similar to: avoid copying big object passed into optimize()

Gabriel Becker writes: AFAIK R does not automatically copy function arguments. R actually tries very hard to avoid copying while maintaining "pass by value" functionality. ... R only copies data when you modify an object, not when you simply pass it to a function. This is a bit misleading. R tries to avoid copying by maintaining a count of how many references there are to an

Hi Lars I am not very sure what you really want. However, I am suggesting the following code that enables (1) to obtain the full summary of your data and (2) retrieve only mean of X values as function of factors f1 and f2. library(tidyverse) library(psych) df <- data.frame(matrix(rnorm(40), 10, 4), f1 = gl(3, 10, labels = letters[1:3]), f2 = gl(3, 10, labels

Hi All, i have a problem in understanding what the assignment operator '<-' really is doing. If i create two numeric arrays in R and copy one into the other with '<-' and afterwards change one array by calling a C function, both arrays are changed! The problem I am facing can easily be seen in the following example: (The following R code and the C function is attached and

Hi Lars, Two comments: 1. You can achieve what you want with a slight modification of your definition of s(), using the hint from the error message that you need an argument '.': s <- function(.) { dplyr::summarise(., x1m = mean(X1), x2m = mean(X2), x3m = mean(X3), x4m = mean(X4)) } 2. You have not given a great test case in

Hi, I have big .csv file. I would like to filter that file into a new table. For example, I have .csv file as below: f1 f2 f3 f4 f5 f6 f7 f9 f10 f11 t1 1 0 1 0 1 0 0 0 0 1 t2 1 0 0 0 0 1 1 1 1 1 t3 0 0 0 0 0 0 0 0 0 0 t4 1 0 0 0 1 0 0 0 0 0 t5 0 0 0 0 0 0 0 0 0 0 t6 0 0 0 0 0 0 0 0 0 0

Dear group, Is there a way I could avoid the sort of duplication illustrated below? i.e., I have the same dplyr::summarise function on different group_by arguments. So I'd like to create a single summarise function that could be applied to both. My attempt below fails. df <- data.frame(matrix(rnorm(40), 10, 4), f1 = gl(3, 10, labels = letters[1:3]), f2 =

I've been playing with passing arguments to .C(), and found that replacing as.double(x) with if(is.double(x)) x else as.double(x) saves time and avoids one copy, in the case that x is already double. I suggest modifying as.double to avoid the extra copy and just return x, when x is already double. Similarly for as.integer, etc. [[alternative HTML version deleted]]

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, $ R --vanilla R version 2.14.1 (2011-12-22) Platform: i686-pc-linux-gnu (32-bit) > DF = data.frame(a=1:3,b=4:6) > DF a b 1 1 4 2 2 5 3 3 6 > tracemem(DF) [1] "<0x8898098>" > names(DF)[2]="B" tracemem[0x8898098 -> 0x8763e18]: tracemem[0x8763e18 -> 0x8766be8]: tracemem[0x8766be8 -> 0x8766b68]: > DF a B 1 1 4 2 2 5 3 3 6 > Are those 3

Is putting a variable into a list a deep copy (and is tracemem the correct way to confirm)? warmstrong at krypton:~/dvl/R.packages$ R > x <- rnorm(1000) > tracemem(x) [1] "<0x3214c90>" > x.list <- list(x.in.list=x) tracemem[0x3214c90 -> 0x2af0a20]: > Is it possible to put a variable into a list without causing a deep copy (i.e. if you _really_ want the

Dear users, i'm using the sem package in R, because i need to improve a confermative factor analisys. I have so many questions in my survey, and i suppose, for example, that Question 1 (Q1) Q2 and Q3 explain the same thing (factor F1), Q4,Q5 and Q6 explain F2 and Q7 and Q8 explain F3... For check that what i supposed is true, i run this code to see if the values of loadings are big or not.

Hi, f1 <- function(){ browser() print("aaa") } f2 <- function(){ a <- 12 eval(envir = parent.frame(), bquote({ b <- .(a) })) } Now do, f1() and enter n RET and then {1+2}: Browse[2]> {1 + 2} debug at #1: 1 + 2 Browse[3]> {} is now being debugged. This was never bothering me

> -----Original Message----- > From: William Dunlap > Sent: Tuesday, November 25, 2008 9:16 AM > To: 'johannes_graumann at web.de' > Subject: Re: [R] Efficient passing through big data.frame and > modifying select fields > > > Johannes Graumann johannes_graumann at web.de > > Tue Nov 25 15:16:01 CET 2008 > > > > Hi all, > > > >

Hi Expert I want to use dtrace to monitor the content change of one file. I made following scripts, #!/usr/sbin/dtrace -s inline int MYPID = $1; syscall::write:entry /pid == MYPID/ { tracemem(arg1, arg2); printf("\n"); } It always has an following error bash-3.00$ sudo dumpFIFO.dtrace 3836 dtrace: failed to compile script ./dumpFIFO.dtrace: line 19: tracemem( ) argument #2

There's a StackOverflow question https://stackoverflow.com/q/22024082/2554330 that references this text from ?missing: "Currently missing can only be used in the immediate body of the function that defines the argument, not in the body of a nested function or a local call. This may change in the future." Someone pointed out (in https://stackoverflow.com/a/58169498/2554330)

Hi, I've noticed that as.vector() always allocates a new object, e.g. > x <- 1:10; > x <- as.vector(x); > tracemem(x); [1] "<0x0000000005622db8" > x <- as.vector(x); tracemem[0x0000000005622db8 -> 0x0000000005622ec0]: as.vector > x <- as.vector(x); tracemem[0x0000000005622ec0 -> 0x0000000005622f18]: as.vector > x <- as.vector(x);

I don't understand your point. Which version is better does NOT depend on what inputs are passed to the function. The compiled code for (as per llvm) f1 will always take less time to execute than f2. for x > 0 => T(f1) < T(f2) for x <= 0 => T(f1) = T(f2) where T() is the time to execute the given function. So always T(f1) <= T(f2). I would call this a missed

N_conreq:entry { self->x=1; calledaddr=(struct xaddrf *)arg3; callingaddr=(struct xaddrf *)arg4; trace(calledaddr->link_id); tracemem(calledaddr->DTE_MAC.lsap_add, 80); trace(callingaddr->link_id); tracemem(callingaddr->DTE_MAC.lsap_add, 80); } 0 -> N_conreq 255

Hi all, I've been trying to get a better handle on what manipulations lead R to duplicate a vector, creating small experiments and using tracemem to observe what happens (all in 2.15.1). That's lead me to a few questions, illustrated using the snippet below. x <- 1:10 tracemem(x) # [1] "<0x1058f8238>" x[5] <- 5 # tracemem[0x1058f8238 -> 0x105994ab0]: x[11] <-

Hello all, A friend recently brought to my attention that vector assignment actually recreates the entire vector on which the assignment is performed. So for example, the code: x[10]<- NA # The original call (short version) Is really doing this: x<- replace(x, list=10, values=NA) # The original call (long version) # assigning a whole new vector to x Which is actually doing this: x<-