I have written an R script which includes a loop that retrieves external (web) data. The format of the data are most of the time the same, however sometimes the format changes in an unpredictable way and my loop is crashing (stops running).
Is there a way to continue code execution regardless the error? I am looking for something similar to "On error Resume Next" from VBA.
Thank you in advance.
Use try or tryCatch.
for(i in something)
{
res <- try(expression_to_get_data)
if(inherits(res, "try-error"))
{
#error handling code, maybe just skip this iteration using
next
}
#rest of iteration for case of no error
}
The modern way to do this uses purrr::possibly.
First, write a function that gets your data, get_data().
Then modify the function to return a default value in the case of an error.
get_data2 <- possibly(get_data, otherwise = NA)
Now call the modified function in the loop.
for(i in something) {
res <- get_data2(i)
}
You can use try:
# a has not been defined
for(i in 1:3)
{
if(i==2) try(print(a),silent=TRUE)
else print(i)
}
How about these solutions on this related question :
Is there a way to `source()` and continue after an error?
Either parse(file = "script.R") followed by a loop'd try(eval()) on each expression in the result.
Or the evaluate package.
If all you need to do is a small piece of clean up, then on.exit() may be the simplest option. It will execute the expression "when the current function exits (either naturally or as the result of an error)" (documentation here).
For example, the following will delete my_large_dataframe regardless of whether output_to_save gets created.
on.exit(rm("my_large_dataframe"))
my_large_dataframe = function_that_does_not_error()
output_to_save = function_that_does_error(my_large_dataframe)
Related
I have a loop in R that does very time-consuming calculations. I can set a max-iterations variable to make sure it doesn't continue forever (e.g. if it is not converging), and gracefully return meaningful output.
But sometimes the iterations could be stopped way before max-iterations is reached. Anyone who has an idea about how to give the user the opportunity to interrupt a loop - without having to wait for user input after each iteration? Preferably something that works in RStudio on all platforms.
I cannot find a function that listens for keystrokes or similar user input without halting until something is done by the user. Another solution would be to listen for a global variable change. But I don't see how I could change such a variable value when a script is running.
The best idea I can come up with is to make another script that creates a file that the first script checks for the existence of, and then breaks if it is there. But that is indeed an ugly hack.
Inspired by Edo's reply, here is an example of what I want to do:
test.it<-function(t) {
a <- 0
for(i in 1:10){
a <- a + 1
Sys.sleep(t)
}
print(a)
}
test.it(1)
As you see, when I interrupt by hitting the read button in RStudio, I break out of the whole function, not just the loop.
Also inspired by Edo's response I discovered the withRestarts function, but I don't think it catches interrupts.
I tried to create a loop as you described it.
a <- 0
for(i in 1:10){
a <- a + 1
Sys.sleep(1)
if(i == 5) break
}
print(a)
If you let it go till the end, a will be equal to 5, because of the break.
If you stop it manually by clicking on the STOP SIGN on the Rstudio Console, you get a lower number.
So it actually works as you would like.
If you want a better answer, you should post a reproducible example of your code.
EDIT
Based on the edit you posted... Try with this.
It's a trycatch solution that returns the last available a value
test_it <- function(t) {
a <- 0
tryCatch(
for(i in 1:10){
a <- a + 1
message("I'm at ", i)
Sys.sleep(t)
if(i==5) break
},
interrupt = function(e){a}
)
a
}
test_it(1)
If you stop it by clicking the Stop Sign, it returns the last value a is equal to.
I'm running a list of strings though an API for an NLP processing. Sometimes the number of tokens is too short and the API returns an error. I've already excluded smaller strings, but sometimes there's an inconsistency (for example what I deem quickly to be a token is rejected by the API). There are only a few, but it's a very long list and I want to let it run through the night without checking on it every once in a while.
So that's why I need for the loop to continue even after an error.
So I already fixed the problem but it's not optimal, I used the try command to check if there is an error:
for(i in 1:nrow(df){
#First I've filtered out what content is clearly too short:
if(sapply(strsplit(df$Content[i], " "), length) > 19){
res <- try(temp_analysis <- gl_nlp(df$Content[i], language = "en"))
if(inherits(res, "try-error"))
{
next
}
temp_analysis <- gl_nlp(df$Content[i], language = "en")
And then some other stuff here
}
}
This works quite fine, but the problem is that it access the API twice and is thus slower and bills me twice.
So is there a way to get this same effect, but without using the try command? Or a derivative of this command that doesn't actually need to call on the API first?
Thanks in advance.
You don't need to access the API twice with try. Here is a simple example:
res <- try(a)
#Error in try(a) : object 'a' not found
class(res)
#[1] "try-error"
a <- 1
res <- try(a)
res
#[1] 1
I have a function that validates a url based on input from a user. The way the function is set up, if the url doesn't validate (either because it doesn't exist or because it is a duplicate) the function simply ends.
How do I make it so that if the url isn't validated, the user goes back to the input dialogue to start the validation process over again instead of just ending the function?
exfun <- function(){
x <- toupper(readline("Do you want to do the function? Y/N......."))
if (x == "Y"){
writeLines("This is where the function body would be, but it's huge so for the sake of this StackExchange question, we'll just make it a simple thing")
} else
writeLines("Well then why did you start the function? We'll try this again")
#This is where I would like the function to return to the "x<-...." line.
There are a few places where I would like to be able to "return to line X" as there are at least two validation points. I have it set up as a series of if else arguments which else into a message. How can I make it else into a message and bring the user back to the beginning/a previous validation test?
I fiddled with the repeat function, but couldn't get it to return from if else correctly.
I'm not really answering your question, but I'll give you an example that might help you (and probably someone will give a hint to improve this).
You can set a while loop and put conditions to continue the loop or to end it, simply like this:
i <- 1
while (T) {
print(i)
i <- i + 1
if (i==5) {
print("NEXT")
next
}
if (i==10) break
}
As you see, the if with next conditions do something in your code, and keep running it after the command print("NEXT"). Also, the break is inside a condition to stop your loop.
I hope it helps, because with your example is difficult to give a full answer.
Iam using R to run a large number using input from a delimited table which is compost of 40000 row and 4 col. iam trying to implement the t test ,p value , but i have error which is (the data are essentially constant) , i used the for loop and apply for both case i had same issue the code is:
NormData3= NormData1[1:40000,1:5]
for(i in 1:nrow(NormData3)) {
g1=NormData3[i,2:3]
g2=NormData3[i,4:5]
p[i]=t.test(g1,g2,var.equal=TRUE)$p.value
}
I don't know what is the problem ??
It's nice that the software recognizes situations in which a sensible
answer can't be computed. At that point, there are two possible actions:
(1) stop with an informative error, and (2) silently return NA.
If you are running this in a iterative loop, you want the second output. Here is the small function for that :
my.t.test.p.value <- function(...) {
obj<-try(t.test(...), silent=TRUE)
if (is(obj, "try-error")) return(NA) else return(obj$p.value)
}
Use this function instead of t.test in your code. This will not disturb your loop and allows it to continue.
I am running the following code:
disc<-for (i in 1:33) {
m=n[i]
xbar<-sum(data[i,],na.rm=TRUE)/m
Sx <- sqrt(sum((data[i,]-xbar)^2,na.rm=TRUE)/(m-1))
Sx
i=i+1}
Running it:
>disc
NULL
Why is it giving me NULL?
This is from the documentation for for, accessible via ?`for`:
‘for’, ‘while’ and ‘repeat’ return ‘NULL’ invisibly.
Perhaps you are looking for something along the following lines:
library(plyr)
disc <- llply(1:33, function(i) {
m=n[i]
xbar<-sum(data[i,],na.rm=TRUE)/m
Sx <- sqrt(sum((data[i,]-xbar)^2,na.rm=TRUE)/(m-1))
Sx
})
Other variants exists -- the ll in llply stands for "list in, list out". Perhaps your intended final result is a data frame or an array -- appropriate functions exist.
The code above is a plain transformation of your example. We might be able to do better by splitting data right away and forgetting the otherwise useless count variable i (untested, as you have provided no data):
disc <- daply(cbind(data, n=n), .(), function(data.i) {
m=data.i$n
xbar<-sum(data.i,na.rm=TRUE)/m
sqrt(sum((data.i-xbar)^2,na.rm=TRUE)/(m-1))
})
See also the plyr website for more information.
Related (if not a duplicate): R - How to turn a loop to a function in R
krlmlr's answer shows you how to fix your code, but to explain your original problem in more abstract terms: A for loop allows you to run the same piece of code multiple times, but it doesn't store the results of running that code for you- you have to do that yourself.
Your current code only really assigns a single value, Sx, for each run of the for loop. On the next run, a new value is put into the Sx variable, so you lose all the previous values. At the end, you'll just end up with whatever the value of Sx was on the last run through the loop.
To save the results of a for loop, you generally need to add them to a vector as you go through, e.g.
# Create the empty results vector outside the loop
results = numeric(0)
for (i in 1:10) {
current_result = 3 + i
results = c(results, current_result)
}
In R for can't return a value. The unique manner to return a value is within a function. So the solution here, is to wrap your loop within a function. For example:
getSx <- function(){
Sx <- 0
disc <- for (i in 1:33) {
m=n[i]
xbar <- sum(data[i,],na.rm=TRUE)/m
Sx <- sqrt(sum((data[i,]-xbar)^2,na.rm=TRUE)/(m-1))
}
Sx
}
Then you call it:
getSx()
Of course you can avoid the side effect of using a for by lapply or by giving a vectorized But this is another problem: You should maybe give a reproducible example and explain a little bit what do you try to compute.