PING
0.9
Statistical data handling and processing in production environment
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Perform a 'bulk-renaming' of the variables of a given table.
idsn
: input reference dataset, whose variables shall be renamed;var
: (option) list of variables that should be renamed; this parameter is incompatible with the parameter ex_var
below; default: var
is empty, and all variables present in the input dataset idsn
will be renamed (unless ex_var
is not empty);ex_var
: (option) list of variables that should not be renamed; this parameter is incompatible with the parameter var
below;typically the identifying variables which will be used to perform the matching shall not be renamed; default: ex_var
is empty;suff
: (option) generic suffix to be added to the names of the variables; default: suff=_new
, i.e. a variable a
in idsn
will be renamed as a_new
;ilib
: (option) name of the input library; by default: empty, i.e. WORK
is used.odsn
: (option) name of the output dataset (stored in the olib
library), that will contain the exact same data than idsn
, where all variables defined by var
and/or excluding those defined by ex_var
are renamed as a concatenation of their former name and suff
; default: odsn=idsn
and the input dataset idsn
is updated instead;olib
: (option) name of the output library; by default: empty, and the value of ilib
is used.Let us consider test dataset #5 in WORKing directory:
f | e | d | c | b | a |
---|---|---|---|---|---|
. | 1 | 2 | 3 | . | 5 |
then both calls to the macro below:
will return the exact same dataset out1=out2
(in WORKing directory) below:
f | e | d2 | c2 | b | a2 |
---|---|---|---|---|---|
. | 1 | 2 | 3 | . | 5 |
Run macro %_example_var_rename
for more examples.
var_rename
can be used for this purpose.var
and ex_var
is passed, all variables present in the input dataset idsn
are renamed.