Challenging layout modification using Tables/collect command

Paul Bergmann

Join Date: Nov 2014
Posts: 6

Challenging layout modification using Tables/collect command

30 Apr 2021, 12:32

I am attempting to use the new Tables/collect features of Stata 17.

I have 7 race variables encoded 0 "No" 1 "Yes". I have managed to create a table with a stacked layout that drops the "No" values for each race variable, as they are redundant.

However, I am having difficulty figuring out how to combine those race variables to appear as though they are levels under a single dimension. Is that even possible?

In other words, How do I go from this:

Code:

----------------------------------------------------------
                                       Frequency   Percent
----------------------------------------------------------

Age - [Mean (SD)]                          30.62    (5.26)
Age - [Min Max]                               15        60
Sex                                                       
  Female                                    1757    85.96%
  Male                                       124     6.07%
  Unknown                                    163     7.97%
Race: White                                               
  Yes                                        451    72.16%
Race: Black                                               
  Yes                                        110    17.60%
...

To this:

Code:

----------------------------------------------------------
                                       Frequency   Percent
----------------------------------------------------------

Age - [Mean (SD)]                          30.62    (5.26)
Age - [Min Max]                               15        60
Sex                                                       
  Female                                    1757    85.96%
  Male                                       124     6.07%
  Unknown                                    163     7.97%
Race                                              
  White                                      451    72.16%
  Black                                      110    17.60%
...

Subjects were able to endorse more than one race, so generating a new composite variable from the individual race variables is not an option.

Here is more info about the collection:

Code:

. collect dims

Collection dimensions
Collection: Table
-----------------------------------------
                   Dimension   No. levels
-----------------------------------------
Layout, style, header, label
                      cmdset   1         
                     colname   50        
                     command   1         
                      result   6         
                     statcmd   6         
                         var   50        

Header, label
                 resp_gender
             resp_race_white
             resp_race_black
             ...
             
Style only
                border_block   4         
                   cell_type   4         
-----------------------------------------

. collect levelsof var

Collection: Table
 Dimension: var
    Levels: resp_age resp_age_minmax
            0.resp_gender 1.resp_gender 2.resp_gender 0.resp_race_white
            1.resp_race_white 0.resp_race_black 1.resp_race_black ...

Tags: None

Rich Goldstein

Join Date: Mar 2014

Posts: 4258
#2

30 Apr 2021, 12:54

you say, "Subjects were able to endorse more than one race, so generating a new composite variable from the individual race variables is not an option." you could always make a category called "multi-racial" for those that endorsed more than 1 race and that would be an easy "solution" to your -collect tables- issue; as a byproduct, your numbers would now add to the correct total which they will not do if you double-count them as you seem to want to do
Comment
Paul Bergmann

Join Date: Nov 2014

Posts: 6
#3

02 May 2021, 12:51

Thank you for responding, Rich. I do not want to to go the "multi-racial" category route.

I would very much like to know if and how one might use collect to to manipulate the row headers to achieve the virtual race variable I describe in the OP. This is more a question of how to achieve a certain presentation than how to construct an actual variable. If anyone has had any success doing something like that with this very new -and cool- feature, I would love to see how it is done.
Comment

Jeff Pitblado (StataCorp)

StataCorp Employee

Join Date: Mar 2014
Posts: 549

14 May 2021, 10:29

Short answer:

You can collect recode the race indicators to be levels of a common race factor in the collection.

Long example:

I want to show how this can be done, so I simulated a dataset similar to Paul's.

Code:

* simulate some data
set seed 17
set obs 2044
gen age = runiformint(15,60)
label var age "Age"
gen sex = runiform() > .85
replace sex = 2 if runiform() > .95
label var sex "Sex"
label define sex 0 "Female" 1 "Male" 2 "Unknown"
label values sex sex
label define noyes 0 "No" 1 "Yes"
local k_x 7
forvalues i = 1/`k_x' {
    gen x`i' = runiform() > .5
    label values x`i' noyes
}

* compute statistics
table,    stat(mean age) stat(sd age) stat(min age) stat(max age) ///
    stat(fvfrequency sex) stat(fvpercent sex) ///
    stat(fvfrequency x*) stat(fvpercent x*)

* first remove the vertical border
collect style cell border_block, border(right, pattern(nil))
collect preview

* query dimensions in the layout
collect layout
collect label list result, all
collect label list var, all

* shorten some labels
collect label levels result ///
    fvfrequency "Freq." ///
    fvpercent "Percent" ///
    sd "SD" ///
    min "Min" ///
    max "max" ///
    , modify
collect preview

* change layout
collect layout (var) (result)

* split age values so we can stack them
collect recode var age = age_mu_sd, fortags(result[mean sd])
collect recode var age = age_range, fortags(result[min max])
collect preview

* add labels for the new var levels
collect label levels var ///
    age_mu_sd "Age - [Mean (SD)]" ///
    age_range "Age - [Min Max]"
collect preview

* realign columns
collect recode result ///
    mean = column1 ///
    sd = column2 ///
    min = column1 ///
    max = column2 ///
    fvfrequency = column1 ///
    fvpercent = column2
collect preview

* oops, let's clear the autolevels for result to get a table
collect style autolevels result, clear
collect preview

* label the new result levels
collect label levels result column1 "Frequency" column2 "Percent"
collect preview

* format the age results
collect style cell var[age_range], nformat(%5.0f)
collect style cell var[age_mu_sd], nformat(%5.2f)
collect style cell var[age_mu_sd]#result[column2], sformat("(%s)")
collect preview

* stack and unbind factor variables in the row headers
collect style row stack, nobinder
collect preview

* recode the Yes levels of the x# factor variables to a single factor
* variable
forvalues i = 1/`k_x' {
    collect recode var 1.x`i' = `i'.x 
}
collect preview

* update layout to select only the var levels we care about
collect layout (var[age_mu_sd age_range i.sex i.x]) (result)

* format the factor variable statistics
collect style cell var[sex x]#result[column2], nformat(%5.2f) sformat("%s%%")
collect preview

* label var x and its levels
collect label levels var x "Race"
forvalues i = 1/`k_x' {
    collect label levels x `i' "Race label `i'"
}
collect preview

* you can hide the column headers
collect style header result, title(hide) level(hide)
collect preview

Here is a log of the above:

Code:

. * simulate some data
. set seed 17

. set obs 2044
Number of observations (_N) was 0, now 2,044.

. gen age = runiformint(15,60)

. label var age "Age"

. gen sex = runiform() > .85

. replace sex = 2 if runiform() > .95
(110 real changes made)

. label var sex "Sex"

. label define sex 0 "Female" 1 "Male" 2 "Unknown"

. label values sex sex

. label define noyes 0 "No" 1 "Yes"

. local k_x 7

. forvalues i = 1/`k_x' {
  2.         gen x`i' = runiform() > .5
  3.         label values x`i' noyes
  4. }

. 
. * compute statistics
. table,  stat(mean age) stat(sd age) stat(min age) stat(max age) ///
>         stat(fvfrequency sex) stat(fvpercent sex) ///
>         stat(fvfrequency x*) stat(fvpercent x*)

-------------------------------------
Mean                      |          
  Age                     |  37.63796
Standard deviation        |          
  Age                     |  13.03153
Minimum value             |          
  Age                     |        15
Maximum value             |          
  Age                     |        60
Factor variable frequency |          
  Sex=Female              |     1,621
  Sex=Male                |       313
  Sex=Unknown             |       110
  x1=No                   |     1,053
  x1=Yes                  |       991
  x2=No                   |     1,027
  x2=Yes                  |     1,017
  x3=No                   |     1,027
  x3=Yes                  |     1,017
  x4=No                   |     1,042
  x4=Yes                  |     1,002
  x5=No                   |     1,021
  x5=Yes                  |     1,023
  x6=No                   |     1,007
  x6=Yes                  |     1,037
  x7=No                   |     1,010
  x7=Yes                  |     1,034
Factor variable percent   |          
  Sex=Female              |     79.31
  Sex=Male                |     15.31
  Sex=Unknown             |      5.38
  x1=No                   |     51.52
  x1=Yes                  |     48.48
  x2=No                   |     50.24
  x2=Yes                  |     49.76
  x3=No                   |     50.24
  x3=Yes                  |     49.76
  x4=No                   |     50.98
  x4=Yes                  |     49.02
  x5=No                   |     49.95
  x5=Yes                  |     50.05
  x6=No                   |     49.27
  x6=Yes                  |     50.73
  x7=No                   |     49.41
  x7=Yes                  |     50.59
-------------------------------------

. 
. * first remove the vertical border
. collect style cell border_block, border(right, pattern(nil))

. collect preview

-----------------------------------
Mean                               
  Age                      37.63796
Standard deviation                 
  Age                      13.03153
Minimum value                      
  Age                            15
Maximum value                      
  Age                            60
Factor variable frequency          
  Sex=Female                  1,621
  Sex=Male                      313
  Sex=Unknown                   110
  x1=No                       1,053
  x1=Yes                        991
  x2=No                       1,027
  x2=Yes                      1,017
  x3=No                       1,027
  x3=Yes                      1,017
  x4=No                       1,042
  x4=Yes                      1,002
  x5=No                       1,021
  x5=Yes                      1,023
  x6=No                       1,007
  x6=Yes                      1,037
  x7=No                       1,010
  x7=Yes                      1,034
Factor variable percent            
  Sex=Female                  79.31
  Sex=Male                    15.31
  Sex=Unknown                  5.38
  x1=No                       51.52
  x1=Yes                      48.48
  x2=No                       50.24
  x2=Yes                      49.76
  x3=No                       50.24
  x3=Yes                      49.76
  x4=No                       50.98
  x4=Yes                      49.02
  x5=No                       49.95
  x5=Yes                      50.05
  x6=No                       49.27
  x6=Yes                      50.73
  x7=No                       49.41
  x7=Yes                      50.59
-----------------------------------

. 
. * query dimensions in the layout
. collect layout

Collection: Table
      Rows: result#var
   Table 1: 44 x 1

-----------------------------------
Mean                               
  Age                      37.63796
Standard deviation                 
  Age                      13.03153
Minimum value                      
  Age                            15
Maximum value                      
  Age                            60
Factor variable frequency          
  Sex=Female                  1,621
  Sex=Male                      313
  Sex=Unknown                   110
  x1=No                       1,053
  x1=Yes                        991
  x2=No                       1,027
  x2=Yes                      1,017
  x3=No                       1,027
  x3=Yes                      1,017
  x4=No                       1,042
  x4=Yes                      1,002
  x5=No                       1,021
  x5=Yes                      1,023
  x6=No                       1,007
  x6=Yes                      1,037
  x7=No                       1,010
  x7=Yes                      1,034
Factor variable percent            
  Sex=Female                  79.31
  Sex=Male                    15.31
  Sex=Unknown                  5.38
  x1=No                       51.52
  x1=Yes                      48.48
  x2=No                       50.24
  x2=Yes                      49.76
  x3=No                       50.24
  x3=Yes                      49.76
  x4=No                       50.98
  x4=Yes                      49.02
  x5=No                       49.95
  x5=Yes                      50.05
  x6=No                       49.27
  x6=Yes                      50.73
  x7=No                       49.41
  x7=Yes                      50.59
-----------------------------------

. collect label list result, all

  Collection: Table
   Dimension: result
       Label: Result
Level labels:
 fvfrequency  Factor variable frequency
   fvpercent  Factor variable percent
         max  Maximum value
        mean  Mean
         min  Minimum value
          sd  Standard deviation

. collect label list var, all

  Collection: Table
   Dimension: var
       Label: Statistic option variable
Level labels:
         age  Age
         sex  Sex
          x1  
          x2  
          x3  
          x4  
          x5  
          x6  
          x7  

. 
. * shorten some labels
. collect label levels result ///
>         fvfrequency "Freq." ///
>         fvpercent "Percent" ///
>         sd "SD" ///
>         min "Min" ///
>         max "max" ///
>         , modify

. collect preview

-----------------------
Mean                   
  Age          37.63796
SD                     
  Age          13.03153
Min                    
  Age                15
max                    
  Age                60
Freq.                  
  Sex=Female      1,621
  Sex=Male          313
  Sex=Unknown       110
  x1=No           1,053
  x1=Yes            991
  x2=No           1,027
  x2=Yes          1,017
  x3=No           1,027
  x3=Yes          1,017
  x4=No           1,042
  x4=Yes          1,002
  x5=No           1,021
  x5=Yes          1,023
  x6=No           1,007
  x6=Yes          1,037
  x7=No           1,010
  x7=Yes          1,034
Percent                
  Sex=Female      79.31
  Sex=Male        15.31
  Sex=Unknown      5.38
  x1=No           51.52
  x1=Yes          48.48
  x2=No           50.24
  x2=Yes          49.76
  x3=No           50.24
  x3=Yes          49.76
  x4=No           50.98
  x4=Yes          49.02
  x5=No           49.95
  x5=Yes          50.05
  x6=No           49.27
  x6=Yes          50.73
  x7=No           49.41
  x7=Yes          50.59
-----------------------

. 
. * change layout
. collect layout (var) (result)

Collection: Table
      Rows: var
   Columns: result
   Table 1: 18 x 6

--------------------------------------------------------------
                 Mean         SD   Min   max   Freq.   Percent
--------------------------------------------------------------
Age          37.63796   13.03153    15    60                  
Sex=Female                                     1,621     79.31
Sex=Male                                         313     15.31
Sex=Unknown                                      110      5.38
x1=No                                          1,053     51.52
x1=Yes                                           991     48.48
x2=No                                          1,027     50.24
x2=Yes                                         1,017     49.76
x3=No                                          1,027     50.24
x3=Yes                                         1,017     49.76
x4=No                                          1,042     50.98
x4=Yes                                         1,002     49.02
x5=No                                          1,021     49.95
x5=Yes                                         1,023     50.05
x6=No                                          1,007     49.27
x6=Yes                                         1,037     50.73
x7=No                                          1,010     49.41
x7=Yes                                         1,034     50.59
--------------------------------------------------------------

. 
. * split age values so we can stack them
. collect recode var age = age_mu_sd, fortags(result[mean sd])
(2 items recoded in collection Table)

. collect recode var age = age_range, fortags(result[min max])
(2 items recoded in collection Table)

. collect preview

--------------------------------------------------------------
                 Mean         SD   Min   max   Freq.   Percent
--------------------------------------------------------------
age_mu_sd    37.63796   13.03153                              
age_range                           15    60                  
Sex=Female                                     1,621     79.31
Sex=Male                                         313     15.31
Sex=Unknown                                      110      5.38
x1=No                                          1,053     51.52
x1=Yes                                           991     48.48
x2=No                                          1,027     50.24
x2=Yes                                         1,017     49.76
x3=No                                          1,027     50.24
x3=Yes                                         1,017     49.76
x4=No                                          1,042     50.98
x4=Yes                                         1,002     49.02
x5=No                                          1,021     49.95
x5=Yes                                         1,023     50.05
x6=No                                          1,007     49.27
x6=Yes                                         1,037     50.73
x7=No                                          1,010     49.41
x7=Yes                                         1,034     50.59
--------------------------------------------------------------

. 
. * add labels for the new var levels
. collect label levels var ///
>         age_mu_sd "Age - [Mean (SD)]" ///
>         age_range "Age - [Min Max]"

. collect preview

--------------------------------------------------------------------
                       Mean         SD   Min   max   Freq.   Percent
--------------------------------------------------------------------
Age - [Mean (SD)]  37.63796   13.03153                              
Age - [Min Max]                           15    60                  
Sex=Female                                           1,621     79.31
Sex=Male                                               313     15.31
Sex=Unknown                                            110      5.38
x1=No                                                1,053     51.52
x1=Yes                                                 991     48.48
x2=No                                                1,027     50.24
x2=Yes                                               1,017     49.76
x3=No                                                1,027     50.24
x3=Yes                                               1,017     49.76
x4=No                                                1,042     50.98
x4=Yes                                               1,002     49.02
x5=No                                                1,021     49.95
x5=Yes                                               1,023     50.05
x6=No                                                1,007     49.27
x6=Yes                                               1,037     50.73
x7=No                                                1,010     49.41
x7=Yes                                               1,034     50.59
--------------------------------------------------------------------

. 
. * realign columns
. collect recode result ///
>         mean = column1 ///
>         sd = column2 ///
>         min = column1 ///
>         max = column2 ///
>         fvfrequency = column1 ///
>         fvpercent = column2
(38 items recoded in collection Table)

. collect preview

Your layout specification does not identify any items.

. 
. * oops, let's clear the autolevels for result to get a table
. collect style autolevels result, clear

. collect preview

--------------------------------------
                    column1    column2
--------------------------------------
Age - [Mean (SD)]  37.63796   13.03153
Age - [Min Max]          15         60
Sex=Female             1621   79.30528
Sex=Male                313   15.31311
Sex=Unknown             110   5.381605
x1=No                  1053   51.51663
x1=Yes                  991   48.48337
x2=No                  1027   50.24462
x2=Yes                 1017   49.75538
x3=No                  1027   50.24462
x3=Yes                 1017   49.75538
x4=No                  1042   50.97847
x4=Yes                 1002   49.02153
x5=No                  1021   49.95108
x5=Yes                 1023   50.04892
x6=No                  1007   49.26614
x6=Yes                 1037   50.73386
x7=No                  1010   49.41292
x7=Yes                 1034   50.58708
--------------------------------------

. 
. * label the new result levels
. collect label levels result column1 "Frequency" column2 "Percent"

. collect preview

---------------------------------------
                   Frequency    Percent
---------------------------------------
Age - [Mean (SD)]   37.63796   13.03153
Age - [Min Max]           15         60
Sex=Female              1621   79.30528
Sex=Male                 313   15.31311
Sex=Unknown              110   5.381605
x1=No                   1053   51.51663
x1=Yes                   991   48.48337
x2=No                   1027   50.24462
x2=Yes                  1017   49.75538
x3=No                   1027   50.24462
x3=Yes                  1017   49.75538
x4=No                   1042   50.97847
x4=Yes                  1002   49.02153
x5=No                   1021   49.95108
x5=Yes                  1023   50.04892
x6=No                   1007   49.26614
x6=Yes                  1037   50.73386
x7=No                   1010   49.41292
x7=Yes                  1034   50.58708
---------------------------------------

. 
. * format the age results
. collect style cell var[age_range], nformat(%5.0f)

. collect style cell var[age_mu_sd], nformat(%5.2f)

. collect style cell var[age_mu_sd]#result[column2], sformat("(%s)")

. collect preview

---------------------------------------
                   Frequency    Percent
---------------------------------------
Age - [Mean (SD)]      37.64    (13.03)
Age - [Min Max]           15         60
Sex=Female              1621   79.30528
Sex=Male                 313   15.31311
Sex=Unknown              110   5.381605
x1=No                   1053   51.51663
x1=Yes                   991   48.48337
x2=No                   1027   50.24462
x2=Yes                  1017   49.75538
x3=No                   1027   50.24462
x3=Yes                  1017   49.75538
x4=No                   1042   50.97847
x4=Yes                  1002   49.02153
x5=No                   1021   49.95108
x5=Yes                  1023   50.04892
x6=No                   1007   49.26614
x6=Yes                  1037   50.73386
x7=No                   1010   49.41292
x7=Yes                  1034   50.58708
---------------------------------------

. 
. * stack and unbind factor variables in the row headers
. collect style row stack, nobinder

. collect preview

---------------------------------------
                   Frequency    Percent
---------------------------------------
Age - [Mean (SD)]      37.64    (13.03)
Age - [Min Max]           15         60
Sex                                    
  Female                1621   79.30528
  Male                   313   15.31311
  Unknown                110   5.381605
x1                                     
  No                    1053   51.51663
  Yes                    991   48.48337
x2                                     
  No                    1027   50.24462
  Yes                   1017   49.75538
x3                                     
  No                    1027   50.24462
  Yes                   1017   49.75538
x4                                     
  No                    1042   50.97847
  Yes                   1002   49.02153
x5                                     
  No                    1021   49.95108
  Yes                   1023   50.04892
x6                                     
  No                    1007   49.26614
  Yes                   1037   50.73386
x7                                     
  No                    1010   49.41292
  Yes                   1034   50.58708
---------------------------------------

. 
. * recode the Yes levels of the x# factor variables to a single factor
. * variable
. forvalues i = 1/`k_x' {
  2.         collect recode var 1.x`i' = `i'.x 
  3. }
(2 items recoded in collection Table)
(2 items recoded in collection Table)
(2 items recoded in collection Table)
(2 items recoded in collection Table)
(2 items recoded in collection Table)
(2 items recoded in collection Table)
(2 items recoded in collection Table)

. collect preview

---------------------------------------
                   Frequency    Percent
---------------------------------------
Age - [Mean (SD)]      37.64    (13.03)
Age - [Min Max]           15         60
Sex                                    
  Female                1621   79.30528
  Male                   313   15.31311
  Unknown                110   5.381605
x                                      
  1                      991   48.48337
x1                                     
  No                    1053   51.51663
x                                      
  2                     1017   49.75538
x2                                     
  No                    1027   50.24462
x                                      
  3                     1017   49.75538
x3                                     
  No                    1027   50.24462
x                                      
  4                     1002   49.02153
x4                                     
  No                    1042   50.97847
x                                      
  5                     1023   50.04892
x5                                     
  No                    1021   49.95108
x                                      
  6                     1037   50.73386
x6                                     
  No                    1007   49.26614
x                                      
  7                     1034   50.58708
x7                                     
  No                    1010   49.41292
---------------------------------------

. 
. * update layout to select only the var levels we care about
. collect layout (var[age_mu_sd age_range i.sex i.x]) (result)

Collection: Table
      Rows: var[age_mu_sd age_range i.sex i.x]
   Columns: result
   Table 1: 14 x 2

---------------------------------------
                   Frequency    Percent
---------------------------------------
Age - [Mean (SD)]      37.64    (13.03)
Age - [Min Max]           15         60
Sex                                    
  Female                1621   79.30528
  Male                   313   15.31311
  Unknown                110   5.381605
x                                      
  1                      991   48.48337
  2                     1017   49.75538
  3                     1017   49.75538
  4                     1002   49.02153
  5                     1023   50.04892
  6                     1037   50.73386
  7                     1034   50.58708
---------------------------------------

. 
. * format the factor variable statistics
. collect style cell var[sex x]#result[column2], nformat(%5.2f) sformat("%s%%")

. collect preview

--------------------------------------
                   Frequency   Percent
--------------------------------------
Age - [Mean (SD)]      37.64   (13.03)
Age - [Min Max]           15        60
Sex                                   
  Female                1621    79.31%
  Male                   313    15.31%
  Unknown                110     5.38%
x                                     
  1                      991    48.48%
  2                     1017    49.76%
  3                     1017    49.76%
  4                     1002    49.02%
  5                     1023    50.05%
  6                     1037    50.73%
  7                     1034    50.59%
--------------------------------------

. 
. * label var x and its levels
. collect label levels var x "Race"

. forvalues i = 1/`k_x' {
  2.         collect label levels x `i' "Race label `i'"
  3. }

. collect preview

--------------------------------------
                   Frequency   Percent
--------------------------------------
Age - [Mean (SD)]      37.64   (13.03)
Age - [Min Max]           15        60
Sex                                   
  Female                1621    79.31%
  Male                   313    15.31%
  Unknown                110     5.38%
Race                                  
  Race label 1           991    48.48%
  Race label 2          1017    49.76%
  Race label 3          1017    49.76%
  Race label 4          1002    49.02%
  Race label 5          1023    50.05%
  Race label 6          1037    50.73%
  Race label 7          1034    50.59%
--------------------------------------

. 
. * you can hide the column headers
. collect style header result, title(hide) level(hide)

. collect preview

----------------------------------
Age - [Mean (SD)]  37.64   (13.03)
Age - [Min Max]       15        60
Sex                               
  Female            1621    79.31%
  Male               313    15.31%
  Unknown            110     5.38%
Race                              
  Race label 1       991    48.48%
  Race label 2      1017    49.76%
  Race label 3      1017    49.76%
  Race label 4      1002    49.02%
  Race label 5      1023    50.05%
  Race label 6      1037    50.73%
  Race label 7      1034    50.59%
----------------------------------

Comment

Paul Bergmann

Join Date: Nov 2014

Posts: 6
#5

17 May 2021, 06:02

Thank you, Jeff! This is exactly what I need.
Comment
ATTOM Ibrahim

Join Date: Nov 2023

Posts: 2
#6

19 Nov 2023, 21:22

Hi every One,

Please can someone help me. I want to avoid automaticaly some levels of dimension in "collect layout command" after a table commande, rather thnan list all dimension's levels needed. Below an illustration!
Comment

ATTOM Ibrahim

Join Date: Nov 2023
Posts: 2

19 Nov 2023, 21:25

HTML Code:

capture log close
webuse nhanes2l
table region sex

. table region sex

----------------------------------
        |            Sex          
        |   Male   Female    Total
--------+-------------------------
Region  |                         
  NE    |  1.018    1.078    2.096
  MW    |  1.310    1.464    2.774
  S     |  1.332    1.521    2.853
  W     |  1.255    1.373    2.628
  Total |  4.915    5.436   10.351
----------------------------------

. collect dims

Collection dimensions
Collection: Table
-----------------------------------------
                   Dimension   No. levels
-----------------------------------------
Layout, style, header, label
                      cmdset   1         
                     command   1         
                      region   5         
                      result   1         
                         sex   3         
                     statcmd   1         

Style only
                border_block   4         
                   cell_type   4         
-----------------------------------------

collect label list region, all

  Collection: Table
   Dimension: region
       Label: Region
Level labels:
          .m  Total
           1  NE
           2  MW
           3  S
           4  W


*# I want somthing to avoid automticaly (".m total") in dimension region when typind 
*# "collect layout (region) (sex). I do not want to type, it seams painful with a dimension containing more levels, 20 for example :
 collect layout (region&#91;1 2 3 4&#93;) (sex)
 
*# Thank you in advance!

Collection: Table
      Rows: region&#91;1 2 3 4&#93;
   Columns: sex
   Table 1: 5 x 3

-------------------------------
       |           Sex         
       |  Male   Female   Total
-------+-----------------------
Region |                       
  NE   |  1018     1078    2096
  MW   |  1310     1464    2774
  S    |  1332     1521    2853
  W    |  1255     1373    2628
-------------------------------

*# Thank you in advance!

Last edited by ATTOM Ibrahim; 19 Nov 2023, 21:41.

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Challenging layout modification using Tables/collect command

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