Error: Vector Memory Exhausted (Limit Reached?) [FIXED]

The RStudio IDE(integrated development environment) uses the Qt framework for its graphical user interface and is partially written in C++. I will discuss the “error: vector memory exhausted (limit reached?)”. Mostly, this error happens when Rstudio or your system needs more allotted memory for the current task. To fix it, try the methods I mentioned here.

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Key Takeaways

• This error is related to memory problems in which the program is running.
• This error occurs when your system needs more RAM for the present work or when Rstudio needs more allocated memory.
• To fix this error, look for a computer system with more RAM, use parallel computing, change r_max_vsize in Rstudio, and increase memory manually in R.

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I suggest you try these simple fixes before delving into advanced methods:

1. Check Internet
2. Restart Router
3. Update System

Look For A Computer System With More RAM

Insufficient RAM on the system is one of the many causes of Vector Memory Exhausted. You need enough RAM if you are working on a larger file. RStudio has the answer for you if you’re wondering how much RAM would be sufficient for your system or work. One of my team members claimed to fix this error by increasing RAM, which has also been a tried fix for other Reddit users.

Use Parallel Computing

A kind of processing known as parallel computing involves running several computations or processes concurrently. Huge problems may frequently be broken down into smaller ones, which can all be handled simultaneously.

There are many parallel packages for RStudio. Parallel computing helps a lot in programming. It saves your time and allows you to work faster.

Starting And Stopping Clusters

library(parallel)

detectCores() #How many cores do you have

[1] 8

. cl <- makeCluster(2) #starting a cluster

#cl <- makeForkCluster(8)

#stopCluster(cl) #Stopping cluster

Sending Libraries To Clusters

clusterEvalQ(cl, {library (tidyverse)

}) #clusterEvalQ make all the clusters run this code as is

[[1]] [1] “forcats” “”stringr” “”dplyr” “purrr”

“”readr” “tidyr”

[7] ““tibble” “ggplot2″ ““tidyverse” “stats”

“graphics” “”grDevices”

[13] “utils” “datasets” “methods” “base”

[[2]] [1] “forcats” “stringr” “”dplyr” “purrr™

“readr” “tidyr”

[7] “tibble” “ggplot2″ ““tidyverse” “stats”

“graphics” “grDevices”

[13] “utils” “datasets” “methods” “base”

Sending Functions And Variables To Clusters

a<- 2

square <- function (num) {num**2}

clusterExport(cl,c(“a”,”square”)) #sends

variables and functions to clusters

clusterEvalQ(cl, {

print(c(a,square(a)))

})

[[1]] [1] 2   4

[[2]] [1] 2   4

Change r_max_vsize In Rstudio

The following commands should be entered in your terminal to modify r max vsize in RStudio:

1, cd ~

2, Touch.Renviron

3, open .Renviron

Save the following line as the file’s first one once. Renviron has opened:

4 lastly, Save r_max_vsizwe = ___GB.

Change r_max_vsize On The Command Line

R max vsize will be modified using the next line on the command line:

Sys.setenv(‘R_MAX_VSIZE’=_______M)

On the other hand, you may expand RStudio memory on Windows by using the memory.limit() method.

Increase Memory Manually In R

1. The first step is to right-click the R software icon on your desktop and select Properties.
2. Next, locate the Target field on the Shortcut tab.
3. Then, modify the target field by adding – -max-mem-size= __amount of memory you require.
4. After that, the previous code will allot the R program to the specified amount of memory.
5. Click OK.

Final Verdict

Encountering the “error: vector memory exhausted (limit reached?)” in RStudio often indicates insufficient system memory for the task. To address this, consider increasing your computer’s RAM, exploring parallel computing for more efficient processing, adjusting the r_max_vsize in RStudio through the terminal, and manually boosting memory in R. Before opting for advanced methods, try simple fixes like checking internet connectivity, restarting the router, and updating your system.

Here are my other guides on similar errors:

• Remote Rejected Internal Server Error
• Error In The file(file, “rt”)
• Error Validating Basket Doordash