# Recursive self-modification

The {pipeflow} package aims to offer a lean and intuitive interface that enables new users to get started quickly without having to learn a lot of new concepts and functions.

At the same time, it was also designed to provide easy access to the underlying data structures to allow advanced users to modify the pipeline basically in any way they want. As such pipelines not only can be modified before but also during execution, respectively. This opens up a wide range of possibilities, for example, to change parameters based on intermediate results or even to modify the pipeline structure itself during a pipeline run. In the following, we will show some examples of how this can be done.

### The pipeline object

Let’s first define a pipeline, which fits a linear model, checks it’s residuals for normality using the Shapiro-Wilk test, and plots the residuals.

    library(pipeflow)

    pip <- pip_new("my-pipeline") |>
        pip_add(
            "data",
            function(data = NULL) data
        ) |>
        pip_add(
            "fit",
            function(
                data = ~data,
                xVar = "x",
                yVar = "y"
            ) {
                lm(paste(yVar, "~", xVar), data = data)
            }
        ) |>
        pip_add(
            "residual_shapiro_p_value",
            function(fit = ~fit) {
                residuals <- residuals(fit)
                shapiro.test(residuals)$p.value
            }
        ) |>
        pip_add(
            "plot",
            function(
                fit = ~fit,
                pointColor = "black"
            ) {
                require(ggplot2, quietly = TRUE)
                data <- data.frame(
                    fitted = predict(fit),
                    residuals = residuals(fit)
                )

                ggplot(data, aes(x = fitted, y = residuals)) +
                    geom_point(shape = 21, color = pointColor) +
                    geom_hline(yintercept = 0, linetype = "dashed") +
                    theme_minimal()
            }
        )

If you have followed the previous vignettes, you by now are used to the pipeline overview.

    pip
    # <pipeflow_pip> my-pipeline (4 steps)
    # ------------------------------------
    #                        step depends    out state
    # 1:                     data         [NULL]   new
    # 2:                      fit    data [NULL]   new
    # 3: residual_shapiro_p_value     fit [NULL]   new
    # 4:                     plot     fit [NULL]   new

To inspect the internal structure, let’s start with the class.

    class(pip)
    # [1] "pipeflow_pip" "environment"

As we can see, the pipeline object is stored in an environment.

    ls(pip)
    # [1] "name"     "pipeline"

There is the “name” of the pipeline, which is just a character string. The most interesting object is the “pipeline”, object, which under the hood is a `data.table` object containing all the information about the steps, their dependencies, meta information, and so on.

    data.class(pip$pipeline)
    # [1] "data.table"

    pip$pipeline
    #                        step           fun    params                              signature depends
    #                      <char>        <list>    <list>                                 <char>  <list>
    # 1:                     data <function[1]> <list[1]>                          (data = NULL)
    # 2:                      fit <function[1]> <list[3]> (data = ~data, xVar = "x", yVar = "y")    data
    # 3: residual_shapiro_p_value <function[1]> <list[1]>                           (fit = ~fit)     fit
    # 4:                     plot <function[1]> <list[2]>     (fit = ~fit, pointColor = "black")     fit
    #       out  state   tags                time locked   exec .nodeId    .indeps
    #    <list> <char> <list>              <POSc> <lgcl> <char>   <int>     <list>
    # 1: [NULL]    new        2026-06-14 22:27:11  FALSE   auto       0       data
    # 2: [NULL]    new        2026-06-14 22:27:11  FALSE   auto       1  xVar,yVar
    # 3: [NULL]    new        2026-06-14 22:27:11  FALSE   auto       2
    # 4: [NULL]    new        2026-06-14 22:27:11  FALSE   auto       3 pointColor

### Changing pipeline parameters at runtime

First, we set some data and parameters and run the pipeline as usual.

    pip |> pip_set_params(list(data = airquality, xVar = "Ozone", yVar = "Temp"))

    pip_run(pip)
    # info [2026-06-14 20:27:12.182 UTC]: Start run of pipeflow_pip 'my-pipeline'
    # info [2026-06-14 20:27:12.182 UTC]: Step 1/4 data
    # info [2026-06-14 20:27:12.183 UTC]: Step 2/4 fit
    # info [2026-06-14 20:27:12.185 UTC]: Step 3/4 residual_shapiro_p_value
    # info [2026-06-14 20:27:12.186 UTC]: Step 4/4 plot
    # info [2026-06-14 20:27:12.226 UTC]: Finished run of pipeflow_pip 'my-pipeline'

    pip[["plot", "out"]]

Now let’s imagine, we want to change the color of the points in the plot depending on the Shapiro-Wilk test result. The obvious way to do this would be to change the `plot` step by passing the test result to the `plot` step function and change the color there.

However, here we are interested in another way that would keep the `plot` function unchanged. For example, we could run the pipeline a second time as follows:

    if (pip[["residual_shapiro_p_value", "out"]] < 0.05) {
        pip |>
            pip_set_params(list(pointColor = "red")) |>
            pip_run()
    }
    # info [2026-06-14 20:27:12.428 UTC]: Start run of pipeflow_pip 'my-pipeline'
    # info [2026-06-14 20:27:12.428 UTC]: Step 1/4 data - skipping done step
    # info [2026-06-14 20:27:12.428 UTC]: Step 2/4 fit - skipping done step
    # info [2026-06-14 20:27:12.429 UTC]: Step 3/4 residual_shapiro_p_value - skipping done step
    # info [2026-06-14 20:27:12.429 UTC]: Step 4/4 plot
    # info [2026-06-14 20:27:12.475 UTC]: Finished run of pipeflow_pip 'my-pipeline'

    pip[["plot", "out"]]

As was mentioned in another vignette, this solution is not ideal, as it requires to run additional code around the pipeline. We rather want to set the parameter from within the pipeline during execution.

Luckily, the pipeline by default assigns itself to the `.self` parameter to potentially be used in any step functions. With this in mind, we update the `residual_shapiro_p_value` step as follows:

    pip |> pip_replace(
        "residual_shapiro_p_value",
        function(
            fit = ~fit,
            .self = NULL
        ) {
            residuals <- residuals(fit)
            p <- shapiro.test(residuals)$p.value

            if (p < 0.05) {
                .self |> pip_set_params(list(pointColor = "blue"))
            }

            p
        }
    )

Now we just have to make sure to set the `.self` parameter.

    pip_run(pip)
    # info [2026-06-14 20:27:12.700 UTC]: Start run of pipeflow_pip 'my-pipeline'
    # info [2026-06-14 20:27:12.700 UTC]: Step 1/4 data - skipping done step
    # info [2026-06-14 20:27:12.700 UTC]: Step 2/4 fit - skipping done step
    # info [2026-06-14 20:27:12.700 UTC]: Step 3/4 residual_shapiro_p_value
    # info [2026-06-14 20:27:12.704 UTC]: Step 4/4 plot
    # info [2026-06-14 20:27:12.743 UTC]: Finished run of pipeflow_pip 'my-pipeline'

    pip[["plot", "out"]]

This simple “trick” opens up a wide range of possibilities for pipeline modifications at runtime. As we will show in the next section, this is not limited to changing parameters but can also be used to modify the very own pipeline structure.

### Changing pipeline structure at runtime

Subsequently, the pipeline steps will be comprised only of very basic functions in order to keep matters simple. The focus here is on the pipeline structure and how it can be modified at runtime.

    pip <- pip_new("my-pipeline") |>
        pip_add("init", function(xInit = 0) xInit) |>
        pip_add("f1", function(x = ~init) x + 1) |>
        pip_add("f2", function(x = ~f1) x + 2) |>
        pip_add("f3", function(x = ~f2) x + 3)

This pipeline just adds 1, 2, and 3 to the initial value, respectively.

    pip_run(pip)
    # info [2026-06-14 20:27:12.950 UTC]: Start run of pipeflow_pip 'my-pipeline'
    # info [2026-06-14 20:27:12.950 UTC]: Step 1/4 init
    # info [2026-06-14 20:27:12.951 UTC]: Step 2/4 f1
    # info [2026-06-14 20:27:12.952 UTC]: Step 3/4 f2
    # info [2026-06-14 20:27:12.953 UTC]: Step 4/4 f3
    # info [2026-06-14 20:27:12.955 UTC]: Finished run of pipeflow_pip 'my-pipeline'

    pip
    # <pipeflow_pip> my-pipeline (4 steps)
    # ------------------------------------
    #    step depends out state
    # 1: init           0  done
    # 2:   f1    init   1  done
    # 3:   f2      f1   3  done
    # 4:   f3      f2   6  done

The `out` column in the table shows the output of each step. Now let’s modify step `f2` that in turn will modify `f3` at runtime based on the interim result passed into `f2`.

#### Modify steps

    pip |> pip_replace(
        "f2",
        function(
            x = ~f1,
            .self = NULL
        ) {
            if (x > 10) {
                .self |> pip_replace("f3", function(x = ~f1) x * 3)
                return(x / 2)
            }
            x + 2
        }
    )

Basically, step `f2` now checks if the input is greater than 10, and if so, it replaces step `f3` with a new step now referencing `f1` that multiplies the input passed from `f1` by 3 and returns half of the input.

To see this, let’s try it with an input of 15.

    pip |>
        pip_set_params(list(xInit = 15)) |>
        pip_run()
    # info [2026-06-14 20:27:13.032 UTC]: Start run of pipeflow_pip 'my-pipeline'
    # info [2026-06-14 20:27:13.033 UTC]: Step 1/4 init
    # info [2026-06-14 20:27:13.034 UTC]: Step 2/4 f1
    # info [2026-06-14 20:27:13.036 UTC]: Step 3/4 f2
    # info [2026-06-14 20:27:13.040 UTC]: Step 4/4 f3
    # info [2026-06-14 20:27:13.041 UTC]: Finished run of pipeflow_pip 'my-pipeline'

    pip
    # <pipeflow_pip> my-pipeline (4 steps)
    # ------------------------------------
    #    step depends out state
    # 1: init          15  done
    # 2:   f1    init  16  done
    # 3:   f2      f1   8  done
    # 4:   f3      f1  48  done

We see that both the output of the pipeline and the dependencies of the last step have changed. Let’s confirm by inspecting the function of the last step.

    pip[["f3", "fun"]]
    # function (x = ~f1)
    # x * 3
    # <environment: 0x0000028e4811e6c0>

#### Insert and remove steps

For our last example, we get even more hacky an dinstead of just replacing, we will go a bit further to insert and remove steps. The pipeline definition is as follows:

    pip <- pip_new("my-pipeline") |>
        pip_add("init", function(xInit = 0) xInit) |>
        pip_add("f1", function(x = ~init) x + 1) |>
        pip_add(
            "f2",
            function(
                x = ~f1,
                .self = NULL
            ) {
                if (x > 10) {
                    .self |>
                        pip_add(
                            "f2a",
                            function(x = ~f1) x + 21,
                            after = "f1",
                        ) |>
                        pip_add(
                            "f2b",
                            function(x = ~f2a) x + 22,
                            after = "f2a"
                        ) |>
                        pip_replace(
                            "f3",
                            function(x = ~f2b) {
                                x + 30
                            }
                        ) |>
                        pip_remove("f2")
                }
                x + 2
            }
        ) |>
        pip_add("f3", function(x = ~f2) x + 3)

Basically, if the input is greater than 10, we insert two new steps `f2a` and `f2b` after `f1`, remove `f2`, and replace `f3` with a new step that adds 30 to the input. Let’s first run with the initial value of 0 to see the original output.

    pip_run(pip)
    # info [2026-06-14 20:27:13.158 UTC]: Start run of pipeflow_pip 'my-pipeline'
    # info [2026-06-14 20:27:13.158 UTC]: Step 1/4 init
    # info [2026-06-14 20:27:13.159 UTC]: Step 2/4 f1
    # info [2026-06-14 20:27:13.160 UTC]: Step 3/4 f2
    # info [2026-06-14 20:27:13.162 UTC]: Step 4/4 f3
    # info [2026-06-14 20:27:13.164 UTC]: Finished run of pipeflow_pip 'my-pipeline'

    pip
    # <pipeflow_pip> my-pipeline (4 steps)
    # ------------------------------------
    #    step depends out state
    # 1: init           0  done
    # 2:   f1    init   1  done
    # 3:   f2      f1   3  done
    # 4:   f3      f2   6  done

Next, we set the initial value to 11 to trigger the changes.

    pip |>
        pip_set_params(list(xInit = 11)) |>
        pip_run()
    # info [2026-06-14 20:27:13.202 UTC]: Start run of pipeflow_pip 'my-pipeline'
    # info [2026-06-14 20:27:13.203 UTC]: Step 1/4 init
    # info [2026-06-14 20:27:13.203 UTC]: Step 2/4 f1
    # info [2026-06-14 20:27:13.204 UTC]: Step 3/4 f2
    # info [2026-06-14 20:27:13.223 UTC]: Step 4/4 f3
    # info [2026-06-14 20:27:13.225 UTC]: Finished run of pipeflow_pip 'my-pipeline'

    pip
    # <pipeflow_pip> my-pipeline (5 steps)
    # ------------------------------------
    #    step depends    out state
    # 1: init             11  done
    # 2:   f1    init     12  done
    # 3:  f2a      f1 [NULL]   new
    # 4:  f2b     f2a         done
    # 5:   f3     f2b [NULL]   new

While the structure has changed as expected, some steps were not yet run. In fact, since originally step `f3`came after `f2`, and in contrast to what the log is showing, instead of step `f3`, actually the new step `f2b` was run, albeit with x = NULL as input.

So to have the true results, we need to re-init the parameter and need to re-run the pipeline.

    pip |>
        pip_set_params(list(xInit = 11)) |>
        pip_run()
    # info [2026-06-14 20:27:13.264 UTC]: Start run of pipeflow_pip 'my-pipeline'
    # info [2026-06-14 20:27:13.265 UTC]: Step 1/5 init
    # info [2026-06-14 20:27:13.265 UTC]: Step 2/5 f1
    # info [2026-06-14 20:27:13.266 UTC]: Step 3/5 f2a
    # info [2026-06-14 20:27:13.268 UTC]: Step 4/5 f2b
    # info [2026-06-14 20:27:13.269 UTC]: Step 5/5 f3
    # info [2026-06-14 20:27:13.270 UTC]: Finished run of pipeflow_pip 'my-pipeline'

    pip
    # <pipeflow_pip> my-pipeline (5 steps)
    # ------------------------------------
    #    step depends out state
    # 1: init          11  done
    # 2:   f1    init  12  done
    # 3:  f2a      f1  33  done
    # 4:  f2b     f2a  55  done
    # 5:   f3     f2b  85  done

Now the output of all steps is as expected. If we want to use {pipeflow} in production, obviously, having to re-run the pipeline and temporarily showing a wrong log is not ideal. Luckily, {pipeflow} provides a built-in solution for this.

First, we have to make sure that any step modifying the pipeline structure returns the modified pipeline object itself, so let’s redefine the pipeline as follows:

    pip <- pip_new("my-pipeline") |>
        pip_add("init", function(xInit = 0) xInit) |>
        pip_add("f1", function(x = ~init) x + 1) |>
        pip_add(
            "f2",
            function(
                x = ~f1,
                .self = NULL
            ) {
                if (x > 10) {
                    .self |>
                        pip_add(
                            "f2a",
                            function(x = ~f1) x + 21,
                            after = "f1",
                        ) |>
                        pip_add(
                            "f2b",
                            function(x = ~f2a) x + 22,
                            after = "f2a"
                        ) |>
                        pip_replace(
                            "f3",
                            function(x = ~f2b) {
                                x + 30
                            }
                        ) |>
                        pip_remove("f2")
                    return(.self) # <-- return modified pipeline
                }
                x + 2
            }
        ) |>
        pip_add("f3", function(x = ~f2) x + 3)

Then let’s run the pipeline again while also setting the `recursive` argument to `TRUE` and have a closer look at the log.

    pip |>
        pip_set_params(list(xInit = 11)) |>
        pip_run(recursive = TRUE)
    # info [2026-06-14 20:27:13.348 UTC]: Start run of pipeflow_pip 'my-pipeline'
    # info [2026-06-14 20:27:13.348 UTC]: Step 1/4 init
    # info [2026-06-14 20:27:13.349 UTC]: Step 2/4 f1
    # info [2026-06-14 20:27:13.350 UTC]: Step 3/4 f2
    # info [2026-06-14 20:27:13.365 UTC]: Abort pipeline execution and restart on returned pipeline.
    # info [2026-06-14 20:27:13.365 UTC]: Start run of pipeflow_pip 'my-pipeline'
    # info [2026-06-14 20:27:13.365 UTC]: Step 1/5 init
    # info [2026-06-14 20:27:13.365 UTC]: Step 2/5 f1
    # info [2026-06-14 20:27:13.367 UTC]: Step 3/5 f2a
    # info [2026-06-14 20:27:13.368 UTC]: Step 4/5 f2b
    # info [2026-06-14 20:27:13.370 UTC]: Step 5/5 f3
    # info [2026-06-14 20:27:13.371 UTC]: Finished run of pipeflow_pip 'my-pipeline'

As you can see, the run is now automatically aborted right after the pipeline was modified and, since we have set `recursive = TRUE`, the pipeline is also restarted automatically based on the new structure. As a result, the log now is fully aligned with the performed pipeline run.

Looking at the final pipeline overview, we see that the output matches the expected output of the modified pipeline.

    pip
    # <pipeflow_pip> my-pipeline (5 steps)
    # ------------------------------------
    #    step depends out state
    # 1: init          11  done
    # 2:   f1    init  12  done
    # 3:  f2a      f1  33  done
    # 4:  f2b     f2a  55  done
    # 5:   f3     f2b  85  done

In summary, this was just a silly example to show some possibilities and I leave it to the user to come up with more sensible and complex use cases.

Lastly note that since you have full access to the pipeline object, of course, you can get even more hacky, but be aware that some additional operations are done under the hood when steps are added or removed. It is therefore not recommended to “manually” manipulate the internal data.table object in terms of removing or adding rows, or changing important columns such as `depends` or `.nodeId` as this immediately would invalidate the internal consistency of the dependency graph.

On the other hand, changing entries in columns such as `tags`, `time`, `state` or `output` is generally not critical. If in doubt, just try and see what works.