Recursive self-modification
Source:vignettes/v06-self-modify-pipeline.Rmd
v06-self-modify-pipeline.RmdThe {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)$p.value
}
) |>
pip_add(
"plot",
function(
fit = ~fit,
pointColor = "black"
) {
require(ggplot2, quietly = TRUE)
data <- data.frame(
fitted = predict(fit),
residuals = residuals(fit)
)
ggplot(data, aes(x = fitted, y = residuals)) +
geom_point(shape = 21, color = pointColor) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_minimal()
}
)If you have followed the previous vignettes, you by now are used to the pipeline overview.
pip
# <pipeflow_pip> my-pipeline (4 steps)
# ------------------------------------
# step depends out state
# 1: data [NULL] new
# 2: fit data [NULL] new
# 3: residual_shapiro_p_value fit [NULL] new
# 4: plot fit [NULL] newTo inspect the internal structure, let’s start with the class.
class(pip)
# [1] "pipeflow_pip" "environment"As we can see, the pipeline object is stored in an environment.
ls(pip)
# [1] "name" "pipeline"There is the “name” of the pipeline, which is just a character
string. The most interesting object is the “pipeline”, object, which
under the hood is a data.table object containing all the
information about the steps, their dependencies, meta information, and
so on.
data.class(pip$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 2026-06-20 21:20:35 FALSE auto 0 data
# 2: [NULL] new 2026-06-20 21:20:35 FALSE auto 1 xVar,yVar
# 3: [NULL] new 2026-06-20 21:20:35 FALSE auto 2
# 4: [NULL] new 2026-06-20 21:20:35 FALSE auto 3 pointColorChanging 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 [2026-06-20 19:20:35.700 UTC]: Start run of pipeflow_pip 'my-pipeline'
# info [2026-06-20 19:20:35.701 UTC]: Step 1/4 data
# info [2026-06-20 19:20:35.702 UTC]: Step 2/4 fit
# info [2026-06-20 19:20:35.706 UTC]: Step 3/4 residual_shapiro_p_value
# info [2026-06-20 19:20:35.708 UTC]: Step 4/4 plot
# info [2026-06-20 19:20:36.432 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 [2026-06-20 19:20:36.766 UTC]: Start run of pipeflow_pip 'my-pipeline'
# info [2026-06-20 19:20:36.766 UTC]: Step 1/4 data - skipping done step
# info [2026-06-20 19:20:36.766 UTC]: Step 2/4 fit - skipping done step
# info [2026-06-20 19:20:36.766 UTC]: Step 3/4 residual_shapiro_p_value - skipping done step
# info [2026-06-20 19:20:36.767 UTC]: Step 4/4 plot
# info [2026-06-20 19:20:36.829 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 [2026-06-20 19:20:37.142 UTC]: Start run of pipeflow_pip 'my-pipeline'
# info [2026-06-20 19:20:37.143 UTC]: Step 1/4 data - skipping done step
# info [2026-06-20 19:20:37.143 UTC]: Step 2/4 fit - skipping done step
# info [2026-06-20 19:20:37.143 UTC]: Step 3/4 residual_shapiro_p_value
# info [2026-06-20 19:20:37.147 UTC]: Step 4/4 plot
# info [2026-06-20 19:20:37.190 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 [2026-06-20 19:20:37.473 UTC]: Start run of pipeflow_pip 'my-pipeline'
# info [2026-06-20 19:20:37.473 UTC]: Step 1/4 init
# info [2026-06-20 19:20:37.474 UTC]: Step 2/4 f1
# info [2026-06-20 19:20:37.476 UTC]: Step 3/4 f2
# info [2026-06-20 19:20:37.481 UTC]: Step 4/4 f3
# info [2026-06-20 19:20:37.483 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 doneThe 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 [2026-06-20 19:20:37.601 UTC]: Start run of pipeflow_pip 'my-pipeline'
# info [2026-06-20 19:20:37.601 UTC]: Step 1/4 init
# info [2026-06-20 19:20:37.602 UTC]: Step 2/4 f1
# info [2026-06-20 19:20:37.604 UTC]: Step 3/4 f2
# info [2026-06-20 19:20:37.609 UTC]: Step 4/4 f3
# info [2026-06-20 19:20:37.611 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 doneWe 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: 0x000001c07570ce48>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 [2026-06-20 19:20:37.783 UTC]: Start run of pipeflow_pip 'my-pipeline'
# info [2026-06-20 19:20:37.783 UTC]: Step 1/4 init
# info [2026-06-20 19:20:37.784 UTC]: Step 2/4 f1
# info [2026-06-20 19:20:37.786 UTC]: Step 3/4 f2
# info [2026-06-20 19:20:37.787 UTC]: Step 4/4 f3
# info [2026-06-20 19:20:37.789 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 doneNext, we set the initial value to 11 to trigger the changes.
pip |>
pip_set_params(list(xInit = 11)) |>
pip_run()
# info [2026-06-20 19:20:37.848 UTC]: Start run of pipeflow_pip 'my-pipeline'
# info [2026-06-20 19:20:37.848 UTC]: Step 1/4 init
# info [2026-06-20 19:20:37.849 UTC]: Step 2/4 f1
# info [2026-06-20 19:20:37.850 UTC]: Step 3/4 f2
# info [2026-06-20 19:20:37.874 UTC]: Step 4/4 f3
# info [2026-06-20 19:20:37.876 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] newWhile the structure has changed as expected, some steps were not yet
run. In fact, since originally step f3came 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 [2026-06-20 19:20:37.937 UTC]: Start run of pipeflow_pip 'my-pipeline'
# info [2026-06-20 19:20:37.937 UTC]: Step 1/5 init
# info [2026-06-20 19:20:37.937 UTC]: Step 2/5 f1
# info [2026-06-20 19:20:37.939 UTC]: Step 3/5 f2a
# info [2026-06-20 19:20:37.941 UTC]: Step 4/5 f2b
# info [2026-06-20 19:20:37.942 UTC]: Step 5/5 f3
# info [2026-06-20 19:20:37.944 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 doneNow 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 [2026-06-20 19:20:38.064 UTC]: Start run of pipeflow_pip 'my-pipeline'
# info [2026-06-20 19:20:38.064 UTC]: Step 1/4 init
# info [2026-06-20 19:20:38.065 UTC]: Step 2/4 f1
# info [2026-06-20 19:20:38.066 UTC]: Step 3/4 f2
# info [2026-06-20 19:20:38.084 UTC]: Abort pipeline execution and restart on returned pipeline.
# info [2026-06-20 19:20:38.084 UTC]: Start run of pipeflow_pip 'my-pipeline'
# info [2026-06-20 19:20:38.084 UTC]: Step 1/5 init
# info [2026-06-20 19:20:38.084 UTC]: Step 2/5 f1
# info [2026-06-20 19:20:38.086 UTC]: Step 3/5 f2a
# info [2026-06-20 19:20:38.087 UTC]: Step 4/5 f2b
# info [2026-06-20 19:20:38.089 UTC]: Step 5/5 f3
# info [2026-06-20 19:20:38.091 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 doneIn 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.