pipeflow
Lightweight, General-Purpose Data Analysis Pipelines

Package index
Search the pipeflow package
Vignettes
Functions
106
Source code
21
Man pages
32
Home
/
CRAN
/
pipeflow
/
Pipeline: Pipeline Class

Pipeline: Pipeline Class
In pipeflow: Lightweight, General-Purpose Data Analysis Pipelines

PipelineR Documentation

Pipeline Class

Description

This class implements an analysis pipeline. A pipeline consists of a sequence of analysis steps, which can be added one by one. Each added step may or may not depend on one or more previous steps. The pipeline keeps track of the dependencies among these steps and will ensure that all dependencies are met on creation of the pipeline, that is, before the the pipeline is run. Once the pipeline is run, the output is stored in the pipeline along with each step and can be accessed later. Different pipelines can be bound together while preserving all dependencies within each pipeline.

Public fields

name

string name of the pipeline

pipeline

data.table the pipeline where each row represents one step.

Methods

Public methods

Method new()

constructor

Usage
Pipeline$new(name, data = NULL, logger = NULL)
Arguments
name

the name of the Pipeline

data

optional data used at the start of the pipeline. The data also can be set later using the set_data function.

logger

custom logger to be used for logging. If no logger is provided, the default logger is used, which should be sufficient for most use cases. If you do want to use your own custom log function, you need to provide a function that obeys the following form:

⁠function(level, msg, ...) { your custom logging code here }⁠

The level argument is a string and will be one of info, warn, or error. The msg argument is a string containing the message to be logged. The ... argument is a list of named parameters, which can be used to add additional information to the log message. Currently, this is only used to add the context in case of a step giving a warning or error.

Note that with the default logger, the log layout can be altered any time via set_log_layout().

Returns

returns the Pipeline object invisibly

Examples
p <- Pipeline$new("myPipe", data = data.frame(x = 1:8))
p

# Passing custom logger
my_logger <- function(level, msg, ...) {
   cat(level, msg, "\n")
}
p <- Pipeline$new("myPipe", logger = my_logger)

Method add()

Add pipeline step

Usage
Pipeline$add(
  step,
  fun,
  params = list(),
  description = "",
  group = step,
  keepOut = FALSE
)
Arguments
step

string the name of the step. Each step name must be unique.

fun

function or name of the function to be applied at the step. Both existing and anonymous/lambda functions can be used. All function parameters must have default values. If a parameter is missing a default value in the function signature, alternatively, it can be set via the params argument (see Examples section with mean() function).

params

list list of parameters to set or overwrite parameters of the passed function.

description

string optional description of the step

group

string output collected after pipeline execution (see function collect_out) is grouped by the defined group names. By default, this is the name of the step, which comes in handy when the pipeline is copy-appended multiple times to keep the results of the same function/step grouped at one place.

keepOut

logical if FALSE (default) the output of the step is not collected when calling collect_out after the pipeline run. This option is used to only keep the results that matter and skip intermediate results that are not needed. See also function collect_out for more details.

Returns

returns the Pipeline object invisibly

Examples
# Add steps with lambda functions
p <- Pipeline$new("myPipe", data = 1)
p$add("s1", \(x = ~data) 2*x)  # use input data
p$add("s2", \(x = ~data, y = ~s1) x * y)
try(p$add("s2", \(z = 3) 3)) # error: step 's2' exists already
try(p$add("s3", \(z = ~foo) 3)) # dependency 'foo' not found
p

# Add step with existing function
p <- Pipeline$new("myPipe", data = c(1, 2, NA, 3, 4))
p$add("calc_mean", mean, params = list(x = ~data, na.rm = TRUE))
p$run()$get_out("calc_mean")

# Step description
p <- Pipeline$new("myPipe", data = 1:10)
p$add("s1", \(x = ~data) 2*x, description = "multiply by 2")
print(p)
print(p, verbose = TRUE) # print all columns

# Group output
p <- Pipeline$new("myPipe", data = data.frame(x = 1:5, y = 1:5))
p$add("prep_x", \(data = ~data) data$x, group = "prep")
p$add("prep_y", \(data = ~data) (data$y)^2, group = "prep")
p$add("sum", \(x = ~prep_x, y = ~prep_y) x + y)
p$run()$collect_out(all = TRUE)

Method append()

Append another pipeline When appending, pipeflow takes care of potential name clashes with respect to step names and dependencies, that is, if needed, it will automatically adapt step names and dependencies to make sure they are unique in the merged pipeline.

Usage
Pipeline$append(p, outAsIn = FALSE, tryAutofixNames = TRUE, sep = ".")
Arguments
p

Pipeline object to be appended.

outAsIn

logical if TRUE, output of first pipeline is used as input for the second pipeline.

tryAutofixNames

logical if TRUE, name clashes are tried to be automatically resolved by appending the 2nd pipeline's name. Only set to FALSE, if you know what you are doing.

sep

string separator used when auto-resolving step names

Returns

returns new combined Pipeline.

Examples
# Append pipeline
p1 <- Pipeline$new("pipe1")
p1$add("step1", \(x = 1) x)
p2 <- Pipeline$new("pipe2")
p2$add("step2", \(y = 1) y)
p1$append(p2)

# Append pipeline with potential name clashes
p3 <- Pipeline$new("pipe3")
p3$add("step1", \(z = 1) z)
p1$append(p2)$append(p3)

# Use output of first pipeline as input for second pipeline
p1 <- Pipeline$new("pipe1", data = 8)
p2 <- Pipeline$new("pipe2")
p1$add("square", \(x = ~data) x^2)
p2$add("log2", \(x = ~data) log2(x))

p12 <- p1$append(p2, outAsIn = TRUE)
p12$run()$get_out("log2")
p12

# Custom name separator
p1$append(p2, sep = "___")

Method append_to_step_names()

Appends string to all step names and takes care of updating step dependencies accordingly.

Usage
Pipeline$append_to_step_names(postfix, sep = ".")
Arguments
postfix

string to be appended to each step name.

sep

string separator between step name and postfix.

Returns

returns the Pipeline object invisibly

Examples
p <- Pipeline$new("pipe")
p$add("step1", \(x = 1) x)
p$add("step2", \(y = 1) y)
p$append_to_step_names("new")
p
p$append_to_step_names("foo", sep = "__")
p

Method collect_out()

Collect output afer pipeline run, by default, from all steps for which keepOut was set to TRUE. The output is grouped by the group names (see group parameter in function add), which by default are set identical to the step names.

Usage
Pipeline$collect_out(groupBy = "group", all = FALSE)
Arguments
groupBy

string column of pipeline by which to group the output.

all

logical if TRUE all output is collected regardless of the keepOut flag. This can be useful for debugging.

Returns

list containing the output, named after the groups, which, by default, are the steps.

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("step1", \(x = ~data) x + 2)
p$add("step2", \(x = ~step1) x + 2, keepOut = TRUE)
p$run()
p$collect_out()
p$collect_out(all = TRUE) |> str()

# Grouped output
p <- Pipeline$new("pipe", data = 1:2)
p$add("step1", \(x = ~data) x + 2, group = "add")
p$add("step2", \(x = ~step1, y = 2) x + y, group = "add")
p$add("step3", \(x = ~data) x * 3, group = "mult")
p$add("step4", \(x = ~data, y = 2) x * y, group = "mult")
p
p$run()
p$collect_out(all = TRUE) |> str()

# Grouped by state
p$set_params(list(y = 5))
p
p$collect_out(groupBy = "state", all = TRUE) |> str()

Method discard_steps()

Discard all steps that match a given pattern.

Usage
Pipeline$discard_steps(pattern, recursive = FALSE, fixed = TRUE, ...)
Arguments
pattern

string containing a regular expression (or character string for fixed = TRUE) to be matched.

recursive

logical if TRUE the step is removed together with all its downstream dependencies.

fixed

logical If TRUE, pattern is a string to be matched as is. Overrides all conflicting arguments.

...

further arguments passed to grep().

Returns

the Pipeline object invisibly

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(x = ~data) x + 1)
p$add("add2", \(x = ~add1) x + 2)
p$add("mult3", \(x = ~add1) x * 3)
p$add("mult4", \(x = ~add2) x * 4)
p

p$discard_steps("mult")
p

# Re-add steps
p$add("mult3", \(x = ~add1) x * 3)
p$add("mult4", \(x = ~add2) x * 4)
p
# Discarding 'add1' does not work ...
try(p$discard_steps("add1"))

# ... unless we enforce to remove its downstream dependencies as well
p$discard_steps("add1", recursive = TRUE)   # this works
p

# Trying to discard non-existent steps is just ignored
p$discard_steps("non-existent")

Method get_data()

Get data

Usage
Pipeline$get_data()
Returns

the output defined in the data step, which by default is the first step of the pipeline

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$get_data()
p$set_data(3:4)
p$get_data()

Method get_depends()

Get all dependencies defined in the pipeline

Usage
Pipeline$get_depends()
Returns

named list of dependencies for each step

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(x = ~data) x + 1)
p$add("add2", \(x = ~data, y = ~add1) x + y)
p$get_depends()

Method get_depends_down()

Get all downstream dependencies of given step, by default descending recursively.

Usage
Pipeline$get_depends_down(step, recursive = TRUE)
Arguments
step

string name of step

recursive

logical if TRUE, dependencies of dependencies are also returned.

Returns

list of downstream dependencies

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(x = ~data) x + 1)
p$add("add2", \(x = ~data, y = ~add1) x + y)
p$add("mult3", \(x = ~add1) x * 3)
p$add("mult4", \(x = ~add2) x * 4)
p$get_depends_down("add1")
p$get_depends_down("add1", recursive = FALSE)

Method get_depends_up()

Get all upstream dependencies of given step, by default descending recursively.

Usage
Pipeline$get_depends_up(step, recursive = TRUE)
Arguments
step

string name of step

recursive

logical if TRUE, dependencies of dependencies are also returned.

Returns

list of upstream dependencies

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(x = ~data) x + 1)
p$add("add2", \(x = ~data, y = ~add1) x + y)
p$add("mult3", \(x = ~add1) x * 3)
p$add("mult4", \(x = ~add2) x * 4)
p$get_depends_up("mult4")
p$get_depends_up("mult4", recursive = FALSE)

Method get_graph()

Visualize the pipeline as a graph.

Usage
Pipeline$get_graph(groups = NULL)
Arguments
groups

character if not NULL, only steps belonging to the given groups are considered.

Returns

two data frames, one for nodes and one for edges ready to be used with the visNetwork package.

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(data = ~data, x = 1) x + data)
p$add("add2", \(x = 1, y = ~add1) x + y)
p$add("mult1", \(x = ~add1, y = ~add2) x * y)
graph <- pipe_get_graph(p)
graph

if (require("visNetwork", quietly = TRUE)) {
    do.call(visNetwork, args = p$get_graph())
}

Method get_out()

Get output of given step

Usage
Pipeline$get_out(step)
Arguments
step

string name of step

Returns

the output at the given step.

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(x = ~data) x + 1)
p$add("add2", \(x = ~data, y = ~add1) x + y)
p$run()
p$get_out("add1")
p$get_out("add2")

Method get_params()

Set unbound function parameters defined in the pipeline where 'unbound' means parameters that are not linked to other steps. Trying #' to set parameters that don't exist in the pipeline is ignored, by default, with a warning.

Usage
Pipeline$get_params(ignoreHidden = TRUE)
Arguments
ignoreHidden

logical if TRUE, hidden parameters (i.e. all names starting with a dot) are ignored and thus not returned.

Returns

list of parameters, sorted and named by step. Steps with no parameters are filtered out.

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(data = ~data, x = 1) x + data)
p$add("add2", \(x = 1, y = 2, .z = 3) x + y + .z)
p$add("add3", \() 1 + 2)
p$get_params() |> str()
p$get_params(ignoreHidden = FALSE) |> str()

Method get_params_at_step()

Get all unbound (i.e. not referring to other steps) at given step name.

Usage
Pipeline$get_params_at_step(step, ignoreHidden = TRUE)
Arguments
step

string name of step

ignoreHidden

logical if TRUE, hidden parameters (i.e. all names starting with a dot) are ignored and thus not returned.

Returns

list of parameters defined at given step.

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(data = ~data, x = 1) x + data)
p$add("add2", \(x = 1, y = 2, .z = 3) x + y + .z)
p$add("add3", \() 1 + 2)
p$get_params_at_step("add2")
p$get_params_at_step("add2", ignoreHidden = FALSE)
p$get_params_at_step("add3")

Method get_params_unique()

Get all unbound (i.e. not referring to other steps) parameters defined in the pipeline, but only list each parameter once. The values of the parameters, will be the values of the first step where the parameter was defined. This is particularly useful after the parameters where set using the set_params function, which will set the same value for all steps.

Usage
Pipeline$get_params_unique(ignoreHidden = TRUE)
Arguments
ignoreHidden

logical if TRUE, hidden parameters (i.e. all names starting with a dot) are ignored and thus not returned.

Returns

list of unique parameters

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(data = ~data, x = 1) x + data)
p$add("add2", \(x = 1, y = 2, .z = 3) x + y + .z)
p$add("mult1", \(x = 1, y = 2, .z = 3, b = ~add2) x * y * b)
p$get_params_unique()
p$get_params_unique(ignoreHidden = FALSE)

Method get_params_unique_json()

Get all unique function parameters in json format.

Usage
Pipeline$get_params_unique_json(ignoreHidden = TRUE)
Arguments
ignoreHidden

logical if TRUE, hidden parameters (i.e. all names starting with a dot) are ignored and thus not returned.

Returns

list flat unnamed json list of unique function parameters

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(data = ~data, x = 1) x + data)
p$add("add2", \(x = 1, y = 2, .z = 3) x + y + .z)
p$add("mult1", \(x = 1, y = 2, .z = 3, b = ~add2) x * y * b)
p$get_params_unique_json()
p$get_params_unique_json(ignoreHidden = FALSE)

Method get_step()

Get step of pipeline

Usage
Pipeline$get_step(step)
Arguments
step

string name of step

Returns

data.table row containing the step.

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(data = ~data, x = 1) x + data)
p$add("add2", \(x = 1, y = 2, z = ~add1) x + y + z)
p$run()
add1 <- p$get_step("add1")
print(add1)
add1[["params"]]
add1[["fun"]]
try()
try(p$get_step("foo")) # error: step 'foo' does not exist

Method get_step_names()

Get step names of pipeline

Usage
Pipeline$get_step_names()
Returns

character vector of step names

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p$get_step_names()

Method get_step_number()

Get step number

Usage
Pipeline$get_step_number(step)
Arguments
step

string name of step

Returns

the step number in the pipeline

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p$get_step_number("f2")

Method has_step()

Check if pipeline has given step

Usage
Pipeline$has_step(step)
Arguments
step

string name of step

Returns

logical whether step exists

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p$has_step("f2")
p$has_step("foo")

Method insert_after()

Insert step after a certain step

Usage
Pipeline$insert_after(afterStep, step, ...)
Arguments
afterStep

string name of step after which to insert

step

string name of step to insert

...

further arguments passed to add method of the pipeline

Returns

returns the Pipeline object invisibly

Examples
p <- Pipeline$new("pipe", data = 1)
p$add("f1", \(x = 1) x)
p$add("f2", \(x = ~f1) x)
p$insert_after("f1", "f3", \(x = ~f1) x)
p

Method insert_before()

Insert step before a certain step

Usage
Pipeline$insert_before(beforeStep, step, ...)
Arguments
beforeStep

string name of step before which to insert

step

string name of step to insert

...

further arguments passed to add method of the pipeline

Returns

returns the Pipeline object invisibly

Examples
p <- Pipeline$new("pipe", data = 1)
p$add("f1", \(x = 1) x)
p$add("f2", \(x = ~f1) x)
p$insert_before("f2", "f3", \(x = ~f1) x)
p

Method length()

Length of the pipeline aka number of pipeline steps.

Usage
Pipeline$length()
Returns

numeric length of pipeline.

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p$length()

Method lock_step()

Locking a step means that both its parameters and its output (given it has output) are locked such that neither setting new pipeline parameters nor future pipeline runs can change the current parameter and output content.

Usage
Pipeline$lock_step(step)
Arguments
step

string name of step

Returns

the Pipeline object invisibly

Examples
p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = 1, data = ~data) x + data)
p$add("add2", \(x = 1, data = ~data) x + data)
p$run()
p$get_out("add1")
p$get_out("add2")
p$lock_step("add1")

p$set_data(3)
p$set_params(list(x = 3))
p$run()
p$get_out("add1")
p$get_out("add2")

Method pop_step()

Drop last step from the pipeline.

Usage
Pipeline$pop_step()
Returns

string the name of the step that was removed

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p
p$pop_step() # "f2"
p

Method pop_steps_after()

Drop all steps after the given step.

Usage
Pipeline$pop_steps_after(step)
Arguments
step

string name of step

Returns

character vector of steps that were removed.

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p$add("f3", \(z = 1) z)
p$pop_steps_after("f1")  # "f2", "f3"
p

Method pop_steps_from()

Drop all steps from and including the given step.

Usage
Pipeline$pop_steps_from(step)
Arguments
step

string name of step

Returns

character vector of steps that were removed.

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p$add("f3", \(z = 1) z)
p$pop_steps_from("f2")  # "f2", "f3"
p

Method print()

Print the pipeline as a table.

Usage
Pipeline$print(verbose = FALSE)
Arguments
verbose

logical if TRUE, print all columns of the pipeline, otherwise only the most relevant columns are displayed.

Returns

the Pipeline object invisibly

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p$print()

Method remove_step()

Remove certain step from the pipeline. If other steps depend on the step to be removed, an error is given and the removal is blocked, unless recursive was set to TRUE.

Usage
Pipeline$remove_step(step, recursive = FALSE)
Arguments
step

string the name of the step to be removed.

recursive

logical if TRUE the step is removed together with all its downstream dependencies.

Returns

the Pipeline object invisibly

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(data = ~data, x = 1) x + data)
p$add("add2", \(x = 1, y = ~add1) x + y)
p$add("mult1", \(x = 1, y = ~add2) x * y)
p$remove_step("mult1")
p
try(p$remove_step("add1"))  # fails because "add2" depends on "add1"
p$remove_step("add1", recursive = TRUE)  # removes "add1" and "add2"
p

Method rename_step()

Safely rename a step in the pipeline. If new step name would result in a name clash, an error is given.

Usage
Pipeline$rename_step(from, to)
Arguments
from

string the name of the step to be renamed.

to

string the new name of the step.

Returns

the Pipeline object invisibly

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(data = ~data, x = 1) x + data)
p$add("add2", \(x = 1, y = ~add1) x + y)
p
try(p$rename_step("add1", "add2"))  # fails because "add2" exists
p$rename_step("add1", "first_add")  # Ok
p

Method replace_step()

Replaces an existing pipeline step.

Usage
Pipeline$replace_step(
  step,
  fun,
  params = list(),
  description = "",
  group = step,
  keepOut = FALSE
)
Arguments
step

string the name of the step to be replaced. Step must exist.

fun

string or function operation to be applied at the step. Both existing and lambda/anonymous functions can be used.

params

list list of parameters to overwrite default parameters of existing functions.

description

string optional description of the step

group

string grouping information (by default the same as the name of the step. Any output collected later (see function collect_out by default is put together by these group names. This, for example, comes in handy when the pipeline is copy-appended multiple times to keep the results of the same function/step at one place.

keepOut

logical if FALSE the output of the function will be cleaned at the end of the whole pipeline execution. This option is used to only keep the results that matter.

Returns

the Pipeline object invisibly

Examples
p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = ~data, y = 1) x + y)
p$add("add2", \(x = ~data, y = 2) x + y)
p$add("mult", \(x = 1, y = 2) x * y, keepOut = TRUE)
p$run()$collect_out()
p$replace_step("mult", \(x = ~add1, y = ~add2) x * y, keepOut = TRUE)
p$run()$collect_out()
try(p$replace_step("foo", \(x = 1) x))   # step 'foo' does not exist

Method reset()

Resets the pipeline to the state before it was run. This means that all output is removed and the state of all steps is reset to 'New'.

Usage
Pipeline$reset()
Returns

returns the Pipeline object invisibly

Examples
p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p$run()
p
p$reset()
p

Method run()

Run all new and/or outdated pipeline steps.

Usage
Pipeline$run(
  force = FALSE,
  recursive = TRUE,
  cleanUnkept = FALSE,
  progress = NULL,
  showLog = TRUE
)
Arguments
force

logical if TRUE all steps are run regardless of whether they are outdated or not.

recursive

logical if TRUE and a step returns a new pipeline, the run of the current pipeline is aborted and the new pipeline is run recursively.

cleanUnkept

logical if TRUE all output that was not marked to be kept is removed after the pipeline run. This option can be useful if temporary results require a lot of memory.

progress

function this parameter can be used to provide a custom progress function of the form ⁠function(value, detail)⁠, which will show the progress of the pipeline run for each step, where value is the current step number and detail is the name of the step.

showLog

logical should the steps be logged during the pipeline run?

Returns

returns the Pipeline object invisibly

Examples
# Simple pipeline
p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = ~data, y = 1) x + y)
p$add("add2", \(x = ~add1, z = 2) x + z)
p$add("final", \(x = ~add1, y = ~add2) x * y, keepOut = TRUE)
p$run()$collect_out()
p$set_params(list(z = 4))  # outdates steps add2 and final
p
p$run()$collect_out()
p$run(cleanUnkept = TRUE)  # clean up temporary results
p

# Recursive pipeline
p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = ~data, y = 1) x + y)
p$add("new_pipe", \(x = ~add1) {
    pp <- Pipeline$new("new_pipe", data = x)
    pp$add("add1", \(x = ~data) x + 1)
    pp$add("add2", \(x = ~add1) x + 2, keepOut = TRUE)
    }
)
p$run(recursive = TRUE)$collect_out()

# Run pipeline with progress bar
p <- Pipeline$new("pipe", data = 1)
p$add("first step", \() Sys.sleep(1))
p$add("second step", \() Sys.sleep(1))
p$add("last step", \() Sys.sleep(1))
pb <- txtProgressBar(min = 1, max = p$length(), style = 3)
fprogress <- function(value, detail) {
   setTxtProgressBar(pb, value)
}
p$run(progress = fprogress, showLog = FALSE)

Method run_step()

Run given pipeline step possibly together with upstream and downstream dependencies.

Usage
Pipeline$run_step(
  step,
  upstream = TRUE,
  downstream = FALSE,
  cleanUnkept = FALSE
)
Arguments
step

string name of step

upstream

logical if TRUE, run all dependent upstream steps first.

downstream

logical if TRUE, run all depdendent downstream afterwards.

cleanUnkept

logical if TRUE all output that was not marked to be kept is removed after the pipeline run. This option can be useful if temporary results require a lot of memory.

Returns

returns the Pipeline object invisibly

Examples
p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = ~data, y = 1) x + y)
p$add("add2", \(x = ~add1, z = 2) x + z)
p$add("mult", \(x = ~add1, y = ~add2) x * y)
p$run_step("add2")
p$run_step("add2", downstream = TRUE)
p$run_step("mult", upstream = TRUE)

Method set_data()

Set data in first step of pipeline.

Usage
Pipeline$set_data(data)
Arguments
data

initial data set

Returns

returns the Pipeline object invisibly

Examples
p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = ~data, y = 1) x + y, keepOut = TRUE)
p$run()$collect_out()
p$set_data(3)
p$run()$collect_out()

Method set_data_split()

This function can be used to apply the pipeline repeatedly to various data sets. For this, the pipeline split-copies itself by the list of given data sets. Each sub-pipeline will have one of the data sets set as input data. The step names of the sub-pipelines will be the original step names plus the name of the data set.

Usage
Pipeline$set_data_split(
  dataList,
  toStep = character(),
  groupBySplit = TRUE,
  sep = "."
)
Arguments
dataList

list of data sets

toStep

string step name marking optional subset of the pipeline, at which the data split should be applied to.

groupBySplit

logical whether to set step groups according to data split.

sep

string separator to be used between step name and data set name when creating the new step names.

Returns

new combined Pipeline with each sub-pipeline having set one of the data sets.

Examples
# Split by three data sets
dataList <- list(a = 1, b = 2, c = 3)
p <- Pipeline$new("pipe")
p$add("add1", \(x = ~data) x + 1, keepOut = TRUE)
p$add("mult", \(x = ~data, y = ~add1) x * y, keepOut = TRUE)
p$set_data_split(dataList)
p
p$run()$collect_out() |> str()

# Don't group output by split
p <- Pipeline$new("pipe")
p$add("add1", \(x = ~data) x + 1, keepOut = TRUE)
p$add("mult", \(x = ~data, y = ~add1) x * y, keepOut = TRUE)
p$set_data_split(dataList, groupBySplit = FALSE)
p
p$run()$collect_out() |> str()

# Split up to certain step
p <- Pipeline$new("pipe")
p$add("add1", \(x = ~data) x + 1)
p$add("mult", \(x = ~data, y = ~add1) x * y)
p$add("average_result", \(x = ~mult) mean(unlist(x)), keepOut = TRUE)
p
p$get_depends()[["average_result"]]

p$set_data_split(dataList, toStep = "mult")
p
p$get_depends()[["average_result"]]

p$run()$collect_out()

Method set_keep_out()

Change the keepOut flag at a given pipeline step, which determines whether the output of that step is collected when calling collect_out() after the pipeline was run.

Usage
Pipeline$set_keep_out(step, keepOut = TRUE)
Arguments
step

string name of step

keepOut

logical whether to keep output of step

Returns

the Pipeline object invisibly

Examples
p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = ~data, y = 1) x + y, keepOut = TRUE)
p$add("add2", \(x = ~data, y = 2) x + y)
p$add("mult", \(x = ~add1, y = ~add2) x * y)
p$run()$collect_out()
p$set_keep_out("add1", keepOut = FALSE)
p$set_keep_out("mult", keepOut = TRUE)
p$collect_out()

Method set_params()

Set parameters in the pipeline. If a parameter occurs in several steps, the parameter is set commonly in all steps. Trying to set parameters that don't exist in the pipeline is ignored, by default, with a warning.

Usage
Pipeline$set_params(params, warnUndefined = TRUE)
Arguments
params

list of parameters to be set

warnUndefined

logical whether to give a warning when trying to set a parameter that is not defined in the pipeline.

Returns

returns the Pipeline object invisibly

Examples
p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = ~data, y = 2) x + y)
p$add("add2", \(x = ~data, y = 3) x + y)
p$add("mult", \(x = 4, z = 5) x * z)
p$get_params()
p$set_params(list(x = 3, y = 3))
p$get_params()
p$set_params(list(x = 5, z = 3))
p$get_params()
suppressWarnings(
    p$set_params(list(foo = 3)) # gives warning as 'foo' is undefined
)
p$set_params(list(foo = 3), warnUndefined = FALSE)

Method set_params_at_step()

Set unbound function parameters defined at given pipeline step where 'unbound' means parameters that are not linked to other steps.

Usage
Pipeline$set_params_at_step(step, params)
Arguments
step

string the name of the step

params

list of parameters to be set

Returns

returns the Pipeline object invisibly

Examples
p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = ~data, y = 2, z = 3) x + y)
p$set_params_at_step("add1", list(y = 5, z = 6))
p$get_params()
try(p$set_params_at_step("add1", list(foo = 3))) # foo not defined

Method split()

Splits pipeline into its independent parts.

Usage
Pipeline$split()
Returns

list of Pipeline objects

Examples
# Example for two independent calculation paths
p <- Pipeline$new("pipe", data = 1)
p$add("f1", \(x = ~data) x)
p$add("f2", \(x = 1) x)
p$add("f3", \(x = ~f1) x)
p$add("f4", \(x = ~f2) x)
p$split()

# Example of split by three data sets
dataList <- list(a = 1, b = 2, c = 3)
p <- Pipeline$new("pipe")
p$add("add1", \(x = ~data) x + 1, keepOut = TRUE)
p$add("mult", \(x = ~data, y = ~add1) x * y, keepOut = TRUE)
pipes <- p$set_data_split(dataList)$split()
pipes

Method unlock_step()

Unlock previously locked step. If step was not locked, the command is ignored.

Usage
Pipeline$unlock_step(step)
Arguments
step

string name of step

Returns

the Pipeline object invisibly

Examples
p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = 1, data = ~data) x + data)
p$add("add2", \(x = 1, data = ~data) x + data)
p$lock_step("add1")
p$set_params(list(x = 3))
p$get_params()
p$unlock_step("add1")
p$set_params(list(x = 3))
p$get_params()

Method clone()

The objects of this class are cloneable with this method.

Usage
Pipeline$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Author(s)

Roman Pahl

Examples


## ------------------------------------------------
## Method `Pipeline$new`
## ------------------------------------------------

p <- Pipeline$new("myPipe", data = data.frame(x = 1:8))
p

# Passing custom logger
my_logger <- function(level, msg, ...) {
   cat(level, msg, "\n")
}
p <- Pipeline$new("myPipe", logger = my_logger)

## ------------------------------------------------
## Method `Pipeline$add`
## ------------------------------------------------

# Add steps with lambda functions
p <- Pipeline$new("myPipe", data = 1)
p$add("s1", \(x = ~data) 2*x)  # use input data
p$add("s2", \(x = ~data, y = ~s1) x * y)
try(p$add("s2", \(z = 3) 3)) # error: step 's2' exists already
try(p$add("s3", \(z = ~foo) 3)) # dependency 'foo' not found
p

# Add step with existing function
p <- Pipeline$new("myPipe", data = c(1, 2, NA, 3, 4))
p$add("calc_mean", mean, params = list(x = ~data, na.rm = TRUE))
p$run()$get_out("calc_mean")

# Step description
p <- Pipeline$new("myPipe", data = 1:10)
p$add("s1", \(x = ~data) 2*x, description = "multiply by 2")
print(p)
print(p, verbose = TRUE) # print all columns

# Group output
p <- Pipeline$new("myPipe", data = data.frame(x = 1:5, y = 1:5))
p$add("prep_x", \(data = ~data) data$x, group = "prep")
p$add("prep_y", \(data = ~data) (data$y)^2, group = "prep")
p$add("sum", \(x = ~prep_x, y = ~prep_y) x + y)
p$run()$collect_out(all = TRUE)

## ------------------------------------------------
## Method `Pipeline$append`
## ------------------------------------------------

# Append pipeline
p1 <- Pipeline$new("pipe1")
p1$add("step1", \(x = 1) x)
p2 <- Pipeline$new("pipe2")
p2$add("step2", \(y = 1) y)
p1$append(p2)

# Append pipeline with potential name clashes
p3 <- Pipeline$new("pipe3")
p3$add("step1", \(z = 1) z)
p1$append(p2)$append(p3)

# Use output of first pipeline as input for second pipeline
p1 <- Pipeline$new("pipe1", data = 8)
p2 <- Pipeline$new("pipe2")
p1$add("square", \(x = ~data) x^2)
p2$add("log2", \(x = ~data) log2(x))

p12 <- p1$append(p2, outAsIn = TRUE)
p12$run()$get_out("log2")
p12

# Custom name separator
p1$append(p2, sep = "___")

## ------------------------------------------------
## Method `Pipeline$append_to_step_names`
## ------------------------------------------------

p <- Pipeline$new("pipe")
p$add("step1", \(x = 1) x)
p$add("step2", \(y = 1) y)
p$append_to_step_names("new")
p
p$append_to_step_names("foo", sep = "__")
p

## ------------------------------------------------
## Method `Pipeline$collect_out`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("step1", \(x = ~data) x + 2)
p$add("step2", \(x = ~step1) x + 2, keepOut = TRUE)
p$run()
p$collect_out()
p$collect_out(all = TRUE) |> str()

# Grouped output
p <- Pipeline$new("pipe", data = 1:2)
p$add("step1", \(x = ~data) x + 2, group = "add")
p$add("step2", \(x = ~step1, y = 2) x + y, group = "add")
p$add("step3", \(x = ~data) x * 3, group = "mult")
p$add("step4", \(x = ~data, y = 2) x * y, group = "mult")
p
p$run()
p$collect_out(all = TRUE) |> str()

# Grouped by state
p$set_params(list(y = 5))
p
p$collect_out(groupBy = "state", all = TRUE) |> str()

## ------------------------------------------------
## Method `Pipeline$discard_steps`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(x = ~data) x + 1)
p$add("add2", \(x = ~add1) x + 2)
p$add("mult3", \(x = ~add1) x * 3)
p$add("mult4", \(x = ~add2) x * 4)
p

p$discard_steps("mult")
p

# Re-add steps
p$add("mult3", \(x = ~add1) x * 3)
p$add("mult4", \(x = ~add2) x * 4)
p
# Discarding 'add1' does not work ...
try(p$discard_steps("add1"))

# ... unless we enforce to remove its downstream dependencies as well
p$discard_steps("add1", recursive = TRUE)   # this works
p

# Trying to discard non-existent steps is just ignored
p$discard_steps("non-existent")

## ------------------------------------------------
## Method `Pipeline$get_data`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$get_data()
p$set_data(3:4)
p$get_data()

## ------------------------------------------------
## Method `Pipeline$get_depends`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(x = ~data) x + 1)
p$add("add2", \(x = ~data, y = ~add1) x + y)
p$get_depends()

## ------------------------------------------------
## Method `Pipeline$get_depends_down`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(x = ~data) x + 1)
p$add("add2", \(x = ~data, y = ~add1) x + y)
p$add("mult3", \(x = ~add1) x * 3)
p$add("mult4", \(x = ~add2) x * 4)
p$get_depends_down("add1")
p$get_depends_down("add1", recursive = FALSE)

## ------------------------------------------------
## Method `Pipeline$get_depends_up`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(x = ~data) x + 1)
p$add("add2", \(x = ~data, y = ~add1) x + y)
p$add("mult3", \(x = ~add1) x * 3)
p$add("mult4", \(x = ~add2) x * 4)
p$get_depends_up("mult4")
p$get_depends_up("mult4", recursive = FALSE)

## ------------------------------------------------
## Method `Pipeline$get_graph`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(data = ~data, x = 1) x + data)
p$add("add2", \(x = 1, y = ~add1) x + y)
p$add("mult1", \(x = ~add1, y = ~add2) x * y)
graph <- pipe_get_graph(p)
graph

if (require("visNetwork", quietly = TRUE)) {
    do.call(visNetwork, args = p$get_graph())
}

## ------------------------------------------------
## Method `Pipeline$get_out`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(x = ~data) x + 1)
p$add("add2", \(x = ~data, y = ~add1) x + y)
p$run()
p$get_out("add1")
p$get_out("add2")

## ------------------------------------------------
## Method `Pipeline$get_params`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(data = ~data, x = 1) x + data)
p$add("add2", \(x = 1, y = 2, .z = 3) x + y + .z)
p$add("add3", \() 1 + 2)
p$get_params() |> str()
p$get_params(ignoreHidden = FALSE) |> str()

## ------------------------------------------------
## Method `Pipeline$get_params_at_step`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(data = ~data, x = 1) x + data)
p$add("add2", \(x = 1, y = 2, .z = 3) x + y + .z)
p$add("add3", \() 1 + 2)
p$get_params_at_step("add2")
p$get_params_at_step("add2", ignoreHidden = FALSE)
p$get_params_at_step("add3")

## ------------------------------------------------
## Method `Pipeline$get_params_unique`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(data = ~data, x = 1) x + data)
p$add("add2", \(x = 1, y = 2, .z = 3) x + y + .z)
p$add("mult1", \(x = 1, y = 2, .z = 3, b = ~add2) x * y * b)
p$get_params_unique()
p$get_params_unique(ignoreHidden = FALSE)

## ------------------------------------------------
## Method `Pipeline$get_params_unique_json`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(data = ~data, x = 1) x + data)
p$add("add2", \(x = 1, y = 2, .z = 3) x + y + .z)
p$add("mult1", \(x = 1, y = 2, .z = 3, b = ~add2) x * y * b)
p$get_params_unique_json()
p$get_params_unique_json(ignoreHidden = FALSE)

## ------------------------------------------------
## Method `Pipeline$get_step`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(data = ~data, x = 1) x + data)
p$add("add2", \(x = 1, y = 2, z = ~add1) x + y + z)
p$run()
add1 <- p$get_step("add1")
print(add1)
add1[["params"]]
add1[["fun"]]
try()
try(p$get_step("foo")) # error: step 'foo' does not exist

## ------------------------------------------------
## Method `Pipeline$get_step_names`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p$get_step_names()

## ------------------------------------------------
## Method `Pipeline$get_step_number`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p$get_step_number("f2")

## ------------------------------------------------
## Method `Pipeline$has_step`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p$has_step("f2")
p$has_step("foo")

## ------------------------------------------------
## Method `Pipeline$insert_after`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1)
p$add("f1", \(x = 1) x)
p$add("f2", \(x = ~f1) x)
p$insert_after("f1", "f3", \(x = ~f1) x)
p

## ------------------------------------------------
## Method `Pipeline$insert_before`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1)
p$add("f1", \(x = 1) x)
p$add("f2", \(x = ~f1) x)
p$insert_before("f2", "f3", \(x = ~f1) x)
p

## ------------------------------------------------
## Method `Pipeline$length`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p$length()

## ------------------------------------------------
## Method `Pipeline$lock_step`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = 1, data = ~data) x + data)
p$add("add2", \(x = 1, data = ~data) x + data)
p$run()
p$get_out("add1")
p$get_out("add2")
p$lock_step("add1")

p$set_data(3)
p$set_params(list(x = 3))
p$run()
p$get_out("add1")
p$get_out("add2")

## ------------------------------------------------
## Method `Pipeline$pop_step`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p
p$pop_step() # "f2"
p

## ------------------------------------------------
## Method `Pipeline$pop_steps_after`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p$add("f3", \(z = 1) z)
p$pop_steps_after("f1")  # "f2", "f3"
p

## ------------------------------------------------
## Method `Pipeline$pop_steps_from`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p$add("f3", \(z = 1) z)
p$pop_steps_from("f2")  # "f2", "f3"
p

## ------------------------------------------------
## Method `Pipeline$print`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p$print()

## ------------------------------------------------
## Method `Pipeline$remove_step`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(data = ~data, x = 1) x + data)
p$add("add2", \(x = 1, y = ~add1) x + y)
p$add("mult1", \(x = 1, y = ~add2) x * y)
p$remove_step("mult1")
p
try(p$remove_step("add1"))  # fails because "add2" depends on "add1"
p$remove_step("add1", recursive = TRUE)  # removes "add1" and "add2"
p

## ------------------------------------------------
## Method `Pipeline$rename_step`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("add1", \(data = ~data, x = 1) x + data)
p$add("add2", \(x = 1, y = ~add1) x + y)
p
try(p$rename_step("add1", "add2"))  # fails because "add2" exists
p$rename_step("add1", "first_add")  # Ok
p

## ------------------------------------------------
## Method `Pipeline$replace_step`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = ~data, y = 1) x + y)
p$add("add2", \(x = ~data, y = 2) x + y)
p$add("mult", \(x = 1, y = 2) x * y, keepOut = TRUE)
p$run()$collect_out()
p$replace_step("mult", \(x = ~add1, y = ~add2) x * y, keepOut = TRUE)
p$run()$collect_out()
try(p$replace_step("foo", \(x = 1) x))   # step 'foo' does not exist

## ------------------------------------------------
## Method `Pipeline$reset`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1:2)
p$add("f1", \(x = 1) x)
p$add("f2", \(y = 1) y)
p$run()
p
p$reset()
p

## ------------------------------------------------
## Method `Pipeline$run`
## ------------------------------------------------

# Simple pipeline
p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = ~data, y = 1) x + y)
p$add("add2", \(x = ~add1, z = 2) x + z)
p$add("final", \(x = ~add1, y = ~add2) x * y, keepOut = TRUE)
p$run()$collect_out()
p$set_params(list(z = 4))  # outdates steps add2 and final
p
p$run()$collect_out()
p$run(cleanUnkept = TRUE)  # clean up temporary results
p

# Recursive pipeline
p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = ~data, y = 1) x + y)
p$add("new_pipe", \(x = ~add1) {
    pp <- Pipeline$new("new_pipe", data = x)
    pp$add("add1", \(x = ~data) x + 1)
    pp$add("add2", \(x = ~add1) x + 2, keepOut = TRUE)
    }
)
p$run(recursive = TRUE)$collect_out()

# Run pipeline with progress bar
p <- Pipeline$new("pipe", data = 1)
p$add("first step", \() Sys.sleep(1))
p$add("second step", \() Sys.sleep(1))
p$add("last step", \() Sys.sleep(1))
pb <- txtProgressBar(min = 1, max = p$length(), style = 3)
fprogress <- function(value, detail) {
   setTxtProgressBar(pb, value)
}
p$run(progress = fprogress, showLog = FALSE)

## ------------------------------------------------
## Method `Pipeline$run_step`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = ~data, y = 1) x + y)
p$add("add2", \(x = ~add1, z = 2) x + z)
p$add("mult", \(x = ~add1, y = ~add2) x * y)
p$run_step("add2")
p$run_step("add2", downstream = TRUE)
p$run_step("mult", upstream = TRUE)

## ------------------------------------------------
## Method `Pipeline$set_data`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = ~data, y = 1) x + y, keepOut = TRUE)
p$run()$collect_out()
p$set_data(3)
p$run()$collect_out()

## ------------------------------------------------
## Method `Pipeline$set_data_split`
## ------------------------------------------------

# Split by three data sets
dataList <- list(a = 1, b = 2, c = 3)
p <- Pipeline$new("pipe")
p$add("add1", \(x = ~data) x + 1, keepOut = TRUE)
p$add("mult", \(x = ~data, y = ~add1) x * y, keepOut = TRUE)
p$set_data_split(dataList)
p
p$run()$collect_out() |> str()

# Don't group output by split
p <- Pipeline$new("pipe")
p$add("add1", \(x = ~data) x + 1, keepOut = TRUE)
p$add("mult", \(x = ~data, y = ~add1) x * y, keepOut = TRUE)
p$set_data_split(dataList, groupBySplit = FALSE)
p
p$run()$collect_out() |> str()

# Split up to certain step
p <- Pipeline$new("pipe")
p$add("add1", \(x = ~data) x + 1)
p$add("mult", \(x = ~data, y = ~add1) x * y)
p$add("average_result", \(x = ~mult) mean(unlist(x)), keepOut = TRUE)
p
p$get_depends()[["average_result"]]

p$set_data_split(dataList, toStep = "mult")
p
p$get_depends()[["average_result"]]

p$run()$collect_out()

## ------------------------------------------------
## Method `Pipeline$set_keep_out`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = ~data, y = 1) x + y, keepOut = TRUE)
p$add("add2", \(x = ~data, y = 2) x + y)
p$add("mult", \(x = ~add1, y = ~add2) x * y)
p$run()$collect_out()
p$set_keep_out("add1", keepOut = FALSE)
p$set_keep_out("mult", keepOut = TRUE)
p$collect_out()

## ------------------------------------------------
## Method `Pipeline$set_params`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = ~data, y = 2) x + y)
p$add("add2", \(x = ~data, y = 3) x + y)
p$add("mult", \(x = 4, z = 5) x * z)
p$get_params()
p$set_params(list(x = 3, y = 3))
p$get_params()
p$set_params(list(x = 5, z = 3))
p$get_params()
suppressWarnings(
    p$set_params(list(foo = 3)) # gives warning as 'foo' is undefined
)
p$set_params(list(foo = 3), warnUndefined = FALSE)

## ------------------------------------------------
## Method `Pipeline$set_params_at_step`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = ~data, y = 2, z = 3) x + y)
p$set_params_at_step("add1", list(y = 5, z = 6))
p$get_params()
try(p$set_params_at_step("add1", list(foo = 3))) # foo not defined

## ------------------------------------------------
## Method `Pipeline$split`
## ------------------------------------------------

# Example for two independent calculation paths
p <- Pipeline$new("pipe", data = 1)
p$add("f1", \(x = ~data) x)
p$add("f2", \(x = 1) x)
p$add("f3", \(x = ~f1) x)
p$add("f4", \(x = ~f2) x)
p$split()

# Example of split by three data sets
dataList <- list(a = 1, b = 2, c = 3)
p <- Pipeline$new("pipe")
p$add("add1", \(x = ~data) x + 1, keepOut = TRUE)
p$add("mult", \(x = ~data, y = ~add1) x * y, keepOut = TRUE)
pipes <- p$set_data_split(dataList)$split()
pipes

## ------------------------------------------------
## Method `Pipeline$unlock_step`
## ------------------------------------------------

p <- Pipeline$new("pipe", data = 1)
p$add("add1", \(x = 1, data = ~data) x + data)
p$add("add2", \(x = 1, data = ~data) x + data)
p$lock_step("add1")
p$set_params(list(x = 3))
p$get_params()
p$unlock_step("add1")
p$set_params(list(x = 3))
p$get_params()

pipeflow documentation built on April 3, 2025, 10:50 p.m.

Related to Pipeline in pipeflow...

pipeflow index
Advanced: How pipelines can modify themselves at runtime" Advanced: How to use pipeflow with split data sets" Collecting output" Combining pipelines" Get started with pipeflow" Modifying existing pipelines"

R Package Documentation

rdrr.io home R language documentation Run R code online

Browse R Packages

CRAN packages Bioconductor packages R-Forge packages GitHub packages

We want your feedback!

Note that we can't provide technical support on individual packages. You should contact the package authors for that.
GitHub issue tracker
ian@mutexlabs.com
Personal blog

 
Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.

Close