Table-related commands in STATA

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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