similar to: What is the most efficient way to assign to PARTS of objects in other frames/environments?

For nested function calls it appears that a combination of environment() and '<<-' will also work: f1 <- function() { x <- rep(NA,4) # Object to be modified by function f2 environment(f2) <- sys.frame(1) # Make f1's environment available to f2 f2(2) # Changes 2nd element of x print(x) } f2 <- function(i) { #

Hi, I've just created: newEnvEval <- function(..., hash=FALSE, parent=parent.frame(), size=29L) { envir <- new.env(hash=hash, parent=parent, size=size); evalq(..., envir=envir); envir; } # newEnvEval() so that I can create an environment and assign objects to it in one go, e.g. env <- newEnvEval({ a <- 1; b <- 2; }); print(env$a); Does this already exists somewhere?

Dear R gurus, I need to know the length of a variable (let's call that X) that is mentioned in a formula. So obviously I look for the environment from which the formula is called and then I have two options: - using eval(parse(text='length(X)'), envir=environment(formula) ) - using length(get('X'), envir=environment(formula) ) a bit of

Can someone help me with the following problem... I have a dataframe with 62 columns a number of these are as.character and a number of these are as.double, I read these into R-1.2.0 as... > Version3.Studies_read.table("c:\\Version3.Studies.dat",sep="\t", as.is=TRUE, header=TRUE,strip.white=TRUE) This is fine up to here, I've checked to see if the data has been

# polygon ignores requests to have its border transparent, look at par(bg="transparent") plot(c(0, 3), 0:1) polygon(c(0, 1, 1, 0), c(0, 0, 1, 1), border=NA, col = 0) polygon(c(1, 2, 2, 1), c(0, 0, 1, 1), border="transparent", col = 0) polygon(c(2, 3, 3, 2), c(0, 0, 1, 1), border=0, col = 0) # a quick fix for erase.screen() is the following erase.screen <- function (n =

Shouldn't the following 4 ways to alter an object in a locked environment either all work or all fail? (All working would be nice, I think.) E <- new.env() assign("var", c(1,2,3,4), envir=E) lockEnvironment(E, bindings=FALSE) E$var[1] <- 101 ; E$var #[1] 101 2 3 local(var[2] <- 102, envir=E) #Error in eval(expr, envir, enclos) : # cannot add

On 7/13/20 4:04 AM, Frank M. Ramaekers Jr. wrote: > Since I upgraded to CentOS8, I cannot get HandBrakeCLI to work: > > # HandBrakeCLI > HandBrakeCLI: error while loading shared libraries: libass.so.5: cannot open shared object file: No such file or directory > > Googling this, it appears the error message is related to ffmpeg, but I don't get any error message with it:

Since I upgraded to CentOS8, I cannot get HandBrakeCLI to work: # HandBrakeCLI HandBrakeCLI: error while loading shared libraries: libass.so.5: cannot open shared object file: No such file or directory Googling this, it appears the error message is related to ffmpeg, but I don't get any error message with it: # ffmpeg ffmpeg version 4.2.3 Copyright (c) 2000-2020 the FFmpeg developers ?

This is a trivial example I set up to see if I could pass an environment and use the variables in it (this is for a function that will be called many times and might need to use a lot of variables that won't be changing, so it seemed more sensible to use an environment). Here's the code: ######################### #The outer function run.internal.env <- function(x) { in.env <-

Try to close the file on the first nfs client before reopening it on the second nfs client. NFS has something called "close-to-open cache consistency". This means that two clients which have the same nfs file open, cannot rely on seeing the updates from the respective other client. If one clients closes, and the other client opens thereafter, it should see the changes. If you want

Hi! Some time ago, I replaced calls to library() with calls to requireNamespace() in my package logmult, in order to follow the new CRAN policies. But I just noticed it broke jackknife/bootstrap using several workers via package parallel. The reason is that I'm running model replicates on the workers, and the formula includes non-standard terms like Mult() which are provided by gnm. If gnm

According to the assign helpfile (0.90.1) the "assign" function is function (x, value, pos = -1, envir = sys.frame(sys.parent()), inherits = FALSE, immediate = TRUE) which is not quite the implementation, which is function (x, value, pos = -1, envir = pos.to.env(pos), inherits = FALSE, immediate = TRUE) This seems to have the following interesting effect. "short.fun"

names(unlist(...)) seems to be able to construct corrupt strings detected via: two identical strings behave different in paste observed in RW1.4.1 and RW1.5.0 pure replication code after output Best Jens Oehlschlägel > l <- names(unlist(list(aa = list(bb = 1)))) > l [1] "aa.bb" > # this is exactly "aa.bb" > identical(l, "aa.bb") [1] TRUE > >

Hi the list, I define a method that want to change an object without assignation (foo(x) and not x<-foo(x)) using deparse and assign. But when the argument of the method does not match *exactly* with the definition of the generic function, assign does not work... Anything wrong? Christophe #------ Does not work ------#

Hi all, I have following environments loaded with my current R session: > search() [1] ".GlobalEnv" "package:stats" "package:graphics" "package:grDevices" [5] "package:utils" "package:datasets" "package:methods" "Autoloads" [9] "package:base" How can I find the objects

For learning purposes mainly I attempted to implement hashes/maps/dictionaries (Python lingua) as S4 classes, see the coding below. I came across some rough S4 edges, but in the end it worked (for one dictionary). When testing ones sees that the dictionaries D1 and D2 share their environments D1 at hash and D2 at hash, though I thought a new and empty environment would be generated each time

Despite the spirited arguments of various R-core folks who feel that mle() doesn't need a "data" argument, and that users would be better off learning to deal with function closures, I am *still* trying to make such things work in a reasonably smooth fashion ... Is there a standard idiom for "merging" environments? i.e., suppose a function has an environment that I want

My understanding is that R will not make copies of lists if there is only one reference to the object. However, I've encountered a case where R does make copies, even though (I think) there should be only one reference to the object. I hope that someone could shed some light on why this is happening. I'll start with a simple example. Below, x is a list with one element, and changing that

Hi the list, I am writing a function in which I need to affect a variable on a higher level. My fnction could be: ++++++++++++++++++ fooBis <- function(x){ nameObject <- deparse(substitute(x)) print(nameObject) assign(nameObject,4,envir=parent.frame()) } > fooBis(e) [1] "e" > e [1] 4 ----------------- (to simplify, this fnction can affect only the number

I would like to be able, inside a function, to create a new function, and use it as part of a formula as an argument to, say, gnls or nlme. for example: MyTop <- function(data=dta) { Cexp <- function(dose,A,B,m){...} Model <- as.formula(paste("y","~ Cexp(",paste(formals(Cexp),collapse =", "),")")) MyCall <-