Table-related commands in STATA

Abbreviations of commands

table	(no abbv.)
tabulate	ta tab
tabstat	(no abbv.)
summarize	su

Differences between table, tabulate, tabstat, summarize

	one-way	two-way	options
table	table v1 create a one-way table of v1 with simple frequency	table v1 v2 create a two-way table of v1 in row† and v2 in column†	,statistics( )
tabulate	tabulate v1 create a one-way table of v1 with detailed frequency	tabulate v1 v2 create a two-way table with v1 in row† and v2 in column†	,chi2 Pearson's chi-squared test; *only for two-way ,summarize(v3) detailed statistics for v3
tabstat	tabstat v1 create a one-way table of v1 with detailed statistics	*no two- or multiple-way table	,statistics( ) ,by(v3) detailed statistics for each of v3
summarize	summarize v1 detailed statistics of v1	*no two- or multiple-way summary	,detail

† row = transverse direction, column = longitudinal direction

Sample data

Suppose we have such a dataset in STATA.

Where,

id	discrete	:Identification number
sex	binary	:Male=0, Female=1
data1	continuous	:Results of a certain test
factorA, B, C	binary	:Negative=0, Positive=1
SES	categorical	:Categories of Socio-Economic Status, divided into four
disease	binary	:Free from a certain disease=0, Having the disease=1

One-way

Summary of sex, a binary variable

table	table sex	Both reports frequency but tabulate is more detailed
tabulate	tabulate sex
tabstat	tabstat sex	Both reports mean but summarize is more detailed
summarize	summarize sex

Summary of data1, a continuous variable

table	table data1	Both reports frequency of each value, which does not make sense
tabulate	tabulate data1
tabstat	tabstat data1	Both reports mean but summarize is more detailed
summarize	summarize data1

Summary of SES, a categorical variable

table	table SES	Both reports frequency but tabulate is more detailed
tabulate	tabulate SES
tabstat	tabstat SES	Both reports mean but summarize is more detailed
summarize	summarize SES

One-way, multiple

table	*Both do not create one-way multiple table
tabulate
tabstat	tabstat sex data1 SES	Reports mean in row (transverse) direction
summarize	summarize sex data1 SES	Reports more details in column (longitudinal) direction

Two-way

Summary of factorA based on sex

table	table sex factorA	Both creates the same table but tabulate is better visualized
tabulate	tabulate sex factorA
tabstat	tabstat factorA, by(sex)	Both reports mean but summarize is more detailed; needs bysort option before the command
summarize	bysort sex: summarize factorA

Summary of sex based on factorA

table	table factorA sex	Both creates the same table but tabulate is better visualized
tabulate	tabulate factorA sex
tabstat	tabstat sex, by(factorA)	Both reports mean but summarize is more detailed; needs bysort option before the command
summarize	bysort factorA: summarize sex

Summary of data1 based on disease

table	*Both do not create a meaningful table for continuous variable
tabulate
tabstat	tabstat data, by(disease)	Both reports mean but summarize is more detailed; needs bysort option before the command
summarize	bysort disease: summarize data1

Two-way with proportions

Summary of factorA based on sex with proportions

table	table sex factorA, statistic(percent)	This calculates proportions of cells compared to the whole without showing raw values
	table sex factorA, statistic(percent, across(sex))	This calculates proportions in column (longitudinal) directions without showing raw values
	tale sex factorA, statistic(percent, across(factorA))	This calculates proportions in row (transverse) directions without showing raw values
tabulate	tabulate sex factorA, column	This calculates proportions in column (longitudinal) directions
	tabulate sex factorA, row	This calculates proportions in row (transverse) directions

Two-way, multiple

Summary of factorA, factorB, factorC based on disease

tabstat	tabstat factorA factorB factorC, by(disease)
	tabstat factorA factorB factorC, by(disease) statistic(sum)	factorA,B,C are binary variables so summations of values provide the positivities of factorA,B,C
	tabstat factorA factorB factorC, by(disease) statistic(n)	statistic(n) (statistic(count) is the same) only counts observations with real values, which only tell non-missing observations

Summary of factorA, factorB, factorC based on SES

tabstat	tabstat factorA factorB factorC, by(SES)
	tabstat factorA factorB factorC, by(SES) statistic(sum)	factorA,B,C are binary variables so summations of values provide the positivities of factorA,B,C
	tabstat factorA factorB factorC, by(SES) statistic(n)	statistic(n) (statistic(count) is the same) only counts observations with real values, which only tell non-missing observations

Two-way of binary/categorical plus summary of continuous

Summary of data1 based on disease and SES

tabulate	tabulate disease SES, summarize(data1)	This tells means, SDs and frequencies of a continuous variable divided in two-way of binary/categorical variables