DataFrame objects

Description

The DataFrame class extends the RectangularData virtual class 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 exceptions have to do with handling of the row names:

1. 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).

2. The row names are not required to be unique.

3. Subsetting by row names does not use partial matching.

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. The row names should be given in row.names; otherwise, they are inherited from the arguments, as in data.frame. Explicitly passing NULL to row.names ensures that there are no rownames. 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.

make_zero_col_DFrame(nrow):

Constructs a zero-column DFrame object with nrow rows. Intended for developers to use in other packages and typically not needed by the end user.

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, make.names=TRUE):

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 propagated from x, if it has any. Otherwise, they are inferred by the data.frame constructor.

Like the as.data.frame() method for class matrix, the method for class DataFrame supports the make.names argument. make.names can be set to TRUE or FALSE to indicate what should happen if the row names of x (or the row names supplied via the row.names argument) are invalid (e.g. contain duplicates). If they are invalid, and make.names is TRUE (the default), they get "fixed" by going thru make.names(*, unique=TRUE). Otherwise (i.e. if make.names is FALSE), an error is raised. Note that unlike the method for class matrix, make.names=NA is not supported.

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.

as.env(x, enclos = parent.frame()):

Creates an environment from x with a symbol for each colnames(x). The values are not actually copied into the environment. Rather, they are dynamically bound using makeActiveBinding. This prevents unnecessary copying of the data from the external vectors into R vectors. The values are cached, so that the data is not copied every time the symbol is accessed.

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.

Displaying

The show() method for DataFrame objects obeys global options showHeadLines and showTailLines for controlling the number of head and tail rows to display. See ?get_showHeadLines for more information.

Author(s)

Michael Lawrence

See Also

• DataFrame-combine for combining DataFrame objects.

• DataFrame-utils for other common operations on DataFrame objects.

• TransposedDataFrame objects.

• RectangularData and SimpleList which DataFrame extends directly.

• get_showHeadLines for controlling the number of DataFrame rows to display.

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",] # no partial match (unlike with data.frame)

## 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