15.7.4 Category Label Positions

/CLABELS {AUTO _|_ {ROWLABELS _|_ COLLABELS}={OPPOSITE _|_ LAYER}}

The CLABELS subcommand controls the position of category labels for the TABLE subcommand that it follows. By default, or if AUTO is specified, category labels for a given variable nest inside the variable’s label on the same axis. For example, the command below results in age categories nesting within the age group variable on the rows axis and gender categories within the gender variable on the columns axis:

CTABLES /TABLE ageGroup BY gender.

		Male	Female
		S3a. GENDER:
		Count	Count
Age group	15 or younger	0	0
16 to 25	594	505
26 to 35	476	491
36 to 45	489	548
46 to 55	526	649
56 to 65	516	731
66 or older	531	943

ROWLABELS=OPPOSITE or COLLABELS=OPPOSITE move row or column variable category labels, respectively, to the opposite axis. The setting affects only the innermost variable or variables, which must be categorical, on the given axis. For example:

CTABLES /TABLE ageGroup BY gender /CLABELS ROWLABELS=OPPOSITE.
CTABLES /TABLE ageGroup BY gender /CLABELS COLLABELS=OPPOSITE.

	15 or younger	16 to 25	26 to 35	36 to 45	46 to 55	56 to 65	66 or older	15 or younger	16 to 25	26 to 35	36 to 45	46 to 55	56 to 65	66 or older
	S3a. GENDER:
	Male	Female
	Count	Count	Count	Count	Count	Count	Count	Count	Count	Count	Count	Count	Count	Count
Age group	0	594	476	489	526	516	531	0	505	491	548	649	731	943
			S3a. GENDER:
			Count
Age group	15 or younger	Male	0
Female	0
16 to 25	Male	594
Female	505
26 to 35	Male	476
Female	491
36 to 45	Male	489
Female	548
46 to 55	Male	526
Female	649
56 to 65	Male	516
Female	731
66 or older	Male	531
Female	943

ROWLABELS=LAYER or COLLABELS=LAYER move the innermost row or column variable category labels, respectively, to the layer axis.

Only one axis’s labels may be moved, whether to the opposite axis or to the layer axis.

Effect on Summary Statistics

CLABELS primarily affects the appearance of tables, not the data displayed in them. However, CTABLES can affect the values displayed for statistics that summarize areas of a table, since it can change the definitions of these areas.

For example, consider the following syntax and output:

CTABLES /TABLE ageGroup BY gender [ROWPCT, COLPCT].

		Row %	Column %	Row %	Column %
		S3a. GENDER:
		Male	Female
Age group	15 or younger	.	.0%	.	.0%
16 to 25	54.0%	19.0%	46.0%	13.1%
26 to 35	49.2%	15.2%	50.8%	12.7%
36 to 45	47.2%	15.6%	52.8%	14.2%
46 to 55	44.8%	16.8%	55.2%	16.8%
56 to 65	41.4%	16.5%	58.6%	18.9%
66 or older	36.0%	17.0%	64.0%	24.4%

Using COLLABELS=OPPOSITE changes the definitions of rows and columns, so that column percentages display what were previously row percentages and the new row percentages become meaningless (because there is only one cell per row):

CTABLES
    /TABLE ageGroup BY gender [ROWPCT, COLPCT]
    /CLABELS COLLABELS=OPPOSITE.

			Row %	Column %
			S3a. GENDER:
Age group	15 or younger	Male	.	.
Female	.	.
16 to 25	Male	100.0%	54.0%
Female	100.0%	46.0%
26 to 35	Male	100.0%	49.2%
Female	100.0%	50.8%
36 to 45	Male	100.0%	47.2%
Female	100.0%	52.8%
46 to 55	Male	100.0%	44.8%
Female	100.0%	55.2%
56 to 65	Male	100.0%	41.4%
Female	100.0%	58.6%
66 or older	Male	100.0%	36.0%
Female	100.0%	64.0%

Moving Categories for Stacked Variables

If CLABELS moves category labels from an axis with stacked variables, the variables that are moved must have the same category specifications (see CTABLES Per-Variable Category Options) and the same value labels.

The following shows both moving stacked category variables and adapting to the changing definitions of rows and columns:

CTABLES /TABLE (likelihoodOfBeingStoppedByPolice
                + likelihoodOfHavingAnAccident) [COLPCT].
CTABLES /TABLE (likelihoodOfBeingStoppedByPolice
                + likelihoodOfHavingAnAccident) [ROWPCT]
  /CLABELS ROW=OPPOSITE.

	Almost certain	Very likely	Somewhat likely	Somewhat unlikely	Very unlikely
		Column %
105b. How likely is it that drivers who have had too much to drink to drive safely will A. Get stopped by the police?	Almost certain	10.2%
Very likely	21.8%
Somewhat likely	40.2%
Somewhat unlikely	19.0%
Very unlikely	8.9%
105b. How likely is it that drivers who have had too much to drink to drive safely will B. Have an accident?	Almost certain	15.9%
Very likely	40.8%
Somewhat likely	35.0%
Somewhat unlikely	6.2%
Very unlikely	2.0%
	Row %	Row %	Row %	Row %	Row %
105b. How likely is it that drivers who have had too much to drink to drive safely will A. Get stopped by the police?	10.2%	21.8%	40.2%	19.0%	8.9%
105b. How likely is it that drivers who have had too much to drink to drive safely will B. Have an accident?	15.9%	40.8%	35.0%	6.2%	2.0%