Package 'LEEF'

Add or replace a function in a queue

Description

Functions starting with add_... do add a function to a queue which is processed by the coresponding run_... command.

The functions do reqquire exactly two arguments with the first named input and the second one named output. They should return either TRUE when run successful, or FALSE when failed. Although, the checking is not yet implemented.

Usage

add(fun, funname, queue)

add_additor(fun)

add_archiver(fun)

add_extractor(fun)

add_pre_processor(fun)

Arguments

fun	function which is run when calling run_...() The functions must not require any arguments!
funname	name of the function
queue	name of queue in getOption("LEEF")

Details

add: The function which is doing the adding - normally the specific add_* functions are used

add_additor: Adding a named function to the queue of additors. If the named function already exists will it be replaced.

add_archiver: Adding a named function to the queue of archivers. If the named function already exists will it be replaced.

These functions do archive the results of the current processing step.

add_extractor: Adding a named function to the queue of extractors. If the named function already exists will it be replaced.

These functions should extract data from the pre-processed data. The extracted data should be usable for the actual analysis to address the actual research question.

add_pre_processor: Adding a named function to the queue of pre-processors. If the named function already exists will it be replaced.

This function should pre-process the raw data. The pre-processed data should be archive ready, i.e. contain the same information as the raw data, be in an open format, and be compressed if possible.

Value

invisibly the function queue.

invisibly the function queue. A list which is processed

invisibly the function queue. A list which is processed

Examples

## Not run: 
## To add the function `cat` to the `additor` queue
add (fun = cat, .queue = "additor")

## To add the function `paste` to the `extractor` queue
add (cat, "cat", "extractor")

## End(Not run)
add_additor( fun = cat )
add_additor( fun = cat )
add_extractor( fun = paste )
add_pre_processor( fun = paste )

---

Run the Control Center shiny app included in the LEEF package.

Description

The Control centre app allows the

• sanity checks of the raw data

• running of the pipeline

Usage

control_center(rootdir = ".")

Arguments

rootdir

Directory in which all the data directories can be found.

Value

return value from runApp()

Examples

## Not run: 
control_center()

## End(Not run)

---

Create folder structure and prepares the pipeline based on the config file

Description

The following steps are done in this function

Usage

init_LEEF(
  config_file = system.file("default_config.yml", package = "LEEF"),
  id = NULL
)

Arguments

config_file	config file to use. If none is specified, cofig.yml in the current working directory will be used.
id	id which will be appended to the name in the config file, using a '.'

Details

1. the config file as specified in the argument config_file is read

2. the folders as specified in the config file are, if they do not exist yet, created. If they are not specified, the following default values are used:

   • general.parameter: ⁠00.general.parameter⁠ - the directory containing general configuratuion files which are used for multiple measurements

   • raw: ⁠0.raw.data⁠ - the raw data

   • pre_processed: ⁠1.pre_processed.data⁠ - the pre-processed Archive Ready Data

   • extracted: ⁠2.extracted.data⁠ = the extracted Research Ready Data

   • archive: ⁠3.archived.data⁠ - the archived data from any of the previous steps or raw data

   • backend: ⁠9.backend⁠ - the backend which contains the Research Ready Data from all pipeline runs before

   • tools: tools - tools needed for running the different processes in the pipeline

3. verifies if a file named sample_metadata.yml exists which contains the metadata of the raw data

4. registers all measurement, archive and backend packages

5. verifies,if all tools are installed and installs them when needed. THis step is specific to the bemovi measurement!!!

Value

invisible TRUE

Examples

## Not run: 
init_LEEF(system.file("default_config.yml", package = "LEEF"))

## End(Not run)

---

LEEF

Description

Meta package for LEEF pipeline

---

List packages on which LEEF depends from the LEEF-UZH repo

Description

This function is a wrapper around tools::package_dependencies("LEEF", which = "all", recursive = TRUE) which returns only the packages which contain .LEEF or LEEF. and the package LEEF itself.

Usage

list_LEEF_packages(recursive = TRUE, versions = FALSE)

Arguments

recursive	logical: should (reverse) dependencies of (reverse) dependencies (and so on) be included? defaults to TRUE
versions	logical: should versions be returned as well.

Details

This function is a convenience function and only returns useful results when all packages which are dependencies of the LEEF package are prefixed with LEEF. or postficxed with .LEEF.

Value

list of all packages which are installed which contain .LEEF or LEEF. and the package LEEF itself

Examples

## Not run: 
list_LEEF_packages()

## End(Not run)

---

Functions to read and write options

Description

Read or write the directories to be used in the processing. Directories do not have to exist and will be created. Content will be overwritten without confirmation! If no parameter is given, the directories will be returned a a list.

Usage

opt_directories(
  general.parameter,
  raw,
  pre_processed,
  extracted,
  archive,
  tools
)

Arguments

general.parameter	character vector of length one containing the directory for the general parameter files
raw	character vector of length one containing the directory for the raw data
pre_processed	character vector of length one containing the directory for the pre_processed data
extracted	character vector of length one containing the directory for the extracted data
archive	character vector of length one containing the directory for the archived data
tools	directory in which the tools are located

Value

list of directories. If values have set, the value before the change.

Examples

opt_directories()

opt_directories(raw = "./temp")

---

Process all ques in the correct order

Description

This function is an example and can be used as a template for processing the queues in a script,. Raw data is always archived using the "none" compression.

Usage

process(submitter, timestamp, process = TRUE, ...)

Arguments

submitter	name of submitter. When provided, will override the one in the 'sample_metadata.yml' file.
timestamp	timestamp for the data. When provided, will override the one in the 'sample_metadata.yml' file.
process	if TRUE, the pipeline will be processed. if FALSE, only the checks of the config file will be done nd no actual processing is happening.
...	additional arguments for the different queues

Value

invisibly TRUE

Examples

## Not run: 
 process()

## End(Not run)

---

Process all queues in the correct order

Description

This function is an example and can be used as a template for processing the queues in a script. It uses the archiver "none" for the raw and pre-processed data, useful for already compressed and large data, e.g. bemovi.

Usage

process_raw_comp_none(submitter, timestamp, ...)

Arguments

submitter	name of the submitter of the data to the pipeline. Will be added to the metadata.
timestamp	timestamp of the submission of the data to the pipeline. This should be in the format YYYYMMDD and will be used to identify the sampling day.
...	additional arguments for the different queues

Value

invisibly TRUE

Examples

## Not run: 
 process()

## End(Not run)

---

Register the functions to be usedfrom packages in the config file

Description

Register the functions to be usedfrom packages in the config file

Usage

register_packages(packages)

Arguments

packages

list of packages. Each element must contain the elements name the name of the package, InstallCommand the command to be executed to install the package, and RegisterCommand the command to be executed to register the functions in a queue

Value

invisibly a list containing the results of the register commands

Examples

## Not run: 
 register_packages(getOption("LEEF")$measurement_packages)

## End(Not run)

---

Run process queue

Description

Run all the functions in the process queue named queue

Usage

run(input, output, queue)

Arguments

input	directory containing the input data in folders with the name of the methodology (e.g. bemovi)
output	directory in which the results will be written in a folder with the name of the methodology (e.g. bemovi)
queue	name of queue in getOption("LEEF")

Value

returns the results of the queue as a vector of length of the queue. If an element is TRUE, the function was run successfully (i.e. returned TRUE)

Examples

## Not run: 
run(
  input = "./input",
  output = "./output",
  queue = "extractor"
)

## End(Not run)

---

Run additors queue

Description

Run all the additors registered with add_additor().

Usage

run_additors()

Value

returns the results of the queue as a vector of length of the queue. If an element is TRUE, the function was run successfully (i.e. returned TRUE)

Examples

## Not run: 
run_additors()

## End(Not run)

---

Run archivers queue

Description

Run all the archivers registered with add_archiver().

Usage

run_archivers(input, output)

Arguments

input	directory to be archive, including subdirectories
output	director in which the archive will be created

Value

returns the results of the queue as a vector of length of the queue. If an element is TRUE, the function was run successfully (i.e. returned TRUE)

Examples

## Not run: 
run_archivers(
  input = "./input",
  output = "./output"
)

## End(Not run)

---

Run extractors queue

Description

Run all the extractors registered with add_extractor().

Usage

run_extractors()

Value

returns the results of the queue as a vector of length of the queue. If an element is TRUE, the function was run successfully (i.e. returned TRUE)

Examples

## Not run: 
run_extractors()

## End(Not run)

---

Run pre_processors queue

Description

Run all the additors registered with add_pre_processor().

Usage

run_pre_processors()

Value

returns the results of the queue as a vector of length of the queue. If an element is TRUE, the function was run successfully (i.e. returned TRUE)

Examples

## Not run: 
run_pre_processors()

## End(Not run)

---

Split bemovi filder into a number of bemovi. folders with a maximum of per_batch video files

Description

Split bemovi filder into a number of bemovi. folders with a maximum of per_batch video files

Usage

split_bemovi(
  per_batch = 30,
  bemovi_dir = file.path(".", "0.raw.data"),
  overwrite = TRUE
)

Arguments

per_batch	maximum number of movies per batch
bemovi_dir	bas directory in which the bemovi directory is located
overwrite	if TRUE, all folders starting with bemovi. in the bemovi_dir will be deleted

Value

the maximum id used

Examples

## Not run: 
split_bemovi(per_batch = 5)

## End(Not run)

---