z-advanced-topic-running-r-or-shell-code-in-nix-from-r.Rmd

---
title: "z - Advanced topic: Running R or Shell Code in Nix from R"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{z-advanced-topic-running-r-or-shell-code-in-nix-from-r}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

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

## **Testing code in evolving software dependency environments with confidence**

Adhering to sound versioning practices is crucial for ensuring the
reproducibility of software. Despite the expertise in software engineering, the
ever-growing complexity and continuous development of new, potentially
disruptive features present significant challenges in maintaining code
functionality over time. This pertains not only to backward compatibility but
also to future-proofing. When code handles critical production loads and relies
on numerous external software libraries, it's likely that these dependencies
will evolve. Infrastructure-as-code and other DevOps principles shine in
addressing these challenges. However, they may appear less approachable and more
labor-intensive to set up for the average R developer.

Are you ready to test your custom R functions and system commands in a a
different environment with isolated software builds that are both pure at build
and at runtime, without leaving the R console?

Let's introduce `with_nix()`. `with_nix()` will evaluate custom R code or shell
commands with command line interfaces provided by Nixpkgs in a Nix environment,
and thereby bring the read-eval-print-loop feeling. Not only can you evaluate
custom R functions or shell commands in Nix environments, but you can also bring
the results back to your current R session as R objects.


## **Two operational modes of computations in environments: 'System-to-Nix' and 'Nix-to-Nix'**

We aim to accommodate various use cases, considering a gradient of declarativity
in individual or sets of software environments based on personal preferences.
There are two main modes for defining and comparing code running through R and
system commands (command line interfaces; CLIs)

1.  **'System-to-Nix'** environments: We assume that you launch an R session
    with an R version defined on your host operating system, either from the
    terminal or an integrated development environment like RStudio. You need to
    make sure that you actively control and know where you installed R and R
    packages from, and at what versions. You may have interactively tested that
    your custom function pipeline worked for the current setup. Most
    importantly, you want to check whether you get your computations running and
    achieve identical results when going back to a Nix revision that represent
    either newer or also older versions of R and package sources.
2.  **'Nix-to-Nix'** environments: Your goals of testing code are the same as in
    1., but you want more fine-grained control in the source environment where
    you launch `with_nix()` from, too. You are probably on the way of getting a
    passionate Nix user.


## **Case study 1: Evolution of base R**

Carefully curated software improves over time, so does R. We pick an example
from the R changelog, the following [literal entry in R
4.2.0](https://cran.r-project.org/doc/manuals/r-release/NEWS.html):

-   "`as.vector()` gains a `data.frame` method which returns a simple named
    list, also clearing a long standing 'FIXME' to enable
    `as.vector(<data.frame>, mode ="list")`. This breaks code relying on
    `as.vector(<data.frame>)` to return the unchanged data frame."

The goal is to illustrate this change in behavior from R versions 4.1.3 and
before to R versions 4.2.0 and later.


### Setting up the (R) software environment with Nix

We first create a isolated directory to prepare for a Nix environment, and write
a custom `.Rprofile` file as well. By default, the R derivation in Nixpkgs
includes the user library at first position (returned by `.libPaths()`). Startup
code written to this local `.Rprofile` will make sure that the system's user
library (`R_LIBS_USER`) is excluded from library paths to load packages from.
This is nice to install packages from a Nix-R session environment in ad-hoc and
interactive manner. However, this comes at the cost that one needs be aware of
potential run-time pollution of packages outside the pool of paths per package
from the nix store. On macOS, we experienced a high-chance of segmentation
faults when accidentally loading packages and linked system libraries from the
system's user library, to give an example. `rix::rix_init()` writes a configuration
that takes care of runtime-pure R package libraries from declaratively defined
Nix builds. Additionally, it modifies `.libPaths()` in the running R session.


```{r parsermd-chunk-1}
library("rix")

path_env_1 <- file.path(".", "_env_1_R-4-1-3")

rix_init(
  project_path = path_env_1,
  rprofile_action = "overwrite",
  message_type = "simple"
)
list.files(path = path_env_1, all.files = TRUE)
```

This will generate the following `.Rprofile` file.


```{r parsermd-chunk-2, echo = FALSE}
cat(readLines(file.path(path_env_1, ".Rprofile")), sep = "\n")
```

Next, we write a `default.nix` file containing Nix expressions that pin R
version 4.2.0 from Nixpkgs.


```{r parsermd-chunk-3}
rix(
  r_ver = "4.1.3",
  overwrite = TRUE,
  project_path = path_env_1
)
```

The following expression is written to default.nix in the subfolder
`./_env_1_R-4-1-3/`.


```{r parsermd-chunk-4, echo = FALSE}
cat(readLines(file.path(path_env_1, "default.nix")), sep = "\n")
```

### Defining and interactively testing custom R code with function(s)

We know have set up the configuration for R 4.1.3 set up in a `default.nix` file
in the folder `./_env_1_R-4-1-3`. Since you are sure you are using an R version
higher 4.2.0 available on your system, you can check what that
`as.vector.data.frame()` S3 method returns a list.


```{r parsermd-chunk-5}
df <- data.frame(a = 1:3, b = 4:6)
as.vector(x = df, mode ="list")
```

This is is different for R versions 4.1.3 and below, where you should get an
identical data frame back.


### Run functioned up code and investigate results produced in pure Nix Rsoftware environments

To formally validate in a 'System-to-Nix' approach that the object returned from
`as.vector.data.frame()` is before `R` \< 4.2.0, we define a function that runs
the computation above.


```{r parsermd-chunk-6}
df_as_vector <- function(x) {
  out <- as.vector(x = x, mode = "list")
  return(out)
}
(out_system_1 <- df_as_vector(x = df))
```

Then, we will evaluate this test code through a `nix-shell` R session. This adds
both build-time and run-time purity with the declarative Nix software
configuration we have made earlier. `with_nix()` leverages the following
principles under the hood:

1.  **Computing on the Language:** Manipulating language objects using code.

2.  **Static Code Analysis:** Detecting global objects and package environments
    in the function call stack of 'expr'. This involves utilizing essential
    functionality from the 'codetools' package, which is recursively iterated.

3.  **Serialization of Dependent R objects:** Saving them to disk and
    deserializing them back into the R session's RAM via a temporary folder.
    This process establishes isolation between two distinct computational
    environments, accommodating both 'System-to-Nix' and 'Nix-to-Nix'
    computational modes. Simultaneously, it facilitates the transfer of input
    arguments, dependencies across the call stack, and outputs of `expr` between
    the Nix-R and the system's R sessions.

This approach guarantees reproducible side effects and effectively streams
messages and errors into the R session. Thereby, the {sys} package facilitates
capturing standard outputs and errors as text output messages. Please be aware
that `with_nix()` will invoke `nix-shell`, which will itself run `nix-build` in
case the Nix derivation (package) for R version 4.1.3 is not yet in your Nix
store. This will take a bit of time to get the cache. When you use the
`exec_mode == "non-blocking"` argument of `with_nix()`, you will see in your
current R console the specific Nix paths that will be downloaded and copied into
your Nix store automatically.


```{r parsermd-chunk-7, eval = FALSE}
# now run it in `nix-shell`; `with_nix()` takes care
# of exporting global objects of `df_as_vector` recursively
out_nix_1 <- with_nix(
  expr = function() df_as_vector(x = df), # wrap to avoid evaluation
  program = "R", 
  exec_mode = "non-blocking", # run as background process
  project_path = path_env_1, 
  message_type = "simple" # you can do `"verbose"`, too
)

# compare results of custom codebase with indentical 
# inputs and different software environments
identical(out_system_1, out_nix_1)

# should return `FALSE` if your system's R versions in
# current interactive R session is R >= 4.2.0
```

### Syntax option for specifying function in `expr` argument of `with_nix()`

In the previous code snippet we wrapped the top-level `expr` function with
`function()` or `function(){}`. As an alternative, you can also provide default
arguments when assigning the function used as `expr` input like this:


```{r parsermd-chunk-8}
df_as_vector <- function(x = df) {
  out <- as.vector(x = x, mode = "list")
  return(out)
}
```

Then, you just supply the name of the function to evaluate with default
arguments.


```{r parsermd-chunk-9, eval = FALSE}
out_nix_1_b <- with_nix(
  expr = df_as_vector, # provide name of function
  program = "R", 
  exec_mode = "non-blocking", # run as background process
  project_path = path_env_1, 
  message_type = "simple" # you can do `"verbose"`, too
)
```

It yields the same results.


```{r parsermd-chunk-10, eval = FALSE}
Reduce(f = identical, list(out_nix_1, out_nix_1_b))
```

### Comparing `as.vector.data.frame()` for both R versions 4.1.3 and 4.2.0 from Nixpkgs

Here follows an example a `Nix-to-Nix` solution, with two subshells to track the
evolution of base R in this specific case. We can verify the breaking changes in
case study 1 in more declarative manner when we use both R 4.1.3 and R 4.2.0
from Nixpkgs. Since we already have defined R 4.1.3 in the *`env`*`_1_R-4-1-3`
subshell, we can use it as a source environment where with_nix() is launched
from. Accordingly, we define the R 4.2.0 environment in a
*`env`*`_1_2_R-4-2-0`using Nix via `rix::rix()`. The latter environment will be
the target environment where `df_as_vector()` will be evaluated in.


```{r parsermd-chunk-11}
library("rix")
path_env_1_2 <- file.path(".", "_env_1_2_R-4-2-0")

rix_init(
  project_path = path_env_1_2,
  rprofile_action = "overwrite",
  message_type = "simple"
)

rix(
  r_ver = "4.2.0",
  overwrite = TRUE,
  project_path = path_env_1_2,
  shell_hook = "R"
)

list.files(path_env_1_2)
```

Now, initiate a new R session as development environment using `nix-shell`. Open
a new terminal at the current working directory of your R session. The provided
expression `default.nix`. defines R 4.1.3 in a "subfolder per subshell"
approach. `nix-shell` will use the expression by `default.nix` and prefer it
over any other `.nix` files, except when you put a `shell.nix` file in that
folder, which takes precedence.


```{sh parsermd-chunk-12, eval = FALSE}
nix-shell --pure ./_env_1_R-4-1-3
```

After some time downloading caches and doing builds, you will enter an R console
session with R 4.1.3. You did not need to type in R first, because we set up a R
shell hook via `rix::rix()`. Next, we define again the target function to test
in R 4.2.0, too.


```{r parsermd-chunk-13, eval = FALSE}
# current Nix-R session with R 4.1.3
df_as_vector <- function(x) {
  out <- as.vector(x = x, mode = "list")
  return(out)
}
(out_nix_1 <- df_as_vector(x = df))
```

```{r parsermd-chunk-14, eval = FALSE}
out_nix_1_2 <- with_nix(
  expr = function() df_as_vector(x = df),
  program = "R",
  exec_mode = "non-blocking", # run as background process
  project_path = path_env_1_2, 
  message_type = "simple" # you can do `"verbose"`, too
)
```

You can now formally compare the outputs of the computation of the same code in
R 4.1.3 vs. R 4.2.0 environments controlled by Nix.


```{r parsermd-chunk-15, eval = FALSE}
identical(out_nix_1, out_nix_1_2)
# yields FALSE
```

## **Case study 2: Breaking changes in {stringr} 1.5.0**

We add one more layer to the reproducibility of the R ecosystem. User libraries
from CRAN or GitHub, one thing that makes R shine is the huge collection of
software packages available from the community.

There was a change introduce in {stringr} 1.5.0; in earlier versions, this
line of code:


```{r parsermd-chunk-16, eval = FALSE}
stringr::str_subset(c("", "a"), "")
```

would return the character `"a"`. However, this behaviour is unexpected:
it really should return an error. This was addressed in versions after
1.5.0:


```{r parsermd-chunk-17, eval = FALSE}
out_system_stringr <- tryCatch(
  expr = {stringr::str_subset(c("", "a"), "")},
  error = function(e)NULL)
```

Since the code returns an error, we wrap it inside `tryCatch()` and return
`NULL` instead of an error (if we wouldn't do that, this vignette could not
compile!).

Let's build a subshell with the latest version of R, but an older version
of `{stringr}`:



```{r parsermd-chunk-18}
library("rix")

path_env_stringr <- file.path(".", "_env_stringr_1.4.1")

rix_init(
  project_path = path_env_stringr,
  rprofile_action = "overwrite",
  message_type = "simple"
)

list.files(path = path_env_stringr, all.files = TRUE)
```

The call below generates a `default.nix` file for the subshell with the
old version of `{stringr}`:


```{r parsermd-chunk-19, eval = FALSE}
rix(
  r_ver = "latest",
  r_pkgs = "stringr@1.4.1",
  overwrite = TRUE,
  project_path = path_env_stringr
)
```

We can now run the code in the subshell


```{r parsermd-chunk-20, eval = FALSE}
out_nix_stringr <- with_nix(
  expr = function() stringr::str_subset(c("", "a"), ""),
  program = "R",
  exec_mode = "non-blocking",
  project_path = path_env_stringr,
  message_type = "simple"
)
```

Here are the last few lines printed on screen:

```
==> `expr` succeeded!

### Finished code evaluation in `nix-shell` ###

* Evaluating `expr` in `nix-shell` returns:
[1] "a"
```

Not only do we see the result of evaluating the code in the subshell, we also
have access to it: `out_nix_stringr` holds this result.

We can now compare the two: the result of the code running in our main session
with the latest version of `{stringr}` and the result of the code running in the
subshell with the old version of `{stringr}`:


```{r parsermd-chunk-21, eval = FALSE}
identical(out_system_stringr, out_nix_stringr)
```

As expected, the result is `FALSE`.


## **Case study 3: Using a subshell to get hard to install dependencies**

Nix subshells are quite useful in cases where you need to use a package that
might be difficult to install, such as `{arrow}`, or other packages that must be
compiled. Depending on your operating system you need to compile `{arrow}` from
source, which can be a frustrating experience, especially if you only need it to
load data and bring it down to a manageable size (using `select()` and
`filter()` for instance). This use cases illustrates how to achieve this.

Let's start by building a subshell that is based on a distinct revision of
nixpkgs, for which we know that arrow compiles on both linux and macOS (darwin).


```{r parsermd-chunk-22, eval = FALSE}
library("rix")

path_env_arrow <- file.path("env_arrow")

rix_init(
  project_path = path_env_arrow,
  rprofile_action = "overwrite",
  message_type = "simple"
)

rix(
  r_ver = "af63e7a15daf283b4ce634006b3767f9c0eb0c58",
  r_pkgs = c("dplyr", "arrow"),
  overwrite = TRUE,
  project_path = path_env_arrow
)
```

This specific revision of R contains `{arrow}` 13. Let's now
suppose that you already have a script with some code to
load and transform some data using `{arrow}`. It may look
something like this:


```{r parsermd-chunk-23, eval = FALSE}
library(arrow)
library(dplyr)

arrow_cars <- arrow_table(cars)

arrow_cars %>%
  filter(speed > 10) %>%
  as.data.frame()
```

To run this code in a subshell, we recommend wrapping it inside a function:


```{r parsermd-chunk-24, eval = FALSE}
arrow_script <- function() {
  library(arrow)
  library(dplyr)

  arrow_cars <- arrow_table(cars)

  arrow_cars %>%
    filter(speed > 10) %>%
    as.data.frame()
}

```

Which we can then run in the subshell:


```{r parsermd-chunk-25, eval = FALSE}
out_nix_arrow <- with_nix(
  expr = function() arrow_script(),
  program = "R",
  exec_mode = "non-blocking",
  project_path = path_env_arrow,
  message_type = "simple"
)

```

This will run the function in the subshell, and its output will be saved in the
`out_nix_arrow` variable, for further manipulation in your main shell/session.