Cross table with three variables plus mean AND respective number of observations/cases

[Sam Peters]

Dear Statalist,

I want to do a cross table, which displays the means of the variable wage. The two other variables are sex and nationality. It works with:

table sex nationality, c(mean wage)

But I also want to display the respective obervations/cases for every combination of sex and nationality - accordingly per mean one number of observations/cases. How can I do this?

Up to now I display the respective number of cases with:

sum wage if nationality==1 & sex==0|sex==1
sum wage if nationality==2 & sex==0|sex==1
sum wage if nationality==3 & sex==0|sex==1
sum wage if nationality==1|nationality==2|nationality==3 & sex==0
sum wage if nationality==1|nationality==2|nationality==3 & sex==1

But I hope there's a more simple solution.

best regards,
Sam Peters

[Rich Goldstein]

if I understand you correctly you already have the basic idea - just add "freq wage" inside your "c()" - this will give you the number of people who have no missing values which, I assume, is what you want

[Marcos Almeida]

If I understood your query right, you may try this:

. sysuse auto
(1978 Automobile Data)

. tabulate rep78 foreign

    Repair |
    Record |       Car type
      1978 |  Domestic    Foreign |     Total
-----------+----------------------+----------
         1 |         2          0 |         2
         2 |         8          0 |         8
         3 |        27          3 |        30
         4 |         9          9 |        18
         5 |         2          9 |        11
-----------+----------------------+----------
     Total |        48         21 |        69


. tabulate rep78 foreign, summarize(mpg) mean

                          Means of Mileage (mpg)

    Repair |
    Record |      Car type
      1978 |  Domestic    Foreign |     Total
-----------+----------------------+----------
         1 |        21          . |        21
         2 |    19.125          . |    19.125
         3 |        19  23.333333 | 19.433333
         4 | 18.444444  24.888889 | 21.666667
         5 |        32  26.333333 | 27.363636
-----------+----------------------+----------
     Total | 19.541667  25.285714 | 21.289855

. tabulate rep78 foreign, summarize(mpg) freq mean

                  Means and Frequencies of Mileage (mpg)

    Repair |
    Record |      Car type
      1978 |  Domestic    Foreign |     Total
-----------+----------------------+----------
         1 |        21          . |        21
           |         2          0 |         2
-----------+----------------------+----------
         2 |    19.125          . |    19.125
           |         8          0 |         8
-----------+----------------------+----------
         3 |        19  23.333333 | 19.433333
           |        27          3 |        30
-----------+----------------------+----------
         4 | 18.444444  24.888889 | 21.666667
           |         9          9 |        18
-----------+----------------------+----------
         5 |        32  26.333333 | 27.363636
           |         2          9 |        11
-----------+----------------------+----------
     Total | 19.541667  25.285714 | 21.289855
           |        48         21 |        69

[Sam Peters]

Yes, exactly - it works! Thank you very much

Best regards,

Sam Peters

[Nick Cox]

Another way to approach this:

. sysuse auto, clear
(1978 Automobile Data)

. egen mean = mean(mpg), by(rep78 foreign) 

. format mean %2.1f 

. bysort rep78 foreign: gen count = _N 

. tabdisp rep78 foreign, c(count mean)

------------------------------
Repair    |
Record    |      Car type     
1978      | Domestic   Foreign
----------+-------------------
        1 |        2          
          |     21.0          
          | 
        2 |        8          
          |     19.1          
          | 
        3 |       27         3
          |     19.0      23.3
          | 
        4 |        9         9
          |     18.4      24.9
          | 
        5 |        2         9
          |     32.0      26.3
          | 
        . |        4         1
          |     23.3      14.0
------------------------------