DataFrame-class {S4Vectors} | R Documentation

## DataFrame objects

### Description

The `DataFrame` class extends the DataTable virtual class and supports the storage of any type of object (with `length` and `[` methods) as columns.

### Details

On the whole, the `DataFrame` behaves very similarly to `data.frame`, in terms of construction, subsetting, splitting, combining, etc. The most notable exception is that the row names are optional. This means calling `rownames(x)` will return `NULL` if there are no row names. Of course, it could return `seq_len(nrow(x))`, but returning `NULL` informs, for example, combination functions that no row names are desired (they are often a luxury when dealing with large data).

As `DataFrame` derives from `Vector`, it is possible to set an `annotation` string. Also, another `DataFrame` can hold metadata on the columns.

For a class to be supported as a column, it must have `length` and `[` methods, where `[` supports subsetting only by `i` and respects `drop=FALSE`. Optionally, a method may be defined for the `showAsCell` generic, which should return a vector of the same length as the subset of the column passed to it. This vector is then placed into a `data.frame` and converted to text with `format`. Thus, each element of the vector should be some simple, usually character, representation of the corresponding element in the column.

### Constructor

`DataFrame(..., row.names = NULL, check.names = TRUE, stringsAsFactors)`: Constructs a `DataFrame` in similar fashion to `data.frame`. Each argument in `...` is coerced to a `DataFrame` and combined column-wise. No special effort is expended to automatically determine the row names from the arguments. The row names should be given in `row.names`; otherwise, there are no row names. This is by design, as row names are normally undesirable when data is large. If `check.names` is `TRUE`, the column names will be checked for syntactic validity and made unique, if necessary.

To store an object of a class that does not support coercion to `DataFrame`, wrap it in `I()`. The class must still have methods for `length` and `[`.

The `stringsAsFactors` argument is ignored. The coercion of column arguments to DataFrame determines whether strings become factors.

### Accessors

In the following code snippets, `x` is a `DataFrame`.

  `dim(x)`: Get the length two integer vector indicating in the first and second element the number of rows and columns, respectively.

  `dimnames(x)`, `dimnames(x) <- value`: Get and set the two element list containing the row names (character vector of length `nrow(x)` or `NULL`) and the column names (character vector of length `ncol(x)`).

### Coercion

  `as(from, "DataFrame")`: By default, constructs a new `DataFrame` with `from` as its only column. If `from` is a `matrix` or `data.frame`, all of its columns become columns in the new `DataFrame`. If `from` is a list, each element becomes a column, recycling as necessary. Note that for the `DataFrame` to behave correctly, each column object must support element-wise subsetting via the `[` method and return the number of elements with `length`. It is recommended to use the `DataFrame` constructor, rather than this interface.

  `as.list(x)`: Coerces `x`, a `DataFrame`, to a `list`.

  `as.data.frame(x, row.names=NULL, optional=FALSE)`: Coerces `x`, a `DataFrame`, to a `data.frame`. Each column is coerced to a `data.frame` and then column bound together. If `row.names` is `NULL`, they are retrieved from `x`, if it has any. Otherwise, they are inferred by the `data.frame` constructor.

  NOTE: conversion of `x` to a `data.frame` is not supported if `x` contains any `list`, `SimpleList`, or `CompressedList` columns.

  `as(from, "data.frame")`: Coerces a `DataFrame` to a `data.frame` by calling `as.data.frame(from)`.

  `as.matrix(x)`: Coerces the `DataFrame` to a `matrix`, if possible.

### Subsetting

In the following code snippets, `x` is a `DataFrame`.

  `x[i,j,drop]`: Behaves very similarly to the `[.data.frame` method, except `i` can be a logical `Rle` object and subsetting by `matrix` indices is not supported. Indices containing `NA`'s are also not supported.

  `x[i,j] <- value`: Behaves very similarly to the `[<-.data.frame` method.

  `x[[i]]`: Behaves very similarly to the `[[.data.frame` method, except arguments `j` and `exact` are not supported. Column name matching is always exact. Subsetting by matrices is not supported.

  `x[[i]] <- value`: Behaves very similarly to the `[[<-.data.frame` method, except argument `j` is not supported.

### Combining

In the following code snippets, `x` is a `DataFrame`.

  `rbind(...)`: Creates a new `DataFrame` by combining the rows of the `DataFrame` objects in `...`. Very similar to `rbind.data.frame`, except in the handling of row names. If all elements have row names, they are concatenated and made unique. Otherwise, the result does not have row names. The return value inherits its metadata from the first argument.

  `cbind(...)`: Creates a new `DataFrame` by combining the columns of the `DataFrame` objects in `...`. Very similar to `cbind.data.frame`. The return value inherits its metadata from the first argument.

### Author(s)

Michael Lawrence

### See Also

- DataTable and SimpleList which DataFrame extends directly.

### Examples

    score <- c(1L, 3L, NA)
    counts <- c(10L, 2L, NA)
    row.names <- c("one", "two", "three")

    df <- DataFrame(score) # single column
    df[["score"]]
    df <- DataFrame(score, row.names = row.names) #with row names
    rownames(df)

    df <- DataFrame(vals = score) # explicit naming
    df[["vals"]]

    # arrays
    ary <- array(1:4, c(2,1,2))
    sw <- DataFrame(I(ary))

    # a data.frame
    sw <- DataFrame(swiss)
    as.data.frame(sw) # swiss, without row names
    # now with row names
    sw <- DataFrame(swiss, row.names = rownames(swiss))
    as.data.frame(sw) # swiss

    # subsetting

    sw[] # identity subset
    sw[,] # same

    sw[NULL] # no columns
    sw[,NULL] # no columns
    sw[NULL,] # no rows

    ## select columns
    sw[1:3]
    sw[,1:3] # same as above
    sw[,"Fertility"]
    sw[,c(TRUE, FALSE, FALSE, FALSE, FALSE, FALSE)]

    ## select rows and columns
    sw[4:5, 1:3]

    sw[1] # one-column DataFrame
    ## the same
    sw[, 1, drop = FALSE]
    sw[, 1] # a (unnamed) vector
    sw[[1]] # the same
    sw[["Fertility"]]

    sw[["Fert"]] # should return 'NULL'

    sw[1,] # a one-row DataFrame
    sw[1,, drop=TRUE] # a list

    ## duplicate row, unique row names are created
    sw[c(1, 1:2),]

    ## indexing by row names
    sw["Courtelary",]
    subsw <- sw[1:5,1:4]
    subsw["C",] # partially matches

    ## row and column names
    cn <- paste("X", seq_len(ncol(swiss)), sep = ".")
    colnames(sw) <- cn
    colnames(sw)
    rn <- seq(nrow(sw))
    rownames(sw) <- rn
    rownames(sw)

    ## column replacement

    df[["counts"]] <- counts
    df[["counts"]]
    df[[3]] <- score
    df[["X"]]
    df[[3]] <- NULL # deletion

[Package S4Vectors version 0.20.1 Index]