subset.audited: Drop Methods for Class Audited
In audited: Automatic Tracing and Display of Changes to Row Count

Description Usage Arguments Details Value Author(s) References See Also Examples

These methods alter the audited object and return it with an updated transaction table. They generate drop transactions.

## S3 method for class 'audited'
x[i, j, drop, id, od]
## S3 method for class 'audited'
subset(x, subset, select, drop = FALSE, id, od = "! subset", ...)
## S3 method for class 'audited'
unique(x, incomparables = FALSE, fromLast = FALSE, id, od = "! unique", ...)
## S3 method for class 'audited'
head(x, n = 6L, ..., id, od = "! head")
## S3 method for class 'audited'
tail(x, n = 6L, ..., id, od = "! tail")
## S3 method for class 'audited'
rbind(..., deparse.level = 1)

`id`	character (scalar); see details
`od`	character (scalar); see details
`x`	audited
`i`	passed to subset operator
`j`	passed to subset operator
`drop`	passed to other functions
`subset`	passed to subset.data.frame
`select`	passed to subset.data.frame
`...`	passed to other functions
`incomparables`	passed to unique
`fromLast`	passed to unique
`n`	passed to head or tail
`deparse.level`	as for rbind.data.frame

The most important arguments here are id and od. All the others are passed through to related functions.

id and od will always have informative defaults. However, you will often want to supply customized values. id is a label for the set of rows that is returned. od is a label for the set of rows that is dropped; it will be used for plotting.

When supplying id or od, remember to maintain the proper number of dimensions. For example,

Theoph[ Theoph$WT > 70, , id = 'heavier', od = 'lighter' ] not

Theoph[ Theoph$WT > 70, id = 'heavier', od = 'lighter' ]

The subset operator, called by all the other functions listed above, deserves special consideration. Argument i controls the rows that are returned. For a data.frame, i may be negative, positive, zero, logical, character, and NA, with varying effects. In particular, use of repeated positive creates duplicates of rows, while use of NA, numeric greater than nrow(x), or character not in row.names(x) creates NA rows. [.audited rejects indices that create NA rows. It allows duplication of rows, but it is an error to both add and drop rows simultaneously. For example, x[c(2,2),] should fail, because record 1 (probably others) is being dropped while a copy of record 2 is being added.

audited

Tim Bergsma

http://metrumrg.googlecode.com