Some types of R objects can be used only in the R session they were created. If used as-is in another R process, such objects often result in an immediate error or in obscure and hard-to-troubleshoot outcomes. Because of this, they cannot be saved to file and re-used at a later time. They can also not be exported to a worker in parallel processing. These objects are sometimes referred to as non-exportable or non-serializable objects. One solution to this problem is to use “marshalling” to encode the R object into an exportable representation that then can be used to re-create a copy of that object in another R process. This package provides a framework for marshalling and unmarshalling R objects such that they can be transferred using functions such as serialize() and unserialize() of base R.

WARNING: This package is currently just a skeleton. Please stay tuned.


When marshal() and unmarshal() methods have been implemented for some of the “non-exportable” object types, it will be possible to use also these objects in a later or another R session and in parallel R workers. Here are some example of functions that will be able to take advantage of marshalling:

  • raw <- serialize(x, connection = NULL) and y <- unserialize(raw) - serialize object x to a raw byte sequence, from which then the original object can be reconstructed
  • save(x, y, file = "xy.RData") and load("xy.RData") - save and reload R objects to a binary file by their names
  • saveRDS(x, "x.rds") and y <- readRDS("x.rds") - save and reload an R object to a binary file
  • save.image(): saves the global environment to an ‘.RData’ file. Next time R is launched, this the global environment is recreated. This function is called by for instance quit(save = "yes")
  • parallel::clusterExport(cl, varlist = c("x", "y")) - export variables x and y to a parallel workers
  • f <- future::future(x * y) - export variables x and y to a parallel workers and evaluate the expression


  1. Phase “Getting Started”
  • [ ] Start on a rudimentary “marshalling” protection framework based on the internal future functions assert_no_references(), find_references() and reference_filters()

  • [ ] Document common cases of packages with non-exportable objects as one or more vignettes. The A Future for R: Non-Exportable Objects vignette is a good start. For each package give at least one example that show the mistake, how to detect it with above protection functions, and when possible give workaround examples. Illustrate with both sequential use cases (e.g. saveRDS() and readRDS()) and with parallelization (e.g. parallel::clusterEvalQ(), foreach::foreach() %dopar% { ... }, and future::future()

  • [ ] Invite R community to report on more cases to build up a knowledge base and make the marshal documentation a go-to reference for explaining the problem

  1. Phase “Detect & Protection”
  • [ ] Detect and report on non-exportable objects via the condition system

    • [ ] objects with external pointers

    • [ ] connection objects

    • [ ] …

  • [ ] Develop set of acceptance filters to handle false positives, e.g. data.table::data.table objects

  • [ ] Develop set of reject filters to handle true positives with specific, more informative error messages, e.g. connection objects

  1. Phase “Marshalling”
  • [ ] Add S3 generic marshal() to re-deconstruct non-exportable objects of certain classes so that they can be re-constructed using unmarshal() afterward, e.g.

    • [ ] ‘XMLAbstractDocument’ of XML, cf. XML::xmlSerializeHook() and XML::xmlDeserializeHook()

    • [ ] base R URL and read-only file connections

    • [ ] Identify packages whose non-exportable objects may be marshalled similarly, e.g. ShortRead and ncdf4

  • [ ] Implement efficient, recursive marshal() for sets such as lists and environments

  • [ ] After marshalling an object, or, say, a list of objects, investigate if we can leverage R’s serialization framework to automatically unmarshal objects via base::unserialize(..., refhook = unmarshal). We might be able to do this by having marshal() appending a “trigger” reference to the marshalled object


R package marshal is only available via GitHub and can be installed in R as:

remotes::install_github("HenrikBengtsson/marshal", ref="master")

Pre-release version

To install the pre-release version that is available in Git branch develop on GitHub, use:

remotes::install_github("HenrikBengtsson/marshal", ref="develop")

This will install the package from source.