Package 'LOLA'

Title: Locus overlap analysis for enrichment of genomic ranges
Description: Provides functions for testing overlap of sets of genomic regions with public and custom region set (genomic ranges) databases. This makes it possible to do automated enrichment analysis for genomic region sets, thus facilitating interpretation of functional genomics and epigenomics data.
Authors: Nathan Sheffield <http://www.databio.org> [aut, cre], Christoph Bock [ctb]
Maintainer: Nathan Sheffield <[email protected]>
License: GPL-3
Version: 1.37.0
Built: 2024-10-30 07:35:33 UTC
Source: https://github.com/bioc/LOLA

Help Index

If you want to test for differential enrichment within your usersets, you can restrict the universe to only regions that are covered in at least one of your sets. This function helps you build just such a restricted universe

Description

If you want to test for differential enrichment within your usersets, you can restrict the universe to only regions that are covered in at least one of your sets. This function helps you build just such a restricted universe

Usage

buildRestrictedUniverse(userSets)

Arguments

userSets

The userSets you will pass to the enrichment calculation.

Value

A restricted universe

Examples

data("sample_input", package="LOLA") # load userSets
restrictedUniverse = buildRestrictedUniverse(userSets)

Check universe appropriateness

Description

Checks to see if the universe is appropriate for the userSets Anything in the userSets should be present in the universe. In addition, 2 different regions in the userSets should not overlap the same region in the universe

Usage

checkUniverseAppropriateness(userSets, userUniverse, cores = 1, fast = FALSE)

Arguments

userSets

Regions of interest
userUniverse

Regions tested for inclusion in userSets
cores

Number of processors
fast

Skip the (slow) test for many-to-many relationships

Value

No return value.

Examples

data("sample_input", package="LOLA") # load userSet
data("sample_universe", package="LOLA") # load userUniverse
checkUniverseAppropriateness(userSets, userUniverse)

cleanws takes multi-line, code formatted strings and just formats them as simple strings

Description

cleanws takes multi-line, code formatted strings and just formats them as simple strings

Usage

cleanws(string)

Arguments

string

string to clean

Value

A string with all consecutive whitespace characters, including tabs and newlines, merged into a single space.

Just a reverser. Reverses the order of arguments and passes them untouched to countOverlapsAny – so you can use it with lapply.

Description

Just a reverser. Reverses the order of arguments and passes them untouched to countOverlapsAny – so you can use it with lapply.

Usage

countOverlapsAnyRev(subj, quer)

Arguments

subj

Subject
quer

Query

Value

Results from countOverlaps

Given a single row from an enrichment table calculation, finds the set of overlaps between the user set and the test set. You can then use these, for example, to get sequences for those regions.

Description

Given a single row from an enrichment table calculation, finds the set of overlaps between the user set and the test set. You can then use these, for example, to get sequences for those regions.

Usage

extractEnrichmentOverlaps(locResult, userSets, regionDB)

Arguments

locResult

Results from runLOLA function
userSets

User sets passed to the runLOLA function
regionDB

Region database used

Value

userSets overlapping the supplied database entry.

Examples

dbPath = system.file("extdata", "hg19", package="LOLA")
regionDB = loadRegionDB(dbLocation=dbPath)
data("sample_universe", package="LOLA")
data("sample_input", package="LOLA")

getRegionSet(regionDB, collections="ucsc_example", filenames="vistaEnhancers.bed")
getRegionSet(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")
getRegionFile(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")


res = runLOLA(userSets, userUniverse, regionDB, cores=1)
locResult = res[2,]
extractEnrichmentOverlaps(locResult, userSets, regionDB)
writeCombinedEnrichment(locResult, "temp_outfolder")

userSetsRedefined =	redefineUserSets(userSets, userUniverse)
resRedefined = runLOLA(userSetsRedefined, userUniverse, regionDB, cores=1)

g = plotTopLOLAEnrichments(resRedefined)

Grab the filename for a a single region set from a database specified by filename.

Description

Like getRegionSet but returns a filename instead of a GRanges object. Given a local filename, returns a complete absolulte path so you can read that file in.

Usage

getRegionFile(dbLocation, filenames, collections = NULL)

Arguments

dbLocation

folder of regionDB
filenames

Filename(s) of a particular region set to grab.
collections

(optional) subset of collections to list

Value

A filename the specified file in the regionDB.

Examples

dbPath = system.file("extdata", "hg19", package="LOLA")
regionDB = loadRegionDB(dbLocation=dbPath)
data("sample_universe", package="LOLA")
data("sample_input", package="LOLA")

getRegionSet(regionDB, collections="ucsc_example", filenames="vistaEnhancers.bed")
getRegionSet(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")
getRegionFile(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")


res = runLOLA(userSets, userUniverse, regionDB, cores=1)
locResult = res[2,]
extractEnrichmentOverlaps(locResult, userSets, regionDB)
writeCombinedEnrichment(locResult, "temp_outfolder")

userSetsRedefined =	redefineUserSets(userSets, userUniverse)
resRedefined = runLOLA(userSetsRedefined, userUniverse, regionDB, cores=1)

g = plotTopLOLAEnrichments(resRedefined)

Grab a single region set from a database, specified by filename.

Description

If you want to work with a LOLA regionDB region set individually, this function can help you. It can extract individual (or subsets of) region sets from either loaded regionDBs, loaded with loadRegionDB(), or from a database on disk, where only the region sets of interest will be loaded.

Usage

getRegionSet(regionDB, filenames, collections = NULL)

Arguments

regionDB

A region database loaded with loadRegionDB().
filenames

Filename(s) of a particular region set to grab.
collections

(optional) subset of collections to list

Value

A GRanges object derived from the specified file in the regionDB.

Examples

dbPath = system.file("extdata", "hg19", package="LOLA")
regionDB = loadRegionDB(dbLocation=dbPath)
data("sample_universe", package="LOLA")
data("sample_input", package="LOLA")

getRegionSet(regionDB, collections="ucsc_example", filenames="vistaEnhancers.bed")
getRegionSet(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")
getRegionFile(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")


res = runLOLA(userSets, userUniverse, regionDB, cores=1)
locResult = res[2,]
extractEnrichmentOverlaps(locResult, userSets, regionDB)
writeCombinedEnrichment(locResult, "temp_outfolder")

userSetsRedefined =	redefineUserSets(userSets, userUniverse)
resRedefined = runLOLA(userSetsRedefined, userUniverse, regionDB, cores=1)

g = plotTopLOLAEnrichments(resRedefined)

Function to run lapply or mclapply, depending on the option set in getOption("mc.cores"), which can be set with setLapplyAlias().

Description

Function to run lapply or mclapply, depending on the option set in getOption("mc.cores"), which can be set with setLapplyAlias().

Usage

lapplyAlias(..., mc.preschedule = TRUE)

Arguments

...

Arguments passed lapply() or mclapply()
mc.preschedule

Argument passed to mclapply

Value

Result from lapply or parallel::mclapply

Lists the region sets for given collection(s) in a region database on disk.

Description

Lists the region sets for given collection(s) in a region database on disk.

Usage

listRegionSets(regionDB, collections = NULL)

Arguments

regionDB

File path to region database
collections

(optional) subset of collections to list

Value

a list of files in the given collections

Examples

dbPath = system.file("extdata", "hg19", package="LOLA")
listRegionSets(dbPath)

converts a list of GRanges into a GRangesList; strips all metadata.

Description

converts a list of GRanges into a GRangesList; strips all metadata.

Usage

listToGRangesList(lst)

Arguments

lst

a list of GRanges objects

Value

a GRangesList object

Helper function to annotate and load a regionDB, a folder with subfolder collections of regions.

Description

Helper function to annotate and load a regionDB, a folder with subfolder collections of regions.

Usage

loadRegionDB(dbLocation, useCache = TRUE, limit = NULL, collections = NULL)

Arguments

dbLocation

folder where your regionDB is stored, or list of such folders
useCache

uses simpleCache to cache and load the results
limit

You can limit the number of regions for testing. Default: NULL (no limit)
collections

Restrict the database loading to this list of collections

Value

regionDB list containing database location, region and collection annotations, and regions GRangesList

Examples

dbPath = system.file("extdata", "hg19", package="LOLA")
regionDB = loadRegionDB(dbLocation=dbPath)

Genome locus overlap analysis.

Description

Run, Lola!

Author(s)

Nathan Sheffield

References

http://github.com/sheffien

Given two regionDBs, (lists returned from loadRegionDB()), This function will combine them into a single regionDB. This will enable you to combine, for example, LOLA Core databases with custom databases into a single analysis.

Description

Given two regionDBs, (lists returned from loadRegionDB()), This function will combine them into a single regionDB. This will enable you to combine, for example, LOLA Core databases with custom databases into a single analysis.

Usage

mergeRegionDBs(dbA, dbB)

Arguments

dbA

First regionDB database.
dbB

Second regionDB database.

Value

A combined regionDB.

Examples

dbPath = system.file("extdata", "hg19", package="LOLA")
regionDB = loadRegionDB(dbPath)
combinedRegionDB = mergeRegionDBs(regionDB, regionDB)

Named list function.

Description

This function is a drop-in replacement for the base list() function, which automatically names your list according to the names of the variables used to construct it. It seemlessly handles lists with some names and others absent, not overwriting specified names while naming any unnamed parameters. Took me awhile to figure this out.

Usage

nlist(...)

Arguments

...

arguments passed to list()

Value

A named list object.

Given some results (you grab the top ones on your own), this plots a barplot visualizing their odds ratios.

Description

Given some results (you grab the top ones on your own), this plots a barplot visualizing their odds ratios.

Usage

plotTopLOLAEnrichments(data)

Arguments

data

A results table returned from runLOLA()

Value

Returns a ggplot2 plot object.

Examples

dbPath = system.file("extdata", "hg19", package="LOLA")
regionDB = loadRegionDB(dbLocation=dbPath)
data("sample_universe", package="LOLA")
data("sample_input", package="LOLA")

getRegionSet(regionDB, collections="ucsc_example", filenames="vistaEnhancers.bed")
getRegionSet(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")
getRegionFile(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")


res = runLOLA(userSets, userUniverse, regionDB, cores=1)
locResult = res[2,]
extractEnrichmentOverlaps(locResult, userSets, regionDB)
writeCombinedEnrichment(locResult, "temp_outfolder")

userSetsRedefined =	redefineUserSets(userSets, userUniverse)
resRedefined = runLOLA(userSetsRedefined, userUniverse, regionDB, cores=1)

g = plotTopLOLAEnrichments(resRedefined)

Imports bed files and creates GRanges objects, using the fread() function from data.table.

Description

Imports bed files and creates GRanges objects, using the fread() function from data.table.

Usage

readBed(file)

Arguments

file

File name of bed file.

Value

GRanges Object

Examples

a = readBed(system.file("extdata", "examples/combined_regions.bed",
package="LOLA"))

Given a bunch of region set files, read in all those flat (bed) files and create a GRangesList object holding all the region sets. This function is used by readRegionGRL to process annotation objects.

Description

Given a bunch of region set files, read in all those flat (bed) files and create a GRangesList object holding all the region sets. This function is used by readRegionGRL to process annotation objects.

Usage

readCollection(filesToRead, limit = NULL)

Arguments

filesToRead

a vector containing bed files
limit

for testing purposes, limit the number of files read. NULL for no limit (default).

Value

A GRangesList with the GRanges in the filesToRead.

Examples

files = list.files(system.file("extdata", "hg19/ucsc_example/regions",
 package="LOLA"), pattern="*.bed")
regionAnno = readCollection(files)

Read collection annotation

Description

Read collection annotation

Usage

readCollectionAnnotation(dbLocation, collections = NULL)

Arguments

dbLocation

Location of the database
collections

Restrict the database loading to this list of collections. Leave NULL to load the entire database (Default).

Value

Collection annotation data.table

Examples

dbPath = system.file("extdata", "hg19", package="LOLA")
collectionAnno = readCollectionAnnotation(dbLocation=dbPath)

Given a database and a collection, this will create the region annotation data.table; either giving a generic table based on file names, or by reading in the annotation data.

Description

Given a database and a collection, this will create the region annotation data.table; either giving a generic table based on file names, or by reading in the annotation data.

Usage

readCollectionFiles(dbLocation, collection, refreshSizes = FALSE)

Arguments

dbLocation

folder where your regionDB is stored.
collection

Collection folder to load
refreshSizes

should I recreate the sizes files documenting how many regions (lines) are in each region set?

Value

A data.table annotating the regions in the collections.

Examples

dbPath = system.file("extdata", "hg19", package="LOLA")
regionAnno = readCollectionFiles(dbLocation=dbPath, "ucsc_example")

This function takes a region annotation object and reads in the regions, returning a GRangesList object of the regions.

Description

This function takes a region annotation object and reads in the regions, returning a GRangesList object of the regions.

Usage

readRegionGRL(
  dbLocation,
  annoDT,
  refreshCaches = FALSE,
  useCache = TRUE,
  limit = NULL
)

Arguments

dbLocation

folder of regiondB
annoDT

output of readRegionSetAnnotation().
refreshCaches

should I recreate the caches?
useCache

uses simpleCache to cache and load the results
limit

for testing purposes, limit the nmber of files read. NULL for no limit (default).

Value

GRangesList object

Examples

dbPath = system.file("extdata", "hg19", package="LOLA")
regionAnno = readRegionSetAnnotation(dbLocation=dbPath)
regionGRL = readRegionGRL(dbLocation= dbPath, regionAnno, useCache=FALSE)

Given a folder containing region collections in subfolders, this function will either read the annotation file if one exists, or create a generic annotation file.

Description

Given a folder containing region collections in subfolders, this function will either read the annotation file if one exists, or create a generic annotation file.

Usage

readRegionSetAnnotation(
  dbLocation,
  collections = NULL,
  refreshCaches = FALSE,
  refreshSizes = TRUE,
  useCache = TRUE
)

Arguments

dbLocation

folder where your regionDB is stored.
collections

Restrict the database loading to this list of collections Leave NULL to load the entire database (Default).
refreshCaches

should I recreate the caches? Default: FALSE
refreshSizes

should I refresh the size files? Default:TRUE
useCache

Use simpleCache to store results and load them?

Value

Region set annotation (data.table)

Examples

dbPath = system.file("extdata", "hg19", package="LOLA")
regionAnno = readRegionSetAnnotation(dbLocation=dbPath)

This function will take the user sets, overlap with the universe, and redefine the user sets as the set of regions in the user universe that overlap at least one region in user sets. this makes for a more appropriate statistical enrichment comparison, as the user sets are actually exactly the same regions found in the universe otherwise, you can get some weird artifacts from the many-to-many relationship between user set regions and universe regions.

Description

This function will take the user sets, overlap with the universe, and redefine the user sets as the set of regions in the user universe that overlap at least one region in user sets. this makes for a more appropriate statistical enrichment comparison, as the user sets are actually exactly the same regions found in the universe otherwise, you can get some weird artifacts from the many-to-many relationship between user set regions and universe regions.

Usage

redefineUserSets(userSets, userUniverse, cores = 1)

Arguments

userSets

Regions of interest
userUniverse

Regions tested for inclusion in userSets
cores

Number of processors

Value

userSets redefined in terms of userUniverse

Examples

dbPath = system.file("extdata", "hg19", package="LOLA")
regionDB = loadRegionDB(dbLocation=dbPath)
data("sample_universe", package="LOLA")
data("sample_input", package="LOLA")

getRegionSet(regionDB, collections="ucsc_example", filenames="vistaEnhancers.bed")
getRegionSet(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")
getRegionFile(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")


res = runLOLA(userSets, userUniverse, regionDB, cores=1)
locResult = res[2,]
extractEnrichmentOverlaps(locResult, userSets, regionDB)
writeCombinedEnrichment(locResult, "temp_outfolder")

userSetsRedefined =	redefineUserSets(userSets, userUniverse)
resRedefined = runLOLA(userSetsRedefined, userUniverse, regionDB, cores=1)

g = plotTopLOLAEnrichments(resRedefined)

This will change the string in filename to have a new extension

Description

This will change the string in filename to have a new extension

Usage

replaceFileExtension(filename, extension)

Arguments

filename

string to convert
extension

new extension

Value

Filename with original extension deleted, replaced by provided extension

Enrichment Calculation

Description

Workhorse function that calculates overlaps between userSets, and then uses a fisher's exact test rank them by significance of the overlap.

Usage

runLOLA(
  userSets,
  userUniverse,
  regionDB,
  minOverlap = 1,
  cores = 1,
  redefineUserSets = FALSE,
  direction = "enrichment"
)

Arguments

userSets

Regions of interest
userUniverse

Regions tested for inclusion in userSets
regionDB

Region DB to check for overlap, from loadRegionDB()
minOverlap

(Default:1) Minimum bases required to count an overlap
cores

Number of processors
redefineUserSets

run redefineUserSets() on your userSets?
direction

Defaults to "enrichment", but may also accept "depletion", which will swap the direction of the fisher test (use 'greater' or less' value passed to the 'alternative' option of fisher.test)

Value

Data.table with enrichment results. Rows correspond to individual pairwise fisher's tests comparing a single userSet with a single databaseSet. The columns in this data.table are: userSet and dbSet: index into their respective input region sets. pvalueLog: -log10(pvalue) from the fisher's exact result; oddsRatio: result from the fisher's exact test; support: number of regions in userSet overlapping databaseSet; rnkPV, rnkOR, rnkSup: rank in this table of p-value, oddsRatio, and Support respectively. The –value is the negative natural log of the p-value returned from a one-sided fisher's exact test. maxRnk, meanRnk: max and mean of the 3 previous ranks, providing a combined ranking system. b, c, d: 3 other values completing the 2x2 contingency table (with support). The remaining columns describe the dbSet for the row.

If you have the qvalue package installed from bioconductor, runLOLA will add a q-value transformation to provide FDR scores automatically.

Examples

dbPath = system.file("extdata", "hg19", package="LOLA")
regionDB = loadRegionDB(dbLocation=dbPath)
data("sample_universe", package="LOLA")
data("sample_input", package="LOLA")

getRegionSet(regionDB, collections="ucsc_example", filenames="vistaEnhancers.bed")
getRegionSet(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")
getRegionFile(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")


res = runLOLA(userSets, userUniverse, regionDB, cores=1)
locResult = res[2,]
extractEnrichmentOverlaps(locResult, userSets, regionDB)
writeCombinedEnrichment(locResult, "temp_outfolder")

userSetsRedefined =	redefineUserSets(userSets, userUniverse)
resRedefined = runLOLA(userSetsRedefined, userUniverse, regionDB, cores=1)

g = plotTopLOLAEnrichments(resRedefined)

Function to sample regions from a GRangesList object, in specified proportion

Description

Function to sample regions from a GRangesList object, in specified proportion

Usage

sampleGRL(GRL, prop)

Arguments

GRL

GRangesList from which to sample
prop

vector with same length as GRL, of values between 0-1, proportion of the list to select

Value

A sampled subset of original GRangesList object.

To make parallel processing a possibility but not required, I use an lapply alias which can point at either the base lapply (for no multicore), or it can point to mclapply, and set the options for the number of cores (what mclapply uses). With no argument given, returns intead the number of cpus currently selected.

Description

To make parallel processing a possibility but not required, I use an lapply alias which can point at either the base lapply (for no multicore), or it can point to mclapply, and set the options for the number of cores (what mclapply uses). With no argument given, returns intead the number of cpus currently selected.

Usage

setLapplyAlias(cores = 0)

Arguments

cores

Number of cpus

Value

None

setSharedDataDir Sets global variable specifying the default data directory.

Description

setSharedDataDir Sets global variable specifying the default data directory.

Usage

setSharedDataDir(sharedDataDir)

Arguments

sharedDataDir

directory where the shared data is stored.

Value

No return value.

Examples

setSharedDataDir("project/data")

Efficiently split a data.table by a column in the table

Description

Efficiently split a data.table by a column in the table

Usage

splitDataTable(DT, splitFactor)

Arguments

DT

Data.table to split
splitFactor

Column to split, which can be a character vector or an integer.

Value

List of data.table objects, split by column

This function will take a single large bed file that is annotated with a column grouping different sets of similar regions, and split it into separate files for use with the LOLA collection format.

Description

This function will take a single large bed file that is annotated with a column grouping different sets of similar regions, and split it into separate files for use with the LOLA collection format.

Usage

splitFileIntoCollection(
  filename,
  splitCol,
  collectionFolder = NULL,
  filenamePrepend = ""
)

Arguments

filename

the file to split
splitCol

factor column that groups the lines in the file by set. It should be an integer.
collectionFolder

name of folder to place the new split files.
filenamePrepend

string to prepend to the filenames. Defaults to blank.

Value

No return value.

Examples

combFile = system.file("extdata", "examples/combined_regions.bed", package="LOLA")
splitFileIntoCollection(combFile, 4)

An example set of regions, sampled from the example database.

Description

A dataset containing a few sample regions.

Usage

data(sample_input)

Format

A GRangesList object

Value

No return value.

Examples

## Not run: 
This is how I produced the sample data sets:
dbPath = system.file("extdata", "hg19", package="LOLA")
regionDB = loadRegionDB(dbLocation= dbPath)
userSetA = reduce(do.call(c, (sampleGRL(regionDB$regionGRL,
prop=c(.1,.25,.05,.05,0)))))
userSetB = reduce(do.call(c, (sampleGRL(regionDB$regionGRL,
prop=c(.2,.05,.05,.05,0)))))

userSets = GRangesList(setA=userSetA, setB=userSetB)
userUniverse = reduce(do.call(c, regionDB$regionGRL))
save(userSets, file="sample_input.RData")
save(userUniverse, file="sample_universe.RData")

## End(Not run)

A reduced GRanges object from the example regionDB database

Description

A reduced GRanges object from the example regionDB database

Usage

data(sample_universe)

Format

A GRanges object

Value

No return value.

Wrapper of write.table that provides defaults to write a simple .tsv file. Passes additional arguments to write.table

Description

Wrapper of write.table that provides defaults to write a simple .tsv file. Passes additional arguments to write.table

Usage

write.tsv(...)

Arguments

...

Additional arguments passed to write.table

Value

No return value

Function for writing output all at once: combinedResults is an table generated by "locationEnrichment()" or by rbinding category/location results. Writes all enrichments to a single file, and also spits out the same data divided into groups based on userSets, and Databases, just for convenience. disable this with an option.

Description

Function for writing output all at once: combinedResults is an table generated by "locationEnrichment()" or by rbinding category/location results. Writes all enrichments to a single file, and also spits out the same data divided into groups based on userSets, and Databases, just for convenience. disable this with an option.

Usage

writeCombinedEnrichment(
  combinedResults,
  outFolder = NULL,
  includeSplits = TRUE
)

Arguments

combinedResults

enrichment results object
outFolder

location to write results on disk
includeSplits

also include individual files for each user set and database?

Value

No return value.

Examples

dbPath = system.file("extdata", "hg19", package="LOLA")
regionDB = loadRegionDB(dbLocation=dbPath)
data("sample_universe", package="LOLA")
data("sample_input", package="LOLA")

getRegionSet(regionDB, collections="ucsc_example", filenames="vistaEnhancers.bed")
getRegionSet(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")
getRegionFile(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")


res = runLOLA(userSets, userUniverse, regionDB, cores=1)
locResult = res[2,]
extractEnrichmentOverlaps(locResult, userSets, regionDB)
writeCombinedEnrichment(locResult, "temp_outfolder")

userSetsRedefined =	redefineUserSets(userSets, userUniverse)
resRedefined = runLOLA(userSetsRedefined, userUniverse, regionDB, cores=1)

g = plotTopLOLAEnrichments(resRedefined)

Given a data table and a factor variable to split on, efficiently divides the table and then writes the different splits to separate files, named with filePrepend and numbered according to split.

Description

Given a data table and a factor variable to split on, efficiently divides the table and then writes the different splits to separate files, named with filePrepend and numbered according to split.

Usage

writeDataTableSplitByColumn(
  DT,
  splitFactor,
  filePrepend = "",
  orderColumn = NULL
)

Arguments

DT

data.table to split
splitFactor

column of DT to split on
filePrepend

notation string to prepend to output files
orderColumn

column of DT to order on (defaults to the first column)

Value

number of splits written