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ValidMind R Package

Prerequisites

Install the required R packages:

install.packages(c("reticulate", "dplyr", "caTools", "knitr", "glue", "plotly", "htmltools", "rmarkdown", "DT", "base64enc"))

You also need a Python environment with the validmind Python package and rpy2 installed:

pip install validmind rpy2

Note: On macOS, if rpy2 fails to find R libraries, rebuild it from source against your installed R:

R_HOME=$(Rscript -e 'cat(R.home())') pip install --no-binary :all: --force-reinstall rpy2

Point python_version to your Python binary (e.g. the one in your project’s .venv).

Installation

You can install ValidMind from CRAN:

install.packages("validmind")

You can also install the package from GitHub using the devtools package:

devtools::install_github("validmind/validmind-library", subdir="r/validmind")

Or you can install the package from source. Ensure you are in the r/validmind directory:

devtools::install()

For local development, you can skip devtools entirely and install directly from the repo path:

install.packages("/path/to/validmind-library/r/validmind", repos = NULL, type = "source")

Configuring the Python path

The R package needs to know which Python binary to use (the one with validmind installed). It reads the VALIDMIND_PYTHON environment variable, falling back to the system Python if not set.

Option 1: .Renviron file (recommended)

Create a .Renviron file in the project root (or ~/.Renviron for a global setting):

VALIDMIND_PYTHON=.venv/bin/python

R reads this file automatically on startup, before any code runs. Relative paths are resolved against the working directory.

Option 2: .env file in the repo

If the project uses Poetry with in-project virtualenvs (.venv/ inside the repo), add to the repo’s .env file:

VALIDMIND_PYTHON=.venv/bin/python

No configuration needed in Docker / CI

When VALIDMIND_PYTHON is not set, the package falls back to Sys.which("python"), which resolves to the system Python — correct for environments where validmind is installed globally.

QuickStart

Connect to your ValidMind profile:

vm_r <- vm(
  api_host="https://api.prod.validmind.ai/api/v1/tracking",
  api_key="<your_api_key_here>",
  api_secret="<your_api_secret_here>",
  model="<your_model_id_here>",
  document="documentation"
)

The python_version parameter is no longer required — it defaults to VALIDMIND_PYTHON or the system Python. The document parameter specifies which document type to associate with the session (e.g. "documentation" or "validation-report").

Quickstart notebooks

See the notebooks/code_sharing/r/ folder for full working examples:

• quickstart_model_documentation.Rmd — End-to-end model documentation workflow: load data, preprocess, train a GLM model, and run the full documentation test suite.
• quickstart_model_validation.Rmd — End-to-end model validation workflow: load data, run data quality tests, train a champion GLM model, and run model evaluation tests.

These notebooks can be run from VS Code (with the R extension), RStudio, or interactively in a terminal R session. When running interactively, launch R from the repository root so that relative dataset paths resolve correctly.

Key APIs available via reticulate

Since the R package returns the full Python validmind module, you can call any Python API directly:

# Preview the documentation template
py_print(vm_r$preview_template())

# Initialize datasets
vm_dataset <- vm_r$init_dataset(dataset=df, input_id="my_dataset", target_column="target")

# Initialize R models
model_path <- save_model(model)
vm_model <- vm_r$init_r_model(model_path=model_path, input_id="model")

# Assign predictions
vm_dataset$assign_predictions(model=vm_model)

# Run the full documentation test suite
vm_r$run_documentation_tests(config=test_config)

# Run individual tests
vm_r$tests$run_test("validmind.data_validation.ClassImbalance", inputs=list(dataset=vm_dataset))$log()

# List available tests
vm_r$tests$list_tests(tags=list("data_quality"), task="classification")
vm_r$tests$list_tasks_and_tags()

Python output in R Jupyter notebooks

When running R notebooks in Jupyter (via IRkernel), Python print() output is not displayed automatically due to a reticulate limitation. Use the py_print() helper to capture and display output from Python functions:

# These produce print() output — wrap with py_print()
py_print(vm_r$preview_template())
py_print(vm_utils$preview_test_config(test_config))

Note: Python logging output (e.g. progress from run_documentation_tests() or assign_predictions()) cannot be captured this way. These functions run silently in R Jupyter — check the ValidMind Platform for results. This is not an issue in terminal R sessions where all Python output is displayed normally.

Troubleshooting

Initializating vm() on Mac

When calling vm() you might see the following error:

OSError: dlopen(/Users/user/validmind-sdk/.venv/lib/python3.11/site-packages/llvmlite/binding/libllvmlite.dylib, 0x0006): Library not loaded: @rpath/libc++.1.dylib
  Referenced from: <F814708F-6874-3A38-AD06-6C06514419D4> /Users/user/validmind-sdk/.venv/lib/python3.11/site-packages/llvmlite/binding/libllvmlite.dylib
  Reason: tried: '/Library/Frameworks/R.framework/Resources/lib/libc++.1.dylib' (no such file), '/Library/Java/JavaVirtualMachines/jdk-11.0.18+10/Contents/Home/lib/server/libc++.1.dylib' (no such file), '/var/folders/c4/typylth55knbkn7qjm8zd0jr0000gn/T/rstudio-fallback-library-path-492576811/libc++.1.dylib' (no such file)

This is typically due to the libc++ library not being found but it’s possible that is already installed and R cannot find it. You can solve this by finding the path to the library and creating a symlink to it.

# Find the path to libc++.1.dylib. This can return multiple results.
sudo find / -name "libc++.1.dylib" 2>/dev/null

If you are using Homebrew, the command above will return a path like /opt/homebrew/Cellar/llvm/.... You can create a symlink to the library by running the following command:

sudo ln -s <path_to_libc++.1.dylib> /Library/Frameworks/R.framework/Resources/lib/libc++.1.dylib

Note that the target argument in the path of libc++ that R was trying to find.

After creating the symlink, you can try calling vm() again.

Issues with Numba when initializing vm() on Mac

You might also see the following error when initializing vm():

Error in py_module_import(module, convert = convert) :
  ImportError: cannot import name 'NumbaTypeError' from partially initialized module 'numba.core.errors' (most likely due to a circular import) (/Users/user/validmind-sdk/.venv/lib/python3.11/site-packages/numba/core/errors.py)

To fix this, you can reinstall Numba:

pip install -U numba

And restart the R session.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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