rix

Reproducible Data Science environments for R with Nix

https://github.com/ropensci/rix

Keywords

Keywords from Contributors

Repository

Reproducible Data Science environments for R with Nix

README.Rmd

---
output: github_document
---

# rix: Reproducible Environments with Nix 

- [Introduction](#introduction)
- [Quick start for returning users](#quick-start-for-returning-users)
- [Getting started for new users](#getting-started-for-new-users)
  - [Docker](#docker)
- [Why Nix? Comparison with Docker+renv/Conda/Guix](#how-is-nix-different-from-dockerrenvgroundhogranganaminicondaguix-or-why-nix)
- [Contributing](#contributing)
- [Thanks](#thanks)
- [Recommended reading](#recommended-reading)


[![R-hub v2](https://github.com/ropensci/rix/actions/workflows/rhub.yaml/badge.svg)](https://github.com/ropensci/rix/actions/workflows/rhub.yaml/)
[![CRAN](https://www.r-pkg.org/badges/version/rix)](https://CRAN.R-project.org/package=rix)
[![runiverse-package rix](https://ropensci.r-universe.dev/badges/rix?scale=1&color=pink&style=round)](https://ropensci.r-universe.dev/rix)
[![Docs](https://img.shields.io/badge/docs-release-blue.svg)](https://docs.ropensci.org/rix/)
[![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/625_status.svg)](https://github.com/ropensci/software-review/issues/625)


```{r, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

## Introduction

`{rix}` is an R package that leverages [Nix](https://nixos.org/), a package
manager focused on reproducible builds. With Nix, you can create
project-specific environments with a custom version of R, its packages, and all
system dependencies (e.g., `GDAL`). Nix ensures full reproducibility, which is
crucial for research and development projects.

Use cases include running web apps (e.g., Shiny, `{plumber}` APIs) or
`{targets}` pipelines with a controlled R environment. Unlike `{renv}`, which
snapshots package versions, `{rix}` provides an entire ecosystem snapshot,
including system-level dependencies.

While Nix has a steep learning curve, `{rix}`

1. simplifies creating Nix expressions, which define reproducible environments,
   also from `renv.lock` files;
2. lets you work interactively in IDEs like RStudio or VS Code, or use Nix in
   CI/CD workflows;
3. makes it easy to create Docker images with the right packages;
4. provides helpers that make it easy to build those environments, evaluate the
   same code in different development environments, and finally to deploy
   software environments in production.

If you want to watch a 5-Minute video introduction, click the image below:


    



Nix includes nearly all CRAN and Bioconductor packages, with the ability to
install specific package versions or GitHub snapshots. Nix also includes Python,
Julia (and many of their respective packages) as well as many, many other tools
(up to 120'000 pieces of software as of writing). Expressions generated by
`{rix}` point to our fork of Nixpkgs which provides improved compatibility for
older versions of R and R packages, especially for Apple Silicon computers.

If you have R installed, you can start straight away from your R session by
first installing `{rix}` from CRAN:

```{r, eval=FALSE}
install.packages("rix")
```

or the development version from R-universe (development versions will generally
have fixes and newer features):

```{r, eval=FALSE}
install.packages("rix", repos = c(
  "https://ropensci.r-universe.dev"
))
```

Now try to generate an expression using `rix()`:

```{r, eval=FALSE}
library("rix")

# Choose the path to your project
# This will create two files: .Rprofile and default.nix
path_default_nix <- "."

rix(
  r_ver = "4.3.3",
  r_pkgs = c("dplyr", "ggplot2"),
  system_pkgs = NULL,
  git_pkgs = NULL,
  ide = "code",
  project_path = path_default_nix,
  overwrite = TRUE,
  print = TRUE
)
```

This will generate two files, `default.nix` and `.Rprofile` in
`project_default_nix`. `default.nix` is the environment definition written in
the Nix programming language, and `.Rprofile` prevents conflicts with library
paths from system-installed R versions, offering better control over your
environment and improving isolation of Nix environments. `.Rprofile` is created
by `rix_init()` which is called automatically by the main function, `rix()`.

It is also possible to provide a date instead of an R version:

```{r, eval=FALSE}
# Choose the path to your project
# This will create two files: .Rprofile and default.nix
path_default_nix <- "."

rix(
  date = "2024-12-14",
  r_pkgs = c("dplyr", "ggplot2"),
  system_pkgs = NULL,
  git_pkgs = NULL,
  ide = "code",
  project_path = path_default_nix,
  overwrite = TRUE,
  print = TRUE
)
```

It is also possible to add Python (and Python packages) and/or Julia (and Julia
packages) to an environment, by passing a list of two elements to the `py_conf`
argument of `rix()` (or `jl_conf` for Julia). This list needs to first specify a
Python or Julia version, and then an atomic character vector of Python (Julia)
packages:

```{r, eval = F}
rix(
  date = "2025-05-21",
  r_pkgs = "ggplot2",
  py_conf = list(
    py_version = "3.12",
    py_pkgs = c("polars", "great-tables")
  ),
  jl_conf = list(
    jl_version = "1.10",
    py_pkgs = c("Arrow", "TidierData")
  ),
  overwrite = TRUE
)
```

It should be noted that while we offer the guarantee that most CRAN and
Bioconductor packages are supported and installable through Nix (less than 5% of
packages aren’t currently supported), the same cannot be said about Python or
Julia packages. Should you require a Python or Julia but cannot successfully
bulid the environment, do open an issue and we’ll see what we can do.

The table below illustrates this all the different types of environment you can
generate:

```{=html}

  

    

      
r_ver or date
      
Intended use
      
State of R version
      
State of CRAN packages
      
State of Bioconductor packages
      
State of other packages in Nixpkgs
    
  
  

    

      
r_ver = "latest-upstream"
      
Start of new project where versions don’t matter
      
Current or previous
      
Outdated (up to 6 months)
      
Outdated (up to 6 months)
      
Current at time of generation
    
    

      
r_ver = "4.4.2" (or other)
      
Reproducing old project or starting a new project where versions don’t matter
      
Same as in `r_ver`, check `available_r()`
      
Outdated (up to 2 months if using latest release)
      
Outdated (up to 2 months if using latest release)
      
Potentially outdated (up to 12 months)
    
    

      
date = "2024-12-14"
      
Reproducing old project or starting a new project using the most recent date
      
Current at that date, check `available_dates()`
      
Current at that date, check `available_dates()`
      
Current at that date, check `available_dates()`
      
Potentially outdated (up to 12 months)
    
    

      
r_ver = "bleeding-edge"
      
To develop against the latest release of CRAN
      
Always current
      
Always current
      
Always current
      
Always current
    
    

      
r_ver = "frozen-edge"
      
To develop against the latest release of CRAN, but manually manage updates
      
Current at time of generation
      
Current at time of generation
      
Current at time of generation
      
Current at time of generation
    
    

      
r_ver = "r-devel"
      
To develop/test against the development version of R
      
Development version
      
Always current
      
Always current
      
Always current
    
    

      
r_ver = "r-devel-bioc-devel"
      
To develop/test against the development version of R and Bioconductor
      
Development version
      
Always current
      
Development version
      
Always current
    
    

      
r_ver = "bioc-devel"
      
To develop/test against the development version of Bioconductor
      
Always current
      
Always current
      
Development version
      
Always current
    
  

```

If you want to benefit from relatively fresh packages and have a stable environment
for production purposes, using a date for `r_ver` is likely your best option.

## Quick-start for returning users

Click to expand

If you're already familiar with Nix and `{rix}`, install Nix using the
[Determinate Systems
installer](https://determinate.systems/posts/determinate-nix-installer): (if
you're using WSL, do check out the [detailed installation
instructions](https://docs.ropensci.org/rix/articles/b1-setting-up-and-using-rix-on-linux-and-windows.html#windows-pre-requisites)
though):

```bash
curl --proto '=https' --tlsv1.2 -sSf -L https://install.determinate.systems/nix | sh -s -- install
```

Then, install the `cachix` client and configure our `rstats-on-nix` cache; this will install
binary versions of many R packages which will speed up the building process of
environments:

```bash
nix-env -iA cachix -f https://cachix.org/api/v1/install
```

then use the cache:

```bash
cachix use rstats-on-nix
```

You only need to do this once per machine you want to use `{rix}` on. Many thanks
to [Cachix](https://www.cachix.org/) for sponsoring the `rstats-on-nix` cache!

`{rix}` also includes a function called `setup_cachix()` which will configure
the cache but it is recommended to use the `cachix` client instead. This is
because `setup_cachix()` will not edit the files that require admin/root
privileges and only edit the user-level files. This may not be enough depending
on how you installed Nix. Using the `cachix` client takes care of everything.

You can then use `{rix}` to build and enter a Nix-based R environment:

```{r, eval=FALSE}
library(rix)

path_default_nix <- "."

rix(
  r_ver = "4.3.3",
  r_pkgs = c("dplyr", "ggplot2"),
  system_pkgs = NULL,
  git_pkgs = NULL,
  ide = "code",
  project_path = path_default_nix,
  overwrite = TRUE,
  print = TRUE
)
```

To build the environment, call `nix_build()`

```{r, eval=FALSE}
# nix_build() is a wrapper around the command line tool `nix-build`
nix_build(project_path = ".")
```

If you don't have R installed, but have the Nix package manager installed, you
can run a temporary terminal session which includes R and the released version
of `{rix}`:

```
nix-shell -p R rPackages.rix
```

or if you prefer the development version of `{rix}`:

```
nix-shell --expr "$(curl -sl https://raw.githubusercontent.com/ropensci/rix/main/inst/extdata/default.nix)"
```

You can then create new development environment definitions, build them, and
start using them.


## Getting started for new users

New to `{rix}` and Nix? Start by reading the `vignette("a-getting-started")`
([online
documentation](https://docs.ropensci.org/rix/articles/a-getting-started.html)).
to learn how to set up and use Nix smoothly.

### Docker

Try Nix inside Docker by following this
`vignette("z-advanced-topic-using-nix-inside-docker")`
[vignette](https://github.com/ropensci/rix/blob/HEAD/vignettes/z-advanced-topic-using-nix-inside-docker.Rmd).

## How is Nix different from Docker+renv/{groundhog}/{rang}/(Ana/Mini)Conda/Guix? or Why Nix?

### Docker + {renv}

Docker and `{renv}` provide robust reproducibility by combining package snapshots
with system-level dependencies. However, for long-term reproducibility, Nix
offers a simpler approach by bundling everything (R, packages, and dependencies)
in a single environment.

### Ana/Miniconda & Mamba

Conda is similar to Nix, but Nix offers immutable environments, making it more
reliable for preventing accidental changes. Nix also supports nearly all CRAN
and Bioconductor packages, which Conda lacks.

### Nix vs. Guix

Guix, like Nix, focuses on reproducibility, but Nix supports more
CRAN/Bioconductor packages and works across Windows, macOS, and Linux.

### Is {rix} all there is?

No, there are other tools that you might want to check out, especially if you
want to set up polyglot environments (even though it is possible to use `{rix}`
to set up an environment with R and Python packages for example).

Take a look at [https://devenv.sh/](https://devenv.sh/) and
[https://prefix.dev/](https://prefix.dev) if you want to explore other tools
that make using Nix easier!

## What's the recommended workflow?

Ideally, you shouldn't be using a system-wide installation of R, and instead use
dedicated Nix environments for each of your projects.

Start a new project by writing a file called `generate_env.R` and write
something like:

```
library(rix)

path_default_nix <- "."

rix(
  r_ver = "4.3.3",                # Change to whatever R version you need
  r_pkgs = c("dplyr", "ggplot2")  # Change to whatever packages you need
  system_pkgs = NULL
  git_pkgs = NULL,
  ide = "code",
  project_path = path_default_nix,
  overwrite = TRUE,
  print = TRUE
)
```

Then use the following command to bootstrap an enivronment with R and `{rix}`
only (from the same directory):

```
nix-shell --expr "$(curl -sl https://raw.githubusercontent.com/ropensci/rix/main/inst/extdata/default.nix)"
```

and then simply run `Rscript generate_env.R` which will run the above script,
thus generating the project's `default.nix`. If you need to add packages, open
the `generate_env.R` file again, modify it, and run it again, do not edit the
`default.nix` directly. Also, commit all the files to version control to avoid
any issues.

## Reproducible pipelines using rixpress

`{rix}` is getting a little sister package, `{rixpress}`, which is heavily
inspired by `{targets}`. If you want to build polyglot data science pipelines
with the power of Nix, give `{rixpress}` [a
try](https://github.com/b-rodrigues/rixpress)!

## Contributing

Refer to `Contributing.md` to learn how to contribute to the package.

Please note that this package is released with a [Contributor Code of
Conduct](https://ropensci.org/code-of-conduct/). By contributing to this
project, you agree to abide by its terms.

## Thanks

Thanks to the [Nix community](https://nixos.org/community/) for making Nix
possible, and thanks to the community of R users on Nix for their work
packaging R and CRAN/Bioconductor packages for Nix (in particular [Justin
Bedő](https://github.com/jbedo), [Rémi Nicole](https://github.com/minijackson),
[nviets](https://github.com/nviets), [Chris
Hammill](https://github.com/cfhammill), [László
Kupcsik](https://github.com/Kupac), [Simon
Lackerbauer](https://github.com/ciil),
[MrTarantoga](https://github.com/MrTarantoga) and every other person from the
[Matrix Nixpkgs R channel](https://matrix.to/#/%23r:nixos.org)).

Finally, thanks to [David Solito](https://www.davidsolito.com/about/) for creating `{rix}`'s logo!

## Recommended reading

- [NixOS’s website](https://nixos.org/)
- [Nixpkgs’s GitHub repository](https://github.com/NixOS/nixpkgs)
- [Nix for R series from Bruno's blog](https://brodrigues.co/index.html#category=nix). Or, in case you like video tutorials, watch [this one on Reproducible R development environments with Nix](https://www.youtube.com/watch?v=c1LhgeTTxaI)
- [nix.dev tutorials](https://nix.dev/tutorials/first-steps/towards-reproducibility-pinning-nixpkgs#pinning-nixpkgs)
- [INRIA's Nix tutorial](https://nix-tutorial.gitlabpages.inria.fr/nix-tutorial/installation.html)
- [Nix pills](https://nixos.org/guides/nix-pills/)
- [Nix for Data Science](https://github.com/nix-community/nix-data-science)
- [NixOS explained](https://christitus.com/nixos-explained/): NixOS is an entire Linux distribution that uses Nix as its package manager.
- [Blog post: Nix with R and devtools](https://rgoswami.me/posts/nix-r-devtools/)
- [Blog post: Statistical Rethinking and Nix](https://rgoswami.me/posts/rethinking-r-nix/)
- [Blog post: Searching and installing old versions of Nix packages](https://lazamar.github.io/download-specific-package-version-with-nix/)

Owner

CodeMeta (codemeta.json)

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  "identifier": "rix",
  "description": "Simplifies the creation of reproducible data science environments using the 'Nix' package manager, as described in Dolstra (2006) <ISBN 90-393-4130-3>. The included `rix()` function generates a complete description of the environment as a `default.nix` file, which can then be built using 'Nix'. This results in project specific software environments with pinned versions of R, packages, linked system dependencies, and other tools or programming languages such as Python or Julia. Additional helpers make it easy to run R code in 'Nix' software environments for testing and production.",
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