I need to test package loading operations (for my multiversion package) and know that unloading namespaces and stuff is dangerous work. So I want to run every test in a fresh R session. Running my tests in parallel does not meet this demand since it will reuse slaves, and these get dirty.
So I thought callr::r would help me out. Unfortunately I am again stuck with the minimally documented reporters it seems.
The following is a minimal example. Placed in file test-mytest.R.
test_that('test 1', {
expect_equal(2+2, 5)
})
reporter_in <- testthat::get_reporter()
# -- 1 --
reporter_out <- callr::r(
function(reporter) {
reporter <- testthat::with_reporter(reporter, {
testthat::test_that("test inside", {
testthat::expect_equal('this', 'wont match')
})
})
},
args = list(reporter = reporter_in),
show = TRUE
)
# -- 2 --
testthat::set_reporter(reporter_out)
# -- 3 --
test_that('test 2', {
expect_equal(2+2, 8)
})
I called this test file using:
# to be able to check the outcome, work with a specific reporter
summary <- testthat::SummaryReporter$new()
testthat::test_file('./tests/testthat/test-mytest.R', reporter = summary)
Which seems to do what I want, but when looking at the results...
> summary$end_reporter()
== Failed ===============================================================================================
-- 1. Failure (test-load_b_pick_last_true.R:5:5): test 1 ------------------------------------------------
2 + 2 (`actual`) not equal to 5 (`expected`).
`actual`: 4
`expected`: 5
== DONE =================================================================================================
...it is only the first test that is returned.
How it works:
An ordinary test is executed.
The reporter, currently in use, is obtained (-- 1 --)
callr::r is used to call a testthat block including a test.
Within the call, I tried using set_reporter, but with_reporter is practically identical.
The callr::r call returns the reporter (tried it with get_reporter(), but with_reporter also returns the reporter (invisibly))
Now the returned reporter seems fine, but when setting it as the actual reporter with set_reporter, it seems that it is not overwriting the actual reporter.
Note that at -- 2 --, the reporter_out contains both test outcomes.
Question
I am not really sure what I expect it to do, but in the end I want the results to be added to the original reporter ((summary or) reporter_in that is, if that is not some kind of copy).
One workaround I can think of would be to move the actual test execution outside of the callr::r call, but gather the testcases inside.
I think it is neat, as long as you can place these helper functions (see the elaborate example) in your package, you can write tests with little overhead.
It doesn't answer how to work with the 'reporter' object though...
Simple example:
test_outcome <- callr::r(
function() {
# devtools::load_all()
list(
check1 = mypackage::sum(5,5), # some imaginary exported functions sum and name.
check2 = mypackage::name()
)
}
)
test_that('My test case', {
expect_equal(test_outcome$check1, 10)
expect_equal(test_outcome$check2, 'Siete')
})
Elaborate example
Note that from .add_test to .exp_true are only function definitions which can better be included in your package so they will be available when being loaded with devtools::load_all(). load_all also loads not-exported functions by default.
test_outcome <- callr::r(
function() {
# devtools::load_all()
# Defining helper functions
tst <- list(desc = 'My first test', tests = list())
.add_test <- function(type, A, B) {
# To show at least something about what is actually tested when returning the result, we can add the actual `.exp_...` call to the test.
call <- as.character(sys.call(-1))
tst$tests[[length(tst$tests) + 1]] <<- list(
type = type, a = A, b = B,
# (I couldn't find a better way to create a nice call string)
call = paste0(call[1], '(', paste0(collapse = ', ', call[2:length(call)]), ')'))
}
.exp_error <- function(expr, exp_msg) {
err_msg <- ''
tryCatch({expr}, error = function(err) {
err_msg <<- err$message
})
.add_test('error', err_msg, exp_msg)
}
.exp_match <- function(expr, regex) {
.add_test('match', expr, regex)
}
.exp_equal <- function(expr, ref) {
.add_test('equal', expr, ref)
}
.exp_false <- function(expr) {
.add_test('false', expr, FALSE)
}
.exp_true <- function(expr) {
.add_test('true', expr, TRUE)
}
# Performing the tests
.exp_match('My name is Siete', 'My name is .*')
.exp_equal(mypackage::sum(5,5), 10) # some imaginary exported functions sum and name.
.exp_match(mypackage::name(), 'Siete')
.exp_false('package:testthat' %in% search())
return(tst)
},
show = TRUE)
# Performing the actual testthat tests:
.run_test_batch <- function(test_outcome) {
test_that(test_outcome$desc, {
for (test in test_outcome$tests) {
# 'test' is a list with the fields 'type', 'a', 'b' and 'call'.
# Where 'type' can contain 'match', 'error', 'true', 'false' or 'equal'.
if (test$type == 'equal') {
with(test, expect_equal(a, b, label = call))
} else if (test$type == 'true') {
expect_true( test$a, label = test$call)
} else if (test$type == 'false') {
expect_false(test$a, label = test$call)
} else if (test$type %in% c('match', 'error')) {
with(test, expect_match(a, b, label = call))
}
}
})
}
.run_test_batch(test_outcome)
When moving the functions to your package you would need the following initialize function too.
tst <- new.env(parent = emptyenv())
tst$desc = ''
tst$tests = list()
.initialize_test <- function(desc) {
tst$desc = desc
tst$tests = list()
}
It works as follows:
An empty list is created: tst
By calling .exp_... functions, tests are added to that list
The list with tests is returned by the function in callr::r
Then we loop over the list and execute every test
Related
I've read a few other SO questions about tryCatch and cuzzins, as well as the documentation:
Exception handling in R
catching an error and then branching logic
How can I check whether a function call results in a warning?
Problems with Plots in Loop
but I still don't understand.
I'm running a loop and want to skip to next if any of a few kinds of errors occur:
for (i in 1:39487) {
# EXCEPTION HANDLING
this.could.go.wrong <- tryCatch(
attemptsomething(),
error=function(e) next
)
so.could.this <- tryCatch(
doesthisfail(),
error=function(e) next
)
catch.all.errors <- function() { this.could.go.wrong; so.could.this; }
catch.all.errors;
#REAL WORK
useful(i); fun(i); good(i);
} #end for
(by the way, there is no documentation for next that I can find)
When I run this, R honks:
Error in value[[3L]](cond) : no loop for break/next, jumping to top level
What basic point am I missing here? The tryCatch's are clearly within the for loop, so why doesn't R know that?
The key to using tryCatch is realising that it returns an object. If there was an error inside the tryCatch then this object will inherit from class error. You can test for class inheritance with the function inherit.
x <- tryCatch(stop("Error"), error = function(e) e)
class(x)
"simpleError" "error" "condition"
Edit:
What is the meaning of the argument error = function(e) e? This baffled me, and I don't think it's well explained in the documentation. What happens is that this argument catches any error messages that originate in the expression that you are tryCatching. If an error is caught, it gets returned as the value of tryCatch. In the help documentation this is described as a calling handler. The argument e inside error=function(e) is the error message originating in your code.
I come from the old school of procedural programming where using next was a bad thing. So I would rewrite your code something like this. (Note that I removed the next statement inside the tryCatch.):
for (i in 1:39487) {
#ERROR HANDLING
possibleError <- tryCatch(
thing(),
error=function(e) e
)
if(!inherits(possibleError, "error")){
#REAL WORK
useful(i); fun(i); good(i);
}
} #end for
The function next is documented inside ?for`.
If you want to use that instead of having your main working routine inside an if, your code should look something like this:
for (i in 1:39487) {
#ERROR HANDLING
possibleError <- tryCatch(
thing(),
error=function(e) e
)
if(inherits(possibleError, "error")) next
#REAL WORK
useful(i); fun(i); good(i);
} #end for
I found other answers very confusing. Here is an extremely simple implementation for anyone who wants to simply skip to the next loop iteration in the event of an error
for (i in 1:10) {
skip_to_next <- FALSE
# Note that print(b) fails since b doesn't exist
tryCatch(print(b), error = function(e) { skip_to_next <<- TRUE})
if(skip_to_next) { next }
}
for (i in -3:3) {
#ERROR HANDLING
possibleError <- tryCatch({
print(paste("Start Loop ", i ,sep=""))
if(i==0){
stop()
}
}
,
error=function(e) {
e
print(paste("Oops! --> Error in Loop ",i,sep = ""))
}
)
if(inherits(possibleError, "error")) next
print(paste(" End Loop ",i,sep = ""))
}
The only really detailed explanation I have seen can be found here: http://mazamascience.com/WorkingWithData/?p=912
Here is a code clip from that blog post showing how tryCatch works
#!/usr/bin/env Rscript
# tryCatch.r -- experiments with tryCatch
# Get any arguments
arguments <- commandArgs(trailingOnly=TRUE)
a <- arguments[1]
# Define a division function that can issue warnings and errors
myDivide <- function(d, a) {
if (a == 'warning') {
return_value <- 'myDivide warning result'
warning("myDivide warning message")
} else if (a == 'error') {
return_value <- 'myDivide error result'
stop("myDivide error message")
} else {
return_value = d / as.numeric(a)
}
return(return_value)
}
# Evalute the desired series of expressions inside of tryCatch
result <- tryCatch({
b <- 2
c <- b^2
d <- c+2
if (a == 'suppress-warnings') {
e <- suppressWarnings(myDivide(d,a))
} else {
e <- myDivide(d,a) # 6/a
}
f <- e + 100
}, warning = function(war) {
# warning handler picks up where error was generated
print(paste("MY_WARNING: ",war))
b <- "changing 'b' inside the warning handler has no effect"
e <- myDivide(d,0.1) # =60
f <- e + 100
return(f)
}, error = function(err) {
# warning handler picks up where error was generated
print(paste("MY_ERROR: ",err))
b <- "changing 'b' inside the error handler has no effect"
e <- myDivide(d,0.01) # =600
f <- e + 100
return(f)
}, finally = {
print(paste("a =",a))
print(paste("b =",b))
print(paste("c =",c))
print(paste("d =",d))
# NOTE: Finally is evaluated in the context of of the inital
# NOTE: tryCatch block and 'e' will not exist if a warning
# NOTE: or error occurred.
#print(paste("e =",e))
}) # END tryCatch
print(paste("result =",result))
One thing I was missing, which breaking out of for loop when running a function inside a for loop in R makes clear, is this:
next doesn't work inside a function.
You need to send some signal or flag (e.g., Voldemort = TRUE) from inside your function (in my case tryCatch) to the outside.
(this is like modifying a global, public variable inside a local, private function)
Then outside the function, you check to see if the flag was waved (does Voldemort == TRUE). If so you call break or next outside the function.
I am trying to extend websocket::Websocket with a method that sends some data and returns the message, so that I can assign it to an object. My question is pretty much identical to https://community.rstudio.com/t/capture-streaming-json-over-websocket/16986. Unfortunately, the user there never revealed how they solved it themselves. My idea was to have the onMessage method return the event$data, i.e. something like:
my_websocket <- R6::R6Class("My websocket",
inherit = websocket::WebSocket,
public = list(
foo = function(x) {
msg <- super$send(paste("x"))
return(msg)
} )
)
load_websocket <- function(){
ws <- my_websocket$new("ws://foo.local")
ws$onMessage(function(event) {
return(event$data)
})
return(ws)
}
my_ws <- load_websocket()
my_ws$foo("hello") # returns NULL
but after spending a good hour on the Websocket source code, I am still completely in the dark as to where exactly the callback happens, "R environment wise".
You need to use super assignment operator <<-. <<- is most useful in conjunction with closures to maintain state. Unlike the usual single arrow assignment (<-) that always works on the current level, the double arrow operator can modify variables in parent levels.
my_websocket <- R6::R6Class("My websocket",
inherit = websocket::WebSocket,
public = list(
foo = function(x) {
msg <<- super$send(paste("x"))
return(msg)
} )
)
load_websocket <- function(){
ws <- my_websocket$new("ws://foo.local")
ws$onMessage(function(event) {
return(event$data)
})
return(ws)
}
my_ws <- load_websocket()
my_ws$foo("hello")
Is it possible to write a function in R which will hold its execution, giving the users control over the console (while in interactive mode of course), meanwhile recording their inputs, and continuing execution either:
after a certain input has been made
or after a certain output has been made
or a certain duration of time has passed
Example: ask the user a question (without using readline() for the answer)
question <- function() {
message("How much is 2 + 2?")
#let users take control of the console
#continue to next statement only if they input "2+2", or "4" or a minute has passed
#meanwhile record their last input similar to ".Last.Value", e.g.:
startTime <- Sys.time()
timeout <- FALSE
lastInput <- lastInput()
while (eval(parse(text = lastInput)) != 4 & !timeout) {
if (difftime(Sys.time(), startTime, units = "mins") > 1) {
timeout <- TRUE
}
lastInput <- lastInput()
}
if (timeout) {
stop("Sorry, timeout.")
} else {
message("Correct! Let's continue with this function:")
}
}
Where lastInput() is a function which "listens" to user input when it changes.
Obviously the above structure is tentative and won't give me what I want, some way to "listen" or "observe" and only react when the user inputs something to the console.
The final user experience should be:
> question()
How much is 2+2?
> #I'm the user, I can do whatever
> head(mtcars)
> plot(1:10)
> 3
> 2 + 2
[1] 4
Correct! Let's continue with this function:
Am I too optimistic or is there some R magic for this?
Thanks to #parth I have looked at swirl's source code and got acquainted with the addTaskCallback function. From the help file:
addTaskCallback registers an R function that is to be called each time a top-level task is completed.
And so we can make R check the users input ("top-level task") with a specific function, responding accordingly.
But since the swirl code is very "heavy", I think I need to supply a minimal example:
swirllike <- function(...){
removeTaskCallback("swirllike")
e <- new.env(globalenv())
e$prompt <- TRUE
e$startTime <- Sys.time()
cb <- function(expr, val, ok, vis, data=e){
e$expr <- expr
e$val <- val
e$ok <- ok
e$vis <- vis
# The result of f() will determine whether the callback
# remains active
return(f(e, ...))
}
addTaskCallback(cb, name = "swirllike")
message("How much is 2+2?")
}
OK, so the swirllike function evokes the 2+2 question, but it also declares a new environment e with some objects the user needs not know. It then adds the swirllike task callback to the task callback list (or rather vector). This "task callback" holds the cb function which calls the f function - the f function will run with every input.
If you run this, make sure you see the swirllike task callback with:
> getTaskCallbackNames()
[1] "swirllike"
Now the f function is similar to my sketch in the question:
f <- function(e, ...){
if (e$prompt) {
if (difftime(Sys.time(), e$startTime, units = "mins") > 1) {
timeout <- TRUE
stop("Sorry, timeout.")
}
if(!is.null(.Last.value) && .Last.value == 4) {
message("Correct! Let's continue with this function:")
e$prompt <- FALSE
while (!e$prompt) {
#continue asking questions or something, but for this example:
break
}
}
}
return(TRUE)
}
And don't forget to remove the swirllike task callback with:
removeTaskCallback("swirllike")
I'm using active bindings in an R6 class to check values before assignment to fields. I thought I could use a closure to generate the bindings as below, but this doesn't work.
The binding isn't evaluated in the way I expect (at all?) because the error shows the closure's name argument. What am I missing?
library(R6)
library(pryr)
# pass a field name to create its binding
generate_binding <- function(name) {
function(value) {
if (!missing(value) && length(value) > 0) {
private$name <- value
}
private$name
}
}
bind_x = generate_binding(x_)
# created as intended:
unenclose(bind_x)
# function (value)
# {
# if (!missing(value) && length(value) > 0) {
# private$x_ <- value
# }
# private$x_
# }
MyClass <- R6::R6Class("MyClass",
private = list(
x_ = NULL
),
active = list(
x = bind_x
),
)
my_class_instance <- MyClass$new()
my_class_instance$x <- "foo"
# Error in private$name <- value :
# cannot add bindings to a locked environment
I think you’re misunderstanding how closures work. unenclose is a red herring here (as it doesn’t actually show you what the closure looks like). The closure contains the statement private$name <- value — it does not contain the statement private$x_ <- value.
The usual solution to this problem would be to rewrite the closure such that the unevaluated name argument is deparsed into its string representation, and then used to subset the private environment (private[[name]] <- value). However, this doesn’t work here since R6 active bindings strip closures of their enclosing environment.
This is where unenclose comes in then:
MyClass <- R6::R6Class("MyClass",
private = list(
x_ = NULL
),
active = list(
x = pryr::unenclose(bind_x)
),
)
Is there a way to set options within an environment? Something like
tmp_env = new.env()
within(tmp_env, options(mc.core = 16))
with(tmp_env, {
# run parallel code here
})
I want to switch between using options(mc.core = 16) and options(mc.core = 1) explicitly and don't want to accidentally set off a parallelized computation.
Use a function or other closure (e.g., local()) to set the option, and use on.exit() to guarantee restoration on exit
fun = function() {
old.opt = options(mc.cores=12)
on.exit(options(old.opt))
## do work
}
You could get fancy with something like (based on with.default)
withp = function(expr, cores=4) {
old.opt = options(mc.cores=cores)
on.exit(options(old.opt))
eval(substitute(expr), enclos=parent.frame())
}
and use
withp({
message("hello")
res <- mclapply(1:20, function(i) Sys.getpid())
table(unlist(res))
}, cores=3)