mteb/BrightProStackoverflowRetrieval huggingface dataset:
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[ base  (version  3.6.2  ) ](/packages/base/versions/3.6.2)

#  libcurlVersion: Report Version of libcurl

##  Description

Report version of ` libcurl ` in use.

##  Usage

    
    
    libcurlVersion()

##  Arguments

##  Value

A character string, with value the ` libcurl ` version in use or ` "" ` if
none is. If ` libcurl ` is available, has attributes

ssl_version

    

A character string naming the SSL/TLS implementation and version, possibly `
"none" ` . It is intended for the version of OpenSSL used, but not all
implementations of ` libcurl ` use OpenSSL --- for example macOS reports `
"SecureTranspart" ` , its wrapper for SSL/TLS.

libssh_version

    

A character string naming the ` libssh ` version, which may or may not be
available (it is used for e.g. ` scp ` and ` sftp ` protocols). Where present,
something like ` "libssh2/1.5.0" ` .

protocols

    

A character vector of the names of supported protocols, also known as
‘schemes’ when part of a URL.

##  Warning

In late 2017 a ` libcurl ` installation was seen divided into two libraries, `
libcurl ` and ` libcurl-feature ` , and the first had been updated but not the
second. As the compiled function recording the version was in the latter, the
version reported by ` libcurlVersion ` was misleading.

##  See Also

` [ extSoftVersion ](/link/extSoftVersion?package=base&version=3.6.2) ` for
versions of other third-party software.

` [ curlGetHeaders ](/link/curlGetHeaders?package=base&version=3.6.2) ` , ` [
download.file ](/link/download.file?package=base&version=3.6.2) ` and ` [ url
](/link/url?package=base&version=3.6.2) ` for functions which (optionally) use
` libcurl ` .

[ http://curl.haxx.se/docs/sslcerts.html
](http://curl.haxx.se/docs/sslcerts.html) and [ http://curl.haxx.se/docs/ssl-
compared.html ](http://curl.haxx.se/docs/ssl-compared.html) for more details
on SSL versions (the current standard being known as TLS). Normally ` libcurl
` used with  R  uses SecureTransport on macOS, OpenSSL on Windows and GnuTLS,
NSS or OpenSSL on Unix-alikes. (At the time of writing Debian-based Linuxen
use GnuTLS and RedHat-based ones use NSS, but it has been announced that
Fedora 27 will switch to OpenSSL.)

##  Examples

[ Run this code
](https://app.datacamp.com/workspace/preview?_tag=rdocs&rdocsPath=packages%2Fbase%2Fversions%2F3.6.2%2Ftopics%2FlibcurlVersion&utm_source=r-docs&utm_medium=docs&utm_term=libcurlVersion&utm_content=run_example_in_workspace)

    
    
    # NOT RUN {
    libcurlVersion()
    # }
    

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[ base  (version  3.6.2  ) ](/packages/base/versions/3.6.2)

#  Sys.time: Get Current Date and Time

##  Description

` Sys.time ` and ` Sys.Date ` returns the system's idea of the current date
with and without time.

##  Usage

    
    
    Sys.time()
    Sys.Date()

##  Arguments

##  Value

` Sys.time ` returns an object of class ` "POSIXct" ` (see [ DateTimeClasses
](/link/DateTimeClasses?package=base&version=3.6.2) ). On almost all systems
it will have sub-second accuracy, possibly microseconds or better. On Windows
it increments in clock ticks (usually 1/60 of a second) reported to
millisecond accuracy.

` Sys.Date ` returns an object of class ` "Date" ` (see [ Date
](/link/Date?package=base&version=3.6.2) ).

##  Details

` Sys.time ` returns an absolute date-time value which can be converted to
various time zones and may return different days.

` Sys.Date ` returns the current day in the current [ time zone
](/link/time%20zone?package=base&version=3.6.2) .

##  See Also

` [ date ](/link/date?package=base&version=3.6.2) ` for the system time in a
fixed-format character string.

` [ Sys.timezone ](/link/Sys.timezone?package=base&version=3.6.2) ` .

` [ system.time ](/link/system.time?package=base&version=3.6.2) ` for
measuring elapsed/CPU time of expressions.

##  Examples

[ Run this code
](https://app.datacamp.com/workspace/preview?_tag=rdocs&rdocsPath=packages%2Fbase%2Fversions%2F3.6.2%2Ftopics%2FSys.time&utm_source=r-docs&utm_medium=docs&utm_term=Sys.time&utm_content=run_example_in_workspace)

    
    
    # NOT RUN {
    Sys.time()
    ## print with possibly greater accuracy:
    op <- options(digits.secs = 6)
    Sys.time()
    options(op)
    
    ## locale-specific version of date()
    format(Sys.time(), "%a %b %d %X %Y")
    
    Sys.Date()
    # }
    

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[ base  (version  3.6.2  ) ](/packages/base/versions/3.6.2)

#  seq.Date: Generate Regular Sequences of Dates

##  Description

The method for ` [ seq ](/link/seq?package=base&version=3.6.2) ` for objects
of class class ` " [ Date ](/link/Date?package=base&version=3.6.2) " `
representing calendar dates.

##  Usage

    
    
    # S3 method for Date
    seq(from, to, by, length.out = NULL, along.with = NULL, …)

##  Arguments

from

starting date. Required

to

end date. Optional.

by

increment of the sequence. Optional. See ‘Details’.

length.out

integer, optional. Desired length of the sequence.

along.with

take the length from the length of this argument.

…

arguments passed to or from other methods.

##  Value

A vector of class ` "Date" ` .

##  Details

` by ` can be specified in several ways.

  * A number, taken to be in days. 

  * A object of class ` [ difftime ](/link/difftime?package=base&version=3.6.2) `

  * A character string, containing one of ` "day" ` , ` "week" ` , ` "month" ` , ` "quarter" ` or ` "year" ` . This can optionally be preceded by a (positive or negative) integer and a space, or followed by ` "s" ` . 

See ` [ seq.POSIXt ](/link/seq.POSIXt?package=base&version=3.6.2) ` for the
details of ` "month" ` .

##  See Also

` [ Date ](/link/Date?package=base&version=3.6.2) `

##  Examples

[ Run this code
](https://app.datacamp.com/workspace/preview?_tag=rdocs&rdocsPath=packages%2Fbase%2Fversions%2F3.6.2%2Ftopics%2Fseq.Date&utm_source=r-docs&utm_medium=docs&utm_term=seq.Date&utm_content=run_example_in_workspace)

    
    
    # NOT RUN {
    ## first days of years
    seq(as.Date("1910/1/1"), as.Date("1999/1/1"), "years")
    ## by month
    seq(as.Date("2000/1/1"), by = "month", length.out = 12)
    ## quarters
    seq(as.Date("2000/1/1"), as.Date("2003/1/1"), by = "quarter")
    
    ## find all 7th of the month between two dates, the last being a 7th.
    st <- as.Date("1998-12-17")
    en <- as.Date("2000-1-7")
    ll <- seq(en, st, by = "-1 month")
    rev(ll[ll > st & ll < en])
    # }
    

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[ micropan  (version  1.0  ) ](/packages/micropan/versions/1.0)

#  fluidity: Computing genomic fluidity for a pan-genome

##  Description

Computes the genomic fluidity, which is a measure of population diversity.

##  Usage

    
    
    fluidity(pan.matrix,n.sim=10)

##  Arguments

pan.matrix

A ` Panmat ` object, see ` [ panMatrix
](/link/panMatrix?package=micropan&version=1.0) ` for details.

n.sim

An integer specifying the number of random samples to use in the computations.

##  Value

A list with two elements, the mean fluidity and its sample standard deviation
over the  n.sim  computed values.

##  Details

The genomic fluidity between two genomes is defined as the number of unique
gene families divided by the total number of gene families (Kislyuk et al,
2011). This is averaged over  n.sim  random pairs of genomes to obtain a
population estimate.

The genomic fluidity between two genomes describes their degree of overlap
with respect to gene cluster content. If the fluidity is 0.0, the two genomes
contain identical gene clusters. If it is 1.0 the two genomes are non-
overlapping. The difference between a Jaccard distance (see ` [ distJaccard
](/link/distJaccard?package=micropan&version=1.0) ` ) and genomic fluidity is
small, they both measure overlap between genomes, but fluidity is computed for
the population by averaging over many pairs, while Jaccard distances are
computed for every pair. Note that only presence/absence of gene clusters are
considered, not multiple occurrences. The input  pan.matrix  is typically
constructed by ` [ panMatrix ](/link/panMatrix?package=micropan&version=1.0) `
.

##  References

Kislyuk, A.O., Haegeman, B., Bergman, N.H., Weitz, J.S. (2011). Genomic
fluidity: an integrative view of gene diversity within microbial populations.
BMC Genomics, 12:32.

##  See Also

` [ panMatrix ](/link/panMatrix?package=micropan&version=1.0) ` , ` [
distJaccard ](/link/distJaccard?package=micropan&version=1.0) ` .

##  Examples

[ Run this code
](https://www.datacamp.com/datalab/preview?_tag=rdocs&rdocsPath=packages%2Fmicropan%2Fversions%2F1.0%2Ftopics%2Ffluidity&utm_source=r-docs&utm_medium=docs&utm_term=fluidity&utm_content=run_example_in_datalab)

    
    
    # Loading two Panmat objects in the micropan package
    data(list=c("Mpneumoniae.blast.panmat","Mpneumoniae.domain.panmat"),package="micropan")
    
    # Fluidity based on a BLAST clustering Panmat object
    fluid.blast <- fluidity(Mpneumoniae.blast.panmat)
    
    # Fluidity based on domain sequence clustering Panmat object
    fluid.domains <- fluidity(Mpneumoniae.domain.panmat)
    

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[ base  (version  3.6.2  ) ](/packages/base/versions/3.6.2)

#  NULL: The Null Object

##  Description

` NULL ` represents the null object in  R  : it is a [ reserved
](/link/reserved?package=base&version=3.6.2) word. ` NULL ` is often returned
by expressions and functions whose value is undefined.

##  Usage

    
    
    NULL
    as.null(x, …)
    is.null(x)

##  Arguments

x

an object to be tested or coerced.

…

ignored.

##  Value

` as.null ` ignores its argument and returns ` NULL ` .

` is.null ` returns ` TRUE ` if its argument's value is ` NULL ` and ` FALSE `
otherwise.

##  Details

` NULL ` can be indexed (see [ Extract
](/link/Extract?package=base&version=3.6.2) ) in just about any syntactically
legal way: whether it makes sense or not, the result is always ` NULL ` .
Objects with value ` NULL ` can be changed by replacement operators and will
be coerced to the type of the right-hand side.

` NULL ` is also used as the empty [ pairlist
](/link/pairlist?package=base&version=3.6.2) : see the examples. Because
pairlists are often promoted to lists, you may encounter ` NULL ` being
promoted to an empty list.

Objects with value ` NULL ` cannot have attributes as there is only one null
object: attempts to assign them are either an error ( ` [ attr
](/link/attr?package=base&version=3.6.2) ` ) or promote the object to an empty
list with attribute(s) ( ` [ attributes
](/link/attributes?package=base&version=3.6.2) ` and ` [ structure
](/link/structure?package=base&version=3.6.2) ` ).

##  References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) _The New S Language_ .
Wadsworth & Brooks/Cole.

##  Examples

[ Run this code
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    # NOT RUN {
    is.null(list())     # FALSE (on purpose!)
    is.null(pairlist()) # TRUE
    is.null(integer(0)) # FALSE
    is.null(logical(0)) # FALSE
    as.null(list(a = 1, b = "c"))
    # }
    

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[ base  (version  3.6.2  ) ](/packages/base/versions/3.6.2)

#  taskCallbackNames: Query the Names of the Current Internal Top-Level Task
Callbacks

##  Description

This provides a way to get the names (or identifiers) for the currently
registered task callbacks that are invoked at the conclusion of each top-level
task. These identifiers can be used to remove a callback.

##  Usage

    
    
    getTaskCallbackNames()

##  Arguments

##  Value

A character vector giving the name for each of the registered callbacks which
are invoked when a top-level task is completed successfully. Each name is the
one used when registering the callbacks and returned as the in the call to ` [
addTaskCallback ](/link/addTaskCallback?package=base&version=3.6.2) ` .

##  See Also

` [ addTaskCallback ](/link/addTaskCallback?package=base&version=3.6.2) ` , `
[ removeTaskCallback ](/link/removeTaskCallback?package=base&version=3.6.2) `
, ` [ taskCallbackManager
](/link/taskCallbackManager?package=base&version=3.6.2) ` \ [
https://developer.r-project.org/TaskHandlers.pdf
](https://developer.r-project.org/TaskHandlers.pdf)

##  Examples

[ Run this code
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    # NOT RUN {
     n <- addTaskCallback(function(expr, value, ok, visible) {
                            cat("In handler\n")
                            return(TRUE)
                          }, name = "simpleHandler")
    
     getTaskCallbackNames()
    
       # now remove it by name
     removeTaskCallback("simpleHandler")
    
    
     h <- taskCallbackManager()
     h$add(function(expr, value, ok, visible) {
                            cat("In handler\n")
                            return(TRUE)
                          }, name = "simpleHandler")
     getTaskCallbackNames()
     removeTaskCallback("R-taskCallbackManager")
    # }
    

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[ base  (version  3.6.2  ) ](/packages/base/versions/3.6.2)

#  interactive: Is R Running Interactively?

##  Description

Return ` TRUE ` when  R  is being used interactively and ` FALSE ` otherwise.

##  Usage

    
    
    interactive()

##  Arguments

##  Details

An interactive  R  session is one in which it is assumed that there is a human
operator to interact with, so for example  R  can prompt for corrections to
incorrect input or ask what to do next or if it is OK to move to the next
plot.

GUI consoles will arrange to start  R  in an interactive session. When  R  is
run in a terminal (via ` Rterm.exe ` on Windows), it assumes that it is
interactive if ` stdin ` is connected to a (pseudo-)terminal and not if `
stdin ` is redirected to a file or pipe. Command-line options  \--interactive
(Unix) and  \--ess  (Windows, ` Rterm.exe ` ) override the default assumption.
(On a Unix-alike, whether the ` readline ` command-line editor is used is
**not** overridden by  \--interactive  .)

Embedded uses of  R  can set a session to be interactive or not.

Internally, whether a session is interactive determines

  * how some errors are handled and reported, e.g.see ` [ stop ](/link/stop?package=base&version=3.6.2) ` and ` [ options ](/link/options?package=base&version=3.6.2) ("showWarnCalls") ` . 

  * whether one of  \--save  ,  \--no-save  or  \--vanilla  is required, and if  R  ever asks whether to save the workspace. 

  * the choice of default graphics device launched when needed and by ` [ dev.new ](/link/dev.new?package=base&version=3.6.2) ` : see ` [ options ](/link/options?package=base&version=3.6.2) ("device") `

  * whether graphics devices ever ask for confirmation of a new page. 

In addition,  R  's own  R  code makes use of ` interactive() ` : for example
` [ help ](/link/help?package=base&version=3.6.2) ` , ` [ debugger
](/link/debugger?package=base&version=3.6.2) ` and ` [ install.packages
](/link/install.packages?package=base&version=3.6.2) ` do.

##  See Also

` [ source ](/link/source?package=base&version=3.6.2) ` , ` [ .First
](/link/.First?package=base&version=3.6.2) `

##  Examples

[ Run this code
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    # NOT RUN {
     .First <- function() if(interactive()) x11()
    # }
    

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[ micropan  (version  1.0  ) ](/packages/micropan/versions/1.0)

#  distJaccard: Computing Jaccard distances between genomes

##  Description

Computes the Jaccard distances between all pairs of genomes.

##  Usage

    
    
    distJaccard(pan.matrix)

##  Arguments

pan.matrix

A ` Panmat ` object, see ` [ panMatrix
](/link/panMatrix?package=micropan&version=1.0) ` for details.

##  Value

A ` dist ` object (see ` [ dist ](/link/dist?package=micropan&version=1.0) ` )
containing all pairwise Jaccard distances between genomes.

##  Details

The Jaccard index between two sets is defined as the size of the interesection
of the sets divided by the size of the union. The Jaccard distance is simply 1
minus the Jaccard index. The Jaccard distance between two genomes describes
their degree of overlap with respect to gene cluster content. If the Jaccard
distance is 0.0, the two genomes contain identical gene clusters. If it is 1.0
the two genomes are non-overlapping. The difference between a genomic fluidity
(see ` [ fluidity ](/link/fluidity?package=micropan&version=1.0) ` ) and a
Jaccard distance is small, they both measure overlap between genomes, but
fluidity is computed for the population by averaging over many pairs, while
Jaccard distances are computed for every pair. Note that only presence/absence
of gene clusters are considered, not multiple occurrences. The input
pan.matrix  is typically constructed by ` [ panMatrix
](/link/panMatrix?package=micropan&version=1.0) ` .

##  See Also

` [ panMatrix ](/link/panMatrix?package=micropan&version=1.0) ` , ` [ fluidity
](/link/fluidity?package=micropan&version=1.0) ` , ` [ dist
](/link/dist?package=micropan&version=1.0) ` .

##  Examples

[ Run this code
](https://www.datacamp.com/datalab/preview?_tag=rdocs&rdocsPath=packages%2Fmicropan%2Fversions%2F1.0%2Ftopics%2FdistJaccard&utm_source=r-docs&utm_medium=docs&utm_term=distJaccard&utm_content=run_example_in_datalab)

    
    
    # Loading two Panmat objects in the micropan package
    data(list=c("Mpneumoniae.blast.panmat","Mpneumoniae.domain.panmat"),package="micropan")
    
    # Jaccard distances based on a BLAST clustering Panmat object
    Jdist.blast <- distJaccard(Mpneumoniae.blast.panmat)
    
    # Jaccard distances based on domain sequence clustering Panmat object
    Jdist.domains <- distJaccard(Mpneumoniae.domain.panmat)
    

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