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How DVC fits into an R project
DVC is an add-on to a Git repository, not a replacement for Git. The DVC installation documentation puts it plainly: “DVC does not replace or include Git.” Git holds source code and small metadata files; DVC tracks data and generated artifacts without adding large data files to ordinary Git history. DVC’s cache holds tracked artifacts locally, while a configured remote lets collaborators transfer them. DVC installation DVC data management
For an R model workflow, the useful division is:
- Git: R scripts, configuration and parameter files,
dvc.yaml, and DVC metadata files. - DVC: tracked data and model artifacts, plus the pipeline state used to determine which stages need to run.
DVC orchestrates declared work; it does not install R, manage R package libraries, or automatically capture every system dependency. Your project remains responsible for its R and system environment and for code that uses declared inputs and produces declared outputs.
Define a pipeline that runs your R scripts
A DVC stage is a shell command with declared dependencies and outputs. That means it can call Rscript, passing file paths or other arguments in the form your script expects. Current pipeline definitions belong in dvc.yaml:
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stages:
prepare:
cmd: Rscript R/prepare.R data/raw.csv data/train.csv
deps:
- R/prepare.R
- data/raw.csv
outs:
- data/train.csv
train:
cmd: Rscript R/train.R data/train.csv models/model.rds
deps:
- R/train.R
- data/train.csv
params:
- train
outs:
- models/model.rds
evaluate:
cmd: Rscript R/evaluate.R data/test.csv models/model.rds reports/metrics.json
deps:
- R/evaluate.R
- data/test.csv
- models/model.rds
outs:
- reports/metrics.jsonThis example assumes those scripts accept the listed arguments and write their results to the declared paths; adjust commands to match your own code. The train parameter section tells DVC to track the relevant parameter values from the project’s parameter file. Parameter-file structure and any parameter substitution in commands must follow DVC’s current syntax. See the DVC pipeline definition reference.
Why declare both inputs and outputs?
The deps list tells DVC what a stage reads, including scripts and data files. The outs list identifies the artifacts it produces. DVC uses dependencies and pipeline state to decide whether work needs to run, and downstream stages can depend on an upstream output. If an input changes, DVC can rerun affected stages while skipping unaffected work. A file that a script reads but that is absent from deps is a hidden input: DVC cannot reliably use it to determine whether the stage is stale.
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Write stages so they can run from their declared inputs and create their outputs predictably. Hidden reads, dependence on undeclared local state, nondeterministic operations, appending to old outputs, or background tasks that outlive the command can undermine reruns. DVC records the workflow and artifact state, but it cannot make nondeterministic code deterministic or guarantee bit-for-bit identical results across machines.
Run and share the workflow
Install DVC separately from Git, and check the installed version with dvc version. Follow DVC’s current installation instructions for the appropriate installation method. A practical setup sequence is:
- Start with a Git repository and initialize DVC in it using the current DVC initialization instructions.
- Add or import the project’s data using DVC’s data-management workflow, rather than committing large data files to ordinary Git history.
- Define stages and their
cmd,deps,params, andoutsindvc.yaml. - Run
dvc reproto execute the required stages in dependency order. Review the outputs and commit the changed source and DVC metadata with Git. - Configure a DVC remote, then use
dvc pushto upload the required tracked artifacts. Push the Git commits as well.
For another person to reproduce the workflow, both transfers matter: the Git repository supplies code and pipeline metadata, while the remote supplies DVC-tracked data and outputs. A Git push alone does not transfer the DVC cache. A collaborator can check out the relevant Git revision and use dvc pull to retrieve the artifacts from a reachable remote before running or inspecting the workflow. DVC data management
Choose pipeline reproduction or experiment runs
Use dvc repro to reproduce a defined pipeline. Use dvc exp run when you want to run parameter variants as experiments and compare their results or metrics. Experiments build on the pipeline definition and can set parameters. Only Git- or DVC-tracked files are saved with an experiment, so stage required files before running queued or temporary experiments. See the DVC experiment-management guide and the dvc exp run command reference.
Pick a remote that suits the project
DVC supports cloud storage such as S3, Azure Blob, and Google Cloud Storage, self-hosted options such as SSH/SFTP and HDFS, and local or mounted storage. It does not prescribe a provider. Choose based on the team’s existing accounts and the practical constraints around the data and workflow:
- Can every collaborator authenticate, and can credentials be handled securely?
- Do the location’s access controls fit the project’s requirements?
- Is the remote reachable when users or automated jobs need to pull and push artifacts?
- What operational costs or maintenance apply?
- Can the data be stored in that location under the project’s requirements?
Consult DVC’s remote-storage documentation for supported remotes and provider-specific setup. Remote setup and authentication depend on the selected storage service.
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What DVC does—and does not—make reproducible
DVC makes the declared workflow and artifact handoffs easier to rerun and share. It does not by itself preserve a complete R execution environment or guarantee that every run will produce identical results. For stronger reproducibility, manage the project’s R version, packages, system dependencies, and relevant machine or hardware conditions alongside the workflow, and write stages that use only declared inputs and produce predictable outputs. The degree of repeatability therefore depends both on what the project records and on how its code behaves.
A note on older R tutorials
Marija Ilić’s DVC R tutorial was originally published on July 24, 2017, and its current page reports an update on November 15, 2025. It is useful as an R-focused illustration of the workflow, but includes historical dvc run examples. For current pipeline setup, use dvc.yaml stage definitions and dvc repro, as described in DVC’s current documentation. DVC R tutorial
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