Using 'new' in Plumber - r

I have a simple function in my Plumber API that looks like the following:
library(methods)
library(plumber)
# Other functions...
#' #param elist The list of events to process as a string
#' #get /process
process_events <- function(elist=""){
setClass("EventPattern", representation(sequence="character", probability="numeric", endProbs="data.frame"))
q <- new("EventPattern", sequence=elist, probability=1, endProbs=data.frame(None=0))
# Further code that should make use of q
}
I start Plumber (locally) and point it to the script containing the api (the above) as:
r <- plumb('/path/to/script/forecast.R')
r$run(port=8000, swagger = TRUE)
And call the function on the address (using PostMan):
http://localhost:8000/process?elist="abcd"
But what I end up getting is 'An Exception Occurred' with the R console saying that:
<simpleError: No method for S4 class:EventPattern>
I realize that the error suggests that a method (a generic) is required, but when I type:
q <- new("EventPattern", sequence=elist, probability=1, endProbs=data.frame(None=0))
locally on my machine (in the R console) it works fine. It suggests to me that something is not fully loaded or available to Plumber, but I have no idea how to fix it. Any ideas?

I have not used setClass and new before. But I've worked with plumber last year. I found using your example that it is trying to return q, and throwing an error because of it.
Adding a print statement seems to prevent an error:
library(methods)
library(plumber)
# Other functions...
#' #param elist The list of events to process as a string
#' #get /process
process_events <- function(elist=""){
setClass("EventPattern", representation(sequence="character", probability="numeric", endProbs="data.frame"))
q <- new("EventPattern", sequence=elist, probability=1, endProbs=data.frame(None=0))
print("Not returning 'q'")
# Further code that should make use of q
}

Related

Use Rcpp function in foreach %dopar%

I have created an Rcpp test package called "test" using the Rcpp package skeleton to try to run c++ code in parallel but keep running into errors. I'm running R 4.1.2 on Mac OS and have updated all parallel computing packages. I added to the package skeleton an R script containing
# wrap c++ function in R function
test_func <- function()
{
return(rcpp_hello_world())
}
# attempt to parallelize
parallelize <- function()
{
# create cluster
cl <- parallel::makeCluster(parallel::detectCores() - 1)
parallel::clusterExport(cl,varlist = c("test_func","rcpp_hello_world"),envir = environment())
doParallel::registerDoParallel(cl)
# call test_func in parallel
res <- foreach::`%dopar%`(foreach::foreach(i = 1:5,.combine = c),ex = {test_func()})
# clean up
parallel::stopCluster(cl)
return(res)
}
I loaded my package using devtools::load_all(), but typing parallelize() in my console I get the error "Error in { : task 1 failed - "object '_test_rcpp_hello_world' not found" ". When I add "_test_rcpp_hello_world" to the clusterExport call I get the error "Error in { : task 1 failed - "NULL value passed as symbol address" ".
Everything works fine when I switch %dopar% to %do%, but I'm hoping to be able to still parallelize.
I know that similar questions have been asked here, but I can't use a solution which calls sourceCpp on each worker (the c++ code in my actual R package is huge and this operation would defeat the purpose of parallelizing).
Any help would be greatly appreciated!!
(Continuing from the comments)
The key is that to execute 'local' code on a node, you cannot send a (compiled) function to the node. The node needs to have it, and the best way it to have the node(s) have access to the same package(s), load them and thus be ready to run code using them. I just glanced at some old slide decks from presentations I gave and I didn't find an perfect example -- but a pointer to a (thirteen-plus (!!) year old) directory of example scripts including this for running (cpu-wise expensive) DieHarder tests on nodes via Rmpi:
#!/usr/bin/env r
suppressMessages(library(Rmpi))
suppressMessages(library(snow))
cl <- NULL
mpirank <- mpi.comm.rank(0)
if (mpirank == 0) {
cl <- makeMPIcluster()
} else { # or are we a slave?
sink(file="/dev/null")
slaveLoop(makeMPImaster())
mpi.finalize()
q()
}
clusterEvalQ(cl, library(RDieHarder))
res <- parLapply(cl, c("mt19937","mt19937_1999",
"mt19937_1998", "R_mersenne_twister"),
function(x) {
dieharder(rng=x, test="operm5",
psamples=100, seed=12345)
})
stopCluster(cl)
print( do.call(rbind, lapply(res, function(x) { x[[1]] } )))
mpi.quit()
The key is in the middle: clusterEvalQ(cl, library(RDieHarder)) All worker nodes are asked to load the RDieHarder package. Conceptually, you want to do the same here, and the foreach family lets you do it too.

R extend Method to dependend Namespace

I've the following Problem:
I'm using the opencpu package to provide my R Package as a web application. In my package I created a RefClass lets call that
.A <- setRefClass(
".A",
fields = c(
id = "integer",
text = "character"
)
)
plus a constructor function:
A <- function(id,text ){return(.A(id,text))}
and on top I wrote a method "toJSON" for the class and also provided an S4 method like this:
.A$methods(
toJSON = function(){
return(sprintf('{\"id\": %s, \"text\": %s}',id,text))
})
setMethod("toJSON", c(".A"),function(x,...){
x$toJSON()
})
So far everthing is fine. When I install the package and run opencpu, I can call the A method without a problem: (POST with parameters e.g.: {id: 123, text: "Hallo World"})
SERVERADRESS/ocpu/library/PACKAGENAME/R/A
But when I want the returned value to be directly converted into JSON I get the following error:
No method for S4 class:.A
A look at the opencpu site, tells that the procedure which is called in this case is:
library(jsonlite)
args <- fromJSON('{"id": 123, "text": "Hallo World"}')
output <- do.call(PKGNAME::A,args)
toJSON(output)
However this runs fine if I run it in a regular R session. But the error becomes reproducable if I change the last line from toJSON(output) to jsonlite::toJSON(output)
Hence I think this might be the problem and I was wundering if I can add my "toJSON" S4 method with signature ".A" to the Namespace of "jsonlite" within my package?
Any Ideas?

Error while running Rmd file but none when executing function directly

I have created an R function which uses a tryCatch construct to skip over errors. The function works when executed as a standalone but gives an error when executing within an Rmarkdown file
The code is as below
for(x in 1:length(aa)){
bowlers <- unique(aa[[x]]$bowler)
for (y in 1:length(bowlers)){
#cat("x=",x,"team",theTeams[x],"\n")
tryCatch(l <- getBowlerWicketDetails(team=theTeams[x],name=bowlers[y],dir="."),
error = function(e) {
print("Error!")
}
)
l <- select(l,bowler,wickets,economyRate)
o <-rbind(o,l)
}
}
I get the following error when executing the code within an Rmd. The error is
Error in select_(.data,.dots=lazyeval::lazy_dots(...),:object l not found calls:Anonymos etc.
I think the code is trying to evaluate even when there is an error in the getBowlerWicketDetails() function when 'l' will not be available. How can this be resolved?
Finally solved the problem by adding an additional check exists(). As I mentioned, directly executing the function seemed to work while executing within Rmd gave the variable does not exist
The modified code was as follows
for(x in 1:length(aa)){
bowlers <- unique(aa[[x]]$bowler)
for (y in 1:length(bowlers)){
#cat("x=",x,"team",theTeams[x],"\n")
tryCatch(l <- getBowlerWicketDetails(team=theTeams[x],name=bowlers[y],dir="."),
error = function(e) {
#print("Error!")
}
)
if(exists("l")){
m <- select(l,bowler,wickets,economyRate)
o <-rbind(o,m)
}
}
}
This worked finally for both.
Thanks all!

Logging console history with errors in R or Rstudio

For educational purposes we are logging all commands that students type in the rstudio console during labs. In addition we would like to store if call was successful or raised an error, to identify students which struggling to get the syntax right.
The best I can come up with is something like this:
options(error = function(){
timestamp("USER ERROR", quiet = TRUE)
})
This adds an ## ERROR comment on the history log when an exception occurs. Thereby we could analyze history files to see which commands were followed by an ## ERROR comment.
However R's internal history system is not well suited for logging because it is in-memory, limited size and needs to be stored manually with savehistory(). Also I would prefer to store log one-line-per-call, i.e. escape linebreaks for multi-line commands.
Is there perhaps a hook or in the R or RStudio console for logging actual executed commands? That would allow me to insert each evaluated expression (and error) in a database along with a username and timestamp.
A possible solution would be to use addTaskCallback or the taskCallbackManager with a function that writes each top-level command to your database. The callback will only fire on the successful completion of a command, so you would still need to call a logging function on an error.
# error handler
logErr <- function() {
# turn logging callback off while we process errors separately
tcbm$suspend(TRUE)
# turn them back on when we're done
on.exit(tcbm$suspend(FALSE))
sc <- sys.calls()
sclen <- length(sc) # last call is this function call
if(sclen > 1L) {
cat("myError:\n", do.call(paste, c(lapply(sc[-sclen], deparse), sep="\n")), "\n")
} else {
# syntax error, so no call stack
# show the last line entered
# (this won't be helpful if it's a parse error in a function)
file1 <- tempfile("Rrawhist")
savehistory(file1)
rawhist <- readLines(file1)
unlink(file1)
cat("myError:\n", rawhist[length(rawhist)], "\n")
}
}
options(error=logErr)
# top-level callback handler
log <- function(expr, value, ok, visible) {
cat(deparse(expr), "\n")
TRUE
}
tcbm <- taskCallbackManager()
tcbm$add(log, name = "log")
This isn't a complete solution, but I hope it gives you enough to get started. Here's an example of what the output looks like.
> f <- function() stop("error")
f <- function() stop("error")
> hi
Error: object 'hi' not found
myError:
hi
> f()
Error in f() : error
myError:
f()
stop("error")

How to make object created within function usable outside

I created a function which produces a matrix as a result, but I can't figure out how to make the output of this function usable outside of the function environment, so that I could for instance save it in csv file.
My code for function is the following:
created function which takes url's from specific site and returns page title:
getTitle <- function(url) {
webpage <- readLines(url)
first.row <- webpage[1]
start <- regexpr("<title>", first.row)
end <- regexpr("</title>", first.row)
title <- substr(first.row,start+7,end-1)
return(title)
}
created function which takes vector of urls and returns n*2 matrix with urls and page titles:
getTitles <- function(pages) {
my.matrix <- matrix(NA, ncol=2, nrow=nrow(pages))
for (i in seq_along(1:nrow(pages))) {
my.matrix[i,1] <- as.character(pages[i,])
my.matrix[i,2] <- getTitle(as.character(pages[i,])) }
return(my.matrix)
print(my.matrix)}
After running this functions on a sample file from here http://goo.gl/D9lLZ which I import with read.csv function and name "mypages" I get the following output:
getTitles(mypages)
[,1] [,2]
[1,] "http://support.google.com/adwords/answer/1704395" "Create your first ad campaign - AdWords Help"
[2,] "http://support.google.com/adwords/answer/1704424" "How costs are calculated in AdWords - AdWords Help"
[3,] "http://support.google.com/adwords/answer/2375470" "Organizing your account for success - AdWords Help"
This is exactly what I need, but I'd love to be able to export this output to csv file or reuse for further manipulations. However, when I try to print(my.matrix), I am getting an error saying "Error: object 'my.matrix' not found"
I feel like it's quite basic gap in my knowledge, but have not been working with R for a while and could not solve that.
Thanks!
Sergey
That's easy: use <<- for assignment to a global.
But then again, global assignment is evil and not functional. Maybe you'd rather return
a list with several results from your function? Looking at your code, it seems that your second function may confuse the return and print. Make sure you return the correct data structure.
A little about functional programming. First of all, when you define your function:
getTitles <- function(pages) {
[...]
return(my.matrix)
print(my.matrix)
}
know that when the function is called it will never reach the print statement. Instead, it will exit right before, with return. So you can remove that print statement, it is useless.
Now the more important stuff. Inside your function, you define and return my.matrix. The object only exists within the scope of the function: as the function exits, what is returned is an unnamed object (and my.matrix is lost.)
In your session, when you call
getTitles(mypages)
the result is printed because you did not assign it. Instead, you should do:
out.matrix <- getTitles(mypages)
Now the result won't be printed but you can definitely do so by typing print(out.matrix) or just out.matrix on a single line. And because you have stored the result in an object, you can now reuse it for further manipulations.
If it help you grasp the concept, this is all the same as calling the c() function from the command line:
c(1, 5, 2) # will return and print a vector
x <- c(1, 5, 2) # will return and assign a vector (not printed.)
Bonus: Really, I don't think you need to define getTitles, but you can use one of the *apply functions. I would try this:
url <- as.character(mypages)
title <- sapply(url, getTitle)
report <- data.frame(url, title)
write.csv(report, file = "report.csv", row.names = FALSE)
Can't you just use <<- to assign it the object to the workspace? The following code works for me and saves the amort_value object.
amortization <- function(cost, downpayment, interest, term) {
amort_value <<- (cost)*(1-downpayment/100)*(interest/1200)*((1+interest/1200)^(term*12))/((1+interest/1200)^(term*12)-1)
sprintf("$%.2f", amort_value)
}
amortization(445000,20,3,15)
amort_value
At the end of the function, you can return the result.
First define the function:
getRangeOf <- function (v) {
numRange <- max(v) - min(v)
return(numRange)
}
Then call it and assign the output to a variable:
scores <- c(60, 65, 70, 92, 99)
scoreRange <- getRangeOf(scores)
From here on use scoreRange in the environment. Any variables or nested functions within your defined function is not accessible to the outside, unless of course, you use <<- to assign a global variable. So in this example, you can't see what numRange is from the outside unless you make it global.
Usually, try to avoid global variables at an early stage. Variables are "encapsulated" so we know which one is used within the current context ("environment"). Global variables are harder to tame.

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