Identifying variables are those variables that describe the (qualitative)
properties that make each observation (as described by the
observed variables) unique.
setIDVar(
schema = NULL,
name = NULL,
type = "character",
value = NULL,
columns = NULL,
rows = NULL,
split = NULL,
merge = NULL,
distinct = FALSE
)[schema(1)]
In case this information is added to an
already existing schema, provide that schema here (overwrites previous
information).
[character(1)]
Name of the new identifying variable.
[character(1)]
data type of the new identifying
variable. Possible values are "c/character", "i/integer",
"n/numeric", "l/logical", "D/Date" or "_/skip".
For "D/Date", the value has to follow the form YYYY-MM-DD,
where dates that don't match that are replaced by NA.
[character(1)]
In case the variable is an implicit
variable (i.e., which is not in the origin table), specify it here.
[integerish(.)]
The column(s) in which the
values of the new variable are recorded.
[integerish(.)]
In case the variable is in several
columns, specify here additionally the row in which the names are
recorded.
[character(1)]
In case the variable is part of a
compound value, this should be a regular expression that splits the
respective value off of that compound value. See
extract on how to set up the regular expression.
[character(1)]
In case a variable is made up of
several columns, this should be the character string that would connect the
two columns (e.g., an empty space " ").
[logical(1)]
whether or not the variable is
distinct from a cluster. This is the case when the variable is not
systematically available for all clusters and thus needs to be registered
separately from clusters.
An object of class schema.
Please also take a look at the currently suggested strategy to set up a schema description.
Other functions to describe table arrangement:
setCluster(),
setFilter(),
setFormat(),
setGroups(),
setObsVar()
# please check the vignette for examples