Existing pipeline
Let’s start where we left off in the Get started with pipeflow vignette, that
is, we have the following pipeline
pip
# step depends out keepOut group state
# <char> <list> <list> <lgcl> <char> <char>
# 1: data <data.frame[10x6]> FALSE data Done
# 2: data_prep data <data.frame[10x7]> FALSE data_prep Done
# 3: model_fit data_prep <lm[13]> FALSE model_fit Done
# 4: model_plot model_fit,data_prep <gg[11]> FALSE model_plot Done
with the following set data
pip$get_data() |> head(3)
# Ozone Solar.R Wind Temp Month Day
# 1 41 190 7.4 67 5 1
# 2 36 118 8.0 72 5 2
# 3 12 149 12.6 74 5 3
Insert new step
Let’s say we want to insert a new step after the
data_prep step that standardizes the y-variable.
pip$insert_after(
afterStep = "data_prep",
step = "standardize",
function(
data = ~data_prep,
yVar = "Ozone"
) {
data[, yVar] <- scale(data[, yVar])
data
}
)
pip
# step depends out keepOut group state
# <char> <list> <list> <lgcl> <char> <char>
# 1: data <data.frame[10x6]> FALSE data Done
# 2: data_prep data <data.frame[10x7]> FALSE data_prep Done
# 3: standardize data_prep [NULL] FALSE standardize New
# 4: model_fit data_prep <lm[13]> FALSE model_fit Done
# 5: model_plot model_fit,data_prep <gg[11]> FALSE model_plot Done
The standardize step is now part of the pipeline, but so
far it is not used by any other step.
Replace existing steps
Let’s revisit the function definition of the model_fit
step
pip$get_step("model_fit")[["fun"]]
# [[1]]
# function (data = ~data_prep, xVar = "Temp.Celsius")
# {
# lm(paste("Ozone ~", xVar), data = data)
# }
To use the standardized data, we need to change the data dependency
such that it refers to the standardize step. Also instead
of a fixed y-variable in the model, we want to pass it as a
parameter.
pip$replace_step(
"model_fit",
function(
data = ~standardize, # <- changed data reference
xVar = "Temp.Celsius",
yVar = "Ozone" # <- new y-variable
) {
lm(paste(yVar, "~", xVar), data = data)
}
)
The model_plot step needs to be updated in a similar
way.
pip$replace_step(
"model_plot",
function(
model = ~model_fit,
data = ~standardize, # <- changed data reference
xVar = "Temp.Celsius",
yVar = "Ozone", # <- new y-variable
title = "Linear model fit"
) {
coeffs <- coefficients(model)
ggplot(data) +
geom_point(aes(.data[[xVar]], .data[[yVar]])) +
geom_abline(intercept = coeffs[1], slope = coeffs[2]) +
labs(title = title)
}
)
The updated pipeline now looks as follows.
pip
# step depends out keepOut group state
# <char> <list> <list> <lgcl> <char> <char>
# 1: data <data.frame[10x6]> FALSE data Done
# 2: data_prep data <data.frame[10x7]> FALSE data_prep Done
# 3: standardize data_prep [NULL] FALSE standardize New
# 4: model_fit standardize [NULL] FALSE model_fit New
# 5: model_plot model_fit,standardize [NULL] FALSE model_plot New
We see that the model_fit and model_plot
steps now use (i.e., depend on) the standardized data. Let’s re-run the
pipeline and inspect the output.
pip$set_params(list(xVar = "Solar.R", yVar = "Wind"))
pip$run()
# INFO [2025-07-26 20:25:23.487] Start run of 'my-pipeline' pipeline:
# INFO [2025-07-26 20:25:23.488] Step 1/5 data - skip 'done' step
# INFO [2025-07-26 20:25:23.489] Step 2/5 data_prep - skip 'done' step
# INFO [2025-07-26 20:25:23.490] Step 3/5 standardize
# INFO [2025-07-26 20:25:23.493] Step 4/5 model_fit
# INFO [2025-07-26 20:25:23.495] Step 5/5 model_plot
# INFO [2025-07-26 20:25:23.500] Finished execution of steps.
# INFO [2025-07-26 20:25:23.501] Done.
pip$get_out("model_fit") |> coefficients()
# (Intercept) Solar.R
# 0.979672739 -0.006625601
pip$get_out("model_plot")
# Warning: Removed 2 rows containing missing values or values outside the scale range
# (`geom_point()`).
Removing steps
Let’s see the pipeline again.
pip
# step depends out keepOut group state
# <char> <list> <list> <lgcl> <char> <char>
# 1: data <data.frame[10x6]> FALSE data Done
# 2: data_prep data <data.frame[10x7]> FALSE data_prep Done
# 3: standardize data_prep <data.frame[10x7]> FALSE standardize Done
# 4: model_fit standardize <lm[13]> FALSE model_fit Done
# 5: model_plot model_fit,standardize <gg[11]> FALSE model_plot Done
When you are trying to remove a step, pipeflow by
default checks if the step is used by any other step, and raises an
error if removing the step would violate the integrity of the
pipeline.
try(pip$remove_step("standardize"))
# Error : cannot remove step 'standardize' because the following steps depend on it: 'model_fit', 'model_plot'
To enforce removing a step together with all its downstream
dependencies, you can use the recursive argument.
pip$remove_step("standardize", recursive = TRUE)
# Removing step 'standardize' and its downstream dependencies: 'model_fit', 'model_plot'
pip
# step depends out keepOut group state
# <char> <list> <list> <lgcl> <char> <char>
# 1: data <data.frame[10x6]> FALSE data Done
# 2: data_prep data <data.frame[10x7]> FALSE data_prep Done
Naturally, the last step never has any downstream dependencies, so it
can be removed without any issues. There is another way to just remove
the last step.
pip$pop_step()
# [1] "data_prep"
pip
# step depends out keepOut group state
# <char> <list> <list> <lgcl> <char> <char>
# 1: data <data.frame[10x6]> FALSE data Done
Replacing steps in a pipeline as shown in this vignette will allow to
re-use existing pipelines and adapt them programmatically to new
requirements. Another way of re-using pipelines is to combine them,
which is shown in the Combining
pipelines vignette.