SciViews socket server

Philippe Grosjean (


The SciViews {svSocket} server implements an R server that is mainly designed to interact with the R prompt from a separate process. The {svSocket} clients and the R console share the same global environment (.GlobalEnv). So, everybody interacts with the same variables. Use cases are to build a separate R console, to monitor or query an R session, or even, to build a complete GUI or IDE on top of R. Examples of such GUIs are Tinn-R and Komodo with the SciViews-K extension.

Quick install and use

The SciViews {svSocket} package provides a stateful, multi-client and preemptive socket server. Socket transactions are operational even when R is busy in its main event loop (calculation done at the prompt). This R socket server uses the excellent asynchronous socket ports management by Tcl, and thus, it needs a working version of Tcl/Tk (>= 8.4), the {tcltk} R package, and R with isTRUE(capabilities("tcltk")).

Install the {svSocket} package as usual:


(For the development version, install first devtools or remotes, and then use something like remotes::github_install("SciViews/svSocket"))

# Start a separate R process with a script that launch a socket server on 8888
# and wait for the varible `done` in `.GlobalEnv` to finish
rscript <- Sys.which("Rscript")
system2(rscript, "--vanilla -e 'svSocket::startSocketServer(8888); while (!exists(\"done\")) Sys.sleep(1)'", wait = FALSE)

Sometimes (on MacOS Catalina, for instance), you are prompted to allow for R to use a socket. Of course, you have to allow to get an operational server.

What this code does is:

As you can see, creating a socket server with {svSocket} is easy. Now, let’s connect to it. It is also very simple:

con <- socketConnection(host = "localhost", port = 8888,
  blocking = FALSE, timeout = 30)

Here, we use the socketConnection() that comes with base R in non-blocking mode and a time out at 30 sec. So, {svSocket} is even not required to connect an R client to our server. The connection is not blocking. We have the control of the prompt immediately.

Now, we can execute code in our R server process with svSocket::evalServer():

evalServer(con, '1 + 1')

The instruction is indeed executed in the server, and the result is returned to the client transparently. You now master two separate R process: the original one where you interact at the R prompt (>), and the server process that you can interact with through evalServer(). To understand this, we will create the variable x on both processes, but with different values:

# Local x
x <- "local"
# x on the server
evalServer(con, 'x <- "server"')

Now, let’s look what is available on the server side:

evalServer(con, 'ls()')
evalServer(con, 'x')

Obviously, there is only one variable, x, with value "server". All right … and in our original process?


We have x, but also con and rsscript. The value of x locally is "local".

As you can see, commands and results are rather similar. And since the processes are different, so are x in both processes.

If you want to transfer data to the server, you can still use evalServer(), with the name you want for the variable on the server instead of a string with R code, and as third argument, the name of the local variable whose the content will be copied to the server. evalServer() manages to send the data to the server (by serializing the data on your side and deserializing it on the server). Here, we will transfer the iris data frame to the server under the iris2 name:

evalServer(con, iris2, iris)
evalServer(con, "ls()")         # iris2 is there
evalServer(con, "head(iris2)")   # ... and its content is OK

For more examples on using evalServer, see its man page with ?evalServer.

Lower-level interaction

The {svSocket} server also allows, of course, for a lower-level interaction with the server, with a lot more options. Here, you send something to the server using cat, file = con) (make sure to end your command by a carriage return \n, or the server will wait indefinitely for the next part of the instruction!), and you read results using readLines(con). Of course, you must wait that R processes the command before reading results back:

# Send a command to the R server (low-level version)
cat('{Sys.sleep(2); "Done!"}\n', file = con)
# Wait for, and get response from the server
res <- NULL
while (!length(res)) {
  res <- readLines(con)

Here you got the results send back as strings. If you want to display them on the client’s console as if it was output there (like evalServer() does), you should use cat( sep = "\n"):

cat(res, "\n")

For convenience, we will wrap all this a function runServer():

runServer <- function(con, code) {
  cat(code, "\n", file = con)
  res <- NULL
  while (!length(res)) {
    res <- readLines(con)
  # Use this instruction to output results as if code was run at the prompt
  #cat(res, "\n")

The transaction is now much easier:

(runServer(con, '{Sys.sleep(2); "Done!"}'))

Configuration and special instructions

Now at the low-level (not within evalServer() but within our runServer() function), one can insert special code <<<X>>> with X being a marker that the server will use on his side. The last instruction we send instructed the server to wait for 2 sec and then, to return "Done!". We have send the instruction to the server and wait for it to finish processing and then, we got the results back. Thus, you original R process is locked down the time the server processes the code. Note that we had to lock it down on our side using the while(!length(res)) construct. It means that communication between the server and the client is asynchronous, but process of the command must be made synchronous. If you want to return immediately in the calling R process before the instruction is processed in the server, you could just consider to drop the while(...) section. This is not a good idea! Indeed, R will send results back and you will read them on the next readLines(con) you issue, and mix it with, perhaps, the result of the next instruction. So, here, we really must tell the R server to send nothing back to us. This is done by inserting the special instruction <<<h>>> on one line (surrounded by \n). This way, we still have to wait for the instruction to be processed on the server, but nothing is returned back to the client. Also, sending <<<H>>> will result into an immediate finalization of the transaction by the server before the instruction is processed.

(runServer(con, '\n<<<H>>>{Sys.sleep(2); "Done!"}'))

Here, we got the result immediately, but it is not the results of the code execution. Our R server simply indicates that he got our code, he parsed it and is about to process it on his side by returning an empty string "".

There are several special instructions you can use. See ?parSocket for further details. The server can be configured to behave in a given way, and that configuration is persistent from one connection to the other for the same client. The function parSocket() allows to change or query the configuration. Its first argument is the name of the client on the server-side… but from the client, you don’t necessarily know which name the server gave to you (one can connect several different clients at the same time, and default name is sock followed by Tcl name of the client socket connection). Using <<<s>>> as a placeholder for this name circumvents the problem. parSocket() returns an environment that contains configuration variables. Here is the list of configuration item for us:

cat(runServer(con, 'ls(envir = svSocket::parSocket(<<<s>>>))'), sep = "\n")

The bare item indicates if the server sends bare results, or also returns a prompt, acting more like a terminal. By default, it returns the bare results. Here is the current value:

cat(runServer(con, 'svSocket::parSocket(<<<s>>>)$bare'), sep = "\n")

And here is how you can change it:

(runServer(con, '\n<<<H>>>svSocket::parSocket(<<<s>>>, bare = FALSE)'))
(runServer(con, '1 + 1'))

When bare = FALSE the server issues the formatted command with a prompt (:> by default) and the result. For more information about {svSocket} server configuration, see ?parSocket. There are also functions to manipulate the pool of clients and their configurations from the server-side, and the server can also send unattended data to client, see ?sendSocketClients. finally, the workhorse function on the server-side is processSocket(). See ?processSocket to learn more about it. You can provide your own process function to the server if you need to.


Don’t forget to close the connection, once you have done using close(con), and you can also close the server from the server-side by using stopSocketServer(). But never use it from the client-side because you will obviously break in the middle of the transaction. If you want to close the server from the client, you have to install a mechanisms that will nicely shut down the server after the transaction is processed. Here, we have installed such a mechanism by detecting the presence of a variable named done on the server-side. So:

# Usually, the client does not stop the server, but it is possible here
evalServer(con, 'done <- NULL') # The server will stop after this transaction

You can also access the {svSocket} server from another language. There is a very basic example written in Tcl in the /etc subdirectory of the {svSocket} package. See the ReadMe.txt file there. The Tcl script SimpleClient.tcl implements a Tcl client in a few dozens of code lines. For other examples, you can inspect the code of SciViews-K, and the code of Tinn-R for a Pascal version. Writing clients in C, Java, Python, etc. should not be difficult if you inspire you from these examples. Finally, there is another implementation of a similar R server through HTTP in the {svHttp} package.

  1. Of course, this port must be free. If not, just use another value.↩︎