, neither.
I'm working in R, and I'd like to define some variables that I (or one of my collaborators) cannot change. In C++ I'd do this:
const std::string path( "/projects/current" );
How do I do this in the R programming language?
Edit for clarity: I know that I can define strings like this in R:
path = "/projects/current"
What I really want is a language construct that guarantees that nobody can ever change the value associated with the variable named "path."
Edit to respond to comments:
It's technically true that const is a compile-time guarantee, but it would be valid in my mind that the R interpreter would throw stop execution with an error message. For example, look what happens when you try to assign values to a numeric constant:
> 7 = 3
Error in 7 = 3 : invalid (do_set) left-hand side to assignment
So what I really want is a language feature that allows you to assign values once and only once, and there should be some kind of error when you try to assign a new value to a variabled declared as const. I don't care if the error occurs at run-time, especially if there's no compilation phase. This might not technically be const by the Wikipedia definition, but it's very close. It also looks like this is not possible in the R programming language.
See lockBinding:
a <- 1
lockBinding("a", globalenv())
a <- 2
Error: cannot change value of locked binding for 'a'
Since you are planning to distribute your code to others, you could (should?) consider to create a package. Create within that package a NAMESPACE. There you can define variables that will have a constant value. At least to the functions that your package uses. Have a look at Tierney (2003) Name Space Management for R
I'm pretty sure that this isn't possible in R. If you're worried about accidentally re-writing the value then the easiest thing to do would be to put all of your constants into a list structure then you know when you're using those values. Something like:
my.consts<-list(pi=3.14159,e=2.718,c=3e8)
Then when you need to access them you have an aide memoir to know what not to do and also it pushes them out of your normal namespace.
Another place to ask would be R development mailing list. Hope this helps.
(Edited for new idea:) The bindenv functions provide an
experimental interface for adjustments to environments and bindings within environments. They allow for locking environments as well as individual bindings, and for linking a variable to a function.
This seems like the sort of thing that could give a false sense of security (like a const pointer to a non-const variable) but it might help.
(Edited for focus:) const is a compile-time guarantee, not a lock-down on bits in memory. Since R doesn't have a compile phase where it looks at all the code at once (it is built for interactive use), there's no way to check that future instructions won't violate any guarantee. If there's a right way to do this, the folks at the R-help list will know. My suggested workaround: fake your own compilation. Write a script to preprocess your R code that will manually substitute the corresponding literal for each appearance of your "constant" variables.
(Original:) What benefit are you hoping to get from having a variable that acts like a C "const"?
Since R has exclusively call-by-value semantics (unless you do some munging with environments), there isn't any reason to worry about clobbering your variables by calling functions on them. Adopting some sort of naming conventions or using some OOP structure is probably the right solution if you're worried about you and your collaborators accidentally using variables with the same names.
The feature you're looking for may exist, but I doubt it given the origin of R as a interactive environment where you'd want to be able to undo your actions.
R doesn't have a language constant feature. The list idea above is good; I personally use a naming convention like ALL_CAPS.
I took the answer below from this website
The simplest sort of R expression is just a constant value, typically a numeric value (a number) or a character value (a piece of text). For example, if we need to specify a number of seconds corresponding to 10 minutes, we specify a number.
> 600
[1] 600
If we need to specify the name of a file that we want to read data from, we specify the name as a character value. Character values must be surrounded by either double-quotes or single-quotes.
> "http://www.census.gov/ipc/www/popclockworld.html"
[1] "http://www.census.gov/ipc/www/popclockworld.html"