R: ff class for data.frames

ffdf {ff} | R Documentation

## ff class for data.frames

### Description

Function 'ffdf' creates ff data.frames stored on disk very similar to 'data.frame'

### Usage

    ffdf(...
    , row.names = NULL
    , ff_split = NULL
    , ff_join = NULL
    , ff_args = NULL
    , update = TRUE
    , BATCHSIZE = .Machine$integer.max
    , BATCHBYTES = getOption("ffbatchbytes")
    , VERBOSE = FALSE)

### Arguments

`...` | `ff` vectors or matrices (optionally wrapped in `I()` that shall be bound together to an ffdf object
`row.names` | A `character` vector. Not recommended for large objects with many rows.
`ff_split` | A vector of character names or integer positions identifying input components to physically split into single ff_vectors. If vector elements have names, these are used as root name for the new ff files.
`ff_join` | A list of vectors with character names or integer positions identifying input components to physically join in the same ff matrix. If list elements have names, these are used to name the new ff files.
`update` | By default (TRUE) new ff files are updated with content of input ff objects. Setting to FALSE prevents this update.
`ff_args` | a list with further arguments passed to `ff` in case that new ff objects are created via 'ff_split' or 'ff_join'
`BATCHSIZE` | passed to `update.ff`
`BATCHBYTES` | passed to `update.ff`
`VERBOSE` | passed to `update.ff`

### Details

By default, creating an 'ffdf' object will NOT create new ff files, instead existing files are referenced. This differs from `data.frame`, which always creates copies of the input objects, most notably in `data.frame(matrix())`, where an input matrix is converted to single columns. ffdf by contrast, will store an input matrix physically as the same matrix and virtually map it to columns. Physically copying a large ff matrix to single ff vectors can be expensive. More generally, ffdf objects have a `physical` and a `virtual` component, which allows very flexible dataframe designs: a physically stored matrix can be virtually mapped to single columns, a couple of physically stored vectors can be virtually mapped to a single matrix. The means to configure these are `I` for the virtual representation and the 'ff_split' and 'ff_join' arguments for the physical representation. An ff matrix wrapped into 'I()' will return the input matrix as a single object, using 'ff_split' will store this matrix as single vectors - and thus create new ff files. 'ff_join' will copy a couple of input vectors into a unified new ff matrix with `dimorder=c(2,1)`, but virtually they will remain single columns. The returned ffdf object has also a `dimorder` attribute, which indicates whether the ffdf object contains a matrix with non-standard dimorder `c(2,1)`, see `dimorderStandard`.
Currently, `virtual windows` are not supported for ffdf.

### Value

A list with components

`physical` | the underlying ff vectors and matrices, to be accessed via `physical`
`virtual` | the virtual features of the ffdf including the virtual-to-physical mapping, to be accessed via `virtual`
`row.names` | the optional row.names, see argument row.names

and class 'ffdf' (NOTE that ffdf dows not inherit from ff)

### Methods

The following methods and functions are available for ffdf objects:

Type | Name | Assign | Comment
|  |  | Basic functions
function | `ffdf` |  | constructor for ffdf objects
generic | `update` |  | updates one ffdf object with the content of another
generic | `clone` |  | clones an ffdf object
method | `print` |  | print ffdf
method | `str` |  | ffdf object structure
|  |  | Class test and coercion
function | `is.ffdf` |  | check if inherits from ff
generic | `as.ffdf` |  | coerce to ff, if not yet
generic | `as.data.frame` |  | coerce to ram data.frame
|  |  | Virtual storage mode
generic | `vmode` |  | get virtual modes for all (virtual) columns
|  |  | Physical attributes
function | `physical` |  | get physical attributes
|  |  | Virtual attributes
function | `virtual` |  | get virtual attributes
method | `length` |  | get length
method | `dim` | `<-` | get dim and set nrow
generic | `dimorder` |  | get the dimorder (non-standard if any component is non-standard)
method | `names` | `<-` | set and get names
method | `row.names` | `<-` | set and get row.names
method | `dimnames` | `<-` | set and get dimnames
method | `pattern` | `<-` | set pattern (rename/move files)
|  |  | Access functions
method | `[` | <- | set and get data.frame content (`[,]`) or get ffdf with less columns (`[]`)
method | `[[` | <- | set and get single column ff object
method | `$` | <- | set and get single column ff object
|  |  | Opening/Closing/Deleting
generic | `is.open` |  | tri-bool is.open status of the physical ff components
method | `open` |  | open all physical ff objects (is done automatically on access)
method | `close` |  | close all physical ff objects
method | `delete` |  | deletes all physical ff files
method | `finalize` |  | call finalizer
|  |  | processing
method | `chunk` |  | create chunked index
method | `sortLevels` |  | sort and recode levels
|  |  | Other

### Note

Note that in theory, accessing a chunk of rows from a matrix with `dimorder=c(2,1)` should be faster than accessing across a bunch of vectors. However, at least under windows, the OS has difficulties filecaching parts from very large files, therefore - until we have partitioning - the recommended physical storage is in single vectors.

### Author(s)

Jens Oehlschlägel

### See Also

`data.frame`, `ff`, for more example see `physical`

### Examples

     m <- matrix(1:12, 3, 4, dimnames=list(c("r1","r2","r3"), c("m1","m2","m3","m4")))
     v <- 1:3
     ffm <- as.ff(m)
     ffv <- as.ff(v)

     d <- data.frame(m, v)
     ffd <- ffdf(ffm, v=ffv, row.names=row.names(ffm))
     all.equal(d, ffd[,])
     ffd
     physical(ffd)

     d <- data.frame(m, v)
     ffd <- ffdf(ffm, v=ffv, row.names=row.names(ffm), ff_split=1)
     all.equal(d, ffd[,])
     ffd
     physical(ffd)

     d <- data.frame(m, v)
     ffd <- ffdf(ffm, v=ffv, row.names=row.names(ffm), ff_join=list(newff=c(1,2)))
     all.equal(d, ffd[,])
     ffd
     physical(ffd)

     d <- data.frame(I(m), I(v))
     ffd <- ffdf(m=I(ffm), v=I(ffv), row.names=row.names(ffm))
     all.equal(d, ffd[,])
     ffd
     physical(ffd)

     rm(ffm,ffv,ffd); gc()

[Package ff version 2.2-13 Index]