I’ve been working for a while with a number of large files containing gene expression data, and I’ve recently run into an issue with loading that data into R, after upgrading to R 3.5.0. After using about 8GB of memory (my mac has 16GB of RAM), if I try to read in another file, I get the following error:
Error: vector memory exhausted (limit reached?)
I found a previous post (Error: vector memory exhausted (limit reached?)) suggesting I try to set the environmental variable R_MAX_VSIZE to a higher value, so I tried the following:
Sys.setenv(R_MAX_VSIZE = 16e9)
However, I still got the same error. Am I not setting the environmental Variable correctly? is there something that I’m missing?
Session info:
R version 3.5.0 (2018-04-23)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.5
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages: [1] data.table_1.11.4
loaded via a namespace (and not attached):
[1] compiler_3.5.0 tools_3.5.0
asked Jul 9, 2018 at 14:47
Graeme FrostGraeme Frost
2,2182 gold badges12 silver badges15 bronze badges
2
For those using Rstudio, I’ve found that setting Sys.setenv('R_MAX_VSIZE'=32000000000) only works on the command line, and that setting that parameter while using Rstudio does not prevent this error:
Error: vector memory exhausted (limit reached?)
After doing some more reading, I found this thread, which clarifies the problem with Rstudio, and identifies a solution, shown below:
Step 1: Open terminal,
Step 2:
cd ~
touch .Renviron
open .Renviron
Step 3: Save the following as the first line of .Renviron:
R_MAX_VSIZE=100Gb
Note: This limit includes both physical and virtual memory; so setting _MAX_VSIZE=16Gb on a machine with 16Gb of physical memory may not prevent this error. You may have to play with this parameter, depending on the specs of your machine
answered Oct 2, 2018 at 16:46
Graeme FrostGraeme Frost
2,2182 gold badges12 silver badges15 bronze badges
R 3.5 has a new system limit on for memory allocation. From the release notes:
The environment variable R_MAX_VSIZE can now be used to specify
the maximal vector heap size. On macOS, unless specified by this
environment variable, the maximal vector heap size is set to the
maximum of 16GB and the available physical memory. This is to
avoid having the R process killed when macOS over-commits memory.
You can override this. You risk overallocating and killing the process, but that is probably what was happening if you hit a hard wall with R 3.4.4 or whatever you were using before.
Execute the following in Terminal to create a temporary environmental variable R_MAX_VSIZE with value 32GB (change to suit):
export R_MAX_VSIZE=32000000000
Or if you don’t want to open Terminal and run that every time you want to start an R session, you can append the same line to your bash profile. Open Terminal and find your bash profile open .bash_profile and, in a text editor, add the line from above.
You will still have to open Terminal and start R from there. You can run R in the terminal by just executing R or you can open the GUI open -n /Applications/R.app.
To make this change in an R session use Sys.setenv('R_MAX_VSIZE'=32000000000)
and to check the value use Sys.getenv('R_MAX_VSIZE')
divibisan
11.1k11 gold badges39 silver badges58 bronze badges
answered Aug 21, 2018 at 20:21
3
A solution for those who might be unfamiliar with the command line can be found here:
In short, the solution is to use the usethis package.
usethis::edit_r_environ() will open the .Renviron which is in your home directory. This .Renviron affects all Rstudio work
usethis::edit_r_environ("project") will open an .Renviron local to your project. Changes made to this file only affect work done in that particular Rstudio project.
Once open, the R_MAX_VSIZE var can be set.
The linked page also links to this blog which describes R’s startup process in great detail.
answered Apr 27, 2019 at 16:33
Mir HenglinMir Henglin
6095 silver badges15 bronze badges
2
I’ve been working for a while with a number of large files containing gene expression data, and I’ve recently run into an issue with loading that data into R, after upgrading to R 3.5.0. After using about 8GB of memory (my mac has 16GB of RAM), if I try to read in another file, I get the following error:
Error: vector memory exhausted (limit reached?)
I found a previous post (Error: vector memory exhausted (limit reached?)) suggesting I try to set the environmental variable R_MAX_VSIZE to a higher value, so I tried the following:
Sys.setenv(R_MAX_VSIZE = 16e9)
However, I still got the same error. Am I not setting the environmental Variable correctly? is there something that I’m missing?
Session info:
R version 3.5.0 (2018-04-23)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.5
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages: [1] data.table_1.11.4
loaded via a namespace (and not attached):
[1] compiler_3.5.0 tools_3.5.0
asked Jul 9, 2018 at 14:47
Graeme FrostGraeme Frost
2,2182 gold badges12 silver badges15 bronze badges
2
For those using Rstudio, I’ve found that setting Sys.setenv('R_MAX_VSIZE'=32000000000) only works on the command line, and that setting that parameter while using Rstudio does not prevent this error:
Error: vector memory exhausted (limit reached?)
After doing some more reading, I found this thread, which clarifies the problem with Rstudio, and identifies a solution, shown below:
Step 1: Open terminal,
Step 2:
cd ~
touch .Renviron
open .Renviron
Step 3: Save the following as the first line of .Renviron:
R_MAX_VSIZE=100Gb
Note: This limit includes both physical and virtual memory; so setting _MAX_VSIZE=16Gb on a machine with 16Gb of physical memory may not prevent this error. You may have to play with this parameter, depending on the specs of your machine
answered Oct 2, 2018 at 16:46
Graeme FrostGraeme Frost
2,2182 gold badges12 silver badges15 bronze badges
R 3.5 has a new system limit on for memory allocation. From the release notes:
The environment variable R_MAX_VSIZE can now be used to specify
the maximal vector heap size. On macOS, unless specified by this
environment variable, the maximal vector heap size is set to the
maximum of 16GB and the available physical memory. This is to
avoid having the R process killed when macOS over-commits memory.
You can override this. You risk overallocating and killing the process, but that is probably what was happening if you hit a hard wall with R 3.4.4 or whatever you were using before.
Execute the following in Terminal to create a temporary environmental variable R_MAX_VSIZE with value 32GB (change to suit):
export R_MAX_VSIZE=32000000000
Or if you don’t want to open Terminal and run that every time you want to start an R session, you can append the same line to your bash profile. Open Terminal and find your bash profile open .bash_profile and, in a text editor, add the line from above.
You will still have to open Terminal and start R from there. You can run R in the terminal by just executing R or you can open the GUI open -n /Applications/R.app.
To make this change in an R session use Sys.setenv('R_MAX_VSIZE'=32000000000)
and to check the value use Sys.getenv('R_MAX_VSIZE')
divibisan
11.1k11 gold badges39 silver badges58 bronze badges
answered Aug 21, 2018 at 20:21
3
A solution for those who might be unfamiliar with the command line can be found here:
In short, the solution is to use the usethis package.
usethis::edit_r_environ() will open the .Renviron which is in your home directory. This .Renviron affects all Rstudio work
usethis::edit_r_environ("project") will open an .Renviron local to your project. Changes made to this file only affect work done in that particular Rstudio project.
Once open, the R_MAX_VSIZE var can be set.
The linked page also links to this blog which describes R’s startup process in great detail.
answered Apr 27, 2019 at 16:33
Mir HenglinMir Henglin
6095 silver badges15 bronze badges
2
Некоторое время я работал с несколькими большими файлами, содержащими данные экспрессии генов, и недавно столкнулся с проблемой загрузки этих данных в R после обновления до R 3.5.0. После использования около 8 ГБ памяти (мой компьютер имеет 16 ГБ ОЗУ), если я пытаюсь прочитать другой файл, я получаю следующую ошибку:
Error: vector memory exhausted (limit reached?)
Я нашел предыдущее сообщение (Ошибка: исчерпана векторная память (достигнут предел?) ), предлагая попытаться установить для переменной среды R_MAX_VSIZE более высокое значение, поэтому я попробовал следующее:
Sys.setenv(R_MAX_VSIZE = 16e9)
Тем не менее, я все еще получил ту же ошибку. Я правильно не устанавливаю переменную среды? я что-то упускаю?
Информация о сессии:
R version 3.5.0 (2018-04-23)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.5
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages: [1] data.table_1.11.4
loaded via a namespace (and not attached):
[1] compiler_3.5.0 tools_3.5.0
3 ответа
Лучший ответ
Для тех, кто использует Rstudio, я обнаружил, что настройка Sys.setenv('R_MAX_VSIZE'=32000000000) работает только в командной строке, и что установка этого параметра при использовании Rstudio не предотвращает эту ошибку:
Error: vector memory exhausted (limit reached?)
После прочтения я обнаружил, что этот поток, который проясняет проблему с Rstudio и определяет решение, показанное ниже:
Шаг 1: Откройте терминал,
Шаг 2:
cd ~
touch .Renviron
open .Renviron
Шаг 3. Сохраните следующее как первую строку .Renviron:
R_MAX_VSIZE=100Gb
Примечание. Этот предел включает как физическую, так и виртуальную память; поэтому установка _MAX_VSIZE = 16 ГБ на машине с 16 ГБ физической памяти может не предотвратить эту ошибку. Возможно, вам придется поиграть с этим параметром, в зависимости от характеристик вашей машины
20
Graeme Frost
2 Окт 2018 в 16:46
R 3.5 имеет новый системный предел для выделения памяти. Из заметок о выпуске:
Переменная среды R_MAX_VSIZE теперь может использоваться для указания максимального размера кучи вектора. В macOS, если эта переменная среды не указана, максимальный размер кучи вектора не должен превышать 16 ГБ и доступной физической памяти. Это сделано для того, чтобы избежать уничтожения процесса R при чрезмерной фиксации памяти в macOS.
Вы можете переопределить это. Вы рискуете перераспределить и убить процесс, но это, вероятно, то, что происходило, если вы ударились о твердую стену с R 3.4.4 или чем-то, что вы использовали ранее.
Выполните в терминале следующее, чтобы создать временную переменную среды R_MAX_VSIZE со значением 32 ГБ (изменить в соответствии с): export R_MAX_VSIZE=32000000000
Или, если вы не хотите открывать терминал и запускать его каждый раз, когда вы хотите начать сеанс R, вы можете добавить эту же строку в свой профиль bash. Откройте Terminal и найдите свой профиль bash open .bash_profile и в текстовом редакторе добавьте строку сверху.
Вам все равно придется открыть терминал и запустить R оттуда. Вы можете запустить R в терминале, просто выполнив R или вы можете открыть графический интерфейс open -n /Applications/R.app.
Чтобы внести это изменение в сеанс R, используйте Sys.setenv('R_MAX_VSIZE'=32000000000) и для проверки значения используйте Sys.getenv('R_MAX_VSIZE')
11
divibisan
26 Авг 2018 в 20:28
Решение для тех, кто может быть незнаком с командной строкой, можно найти здесь :
Короче говоря, решение состоит в том, чтобы использовать пакет usethis.
usethis::edit_r_environ() откроет .Renviron, которое находится в вашем домашнем каталоге. Это .Renviron влияет на всю работу Rstudio
usethis::edit_r_environ("project") откроет локальную .Renviron для вашего проекта. Изменения, внесенные в этот файл, влияют только на работу, выполненную в этом конкретном проекте Rstudio.
После открытия можно установить переменную R_MAX_VSIZE.
Связанная страница также ссылается на этот блог который подробно описывает процесс запуска R
9
Mir Henglin
27 Апр 2019 в 16:33
The error code that reads error: vector memory exhausted (limit reached?) happens when you are working with the R programming language on macOS. Keep on reading as our experts show you tried and tested methods to fix this error. In addition, we’ll also be teaching you expert tips that will help you prevent this error and answer some commonly-asked questions about it.
Contents
- Error: Vector Memory Exhausted (Limit Reached?): Why Are You Getting It?
- – You Are Working With a Large Dataset in RStudio
- – Rstudio Needs More Allocated Memory
- – Your System Needs More RAM for the Current Task
- How To Fix Vector Exhausted Memory
- – Use a System With More RAM
- – Change r_max_vsize on the Command Line
- – Change r_max_vsize in Rstudio
- – Use Parallel Computing
- Additional Information About the Vector Exhausted Memory in R
- – What Is R_max_vsize?
- – Why Is R Using So Much Memory?
- – Does R Have a Memory Limit?
- – What Happens When R Runs Out of Memory?
- – How Do You Increase Memory in Rstudio Mac?
- Conclusion
Error: Vector Memory Exhausted (Limit Reached?): Why Are You Getting It?
You are getting the memory exhausted error because you are probably working with a large dataset in RStudio, your RStudio needs more allocated memory, or your system needs more RAM for the current task. Let’s look at these possible reasons in greater detail.
– You Are Working With a Large Dataset in RStudio
When working with a large dataset in RStudio, you can run into RStudio memory limit. The reason is that every application has a defined amount of memory it can use on a system. For example, if your dataset is between 8 to 10 gigabytes, you’ll notice an error in RStudio.
– Rstudio Needs More Allocated Memory
Your computer system regulates the amount of memory allocated to RStudio, so when you run into a memory error, that means Rstudio has reached its memory limit on the system.
– Your System Needs More RAM for the Current Task
It’s public knowledge that systems have different amounts of installed RAM, so if your RAM is too small for the task, you’ll notice an error: vector memory exhausted (limit reached?) macOS warning.
You can fix the vector exhausted memory error by taking several steps such as using a system with more RAM or using parallel computing. Aside from these, you can also perform the following:
- Use a system with more RAM
- Change r_max_vsize on the command line
- Change r_max_vsize in RStudio
- Use parallel computing
Let’s take a closer look at these solutions:
– Use a System With More RAM
You can get a system with more RAM to solve the memory issues when using the R language in RStudio. On a normal day, a system having 16 GB of RAM should be an easy solution. However, if your dataset needs more than 16 GB of RAM, then you’ll need to invest in more memory. Doing this is a step toward the elimination of the memory limit R Mac error.
– Change r_max_vsize on the Command Line
Before changing the r_max_size, you can check r_max_vsize. However, if you are seeing the memory error, you’ll need to change r_max_size. The change will increase vector memory R and get rid of the memory error.
The following command will change r_max_vsize on the command line:
Sys.setenv(‘R_MAX_VSIZE’=32000000000)
– Change r_max_vsize in Rstudio
Changing the r_max_vsize on the command line might not work for Rstudio. That’s why you can also change it in RStudio itself. To change r_max_vsize in RStudio, open your terminal and type the following commands:
cd ~
touch .Renviron
open .Renviron
The touch command will create a file called .Renviron, while the open command will open the .Renviron file and prepare it for writing. Once .Renviron opens, save the following as the first line in the file:
R_MAX_VSIZE=100Gb
However, if you are on Windows, you can use the memory.limit() function to increase R’s memory. That is because ‘memory.limit()’ is windows-specific.
– Use Parallel Computing
Modern systems have multiple CPU cores, however, the R language uses only a single core. Nevertheless, you can allow R to take advantage of your system’s multiple cores. Your first option is to use the Mclappy function, which will run the process in parallel. The following is an example usage of Mclappy:
mclapply(data_vector, function, mc.cores = detectCores())
However, to use the Mclappy function, you’ll need to download and install a package. The package name is parallel, and you can get parallel using the following command:
install.packages(‘parallel’)
library(parallel)
The second option to use parallel computing in R is via the Future package by Henrik Bengtsson. After the package installation, use the following to increase the memory allocation:
options(future globals maxsize 4000 1024^2)
Additional Information About the Vector Exhausted Memory in R
The vector exhausted memory can prove tricky at times due to the difference in Operating Systems. However, in previous sections of this article, we’ve shown you how to solve it all.
This section is a thematic list of questions related to the error. We’ve analyzed the common questions, and we’ll give you answers that came straight from our experts.
– What Is R_max_vsize?
R_max_vsize is an environmental variable that can be used to declare the maximum vector heap size. On macOS, it’s easy to change the vector heap size, as we’ve detailed in this article.
– Why Is R Using So Much Memory?
R will use a lot of memory when it copies objects because it still occupies the space before deletion even though these copied objects get deleted at a later time. If you don’t want this, you can transfer the space back to the operating system using the gc() function.
GC stands for garbage collection, and it works by checking and freeing up unused space. Moreover, when R needs memory, it’ll call the gc() function by itself. The takeaway from all is that R is memory intensive, so we advise that you have as much memory (RAM) as possible.
What’s more, if you are running R in a virtual machine, allocate enough memory to the virtual machine. The allocated memory should accommodate your datasets when running R.
– Does R Have a Memory Limit?
It depends on the architecture of your system; for a 32bit Windows system, the Memory limit for R is capped at approximately three and a half gigabytes. However, R will use the RAM available in a 64bit version of Windows 7/8/10, macOS Linux CPUs.
Nonetheless, you can check the memory limit using the following:
memory.limit()
To check the current memory being used by R, use the following command:
memory.size()
These commands will give you an insight into R memory usage. About memory.size(), you can execute it to know R memory usage, so you’ll know before you hit the allowed limit indicated by the call to memory.limit().
– What Happens When R Runs Out of Memory?
If you are on Windows, R will tell you when it runs out of memory, and if you install more RAM, you’ll have to reinstall R. This will allow R to use the added memory. In addition, you can prevent R from running out of memory in High Performance computing.
To get started, check out packages that allow you to work with large datasets that are large for the memory. An example of such a package is ff package by Adler et al. that uses a data structure stored on your system disk. Meanwhile, these structures behave as if they are in the RAM. The ff package achieves this feat using transparent mapping of page size in memory.
Another package is the bigmemory by Kane and Emerson. This package allows you to store large objects in memory. After that, you can use an external pointer to refer to these objects. This allows transparent access from R without going over the memory limit of R.
– How Do You Increase Memory in Rstudio Mac?
You can inccrease an application’s memory size when it’s in the Classic environment of Mac OS X by going into the File menu of Rstudio. In Mac OS X, there is no need to set an application’s memory size for built-in applications because Mac OS X makes memory adjustments based on the application’s needs.
Proceed with the following steps:
- Determine the memory in your computer.
- Close the application you’ll to allocate memory to.
- Select the application’s icon. In this case, it’s RStudio.
- Select Get Info or Show Info from the File menu. This will open the application information window.
- In Mac OS X version greater than 10.2.x, click the arrow to the left of “Memory:” As a result, the arrow should face down.
- Meanwhile, in the Mac OS X series of 10.0.x and 10.1.x, select Memory from the pop-up menu.
- Enter the amount of memory you’d like to allocate to the application In the “Preferred size:” field.
Conclusion
This article explained the error about memory exhaustion in R in great detail, and you also learned what it takes to fix the error. Towards the end of the article, we gave you answers to further questions you may have about the error, and as a takeaway, here are the key points of this guide:
- Working with large datasets can lead to a memory limit error.
- You can fix the memory limit error by using more RAM or changing r_max_vsize.
- You can change r_max_vsize via the command line or in RStudio.
- You can check the memory limit in R with memory.limit().
- Packages like ff and bigmemory can prevent you from going over R’s memory limit.
With our detailed guide, you can fix memory issues in R and RStudio without any stress. You should use our article as your guiding light the next time you encounter this error.
- Author
- Recent Posts
Position Is Everything: Your Go-To Resource for Learn & Build: CSS,JavaScript,HTML,PHP,C++ and MYSQL.
Error messages are an unfortunate part of programming, those that result from exceeding the available memory can be more than a nuisance, but a serious problem when they result from overextending the physical memory of the computer. The vector memory exhausted error is this type of error message.
Circumstances of this error message.
Are two main circumstances that can cause this error to happen. The first one is insufficient memory allocation by R Studio itself. The second is a lack of physical memory on your computer. Even with these issues, you will have to be dealing with really large amounts of data for this error message to show up.
Reason for this error message.
The reason why this error message occurs is simply that the program is running out of space to put more data. Whether the reason is the allocation or an actual lack of memory, the program simply runs out of space to put more data. While this is an easy error message to understand, it is a hard one to fix because it may not simply involve tweaking your code to fix or get around the problem.
How to fix this error message.
There are several ways to fix this error message. The simplest fix is to simply to make your file smaller. A couple of ways to do this is to remove duplicates and columns. Another possibility is to remove any data that you will not be using. If the study has a shorter range of time, exclude data that is outside of that time. If there are entire columns of data that you do not use, then go ahead and eliminate them. All of these will reduce the amount of memory needed to hold the data.
If reducing the size of your data does not do the job, then you may want to consider an increase in the size of the physical memory of your computer. Another solution is to keep the data in a database such as SQL where the program accesses it only as needed.
Unfortunately, this problem has no easy copy and paste solution, but requires a lot of effort no matter how you try to solve it. You can solve this problem but not easily. However, it is a chance for your creativity to shine because that is where your solution to this problem will be found.
For: error: vector memory exhausted (limit reached?)
Некоторое время я работал с рядом больших файлов, содержащих данные экспрессии генов, и недавно я столкнулся с проблемой загрузки этих данных в R после обновления до R 3.5.0. После использования около 8 ГБ памяти (мой Mac имеет 16 ГБ ОЗУ), если я попытаюсь прочитать другой файл, я получаю следующую ошибку:
Error: vector memory exhausted (limit reached?)
Я нашел предыдущий пост (Ошибка: память векторов исчерпана (достигнут предел?)), в котором предлагалось установить более высокое значение переменной окружения R_MAX_VSIZE, поэтому я попробовал следующее:
Sys.setenv(R_MAX_VSIZE = 16e9)
Однако у меня все еще была та же ошибка. Я неправильно устанавливаю переменную окружения? что-то мне не хватает?
Информация о сеансе:
R version 3.5.0 (2018-04-23)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.5
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages: [1] data.table_1.11.4
loaded via a namespace (and not attached):
[1] compiler_3.5.0 tools_3.5.0
Перейти к ответу
Данный вопрос помечен как решенный
Ответы
3
R 3.5 имеет новое системное ограничение на выделение памяти. Из примечаний к выпуску:
The environment variable R_MAX_VSIZE can now be used to specify
the maximal vector heap size. On macOS, unless specified by this
environment variable, the maximal vector heap size is set to the
maximum of 16GB and the available physical memory. This is to
avoid having the R process killed when macOS over-commits memory.
Вы можете отменить это. Вы рискуете перераспределить ресурсы и убить процесс, но, вероятно, именно это и произошло, если вы натолкнулись на твердую стену с помощью R 3.4.4 или того, что вы использовали раньше.
Выполните в Терминале следующее, чтобы создать временную переменную среды R_MAX_VSIZE со значением 32 ГБ (изменить в соответствии с требованиями):
export R_MAX_VSIZE=32000000000
Или, если вы не хотите открывать Терминал и запускать его каждый раз, когда вы хотите запустить сеанс R, вы можете добавить ту же строку в свой профиль bash. Откройте Терминал и найдите свой профиль bash open .bash_profile и в текстовом редакторе добавьте строку сверху.
Вам все равно придется открыть Терминал и запустить R оттуда. Вы можете запустить R в терминале, просто выполнив R, или вы можете открыть графический интерфейс open -n /Applications/R.app.
Чтобы внести это изменение в сеанс R, используйте Sys.setenv(‘R_MAX_VSIZE’=32000000000)
и для проверки значения используйте Sys.getenv(‘R_MAX_VSIZE’)
Для тех, кто использует Rstudio, я обнаружил, что установка Sys.setenv(‘R_MAX_VSIZE’=32000000000) работает только в командной строке, и установка этого параметра при использовании Rstudio не предотвращает эту ошибку:
Error: vector memory exhausted (limit reached?)
Прочитав еще немного, я нашел поток это, который проясняет проблему с Rstudio и определяет решение, показанное ниже:
Шаг 1. Откройте терминал,
Шаг 2:
cd ~
touch .Renviron
open .Renviron
Шаг 3: Сохраните следующее как первую строку .Renviron:
R_MAX_VSIZE=100Gb
Примечание. Это ограничение включает как физическую, так и виртуальную память; поэтому установка _MAX_VSIZE = 16Gb на машине с 16Gb физической памяти не может предотвратить эту ошибку. Возможно, вам придется поиграть с этим параметром, в зависимости от характеристик вашей машины.
Решение для тех, кто может быть незнаком с командной строкой, можно найти здесь:
Короче говоря, решение — использовать пакет usethis.
Usethis::edit_r_environ() откроет .Renviron, который находится в вашем домашнем каталоге. Этот .Renviron влияет на всю работу Rstudio
Usethis::edit_r_environ(«project») откроет локальный .Renviron для вашего проекта. Изменения, внесенные в этот файл, влияют только на работу, выполненную в этом конкретном проекте Rstudio.
После открытия можно установить переменную R_MAX_VSIZE.
На связанной странице также есть ссылка на этот блог, который очень подробно описывает процесс запуска R.
Другие вопросы по теме
Ошибка: память векторов исчерпана (достигнут предел?)
Раньше я сохранял файл RData объемом 2,8 ГБ, а теперь пытаюсь загрузить его, чтобы снова поработать над ним, но, как ни странно, не могу. Это дает ошибку
Error: vector memory exhausted (limit reached?)
Это странно, так как раньше я отлично с этим работал. Однако изменилось одно: я обновился до последней версии R 3.5.0. Я видел предыдущий пост с такой же ошибкой, как это, но он не был решен. Я надеялся на решение это, которое увеличивает memory.limit(), но, к сожалению, оно доступно только для Windows.
Кто-нибудь может помочь? Я действительно не понимаю, в чем проблема, так как я мог работать с моим набором данных до обновления, поэтому он не должен вызывать эту ошибку.
Обновление как-то уменьшило ОЗУ, выделенную для R? Можем ли мы вручную увеличить memory.limit() в Mac, чтобы решить эту ошибку?
Brent, 28 июня 2018 г., 17:46
5
9 674
1
Ответ:
Решено
Это изменение было необходимо для решения проблем с чрезмерным выделением памяти операционной системы в Mac OS. Из файла NEWS:
item The environment variable env{R_MAX_VSIZE} can now be used
to specify the maximal vector heap size. On macOS, unless specified
by this environment variable, the maximal vector heap size is set to
the maximum of 16GB and the available physical memory. This is to
avoid having the command{R} process killed when macOS over-commits
memory.
Установите для переменной среды R_MAX_VSIZE значение, подходящее для вашей системы, перед запуском R, и вы сможете прочитать свой файл.
Luke, 28 июня 2018 г., 18:22