R copies for no apparent reason - r

Some R function will make R copy the object AFTER the function call, like nrow, while some others don't, like sum.
For example the following code:
x = as.double(1:1e8)
system.time(x[1] <- 100)
y = sum(x)
system.time(x[1] <- 200) ## Fast (takes 0s), after calling sum
foo = function(x) {
return(sum(x))
}
y = foo(x)
system.time(x[1] <- 300) ## Slow (takes 0.35s), after calling foo
Calling foo is NOT slow, because x isn't copied. However, changing x again is very slow, as x is copied. My guess is that calling foo will leave a reference to x, so when changing it after, R makes another copy.
Any one knows why R does this? Even when the function doesn't change x at all? Thanks.

I definitely recommend Hadley's Advanced R book, as it digs into some of the internals that you will likely find interesting and relevant. Most relevant to your question (and as mentioned by #joran and #lmo), the reason for the slow-down was an additional reference that forced copy-on-modify.
An excerpt that might be beneficial from Memory#Modification:
There are two possibilities:
R modifies x in place.
R makes a copy of x to a new location, modifies the copy, and then
uses the name x to point to the new location.
It turns out that R can do either depending on the circumstances. In
the example above, it will modify in place. But if another variable
also points to x, then R will copy it to a new location. To explore
what’s going on in greater detail, we use two tools from the pryr
package. Given the name of a variable, address() will tell us the
variable’s location in memory and refs() will tell us how many names
point to that location.
Also of interest are the sections on R's C interface and Performance. The pryr package also has tools for working with these sorts of internals in an easier fashion.
One last note from Hadley's book (same Memory section) that might be helpful:
While determining that copies are being made is not hard, preventing
such behaviour is. If you find yourself resorting to exotic tricks to
avoid copies, it may be time to rewrite your function in C++, as
described in Rcpp.

Related

How to speed up writing to a matrix in a reference class in R

Here is a piece of R code that writes to each element of a matrix in a reference class. It runs incredibly slowly, and I’m wondering if I’ve missed a simple trick that will speed this up.
nx = 2000
ny = 10
ref_matrix <- setRefClass(
"ref_matrix",fields = list(data = "matrix"),
)
out <- ref_matrix(data = matrix(0.0,nx,ny))
#tracemem(out$data)
for (iy in 1:ny) {
for (ix in 1:nx) {
out$data[ix,iy] <- ix + iy
}
}
It seems that each write to an element of the matrix triggers a check that involves a copy of the entire matrix. (Uncommenting the tracemen() call shows this.) Now, I’ve found a discussion that seems to confirm this:
https://r-devel.r-project.narkive.com/8KtYICjV/rd-copy-on-assignment-to-large-field-of-reference-class
and this also seems to be covered by Speeding up field access in R reference classes
but in both of these this behaviour can be bypassed by not declaring a class for the field, and this works for the example in the first link which uses a 1D vector, b, which can just be set as b <<- 1:10000. But I’ve not found an equivalent way of creating a 2D array without using a explicit “matrix” instance.
Am I just missing something simple, or is this actually not possible?
Let me add a couple of things. First, I’m very new to R, so could easily have missed something. Second, I’m really just curious about the way reference classes work in this case and whether there’s a simple way to use them efficiently; I’m not looking for a really fast way to set the elements of a matrix - I can do that by not having the matrix in a reference class at all, and if I really care about speed I can write a C routine to do it and call it from R.
Here’s some background that might explain why I’m interested in this, which you’re welcome to ignore.
I got here by wanting to see how different languages, and even different compiler options and different ways of coding the same operation, compared for efficiency when accessing 2D rectangular arrays. I’ve been playing with a test program that creates two 2D arrays of the same size, and calls a subroutine that sets the first to the elements of the second plus their index values. (Almost any operation would do, but this one isn’t completely trivial to optimise.) I have this in a number of languages now, C, C++, Julia, Tcl, Fortran, Swift, etc., even hand-coded assembler (spoiler alert: assembler isn’t worth the effort any more) and thought I’d try R. The obvious implementation in R passes the two arrays to a subroutine that does the work, but because R doesn’t normally pass by reference, that routine has to make a copy of the modified array and return that as the function value. I thought using a reference class would avoid the relatively minor overhead of that copy, so I tried that and was surprised to discover that, far from speeding things up, it slowed them down enormously.
Use outer:
out$data <- outer(1:ny, 1:nx, `+`)
Also, don't use reference classes (or R6 classes) unless you actually need reference semantics. KISS and all that.

How to not fall into R's 'lazy evaluation trap'

"R passes promises, not values. The promise is forced when it is first evaluated, not when it is passed.", see this answer by G. Grothendieck. Also see this question referring to Hadley's book.
In simple examples such as
> funs <- lapply(1:10, function(i) function() print(i))
> funs[[1]]()
[1] 10
> funs[[2]]()
[1] 10
it is possible to take such unintuitive behaviour into account.
However, I find myself frequently falling into this trap during daily development. I follow a rather functional programming style, which means that I often have a function A returning a function B, where B is in some way depending on the parameters with which A was called. The dependency is not as easy to see as in the above example, since calculations are complex and there are multiple parameters.
Overlooking such an issue leads to difficult to debug problems, since all calculations run smoothly - except that the result is incorrect. Only an explicit validation of the results reveals the problem.
What comes on top is that even if I have noticed such a problem, I am never really sure which variables I need to force and which I don't.
How can I make sure not to fall into this trap? Are there any programming patterns that prevent this or that at least make sure that I notice that there is a problem?
You are creating functions with implicit parameters, which isn't necessarily best practice. In your example, the implicit parameter is i. Another way to rework it would be:
library(functional)
myprint <- function(x) print(x)
funs <- lapply(1:10, function(i) Curry(myprint, i))
funs[[1]]()
# [1] 1
funs[[2]]()
# [1] 2
Here, we explicitly specify the parameters to the function by using Curry. Note we could have curried print directly but didn't here for illustrative purposes.
Curry creates a new version of the function with parameters pre-specified. This makes the parameter specification explicit and avoids the potential issues you are running into because Curry forces evaluations (there is a version that doesn't, but it wouldn't help here).
Another option is to capture the entire environment of the parent function, copy it, and make it the parent env of your new function:
funs2 <- lapply(
1:10, function(i) {
fun.res <- function() print(i)
environment(fun.res) <- list2env(as.list(environment())) # force parent env copy
fun.res
}
)
funs2[[1]]()
# [1] 1
funs2[[2]]()
# [1] 2
but I don't recommend this since you will be potentially copying a whole bunch of variables you may not even need. Worse, this gets a lot more complicated if you have nested layers of functions that create functions. The only benefit of this approach is that you can continue your implicit parameter specification, but again, that seems like bad practice to me.
As others pointed out, this might not be the best style of programming in R. But, one simple option is to just get into the habit of forcing everything. If you do this, realize you don't need to actually call force, just evaluating the symbol will do it. To make it less ugly, you could make it a practice to start functions like this:
myfun<-function(x,y,z){
x;y;z;
## code
}
There is some work in progress to improve R's higher order functions like the apply functions, Reduce, and such in handling situations like these. Whether this makes into R 3.2.0 to be released in a few weeks depend on how disruptive the changes turn out to be. Should become clear in a week or so.
R has a function that helps safeguard against lazy evaluation, in situations like closure creation: forceAndCall().
From the online R help documentation:
forceAndCall is intended to help defining higher order functions like apply to behave more reasonably when the result returned by the function applied is a closure that captured its arguments.

Analog to utility classes in R?

I have a couple of functions that convert between coordinate systems, and they all rely on constants from the WGS84 ellipsoid, etc. I'd rather not have these constants pollute the global namespace. Similarly, not all of the functions need to be visible globally.
In Java, I'd encapsulate all the coordinate stuff in a utility class and only expose the coordinate transformation methods.
What's a low-overhead way to do this in R? Ideally, I could:
source("coordinateStuff.R")
at the top of my file and call the "public" functions as needed. It might make a nice package down the road, but that's not a concern right now.
Edit for initial approach:
I started coords.R with:
coords <- new.env()
with(coords, {
## Semi-major axis (center to equator)
a <- 6378137.0
## And so on...
})
The with statement and indentation clearly indicate that something is different about the assignment variables. And it sure beats typing a zillion assign statements.
The first cut at functions looked like:
ecef2geodetic <- function (x,y,z) {
attach(coords)
on.exit(detach(coords))
The on.exit() ensures that we'll leave coords when the function exits. But the attach() statements caused trouble when one function in coords called another in coords. See this question to see how things went from there.
Utility classes in Java are code smell. This is not what you want in R.
There are several ways of solving this in R. For medium / large scale things, the way to go is to put you stuff into a package and use it in the remaining code. That encapsulates your “private” variables nicely and exposes a well-defined interface.
For smaller things, an excellent way of doing this is to put your code into a local call which, as the name suggests, executes its argument in a local scope:
x <- 23
result <- local({
foo <- 42
bar <- x
foo * bar
})
Finally, you can put your objects into a list or environment (there are differences but you may ignore them for now), and then just access them via listname$objname:
coordinateStuff <- list(
foo = function () { cat('42\n') }
bar = 23
)
coordinateStuff$foo()
If you want something similar to your source statement, take a look at my xsource command which solves this to some extent (although it’s work in progress and has several issues!). This would allow you to write
cs <- xsource(coordinateStuff)
# Use cs as if it were an evironment, e.g.
cs$public_function()
# or even:
cs::public_function()
A package is the solution... But for a fast solution you could use Environments http://stat.ethz.ch/R-manual/R-devel/library/base/html/environment.html

Examples of the perils of globals in R and Stata

In recent conversations with fellow students, I have been advocating for avoiding globals except to store constants. This is a sort of typical applied statistics-type program where everyone writes their own code and project sizes are on the small side, so it can be hard for people to see the trouble caused by sloppy habits.
In talking about avoidance of globals, I'm focusing on the following reasons why globals might cause trouble, but I'd like to have some examples in R and/or Stata to go with the principles (and any other principles you might find important), and I'm having a hard time coming up with believable ones.
Non-locality: Globals make debugging harder because they make understanding the flow of code harder
Implicit coupling: Globals break the simplicity of functional programming by allowing complex interactions between distant segments of code
Namespace collisions: Common names (x, i, and so forth) get re-used, causing namespace collisions
A useful answer to this question would be a reproducible and self-contained code snippet in which globals cause a specific type of trouble, ideally with another code snippet in which the problem is corrected. I can generate the corrected solutions if necessary, so the example of the problem is more important.
Relevant links:
Global Variables are Bad
Are global variables bad?
I also have the pleasure of teaching R to undergraduate students who have no experience with programming. The problem I found was that most examples of when globals are bad, are rather simplistic and don't really get the point across.
Instead, I try to illustrate the principle of least astonishment. I use examples where it is tricky to figure out what was going on. Here are some examples:
I ask the class to write down what they think the final value of i will be:
i = 10
for(i in 1:5)
i = i + 1
i
Some of the class guess correctly. Then I ask should you ever write code like this?
In some sense i is a global variable that is being changed.
What does the following piece of code return:
x = 5:10
x[x=1]
The problem is what exactly do we mean by x
Does the following function return a global or local variable:
z = 0
f = function() {
if(runif(1) < 0.5)
z = 1
return(z)
}
Answer: both. Again discuss why this is bad.
Oh, the wonderful smell of globals...
All of the answers in this post gave R examples, and the OP wanted some Stata examples, as well. So let me chime in with these.
Unlike R, Stata does take care of locality of its local macros (the ones that you create with local command), so the issue of "Is this this a global z or a local z that is being returned?" never comes up. (Gosh... how can you R guys write any code at all if locality is not enforced???) Stata has a different quirk, though, namely that a non-existent local or global macro is evaluated as an empty string, which may or may not be desirable.
I have seen globals used for several main reasons:
Globals are often used as shortcuts for variable lists, as in
sysuse auto, clear
regress price $myvars
I suspect that the main usage of such construct is for someone who switches between interactive typing and storing the code in a do-file as they try multiple specifications. Say they try regression with homoskedastic standard errors, heteroskedastic standard errors, and median regression:
regress price mpg foreign
regress price mpg foreign, robust
qreg price mpg foreign
And then they run these regressions with another set of variables, then with yet another one, and finally they give up and set this up as a do-file myreg.do with
regress price $myvars
regress price $myvars, robust
qreg price $myvars
exit
to be accompanied with an appropriate setting of the global macro. So far so good; the snippet
global myvars mpg foreign
do myreg
produces the desirable results. Now let's say they email their famous do-file that claims to produce very good regression results to collaborators, and instruct them to type
do myreg
What will their collaborators see? In the best case, the mean and the median of mpg if they started a new instance of Stata (failed coupling: myreg.do did not really know you meant to run this with a non-empty variable list). But if the collaborators had something in the works, and too had a global myvars defined (name collision)... man, would that be a disaster.
Globals are used for directory or file names, as in:
use $mydir\data1, clear
God only knows what will be loaded. In large projects, though, it does come handy. You would want to define global mydir somewhere in your master do-file, may be even as
global mydir `c(pwd)'
Globals can be used to store an unpredictable crap, like a whole command:
capture $RunThis
God only knows what will be executed; let's just hope it is not ! format c:\. This is the worst case of implicit strong coupling, but since I am not even sure that RunThis will contain anything meaningful, I put a capture in front of it, and will be prepared to treat the non-zero return code _rc. (See, however, my example below.)
Stata's own use of globals is for God settings, like the type I error probability/confidence level: the global $S_level is always defined (and you must be a total idiot to redefine this global, although of course it is technically doable). This is, however, mostly a legacy issue with code of version 5 and below (roughly), as the same information can be obtained from less fragile system constant:
set level 90
display $S_level
display c(level)
Thankfully, globals are quite explicit in Stata, and hence are easy to debug and remove. In some of the above situations, and certainly in the first one, you'd want to pass parameters to do-files which are seen as the local `0' inside the do-file. Instead of using globals in the myreg.do file, I would probably code it as
unab varlist : `0'
regress price `varlist'
regress price `varlist', robust
qreg price `varlist'
exit
The unab thing will serve as an element of protection: if the input is not a legal varlist, the program will stop with an error message.
In the worst cases I've seen, the global was used only once after having been defined.
There are occasions when you do want to use globals, because otherwise you'd have to pass the bloody thing to every other do-file or a program. One example where I found the globals pretty much unavoidable was coding a maximum likelihood estimator where I did not know in advance how many equations and parameters I would have. Stata insists that the (user-supplied) likelihood evaluator will have specific equations. So I had to accumulate my equations in the globals, and then call my evaluator with the globals in the descriptions of the syntax that Stata would need to parse:
args lf $parameters
where lf was the objective function (the log-likelihood). I encountered this at least twice, in the normal mixture package (denormix) and confirmatory factor analysis package (confa); you can findit both of them, of course.
One R example of a global variable that divides opinion is the stringsAsFactors issue on reading data into R or creating a data frame.
set.seed(1)
str(data.frame(A = sample(LETTERS, 100, replace = TRUE),
DATES = as.character(seq(Sys.Date(), length = 100, by = "days"))))
options("stringsAsFactors" = FALSE)
set.seed(1)
str(data.frame(A = sample(LETTERS, 100, replace = TRUE),
DATES = as.character(seq(Sys.Date(), length = 100, by = "days"))))
options("stringsAsFactors" = TRUE) ## reset
This can't really be corrected because of the way options are implemented in R - anything could change them without you knowing it and thus the same chunk of code is not guaranteed to return exactly the same object. John Chambers bemoans this feature in his recent book.
A pathological example in R is the use of one of the globals available in R, pi, to compute the area of a circle.
> r <- 3
> pi * r^2
[1] 28.27433
>
> pi <- 2
> pi * r^2
[1] 18
>
> foo <- function(r) {
+ pi * r^2
+ }
> foo(r)
[1] 18
>
> rm(pi)
> foo(r)
[1] 28.27433
> pi * r^2
[1] 28.27433
Of course, one can write the function foo() defensively by forcing the use of base::pi but such recourse may not be available in normal user code unless packaged up and using a NAMESPACE:
> foo <- function(r) {
+ base::pi * r^2
+ }
> foo(r = 3)
[1] 28.27433
> pi <- 2
> foo(r = 3)
[1] 28.27433
> rm(pi)
This highlights the mess you can get into by relying on anything that is not solely in the scope of your function or passed in explicitly as an argument.
Here's an interesting pathological example involving replacement functions, the global assign, and x defined both globally and locally...
x <- c(1,NA,NA,NA,1,NA,1,NA)
local({
#some other code involving some other x begin
x <- c(NA,2,3,4)
#some other code involving some other x end
#now you want to replace NAs in the the global/parent frame x with 0s
x[is.na(x)] <<- 0
})
x
[1] 0 NA NA NA 0 NA 1 NA
Instead of returning [1] 1 0 0 0 1 0 1 0, the replacement function uses the index returned by the local value of is.na(x), even though you're assigning to the global value of x. This behavior is documented in the R Language Definition.
One quick but convincing example in R is to run the line like:
.Random.seed <- 'normal'
I chose 'normal' as something someone might choose, but you could use anything there.
Now run any code that uses generated random numbers, for example:
rnorm(10)
Then you can point out that the same thing could happen for any global variable.
I also use the example of:
x <- 27
z <- somefunctionthatusesglobals(5)
Then ask the students what the value of x is; the answer is that we don't know.
Through trial and error I've learned that I need to be very explicit in naming my function arguments (and ensure enough checks at the start and along the function) to make everything as robust as possible. This is especially true if you have variables stored in global environment, but then you try to debug a function with a custom valuables - and something doesn't add up! This is a simple example that combines bad checks and calling a global variable.
glob.arg <- "snake"
customFunction <- function(arg1) {
if (is.numeric(arg1)) {
glob.arg <- "elephant"
}
return(strsplit(glob.arg, "n"))
}
customFunction(arg1 = 1) #argument correct, expected results
customFunction(arg1 = "rubble") #works, but may have unexpected results
An example sketch that came up while trying to teach this today. Specifically, this focuses on trying to give intuition as to why globals can cause problems, so it abstracts away as much as possible in an attempt to state what can and cannot be concluded just from the code (leaving the function as a black box).
The set up
Here is some code. Decide whether it will return an error or not based on only the criteria given.
The code
stopifnot( all( x!=0 ) )
y <- f(x)
5/x
The criteria
Case 1: f() is a properly-behaved function, which uses only local variables.
Case 2: f() is not necessarily a properly-behaved function, which could potentially use global assignment.
The answer
Case 1: The code will not return an error, since line one checks that there are no x's equal to zero and line three divides by x.
Case 2: The code could potentially return an error, since f() could e.g. subtract 1 from x and assign it back to the x in the parent environment, where any x element equal to 1 could then be set to zero and the third line would return a division by zero error.
Here's one attempt at an answer that would make sense to statisticsy types.
Namespace collisions: Common names (x, i, and so forth) get re-used, causing namespace collisions
First we define a log likelihood function,
logLik <- function(x) {
y <<- x^2+2
return(sum(sqrt(y+7)))
}
Now we write an unrelated function to return the sum of squares of an input. Because we're lazy we'll do this passing it y as a global variable,
sumSq <- function() {
return(sum(y^2))
}
y <<- seq(5)
sumSq()
[1] 55
Our log likelihood function seems to behave exactly as we'd expect, taking an argument and returning a value,
> logLik(seq(12))
[1] 88.40761
But what's up with our other function?
> sumSq()
[1] 633538
Of course, this is a trivial example, as will be any example that doesn't exist in a complex program. But hopefully it'll spark a discussion about how much harder it is to keep track of globals than locals.
In R you may also try to show them that there is often no need to use globals as you may access the variables defined in the function scope from within the function itself by only changing the enviroment. For example the code below
zz="aaa"
x = function(y) {
zz="bbb"
cat("value of zz from within the function: \n")
cat(zz , "\n")
cat("value of zz from the function scope: \n")
with(environment(x),cat(zz,"\n"))
}

R writing style - require vs. ::

OK, we're all familiar with double colon operator in R. Whenever I'm about to write some function, I use require(<pkgname>), but I was always thinking about using :: instead. Using require in custom functions is better practice than library, since require returns warning and FALSE, unlike library, which returns error if you provide a name of non-existent package.
On the other hand, :: operator gets the variable from the package, while require loads whole package (at least I hope so), so speed differences came first to my mind. :: must be faster than require.
And I did some analysis in order to check that - I've written two simple functions that load read.systat function from foreign package, with require and :: respectively, hence import Iris.syd dataset that ships with foreign package, replicated functions 1000 times each (which was shamelessly arbitrary), and... crunched some numbers.
Strangely (or not) I found significant differences in terms of user CPU and elapsed time, while there were no significant differences in terms of system CPU. And yet more strange conclusion: :: is actually slower! Documentation for :: is very blunt, and just by looking at sources it's obvious that :: should perform better!
require
#!/usr/local/bin/r
## with require
fn1 <- function() {
require(foreign)
read.systat("Iris.syd", to.data.frame=TRUE)
}
## times
n <- 1e3
sink("require.txt")
print(t(replicate(n, system.time(fn1()))))
sink()
double colon
#!/usr/local/bin/r
## with ::
fn2 <- function() {
foreign::read.systat("Iris.syd", to.data.frame=TRUE)
}
## times
n <- 1e3
sink("double_colon.txt")
print(t(replicate(n, system.time(fn2()))))
sink()
Grab CSV data here. Some stats:
user CPU: W = 475366 p-value = 0.04738 MRr = 975.866 MRc = 1025.134
system CPU: W = 503312.5 p-value = 0.7305 MRr = 1003.8125 MRc = 997.1875
elapsed time: W = 403299.5 p-value < 2.2e-16 MRr = 903.7995 MRc = 1097.2005
MRr is mean rank for require, MRc ibid for ::. I must have done something wrong here. It just doesn't make any sense... Execution time for :: seems way faster!!! I may have screwed something up, you shouldn't discard that option...
OK... I've wasted my time in order to see that there is some difference, and I carried out completely useless analysis, so, back to the question:
"Why should one prefer require over :: when writing a function?"
=)
"Why should one prefer require over ::
when writing a function?"
I usually prefer require due to the nice TRUE/FALSE return value that lets me deal with the possibility of the package not being available up front before getting into the code. Crash as early as possible instead of halfway through your analysis.
I only use :: when I need to make sure I am using the correct version of a function, not a version from some other package that is masking the name.
On the other hand, :: operator gets
the variable from the package, while
require loads whole package (at least
I hope so), so speed differences came
first to my mind. :: must be faster
than require.
I think you may be ignoring the effects of lazy loading which is used by the foreign package according to the first page of its manual. Essentially, packages that use lazy loading defer the loading of objects, such as functions, until the objects are called upon for the first time. So your argument that ":: must be faster than require" is not necessarily true as foreign is not loading all of its contents into memory when you attach it with require. For full details on lazy loading, see Prof. Ripley's article in RNews, Volume 4, Issue 2.
Since the time to load a package is almost always small compared to the time you spend trying to figure out what the code you wrote six months ago was about, in this case coding for clarity is the most important thing.
For scripts, having a call to require or library at the start lets you know which packages you need straight away.
Similarly, calling require (or a wrapper like requirePackage in Hmisc or try_require in ggplot2) at the start of a function is the most unambiguous way of showing that you need to use that package.
:: should be reserved for cases when you have naming conflicts between packages – compare, e.g.,
Hmisc::is.discrete
and
plyr::is.discrete

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