R script with user input from command line [duplicate] - r

This question already has answers here:
How to include interactive input in script to be run from the command line
(3 answers)
Closed 5 years ago.
I cannot find a solution to this particular problem, even though more or less similar questions have been questioned before in:
Run R script from command line
http://www.cureffi.org/2014/01/15/running-r-batch-mode-linux/
Running a script from bash is easy enough, however once one needs user interaction I couldn't find a solution. Please consider the example:
userInput<-function(question) {
n = 0
while(n < 1 ){
n <- readline(question)
n <- ifelse(grepl("\\D",n),-1,as.integer(n))
if(is.na(n)){break} # breaks when hit enter
}
return(n)
}
investedLow<- userInput("Invested value in low risk since last time: ")
Now if I save this script as test.R and run it for R --no-save < teste.R the entire script is run and the time for user input does not happen.
The script works fine in Rstudio, for example.
How to wait for user input in a script to be run in the command line?

Here is a total hack, repurposing a very specific purpose-built package for your more-general question:
library(getPass)
userInput<-function(question) {
n = 0
while(n < 1 ){
n <- getPass::getPass(msg = question)
n <- ifelse(grepl("\\D",n),-1,as.integer(n))
if(is.na(n)){break} # breaks when hit enter
}
return(n)
}
investedLow <- userInput("Invested value in low risk since last time: ")
print(investedLow)
Maybe the worst part about this is that getPass hides the user input. There must be a way to modify the source code to fix that.
Update: The getPass author pointed out that the solution could be as simple as using readLines slightly differently:
cat(question)
readLines(file("stdin"), n=1)

Related

How to deal with C stack usage error in R [duplicate]

I'm attempting to run some fairly deep recursive code in R and it keeps giving me this error:
Error: C stack usage is too close to the limit
My output from CStack_info() is:
Cstack_info()
size current direction eval_depth
67108864 8120 1 2
I have plenty of memory on my machine, I'm just trying to figure out how I can increase the CStack for R.
EDIT: Someone asked for a reproducible example. Here's some basic sample code that causes the problem. Running f(1,1) a few times you'll get the error. Note that I've already set --max-ppsize = 500000 and options(expressions=500000) so if you don't set those you might get an error about one of those two things instead. As you can see, the recursion can go pretty deep here and I've got no idea how to get it to work consistently. Thanks.
f <- function(root=1,lambda=1) {
x <- c(0,1);
prob <- c(1/(lambda+1),lambda/(lambda+1));
repeat {
if(root == 0) {
break;
}
else {
child <- sample(x,2,replace=TRUE,prob);
if(child[1] == 0 && child[2] == 0) {
break;
}
if(child[1] == 1) {
child[1] <- f(root=child[1],lambda);
}
if(child[2] == 1 && child[1] == 0) {
child[2] <- f(root=child[2],lambda);
}
}
if(child[1] == 0 && child[2] == 0) {
break;
}
if(child[1] == 1 || child[2] == 1) {
root <- sample(x,1,replace=TRUE,prob);
}
}
return(root)
}
The stack size is an operating system parameter, adjustable per-process (see setrlimit(2)). You can't adjust it from within R as far as I can tell, but you can adjust it from the shell before starting R, with the ulimit command. It works like this:
$ ulimit -s # print default
8192
$ R --slave -e 'Cstack_info()["size"]'
size
8388608
8388608 = 1024 * 8192; R is printing the same value as ulimit -s, but in bytes instead of kilobytes.
$ ulimit -s 16384 # enlarge stack limit to 16 megs
$ R --slave -e 'Cstack_info()["size"]'
size
16777216
To make a permanent adjustment to this setting, add the ulimit command to your shell startup file, so it's executed every time you log in. I can't give more specific directions than that, because it depends on exactly which shell you have and stuff. I also don't know how to do it for logging into a graphical environment (which will be relevant if you're not running R inside a terminal window).
I suspect that, regardless of stack limit, you'll end up with recursions that are too deep. For instance, with lambda = Inf, f(1) leads to an immediate recursion, indefinitely. The depth of the recursion seems to be a random walk, with some probability r of going deeper, 1 - r of finishing the current recursion. By the time you've hit the stack limit, you've made a large number of steps 'deeper'. This implies that r > 1 / 2, and the very large majority of time you'll just continue to recurse.
Also, it seems like it is almost possible to derive an analytic or at least numerical solution even in the face of infinite recursion. One can define p as the probability that f(1) == 1, write implicit expressions for the 'child' states after a single iteration, and equate these with p, and solve. p can then be used as the chance of success in a single draw from a binomial distribution.
This error is not due to memory it is due to recursion. A function is calling itself. This isn't always obvious from examining the definition of only one function. To illustrate the point, here is a minimal example of 2 functions that call each other:
change_to_factor <- function(x){
x <- change_to_character(x)
as.factor(x)
}
change_to_character <- function(x){
x <- change_to_factor(x)
as.character(x)
}
change_to_character("1")
Error: C stack usage 7971600 is too close to the limit
The functions will continue to call each other recursively and will theoretically never complete, even if you increase the limit it will still be exceeded. It is only checks within your system that prevent this from occurring indefinitely and consuming all of the compute resources of your machine. You need to alter the functions to ensure that they won't indefinitely call itself (or each other) recursively.
This happened to me for a completely different reason. I accidentally created a superlong string while combining two columns:
output_table_subset = mutate(big_data_frame,
combined_table = paste0(first_part, second_part, col = "_"))
instead of
output_table_subset = mutate(big_data_frame,
combined_table = paste0(first_part, second_part, sep = "_"))
Took me for ever to figure it out as I never expected the paste to have caused the problem.
I encountered the same problem of receiving the "C stack usage is too close to the limit" error (albeit for another application than the one stated by user2045093 above). I tried zwol's proposal but it didn't work out.
To my own surprise, I could solve the problem by installing the newest version of R for OS X (currently: version 3.2.3) as well as the newest version of R Studio for OS X (currently: 0.99.840), since I am working with R Studio.
Hopefully, this may be of some help to you as well.
One issue here can be that you're calling f inside itself
plop <- function(a = 2){
pouet <- sample(a)
plop(pouet)
}
plop()
Erreur : évaluations trop profondément imbriquées : récursion infinie / options(expressions=) ?
Erreur pendant l'emballage (wrapup) : évaluations trop profondément imbriquées : récursion infinie / options(expressions=) ?
Mine is perhaps a more unique case, but may help the few who have this exact problem:
My case has absolutely nothing to do with space usage, still R gave the:
C stack usage is too close to the limit
I had a defined function which is an upgrade of the base function:
saveRDS()
But,
Accidentally, this defined function was called saveRDS() instead of safe_saveRDS().
Thus, past that definition, when the code got to the line wihch actually uses saveRDS(...) (which calls the original base version, not the upgraded one), it gave the above error and crushed.
So, if you're getting that error when calling some saving function, see if you didn't accidentally run over it.
On Linux, I have permanently increased the size of the stack and memlock memories by doing so :
sudo vi /etc/security/limits.conf
Then, add the following lines at the end of the file.
* soft memlock unlimited
* hard memlock unlimited
* soft stack unlimited
* hard stack unlimited
For everyone's information, I am suddenly running into this with R 3.6.1 on Windows 7 (64-bit). It was not a problem before, and now stack limits seem to be popping up everywhere, when I try to "save(.)" data or even do a "save.image(.)". It's like the serialization is blowing these stacks away.
I am seriously considering dropping back to 3.6.0. Didn't happen there.
I often include a commented-out source("path/to/file/thefile.R") line at the top of an R script, e.g. thefile.R, so I can easily copy-paste this into the terminal to run it. I get this error if I forget to comment out the line, since running the file runs the file, which runs the file, which runs the file, ...
If that is the cause, the solution is simple: comment out the line.
Not sure if we re listing issues here but it happened to me with leaflet().
I was trying to map a dataframe in which a date column was of class POSIXlt.
Changing back to POSIXct solved the issue.
As Martin Morgan wrote... The problem is that you get too deep inside of recursion. If the recursion does not converge at all, you need to break it by your own. I hope this code is going to work, because It is not tested. However at least point should be clear here.
f <- function(root=1,lambda=1,depth=1) {
if(depth > 256){
return(NA)
}
x <- c(0,1);
prob <- c(1/(lambda+1),lambda/(lambda+1));
repeat {
if(root == 0) {
break;
} else {
child <- sample(x,2,replace=TRUE,prob);
if(child[1] == 0 && child[2] == 0) {
break;
}
if(child[1] == 1) {
child[1] <- f(root=child[1],lambda,depth+1);
}
if(child[2] == 1 && child[1] == 0) {
child[2] <- f(root=child[2],lambda,depth+1);
}
}
if(child[1] == NA | child[2] == NA){
return NA;
}
if(child[1] == 0 && child[2] == 0) {
break;
}
if(child[1] == 1 || child[2] == 1) {
root <- sample(x,1,replace=TRUE,prob);
}
}
return(root)
}
If you're using plot_ly check which columns you are passing. It seems that for POSIXdt/ct columns, you have to use as.character() before passing to plotly or you get this exception!
Here is how I encountered this error message. I met this error message when I tried to print a data.table in the console. It turned out it was because I mistakenly made a super super long string (by using collapse in paste() when I shouldn't) in a column.
The package caret has a function called createDataPartition that always results in error when the dataset to be partitioned has more than 1m rows.
Just for your info.
I faced the same issue. This problem won't be solved by reinstalling R or Rstudio or by increasing the stack size. Here is a solution that solved this problem -
If you are sourcing a.R inside b.R and at the same time sourcing b.R inside a.R, then the stack will fill up very fast.
Problem
This is the first file a.R in which b.R is sourced
#---- a.R File -----
source("/b.R")
...
...
#--------------------
This is the second file b.R, in which a.R is sourced
#---- b.R File -----
source("/a.R")
...
...
#--------------------
Solution
Source only one file to avoid the recursive calling of files within each other
#---- a.R File -----
source("/b.R")
...
...
#--------------------
#---- b.R File -----
...
...
#--------------------
OR
#---- a.R File -----
...
...
...
#--------------------
#---- b.R File -----
source("/a.R")
...
...
#--------------------
Another way to cause the same problem:
library(debug)
mtrace(lapply)
The recursive call isn't as obvious here.

Running R script_Readline and Scan does not pause for user input

I have looked at other posts that appeared similar to this question but they have not helped me. This may be just my ignorance of R. Thus I decided to sign up and make my first post on stack-overflow.
I am running an R-script and would like the user to decide either to use one of the two following loops. The code to decide user input looks similar to the one below:
#Define the function
method.choice<-function() {
Method.to.use<-readline("Please enter 'New' for new method and'Old' for old method: ")
while(Method.to.use!="New" && Method.to.use!="Old"){ #Make sure selection is one of two inputs
cat("You have not entered a valid input, try again", "\n")
Method.to.use<-readline("Please enter 'New' for new method and 'Old' for old method: ")
cat("You have selected", Method.to.use, "\n")
}
return(Method.to.use)
}
#Run the function
method.choice()
Then below this I have the two possible choices:
if(Method.to.use=="New") {
for(i in 1:nrow(linelist)){...}
}
if(Method.to.use=="Old"){
for(i in 1:nrow(linelist)){...}
}
My issue is, and what I have read from other posts, is that whether I use "readline", "scan" or "ask", R does not wait for my input. Instead R will use the following lines as the input.
The only way I found that R would pause for input is if the code is all on the same line or if it is run line by line (instead of selecting all the code at once). See example from gtools using "ask":
silly <- function()
{
age <- ask("How old are you? ")
age <- as.numeric(age)
cat("In 10 years you will be", age+10, "years old!\n")
}
This runs with a pause:
silly(); paste("this is quite silly")
This does not wait for input:
silly()
paste("this is quite silly")
Any guidance would be appreciated to ensure I can still run my entire script and have it pause at readline without continuing. I am using R-studio and I have checked that interactive==TRUE.
The only other work-around I found is wrapping my entire script into one main function, which is not ideal for me. This may require me to use <<- to write to my environment.
Thank you in advance.

Kill a calculation programme after user defined time in R

Say my executable is c:\my irectory\myfile.exe and my R script calls on this executeable with system(myfile.exe)
The R script gives parameters to the executable programme which uses them to do numerical calculations. From the ouput of the executable, the R script then tests whether the parameters are good ore not. If they are not good, the parameters are changed and the executable rerun with updated parameters.
Now, as this executable carries out mathematical calculations and solutions may converge only slowly I wish to be able to kill the executable once it has takes to long to carry out the calculations (say 5 seconds)
How do I do this time dependant kill?
PS:
My question is a little related to this one: (time non dependant kill)
how to run an executable file and then later kill or terminate the same process with R in Windows
You can add code to your R function which issued the executable call:
setTimeLimit(elapse=5, trans=T)
This will kill the calling function, returning control to the parent environment (which could well be a function as well). Then use the examples in the question you linked to for further work.
Alternatively, set up a loop which examines Sys.time and if the expected update to the parameter set has not taken place after 5 seconds, break the loop and issue the system kill command to terminate myfile.exe .
There might possibly be nicer ways but it is a solution.
The assumption here is, that myfile.exe successfully does its calculation within 5 seconds
try.wtl <- function(timeout = 5)
{
y <- evalWithTimeout(system(myfile.exe), timeout = timeout, onTimeout= "warning")
if(inherits(y, "try-error")) NA else y
}
case 1 (myfile.exe is closed after successfull calculation)
g <- try.wtl(5)
case 2 (myfile.exe is not closed after successfull calculation)
g <- try.wtl(0.1)
MSDOS taskkill required for case 2 to recommence from the beginnging
if (class(g) == "NULL") {system('taskkill /im "myfile.exe" /f',show.output.on.console = FALSE)}
PS: inspiration came from Time out an R command via something like try()

Error: C stack usage is too close to the limit

I'm attempting to run some fairly deep recursive code in R and it keeps giving me this error:
Error: C stack usage is too close to the limit
My output from CStack_info() is:
Cstack_info()
size current direction eval_depth
67108864 8120 1 2
I have plenty of memory on my machine, I'm just trying to figure out how I can increase the CStack for R.
EDIT: Someone asked for a reproducible example. Here's some basic sample code that causes the problem. Running f(1,1) a few times you'll get the error. Note that I've already set --max-ppsize = 500000 and options(expressions=500000) so if you don't set those you might get an error about one of those two things instead. As you can see, the recursion can go pretty deep here and I've got no idea how to get it to work consistently. Thanks.
f <- function(root=1,lambda=1) {
x <- c(0,1);
prob <- c(1/(lambda+1),lambda/(lambda+1));
repeat {
if(root == 0) {
break;
}
else {
child <- sample(x,2,replace=TRUE,prob);
if(child[1] == 0 && child[2] == 0) {
break;
}
if(child[1] == 1) {
child[1] <- f(root=child[1],lambda);
}
if(child[2] == 1 && child[1] == 0) {
child[2] <- f(root=child[2],lambda);
}
}
if(child[1] == 0 && child[2] == 0) {
break;
}
if(child[1] == 1 || child[2] == 1) {
root <- sample(x,1,replace=TRUE,prob);
}
}
return(root)
}
The stack size is an operating system parameter, adjustable per-process (see setrlimit(2)). You can't adjust it from within R as far as I can tell, but you can adjust it from the shell before starting R, with the ulimit command. It works like this:
$ ulimit -s # print default
8192
$ R --slave -e 'Cstack_info()["size"]'
size
8388608
8388608 = 1024 * 8192; R is printing the same value as ulimit -s, but in bytes instead of kilobytes.
$ ulimit -s 16384 # enlarge stack limit to 16 megs
$ R --slave -e 'Cstack_info()["size"]'
size
16777216
To make a permanent adjustment to this setting, add the ulimit command to your shell startup file, so it's executed every time you log in. I can't give more specific directions than that, because it depends on exactly which shell you have and stuff. I also don't know how to do it for logging into a graphical environment (which will be relevant if you're not running R inside a terminal window).
I suspect that, regardless of stack limit, you'll end up with recursions that are too deep. For instance, with lambda = Inf, f(1) leads to an immediate recursion, indefinitely. The depth of the recursion seems to be a random walk, with some probability r of going deeper, 1 - r of finishing the current recursion. By the time you've hit the stack limit, you've made a large number of steps 'deeper'. This implies that r > 1 / 2, and the very large majority of time you'll just continue to recurse.
Also, it seems like it is almost possible to derive an analytic or at least numerical solution even in the face of infinite recursion. One can define p as the probability that f(1) == 1, write implicit expressions for the 'child' states after a single iteration, and equate these with p, and solve. p can then be used as the chance of success in a single draw from a binomial distribution.
This error is not due to memory it is due to recursion. A function is calling itself. This isn't always obvious from examining the definition of only one function. To illustrate the point, here is a minimal example of 2 functions that call each other:
change_to_factor <- function(x){
x <- change_to_character(x)
as.factor(x)
}
change_to_character <- function(x){
x <- change_to_factor(x)
as.character(x)
}
change_to_character("1")
Error: C stack usage 7971600 is too close to the limit
The functions will continue to call each other recursively and will theoretically never complete, even if you increase the limit it will still be exceeded. It is only checks within your system that prevent this from occurring indefinitely and consuming all of the compute resources of your machine. You need to alter the functions to ensure that they won't indefinitely call itself (or each other) recursively.
This happened to me for a completely different reason. I accidentally created a superlong string while combining two columns:
output_table_subset = mutate(big_data_frame,
combined_table = paste0(first_part, second_part, col = "_"))
instead of
output_table_subset = mutate(big_data_frame,
combined_table = paste0(first_part, second_part, sep = "_"))
Took me for ever to figure it out as I never expected the paste to have caused the problem.
I encountered the same problem of receiving the "C stack usage is too close to the limit" error (albeit for another application than the one stated by user2045093 above). I tried zwol's proposal but it didn't work out.
To my own surprise, I could solve the problem by installing the newest version of R for OS X (currently: version 3.2.3) as well as the newest version of R Studio for OS X (currently: 0.99.840), since I am working with R Studio.
Hopefully, this may be of some help to you as well.
One issue here can be that you're calling f inside itself
plop <- function(a = 2){
pouet <- sample(a)
plop(pouet)
}
plop()
Erreur : évaluations trop profondément imbriquées : récursion infinie / options(expressions=) ?
Erreur pendant l'emballage (wrapup) : évaluations trop profondément imbriquées : récursion infinie / options(expressions=) ?
Mine is perhaps a more unique case, but may help the few who have this exact problem:
My case has absolutely nothing to do with space usage, still R gave the:
C stack usage is too close to the limit
I had a defined function which is an upgrade of the base function:
saveRDS()
But,
Accidentally, this defined function was called saveRDS() instead of safe_saveRDS().
Thus, past that definition, when the code got to the line wihch actually uses saveRDS(...) (which calls the original base version, not the upgraded one), it gave the above error and crushed.
So, if you're getting that error when calling some saving function, see if you didn't accidentally run over it.
On Linux, I have permanently increased the size of the stack and memlock memories by doing so :
sudo vi /etc/security/limits.conf
Then, add the following lines at the end of the file.
* soft memlock unlimited
* hard memlock unlimited
* soft stack unlimited
* hard stack unlimited
For everyone's information, I am suddenly running into this with R 3.6.1 on Windows 7 (64-bit). It was not a problem before, and now stack limits seem to be popping up everywhere, when I try to "save(.)" data or even do a "save.image(.)". It's like the serialization is blowing these stacks away.
I am seriously considering dropping back to 3.6.0. Didn't happen there.
I often include a commented-out source("path/to/file/thefile.R") line at the top of an R script, e.g. thefile.R, so I can easily copy-paste this into the terminal to run it. I get this error if I forget to comment out the line, since running the file runs the file, which runs the file, which runs the file, ...
If that is the cause, the solution is simple: comment out the line.
Not sure if we re listing issues here but it happened to me with leaflet().
I was trying to map a dataframe in which a date column was of class POSIXlt.
Changing back to POSIXct solved the issue.
As Martin Morgan wrote... The problem is that you get too deep inside of recursion. If the recursion does not converge at all, you need to break it by your own. I hope this code is going to work, because It is not tested. However at least point should be clear here.
f <- function(root=1,lambda=1,depth=1) {
if(depth > 256){
return(NA)
}
x <- c(0,1);
prob <- c(1/(lambda+1),lambda/(lambda+1));
repeat {
if(root == 0) {
break;
} else {
child <- sample(x,2,replace=TRUE,prob);
if(child[1] == 0 && child[2] == 0) {
break;
}
if(child[1] == 1) {
child[1] <- f(root=child[1],lambda,depth+1);
}
if(child[2] == 1 && child[1] == 0) {
child[2] <- f(root=child[2],lambda,depth+1);
}
}
if(child[1] == NA | child[2] == NA){
return NA;
}
if(child[1] == 0 && child[2] == 0) {
break;
}
if(child[1] == 1 || child[2] == 1) {
root <- sample(x,1,replace=TRUE,prob);
}
}
return(root)
}
If you're using plot_ly check which columns you are passing. It seems that for POSIXdt/ct columns, you have to use as.character() before passing to plotly or you get this exception!
Here is how I encountered this error message. I met this error message when I tried to print a data.table in the console. It turned out it was because I mistakenly made a super super long string (by using collapse in paste() when I shouldn't) in a column.
The package caret has a function called createDataPartition that always results in error when the dataset to be partitioned has more than 1m rows.
Just for your info.
I faced the same issue. This problem won't be solved by reinstalling R or Rstudio or by increasing the stack size. Here is a solution that solved this problem -
If you are sourcing a.R inside b.R and at the same time sourcing b.R inside a.R, then the stack will fill up very fast.
Problem
This is the first file a.R in which b.R is sourced
#---- a.R File -----
source("/b.R")
...
...
#--------------------
This is the second file b.R, in which a.R is sourced
#---- b.R File -----
source("/a.R")
...
...
#--------------------
Solution
Source only one file to avoid the recursive calling of files within each other
#---- a.R File -----
source("/b.R")
...
...
#--------------------
#---- b.R File -----
...
...
#--------------------
OR
#---- a.R File -----
...
...
...
#--------------------
#---- b.R File -----
source("/a.R")
...
...
#--------------------
Another way to cause the same problem:
library(debug)
mtrace(lapply)
The recursive call isn't as obvious here.

how to prevent the output of R to scroll away in bash? [duplicate]

This question already has answers here:
Closed 12 years ago.
Possible Duplicate:
Equivalent to unix “less” command within R console
I am using R under unix in bash.
Sometimes the output of a command has more lines than the bash.
How do I prevent the output from scrolling away? I.e. is there some equivalent of less and less -S in R?
A way to do this in R is also to redirect to a file:
sink("a_file.txt")
...your_commands...
sink()
I think the page() function is like having | less in an R session. It allows two representations of the object; i) a version you'd get from dput(), and ii) a version you'd get if you print()-ed the object.
dat <- data.frame(matrix(rnorm(2000), ncol = 5))
page(dat, method = "print")
It might be possible to wrap your expression in capture.output, and then page the result to the terminal.
pager <- function(cmd,nlines=10){
output = capture.output(cmd)
pages = seq(1,length(output),by=nlines)
for(p in pages){
f = p
l = min(p+nlines-1,length(output))
cat(paste(output[f:l],"\n"))
readline("*more*")
}
return(invisible(0))
}
Usage: pager(ls()), then hit Return (not space or anything else) at each 'more' prompt.
currently it doesn't return the value. Oh and it fails if there's no output. But you can fix these :)
Or use emacs with ESS and let it all scroll back...
Wouldnt
any_command | more
work fine?
“the bash” has no lines, your terminal has.
You can set the number of lines of your terminal in the settings of that application.
Your question is unclear. If you're talking about using R interactively and accidentally running a command which spits out a huge number of lines, run something like this in your R session: options(max.print=4000)

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