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The Deephaven Core R client is an R package that enables R users to interface with a Deephaven server and perform various server-side operations from the comfort of RStudio or any other R interface.

What can the R client do?

The R Client provides the following functionalities:

  1. Connect to a Deephaven server
    • with anonymous authentication (no username or password)
    • with basic authentication (username and password)
    • with pre-shared key authentication (requires only a key)
    • with custom authentication (general key-value credentials)
  2. Run scripts on the server
    • If the server is equipped with a console, run a script in that console
    • Currently, Python and Groovy are supported
  3. Utilize Deephaven’s vast table API from R


Currently, the R client is only supported on Ubuntu 20.04 or 22.04 and must be built from source.

  1. We need a working installation of R on the machine where the R client will be built. The R client requires R 4.1.2 or newer; you can install R from the standard packages made available by Ubuntu 22.04. If you want a newer R version or if you are running in Ubuntu 20.04, you should install R from CRAN:

    # Download the key and install it
    $ wget -qO- | \
        sudo gpg --dearmor -o /usr/share/keyrings/r-project.gpg
    # Add the R source list to apt's sources list
    $ echo "deb [signed-by=/usr/share/keyrings/r-project.gpg] jammy-cran40/" | \
        sudo tee -a /etc/apt/sources.list.d/r-project.list
    # update the apt package list
    $ apt update
    # install R
    $ sudo apt install r-base r-recommended
  2. Build the cpp-client (and any dependent libraries) according to the instructions in Follow the instructions at least to the point for “Build and install Deephaven C++ client”. At that point you would have both the Deephaven C++ client and any C++ libraries it depends on, all installed in a particular directory of your choosing. In what follows we assume that directory is /path/to/dhcpp. Independently of where that directory is in your chosen installation, a file called should exist on it, and a local subdirectory as well.

  3. Choose a directory where the Deephaven R client source code will live. Here, the source code will be downloaded into a new directory called rdeephaven. Navigate into that directory and clone this subdirectory of deephaven-core using git’s sparse-checkout:

    mkdir rdeephaven
    cd rdeephaven
    git init
    git remote add -f origin
    git config core.sparseCheckout true
    echo "R/rdeephaven" >> .git/info/sparse-checkout
    git pull origin main
  4. Set environment variables from the C++ client installation required for building the package. Use:

    source /path/to/dhcpp/

    where /path/to/dhcpp is the directory you created in step (1) above. You can ensure the environment variables that are necessary for the steps that follow are set by checking their values by running the commands:

    echo $DHCPP

    Both environment variables need to be defined for installing the package in the instructions below. Once the package is installed, you will only need LD_LIBRARY_PATH to be set in the R session where you intend to use the rdeephaven library. If you are starting R from the command line, you can set the environment variable as explained above. If you are using RStudio, see the note in the following point.

    Refer to the instructions on the C++ client installation for more details on the dhcpp directory.

    For faster compilation of the R client and its dependencies (particularly the Arrow R client), use the following commands:

     export NCPUS=`getconf _NPROCESSORS_ONLN`
     export MAKE="make -j$NCPUS"
  5. Start an R console inside the rdeephaven directory. In that console, install the dephaven client dependencies (since we are building from source, dependencies will not be automatically pulled in):

    install.packages(c('Rcpp', 'arrow', 'R6', 'dplyr'))

    Then, exit the R console with quit(). From the rdeephaven directory, build and install the R client:

    cd .. && R CMD build rdeephaven && R CMD INSTALL --no-multiarch --with-keep.source rdeephaven_*.tar.gz && rm rdeephaven_*.tar.gz

    This is needed over the typical install.packages() to ensure that the vignettes get built and installed.


    If using RStudio for this step, the environment variables that were set in step 3 may not persist into the RStudio R environment if RStudio is not a child process of the shell where the environment variables were set (ie, if RStudio is not started from that same shell and after the environment variables are set in that shell). R supports using a .Renviron file for settings like this. You can generate the right content to add to your .Renviron file (or for creating a new one) using the script under etc/

    You can create a new .Renviron file under the deephave-core directory with the lines producing by running the etc/ in the same shell where you set the environment variables; the script will give you the right content for the .Renviron file. Then, create a new R project from the existing deephaven-core directory using RStudio, and the corresponding R session will inherit all the necessary environment variables for successful compilation.

    If RStudio Server is being used, all of the above must be followed for successful compilation. In addition, use the output from the script etc/ and add them to the rserver.conf file for the RStudio Server installation (the location of that file may depend on your particular RStudio server installation, but a common location is /etc/rstudio/rserver.conf).

  6. Now, run


    in the R session, and start using the client!

    For an introduction to the package, run vignette("rdeephaven").


If an error like this occurs in step 4:

client.cpp:7:10: fatal error: deephaven/client/client.h: No such file or directory
 7 | #include "deephaven/client/client.h"
   |          ^~~~~~~~~~~~~~~~~~~~~~~~~~~
compilation terminated.

this means that the C++ compiler does not know where to find the relevant header files for the Deephaven C++ client. This can happen for a handul of reasons: 1. Step 1 was skipped, and the Deephaven C++ client was not installed. In this case, please ensure that the client is installed before attempting to build the R client. 2. The Deephaven C++ client is installed, but the DHCPP environment variable is not set. To test this, run bash echo $DHCPP If this returns an empty string, set DHCPP according to the instructions in step 1 with bash export DHCPP=/path/to/dhcpp 3. The Deephaven C++ client is installed and the DHCPP environment variable is set, but the current project is not configured to allow the compiler to access the Deephaven dhcpp and src directories. This is more difficult to give advice on, as it is an IDE-dependent problem. Consult your IDE’s documentation on C/C++ compiler include paths for more information.

Running the unit tests

The Deephaven R client utilizes R’s testthat package to perform unit tests. In order to run these unit tests, install testthat and the other dependent packages:

install.packages(c('testthat', 'lubridate', 'zoo'))

Then, from an R session with rdeephaven installed, run the unit tests:


Because the Deephaven R client is written in C++ and wrapped with Rcpp, standard R-level debugging is not sufficient for many kinds of problems associated with C++ code. For this reason, debugging the R client must be done with a C++ debugger. We recommend using Valgrind to check for memory bugs, and using gdb for thorough backtraces and general debugging.

Running R with Valgrind

The following was taken from this blog post, which has proven very useful for getting started with Valgrind. 1. Install Valgrind with sudo apt-get install valgrind or sudo yum install valgrind 2. Run R under Valgrind with R -d valgrind

OS-dependent problems may come up in either step, and the simplest solution is to use a Linux machine or VM if one is available. Attempting these steps in a Linux Docker image may also prove difficult, and will certainly fail if the host architecture is not AMD/X86.

Running R with gdb

This article is a good resource for running R with gdb, and also touches on Valgrind use. There are several ways to run R with gdb, and here we only outline the text-based approach given near the bottom of the page. 1. Install gdb with sudo apt-get install gdb or sudo yum install gdb 2. Start gdb with R attached with R -d gdb. This will start a gdb session denoted by (gdb) in the console. 3. In the gdb session, start an R console with (gdb) run

Both Valgrind and gdb debugging is done through a console, and is not interactive from an IDE. There may be a way to make RStudio play well with Valgrind or gdb, but that is beyond the scope of these instructions.

Enabling DEBUG level logging for gRPC and the C++ layer of the Deephaven R client

The C++ component of the Deephaven R client uses the C++ implementation of gRPC to exchange messages with a Deephaven server. gRPC has an internal logging component that can be configured to log to stderr detail information about connection state and messages exchanged between client and server; the Deephaven R client also uses the same logging component to show client state information. This can be useful for debugging purposes. To enable detailed logging, set the environment variable GRPC_VERVOSITY=DEBUG

Code Styling

The Deephaven R client uses the Tidyverse styleguide for code formatting, and implements this style with the styler package. For contributions, ensure that code is properly styled according to Tidyverse standards by running the following code in your R console, where /path/to/rdeephaven is the path to the root directory of this package.


High-level design overview

The R client uses the Deephaven C++ client as the backend for connecting to and communicating with the server. Any Deephaven-specific feature in the R client is, at some level, an API for an equivalent feature in the C++ client. To make Deephaven’s C++ client API available in R, an R6 class provides an R interface to Rcpp wrapped parts of the C++ API. Deephaven’s C++ API can create Arrow tables, and R has an Arrow library. Because Arrow is an in-memory data format, Arrow data can be transferred between R and C++ simply by passing a pointer between the languages and using the Arrow C Stream Interface. No data copies are required.