Existing pipeline
Let’s start where we left off in the Get started with pipeflow vignette, that
is, we have the following pipeline.
pip
# <pipeflow> my-pip (4 steps)
# ---------------------------
# step params depends state out
# 1: data data done <data.frame[10x6]>
# 2: prep df data done <data.frame[10x7]>
# 3: fit data,xVar prep done <lm[13]>
# 4: plot model,data,xVar,xLab,title fit,prep done <ggplot2::ggplot>
# ---------------------------
# <ready> last run: 2026-09-27 20:21:14
with the following set data
pip_get_params(pip)[["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 prep
step that standardizes the y-variable. To do this, we use
pip_add() with the after argument.
pip |> pip_add(
"standardize",
function(data = ~prep,
yVar = "Ozone") {
data[, yVar] <- scale(data[, yVar])
data
},
after = "prep"
)
pip
# <pipeflow> my-pip (5 steps)
# ---------------------------
# step params depends state out
# 1: data data done <data.frame[10x6]>
# 2: prep df data done <data.frame[10x7]>
# 3: standardize data,yVar prep new [NULL]
# 4: fit data,xVar prep done <lm[13]>
# 5: plot model,data,xVar,xLab,title fit,prep done <ggplot2::ggplot>
# ---------------------------
# <ready> last run: 2026-09-27 20:21:14
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 fit
step
pip[["fit", "fun"]]
# function (data = ~prep, xVar = "Temp.Celsius")
# {
# lm(paste("Ozone ~", xVar), data = data)
# }
# <environment: 0x5606deb85658>
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, let’s pass it as a parameter.
pip |> pip_replace(
"fit",
function(data = ~standardize, # <- changed data reference
xVar = "Temp.Celsius",
yVar = "Ozone" # <- new y-variable
) {
lm(paste(yVar, "~", xVar), data = data)
}
)
The plot step needs to be updated in a similar way.
pip |> pip_replace(
"plot",
function(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
# <pipeflow> my-pip (5 steps)
# ---------------------------
# step params depends state out
# 1: data data done <data.frame[10x6]>
# 2: prep df data done <data.frame[10x7]>
# 3: standardize data,yVar prep new [NULL]
# 4: fit data,xVar,yVar standardize new [NULL]
# 5: plot model,data,xVar,yVar,title fit,standardize new [NULL]
# ---------------------------
# <ready> last run: 2026-09-27 20:21:14
We see that the fit and plot steps now use
(i.e., depend on) the standardized data. Let’s re-run the pipeline and
inspect the output.
pip_set_params(pip, params = list(xVar = "Solar.R", yVar = "Wind"))
pip_run(pip)
# info [2026-09-27 18:21:14.159 UTC]: Starting run of pipeflow 'my-pip'
# info [2026-09-27 18:21:14.159 UTC]: Step 1/5 data - skipping done step
# info [2026-09-27 18:21:14.159 UTC]: Step 2/5 prep - skipping done step
# info [2026-09-27 18:21:14.159 UTC]: Step 3/5 standardize
# info [2026-09-27 18:21:14.160 UTC]: Step 4/5 fit
# info [2026-09-27 18:21:14.162 UTC]: Step 5/5 plot
# info [2026-09-27 18:21:14.170 UTC]: Finished run of pipeflow 'my-pip'
pip[["fit", "out"]] |> coefficients()
# (Intercept) Solar.R
# 0.979672739 -0.006625601
Removing steps
Let’s see the pipeline again.
pip
# <pipeflow> my-pip (5 steps)
# ---------------------------
# step params depends state out
# 1: data data done <data.frame[10x6]>
# 2: prep df data done <data.frame[10x7]>
# 3: standardize data,yVar prep done <data.frame[10x7]>
# 4: fit data,xVar,yVar standardize done <lm[13]>
# 5: plot model,data,xVar,yVar,title fit,standardize done <ggplot2::ggplot>
# ---------------------------
# <ready> last run: 2026-09-27 20:21:14
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(pip, "standardize"))
# Error in pip_remove(pip, "standardize") :
# cannot remove step 'standardize' because the following steps depend on it: 'fit', 'plot'
To enforce removing a step together with all its downstream
dependencies, you can use the force argument.
pip_remove(pip, "standardize", force = TRUE)
# Removing step 'standardize' and its downstream dependencies: 'fit', 'plot'
pip
# <pipeflow> my-pip (2 steps)
# ---------------------------
# step params depends state out
# 1: data data done <data.frame[10x6]>
# 2: prep df data done <data.frame[10x7]>
# ---------------------------
# <ready> last run: 2026-09-27 20:21:14
Naturally, the last step never has any downstream dependencies, so it
can be removed without any issues.
last_step <- tail(pip[["step"]], 1)
pip_remove(pip, last_step)
pip
# <pipeflow> my-pip (1 step)
# --------------------------
# step params depends state out
# 1: data data done <data.frame[10x6]>
# --------------------------
# <ready> last run: 2026-09-27 20:21:14
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 Combine
pipelines vignette.