Split, map, and reduce

Motivation

A common scenario is to split a data set into subsets and then apply the same analysis to each part. In context of pipelines, this means that we would like to apply the same pipeline multiple times to each data subset. In addition, we may then want to combine parts of the individual output. As we will see, {pipeflow} provides a built-in function to handle this scenario.

Define pipeline

Let’s first define our pipeline, which, to keep matters simple, just fits a linear model and outputs the model coefficients.

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(
        "coefs",
        function(fit = ~fit) {
            coefficients(fit)
        }
    )

So our pipeline looks like this:

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

Or graphically:

We use the iris data set as our working example.

head(iris)
#   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# 1          5.1         3.5          1.4         0.2  setosa
# 2          4.9         3.0          1.4         0.2  setosa
# 3          4.7         3.2          1.3         0.2  setosa
# 4          4.6         3.1          1.5         0.2  setosa
# 5          5.0         3.6          1.4         0.2  setosa
# 6          5.4         3.9          1.7         0.4  setosa

First, we apply the pipeline to the whole data set.

pip |> pip_set_params(list(
    data = iris,
    xVar = "Sepal.Length",
    yVar = "Sepal.Width"
))

pip_run(pip)
# info [2026-06-14 20:27:10.733 UTC]: Start run of pipeflow_pip 'my-pipeline'
# info [2026-06-14 20:27:10.733 UTC]: Step 1/3 data
# info [2026-06-14 20:27:10.734 UTC]: Step 2/3 fit
# info [2026-06-14 20:27:10.736 UTC]: Step 3/3 coefs
# info [2026-06-14 20:27:10.737 UTC]: Finished run of pipeflow_pip 'my-pipeline'
pip[["coefs", "out"]]
#  (Intercept) Sepal.Length 
#    3.4189468   -0.0618848

Split data

Next, we want to apply the pipeline to each species separately. One way to do this would be to use R’s split function. We can split it by the Species column and then run the pipeline for each subset. For example:

run_pipeline_helper <- function(data) {
    pip |> pip_set_params(list(data = data))
    pip_run(pip)
    pip[["coefs", "out"]]
}

results <- lapply(split(iris, iris$Species), FUN = run_pipeline_helper)
# info [2026-06-14 20:27:10.809 UTC]: Start run of pipeflow_pip 'my-pipeline'
# info [2026-06-14 20:27:10.809 UTC]: Step 1/3 data
# info [2026-06-14 20:27:10.810 UTC]: Step 2/3 fit
# info [2026-06-14 20:27:10.812 UTC]: Step 3/3 coefs
# info [2026-06-14 20:27:10.817 UTC]: Finished run of pipeflow_pip 'my-pipeline'
# info [2026-06-14 20:27:10.821 UTC]: Start run of pipeflow_pip 'my-pipeline'
# info [2026-06-14 20:27:10.821 UTC]: Step 1/3 data
# info [2026-06-14 20:27:10.822 UTC]: Step 2/3 fit
# info [2026-06-14 20:27:10.824 UTC]: Step 3/3 coefs
# info [2026-06-14 20:27:10.825 UTC]: Finished run of pipeflow_pip 'my-pipeline'
# info [2026-06-14 20:27:10.829 UTC]: Start run of pipeflow_pip 'my-pipeline'
# info [2026-06-14 20:27:10.829 UTC]: Step 1/3 data
# info [2026-06-14 20:27:10.829 UTC]: Step 2/3 fit
# info [2026-06-14 20:27:10.831 UTC]: Step 3/3 coefs
# info [2026-06-14 20:27:10.832 UTC]: Finished run of pipeflow_pip 'my-pipeline'
results
# $setosa
#  (Intercept) Sepal.Length 
#   -0.5694327    0.7985283 
# 
# $versicolor
#  (Intercept) Sepal.Length 
#    0.8721460    0.3197193 
# 
# $virginica
#  (Intercept) Sepal.Length 
#    1.4463054    0.2318905

Unfortunately, with this approach we had to create additional code that had to be run outside the pipeline framework. In addition, the run log quickly can become redundant and confusing, as it now contains multiple runs of the same pipeline. Since splitting data sets (or more generally mapping function calls to different subsets of data) is such a common scenario, {pipeflow} also provides a built-in mechanism to handle this case.

Since version 0.4.0, for each step, it is possible to set the so-called execution mode, which by default is exec = "auto". To model the above scenario, we add a new step to our pipeline that splits the data set and set its execution mode to split.

pip <- pip_new("my-split-pip") |>
    pip_add(
        "data",
        function(data = NULL) data
    ) |>
    pip_add(
        "split_data",
        function(
            data = ~data,
            byVar = "by"
        ) {
            split(data, f = data[[byVar]])
        },
        exec = "split" # <-- set execution mode to "split"
    ) |>
    pip_add(
        "fit",
        function(
            data = ~split_data,
            xVar = "x",
            yVar = "y"
        ) {
            lm(paste(yVar, "~", xVar), data = data)
        }
    ) |>
    pip_add(
        "coefs",
        function(fit = ~fit) {
            coefficients(fit)
        }
    )
pip
# <pipeflow_pip> my-split-pip (4 steps)
# -------------------------------------
#          step    depends    out state  exec
# 1:       data            [NULL]   new  auto
# 2: split_data       data [NULL]   new split
# 3:        fit split_data [NULL]   new  auto
# 4:      coefs        fit [NULL]   new  auto

First of all, we see that the pipeline now is printed with an additional column exec marking the split execution mode for the split_data step. This also can be inspected in the graph:

library(visNetwork)
do.call(visNetwork, args = pip_get_graph(pip))

Now what does this execution mode actually do? It basically tells the pipeline that for all steps that depend on the split_data step (directly or indirectly), the results coming from the split step should be treated as lists of results, which should be iterated over.

In our particular example, this means that the fit step will be executed for each data subset coming from the split_data step and likewise the coefs step will be executed for each fitted model coming from the fit step.

Let’s see this in action by running the pipeline.

pip |> pip_set_params(list(
    data = iris,
    xVar = "Sepal.Length",
    yVar = "Sepal.Width",
    byVar = "Species"
))

pip_run(pip)
# info [2026-06-14 20:27:11.058 UTC]: Start run of pipeflow_pip 'my-split-pip'
# info [2026-06-14 20:27:11.058 UTC]: Step 1/4 data
# info [2026-06-14 20:27:11.058 UTC]: Step 2/4 split_data
# info [2026-06-14 20:27:11.060 UTC]: Step 3/4 fit
# info [2026-06-14 20:27:11.063 UTC]: Step 4/4 coefs
# info [2026-06-14 20:27:11.065 UTC]: Finished run of pipeflow_pip 'my-split-pip'

Looking at the pipeline overview, we see that the outputs following the split_data steps are now all lists of results.

pip
# <pipeflow_pip> my-split-pip (4 steps)
# -------------------------------------
#          step    depends                 out state  exec
# 1:       data            <data.frame[150x5]>  done  auto
# 2: split_data       data           <list[3]>  done split
# 3:        fit split_data           <list[3]>  done  auto
# 4:      coefs        fit           <list[3]>  done  auto

Inspecting in particular the output of the coefs step, we see that it is now a list of coefficient tables, one for each species.

pip[["coefs", "out"]]
# $setosa
#  (Intercept) Sepal.Length 
#   -0.5694327    0.7985283 
# 
# $versicolor
#  (Intercept) Sepal.Length 
#    0.8721460    0.3197193 
# 
# $virginica
#  (Intercept) Sepal.Length 
#    1.4463054    0.2318905 
# 
# attr(,"class")
# [1] "list"                 "pipeflow_partitioned"

This matches the output1 we obtained earlier with the helper function but was obtained without the need having to write all this extra code around the pipeline.

Recombine output

While the above approach looks nice already, we are only half way there, because often we will want to recombine the output of all the different subsets in some way. For example, we may want to show the resulting coefficients of the linear models in one summary table.

This is where the reduce execution mode comes into play. Let’s for this matter extend our pipeline by one step at the end.

pip |> pip_add(
    "combine_coefs",
    function(coefs = ~coefs) {
        do.call(rbind, coefs)
    },
    exec = "reduce" # <-- set execution mode to "reduce"
)
pip
# <pipeflow_pip> my-split-pip (5 steps)
# -------------------------------------
#             step    depends                 out state   exec
# 1:          data            <data.frame[150x5]>  done   auto
# 2:    split_data       data           <list[3]>  done  split
# 3:           fit split_data           <list[3]>  done   auto
# 4:         coefs        fit           <list[3]>  done   auto
# 5: combine_coefs      coefs              [NULL]   new reduce

Again, we see that the new step is marked with the execution mode (reduce) in the overview. Graphically, this mode is represented by a circle.

do.call(visNetwork, args = pip_get_graph(pip))

If we now run the pipeline, we see that the output of the combine_coefs step is a combined table of coefficients.

pip_run(pip)
# info [2026-06-14 20:27:11.312 UTC]: Start run of pipeflow_pip 'my-split-pip'
# info [2026-06-14 20:27:11.312 UTC]: Step 1/5 data - skipping done step
# info [2026-06-14 20:27:11.312 UTC]: Step 2/5 split_data - skipping done step
# info [2026-06-14 20:27:11.312 UTC]: Step 3/5 fit - skipping done step
# info [2026-06-14 20:27:11.312 UTC]: Step 4/5 coefs - skipping done step
# info [2026-06-14 20:27:11.313 UTC]: Step 5/5 combine_coefs
# info [2026-06-14 20:27:11.314 UTC]: Finished run of pipeflow_pip 'my-split-pip'

pip[["combine_coefs", "out"]]
#            (Intercept) Sepal.Length
# setosa      -0.5694327    0.7985283
# versicolor   0.8721460    0.3197193
# virginica    1.4463054    0.2318905

There you go :-)

  1. Technically, the output is slightly different, because the returned list has an additional class attribute “pipeflow_partitioned”.↩︎