---
title: "Polyglot pipelines and literate programming with Quarto or R Markdown"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{polyglot}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

This vignette demonstrates how to build a polyglot pipeline and assumes you've
read `vignette("core-functions")`.

For a video version of this vignette, [click here](https://youtu.be/LYtN1aOsTWQ).

You can find all the code of this example
[here](https://github.com/b-rodrigues/rixpress_demos/tree/master/r_python_quarto).
The built Quarto document can be viewed
[here](https://b-rodrigues.github.io/rixpress_demos/r_python_quarto/index.html)
(the pipeline in this vignette is a slightly simplified version). For the Rmd
version, look
[here](https://github.com/b-rodrigues/rixpress_demos/blob/master/r_python_rmd/Readme.md).

For various other examples of polyglot pipelines, check out the folder labeled
`python_r` in this [github
repository](https://github.com/b-rodrigues/rixpress_demos/).

## Analysing the mtcars dataset using R and Python

`{rixpress}` makes it easy to write polyglot (multilingual) data science
pipelines with derivations that run R or Python code. This vignette explains how
you can easily set up such a pipeline.

Let's assume that you only have `Nix` installed on your system, and no R
installation (this is the ideal scenario: if you plan to use `Nix` full-time for
your development environments, you shouldn't have a system-wide installation of
R).

Before installing R and R packages for your pipeline, install
[cachix](https://www.cachix.org/) and configure the `rstats-on-nix` cache. This
way, pre-compiled, binary packages will be used instead of being built from
source. Run the following line in a terminal:

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

then use the cache:

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

There might be a message telling you to add your user to a configuration file by
executing another command. If so, follow the instructions; you only need to do
this once per machine you want to use `{rixpress}` on. Many thanks to
[Cachix](https://www.cachix.org/) for sponsoring the `rstats-on-nix` cache!

Now that the cache is configured, it's time to bootstrap your development
environment. Run this line:

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

This will drop you into a temporary shell with R and both `{rix}` and
`{rixpress}` available. Simply start R by typing `R`, and load `{rixpress}` and
call `rxp_init()` which will generate two files, `gen-env.R` and
`gen-pipeline.R`. You can open `gen-env.R` in your favourite text editor and
define the execution environment there:

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

rix(
  date = "2025-03-31",
  r_pkgs = c("dplyr", "igraph", "reticulate", "quarto"),
  git_pkgs = list(
    package_name = "rixpress",
    repo_url = "https://github.com/ropensci/rixpress",
    commit = "HEAD"
  ),
  py_conf = list(
    py_version = "3.12",
    py_pkgs = c("pandas", "polars", "pyarrow")
  ),
  ide = "none",
  project_path = ".",
  overwrite = TRUE
)
```

Notice the `py_conf` argument to `rix()`: this will install Python and the
listed Python packages in that environment. You'll notice that we add
`{reticulate}` to the list of R packages to install as well; this is primarily
for converting data between R and Python if you're not using a universal format
like JSON. Python build steps are executed in a standard Python shell and do not
require `{reticulate}` for Python code execution itself, so if you're only using
JSON to transfer data, `{reticulate}` is not required. If you prefer, you can
also use `uv` to manage Python and Python packages. While this is not a pure Nix
solution, it is still useful in cases you need a specific Python package that
might not be available through Nix, as not all PyPI packages are available
through Nix. In this case, refer to this
[section](https://docs.ropensci.org/rix/articles/d1-installing-r-packages-in-a-nix-environment.html#installing-python-packages-not-available-via-nixpkgs-impure)
of the *Installing R and Python packages in a Nix environment* vignette from
`{rix}`.

Now that you defined the execution environment of the pipeline, you can run the
`gen-env.R` script, still from the temporary `Nix` shell by running
`source("gen-env.R")`. This will generate the required `default.nix`. Then, quit
R and the temporary shell (CTRL-D or `quit()` in R, `exit` in the terminal) and
then build the environment defined by the freshly generated `default.nix` by
typing `nix-build`. This will now build the execution environment of the
pipeline. You can use this environment to work on your project interactively as
usual. To learn more, check out [`{rix}`](https://docs.ropensci.org/rix/).

You can now edit the pipeline script in `gen-pipeline.R`:

```{r, eval = FALSE}
library(rixpress)
library(igraph)

list(
  rxp_py_file(
    name = mtcars_pl,
    path = 'data/mtcars.csv',
    read_function = "lambda x: pl.read_csv(x, separator='|')"
  ),

  rxp_py(
    # reticulate doesn't support polars DFs yet, so need to convert
    # first to pandas DF
    name = mtcars_pl_am,
    expr = "mtcars_pl.filter(pl.col('am') == 1).to_pandas()"
  ),

  rxp_py2r(
    name = mtcars_am,
    expr = mtcars_pl_am
  ),

  rxp_r(
    name = mtcars_head,
    expr = my_head(mtcars_am),
    user_functions = "functions.R"
  ),

  rxp_r2py(
    name = mtcars_head_py,
    expr = mtcars_head
  ),

  rxp_py(
    name = mtcars_tail_py,
    expr = 'mtcars_head_py.tail()'
  ),

  rxp_py2r(
    name = mtcars_tail,
    expr = mtcars_tail_py
  ),

  rxp_r(
    name = mtcars_mpg,
    expr = dplyr::select(mtcars_tail, mpg)
  ),

  rxp_qmd(
    name = page,
    qmd_file = "my_doc/page.qmd",
    additional_files = c("my_doc/content.qmd", "my_doc/images")
  )
) |>
  rxp_populate(
    project_path = ".",
    py_imports = c(polars = "import polars as pl")
  )
```

As you can see, it starts by reading in some data using the Python `polars`
package, and then converts it to an R data frame for further manipulation,
converts it back to a Python data frame and back to R. You'll notice that at
some point the *head* of the data is computed using a user-defined function
called `my_head()`. User-defined functions should all go into a script called
`functions.R` or `functions.py` and derivations that use them need to be aware
of them by setting the `user_functions` argument. If derivations need further
files to be available in the sandbox, these should be listed in the
`additional_files` argument. A main difference between `rxp_py()` and `rxp_r()`
is that Python code should be passed as a string, and not as an expression.

What's also import for Python is to define how packages should be imported. In
this case, I want `polars` to be imported using `import polars as pl`, so I need
to use the `py_imports` argument of `rxp_populate()`. It is possible to skip
this, but then you'd need to write the entire package name each time:
`polars.read_csv()`. This is sometimes mandatory, for example if you want to
import a package's submodule:

```{r, eval = FALSE}
py_imports = c(pillow = "from PIL import Image")
```

The package is called `pillow`, so `{rixpress}` will write the statement
as `import pillow`, but this will simply not work.

It is also possible to use `adjust_import()` after the creation of the
`pipeline.nix` but more importantly is `add_import()`. This is required in cases
where a built-in Python module needs to be loaded, such as `os`. Because the
`os` module is not listed in the required Python packages in `rix(..., py_conf =
...)` to create the execution environment, it won't get automatically loaded by
`rxp_populate()`. Because of this, if `os` is needed for the pipeline,
`add_import()` is how you can add it. The `vignette("importing-data")` show such
an example.

If you want to use JSON to transfer data between derivations, you should use
the `encoder` and `decoder` arguments respectively:

```{r, eval = FALSE}
library(rixpress)
library(igraph)

list(
  rxp_py_file(
    name = mtcars_pl,
    path = "data/mtcars.csv",
    read_function = "lambda x: pl.read_csv(x, separator='|')"
  ),

  rxp_py(
    name = mtcars_pl_am,
    expr = "mtcars_pl.filter(pl.col('am') == 1)",
    user_functions = "functions.py",
    encoder = "serialize_to_json",
  ),

  rxp_r(
    name = mtcars_head,
    expr = my_head(mtcars_pl_am),
    user_functions = "functions.R",
    decoder = "jsonlite::fromJSON"
  ),

  rxp_r(
    name = mtcars_mpg,
    expr = dplyr::select(mtcars_head, mpg)
  )
) |>
  rxp_populate(
    project_path = ".",
    py_imports = c(polars = "import polars as pl")
  )

# Plot DAG for CI
rxp_dag_for_ci()
```

The Python `serialize_to_json` function is defined in the `functions.py` script
and looks like this:

```
def serialize_to_json(pl_df, path):
    with open(path, 'w') as f:
        f.write(pl_df.write_json())
```

The `encoder` and `decoder` arguments can be used to
serialise objects using any function, for example `qs::save()` or machine
learning-specific functions for specific models, such as those from `xgboost`.

## Building a Quarto or R Markdown document

The last pipeline I want to discuss builds a Quarto document using `rxp_qmd()`
(use `rxp_rmd()` for an R Markdown document). Here again, the `additional_files`
argument is used to make the derivation aware of required files to build the
document. Here is what the source of the document looks like:

````text

---
title: "Loading derivations outputs in a quarto doc"
format:
  html:
    embed-resources: true
    toc: true
---

![Meme](images/meme.png)

Use `rxp_read()` to show object in the document:

```
#| eval: true

rixpress::rxp_read("mtcars_head")
```

```
#| eval: true

rixpress::rxp_read("mtcars_tail")
```

```
#| eval: true

rixpress::rxp_read("mtcars_mpg")
```

{{< include content.qmd >}}

```
#| eval: true

rixpress::rxp_read("mtcars_tail_py")
```

````

Just like in an interactive session, `rxp_read()` is used to retrieve the
objects from the store. See how I refer to the other document `content.qmd` and
the image `meme.png`.

If you want to add further arguments to the Quarto command line tool, you can
use the `args` argument:

```{r, eval = FALSE}
rxp_qmd(
  name = page,
  qmd_file = "my_doc/page.qmd",
  additional_files = c("my_doc/content.qmd", "my_doc/images"),
  args = "--to typst"
)
```

and don't forget to add `typst` to the list of system packages in the call to
`rix()`:

```{r, eval = FALSE}
rix(
  date = "2025-03-31",
  r_pkgs = c("dplyr", "igraph", "reticulate", "quarto"),
  system_pkgs = "typst",
  git_pkgs = list(...
```

For more examples, check out [rixpress_demos
repository](https://github.com/b-rodrigues/rixpress_demos). These examples
demonstrate additional features of `{rixpress}`, including:

- [Using the Python 'xgboost' library and transferring data to R](https://github.com/b-rodrigues/rixpress_demos/tree/master/r_py_xgboost)
- [Importing multiple files at once](https://github.com/b-rodrigues/rixpress_demos/tree/master/many_inputs_example)
- [Using multiple environments instead of a single `default.nix` file](https://github.com/b-rodrigues/rixpress_demos/tree/master/r_multi_envs)

and many others! Don’t hesitate to submit more examples as well!
