# Reproducibility in Microbiome Data Analysis

Reproducibility is a crucial aspect of data analysis, particularly in the context of microbiome data. The ability to consistently replicate an analysis and obtain the same results is essential for ensuring the reliability of findings and facilitating scientific collaboration.

The `dar` package includes two key functions, `export_steps` and `import_steps`, which promote reproducibility in microbiome data analysis. These functions allow you to export the steps of a recipe to a JSON file and then import those steps to reproduce the analysis in a different environment.

## Exporting Steps of a Recipe

The `export_steps` function facilitates the export of a recipe’s steps to a JSON file. This is useful for documenting and sharing the parameters used in the analysis.

Here’s an example of how to use the `export_steps` function:

    library(dar)
    data(metaHIV_phy)

    # Create a recipe with steps
    rec <-
      recipe(metaHIV_phy, "RiskGroup2", "Species") |>
      step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |>
      step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.3 * length(x))") |>
      step_maaslin()

    # Export the steps to a JSON file
    out_file <- tempfile(fileext = ".json")
    export_steps(rec, out_file)

In this example, a recipe with multiple steps is created, and then the steps are exported to a JSON file using the `export_steps` function.

## Importing Steps from a JSON File

The `import_steps` function allows you to import steps from a JSON file and add them to an existing recipe. This is useful when you want to reuse a previously saved set of steps or incorporate steps from another recipe into your current analysis.

Here’s an example of how to use the `import_steps` function:

    # Initialize a recipe with a phyloseq object
    rec <- recipe(metaHIV_phy, "RiskGroup2", "Species")

    # Import the steps from a JSON file
    json_file <- out_file
    rec <- import_steps(rec, json_file)
    rec
    #> ── DAR Recipe ──────────────────────────────────────────────────────────────────
    #> Inputs:
    #>
    #>      ℹ phyloseq object with 451 taxa and 156 samples
    #>      ℹ variable of interes RiskGroup2 (class: character, levels: hts, msm, pwid)
    #>      ℹ taxonomic level Species
    #>
    #> Preporcessing steps:
    #>
    #>      ◉ step_subset_taxa() id = subset_taxa__Gibanica
    #>      ◉ step_filter_taxa() id = filter_taxa__Rhubarb_tart
    #>
    #> DA steps:
    #>
    #>      ◉ step_maaslin() id = maaslin__Coussin_de_Lyon

In this example, an empty recipe is initialized, and then the steps are imported from a JSON file using the `import_steps` function. The imported steps are added to the existing recipe.

Once the recipe is imported, we can choose to add more steps or execute the code using the `prep` function. In this case, we choose to execute `prep` directly.

    ## Execute
    da_results <- prep(rec, parallel = FALSE) |> bake()
    da_results
    #> ── DAR Results ─────────────────────────────────────────────────────────────────
    #> Inputs:
    #>
    #>      ℹ phyloseq object with 101 taxa and 156 samples
    #>      ℹ variable of interes RiskGroup2 (class: character, levels: hts, msm, pwid)
    #>      ℹ taxonomic level Species
    #>
    #> Results:
    #>
    #>      ✔ maaslin__Coussin_de_Lyon diff_taxa = 41
    #>
    #>      ℹ 41 taxa are present in all tested methods
    #>
    #> Bakes:
    #>
    #>      ◉ 1 -> count_cutoff: NULL, weights: NULL, exclude: NULL, id: bake__Fig_roll

## Limitations and Considerations

It’s important to note some limitations and considerations when using the `export_steps` and `import_steps` functions:

- The JSON files generated by `export_steps` only contain the parameters of the recipe steps and bakes, not the original data used in the analysis. Therefore, when importing the steps from a JSON file, ensure that you have access to the same data that was originally used.

- The `export_steps` and `import_steps` functions are specific to the `dar` package and are designed for use in microbiome data analysis. They are not applicable to other types of analyses or packages.

- When importing steps from a JSON file, it’s important to check if the file contains “bake” steps. If so, the recipe will be automatically prepared after importing the steps. This may have implications for runtime and resource requirements of the analysis.

- Make sure you have the correct versions of the dependencies of the `dar` package when exporting and importing recipe steps. Updates in dependencies can affect the compatibility and reproducibility of the analyses.

## Conclusion

Reproducibility is essential in microbiome data analysis, and the `dar` package facilitates this aspect by providing the `export_steps` and `import_steps` functions. These functions allow you to export the steps of a recipe to a JSON file and then import them to reproduce the analysis in a different environment. With these tools, you can effectively document and share your analyses, increasing transparency and the reliability of your results.

## Session info

    devtools::session_info()
    #> ─ Session info ───────────────────────────────────────────────────────────────
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    #>  quarto   1.9.37 @ /usr/local/bin/quarto
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    #>  S7                         0.2.2      2026-04-22 [2] RSPM (R 4.6.0)
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    #>  SparseArray                1.13.2     2026-05-01 [2] https://bioc.r-universe.dev (R 4.6.0)
    #>  sparseMatrixStats          1.25.0     2026-04-28 [2] https://bioc.r-universe.dev (R 4.6.0)
    #>  SQUAREM                    2026.1     2026-03-12 [2] RSPM (R 4.6.0)
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    #>  xfun                       0.57       2026-03-20 [2] RSPM (R 4.6.0)
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    #>
    #>  [1] /tmp/RtmpYCIDqx/Rinst35eb3ee76ca4
    #>  [2] /github/workspace/pkglib
    #>  [3] /usr/local/lib/R/site-library
    #>  [4] /usr/lib/R/site-library
    #>  [5] /usr/lib/R/library
    #>  * ── Packages attached to the search path.
    #>
    #> ──────────────────────────────────────────────────────────────────────────────