How to Install and Uninstall r-cran-ff Package on Kali Linux

Last updated: May 19,2024

1. Install "r-cran-ff" package

Please follow the guidelines below to install r-cran-ff on Kali Linux


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                r-cran-ff
            
                    
    
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2. Uninstall "r-cran-ff" package

Here is a brief guide to show you how to uninstall r-cran-ff on Kali Linux:


    
        
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                sudo apt remove
            
                        
                r-cran-ff
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the r-cran-ff package on Kali Linux

Package: r-cran-ff
Version: 4.0.12+ds-1
Installed-Size: 1567
Maintainer: Debian R Packages Maintainers
Architecture: amd64
Depends: r-api-4.0, r-cran-bit (>= 4.0.0), libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 5)
Recommends: r-cran-testthat (>= 0.11.0)
Size: 973128
SHA256: 5f6032a6a6c7fccfe4f88c3fb6bc1ce3f84bdbb1486a589e99d8767b8cc8416d
SHA1: 7c86276e399566f965d7095e27c9b55268163de5
MD5sum: 3a533419d1cd294427476734fd0ac26a
Description: Memory-Efficient Fast-Access Storage of Large Data
The ff package provides data structures that are stored on
disk but behave (almost) as if they were in RAM by transparently
mapping only a section (pagesize) in main memory - the effective
virtual memory consumption per ff object. ff supports R's standard
atomic data types 'double', 'logical', 'raw' and 'integer' and
non-standard atomic types boolean (1 bit), quad (2 bit unsigned),
nibble (4 bit unsigned), byte (1 byte signed with NAs), ubyte (1 byte
unsigned), short (2 byte signed with NAs), ushort (2 byte unsigned),
single (4 byte float with NAs). For example 'quad' allows efficient
storage of genomic data as an 'A','T','G','C' factor. The unsigned
types support 'circular' arithmetic. There is also support for
close-to-atomic types 'factor', 'ordered', 'POSIXct', 'Date' and
custom close-to-atomic types.
.
ff not only has native C-support for vectors, matrices and arrays
with flexible dimorder (major column-order, major row-order and
generalizations for arrays). There is also a ffdf class not unlike
data.frames and import/export filters for csv files.
ff objects store raw data in binary flat files in native encoding,
and complement this with metadata stored in R as physical and virtual
attributes. ff objects have well-defined hybrid copying semantics,
which gives rise to certain performance improvements through
virtualization. ff objects can be stored and reopened across R
sessions. ff files can be shared by multiple ff R objects
(using different data en/de-coding schemes) in the same process
or from multiple R processes to exploit parallelism. A wide choice of
finalizer options allows one to work with 'permanent' files as well as
creating/removing 'temporary' ff files completely transparent to the
user. On certain OS/Filesystem combinations, creating the ff files
works without notable delay thanks to using sparse file allocation.
Several access optimization techniques such as Hybrid Index
Preprocessing and Virtualization are implemented to achieve good
performance even with large datasets, for example virtual matrix
transpose without touching a single byte on disk. Further, to reduce
disk I/O, 'logicals' and non-standard data types get stored native and
compact on binary flat files i.e. logicals take up exactly 2 bits to
represent TRUE, FALSE and NA.
.
Beyond basic access functions, the ff package also provides
compatibility functions that facilitate writing code for ff and ram
objects and support for batch processing on ff objects (e.g. as.ram,
as.ff, ffapply). ff interfaces closely with functionality from package
'bit': chunked looping, fast bit operations and coercions between
different objects that can store subscript information ('bit',
'bitwhich', ff 'boolean', ri range index, hi hybrid index). This allows
to work interactively with selections of large datasets and quickly
modify selection criteria.
Description-md5:
Homepage: https://cran.r-project.org/package=ff
Section: gnu-r
Priority: optional
Filename: pool/main/r/r-cran-ff/r-cran-ff_4.0.12+ds-1_amd64.deb