Using asdoc & tabstat to compute means by multiple groupvars

[Mattia Gatti]

Dear all,

I would like to create nice descriptive statistics with asdoc and tabstat commands. I would like to compute the means for different variables and groups in my dataset. Here is my dataset

* Example generated by -dataex-. For more info, type help dataex
clear
input long country float idno str15 party float(vote dist_lr dist_red dist_immpoor) byte gender float age
 3  3 "AfD"    0 4.2380953         6       9.9 0 2
 3  3 "SPD"    0 1.3809524 3.0526316      4.15 0 2
 3  3 "CDU"    1  .8571429  5.947369         6 0 2
 3  3 "LINKE"  0 3.5714285  .8947368       2.7 0 2
 3  3 "FDP"    0 1.4285712  8.578947      5.95 0 2
 3  3 "GRUNEN" 0 1.7619047  3.421053       1.7 0 2
10  6 "GP"     0  1.857143        .5  3.333333 1 1
10  6 "FF"     0  .4285712  2.357143  1.833333 1 1
10  6 "Lab"    0 1.4285715  .7857144  2.833333 1 1
10  6 "SF"     0 2.4285715  .2142856  2.333333 1 1
10  6 "FG"     1  .8571429  3.357143         2 1 1
 6  7 "SFP"    0         0 2.2142859 1.0476189 1 3
 6  7 "SDP"    0  2.857143  .7857143  .3809524 1 3
 6  7 "VIHR"   0 2.2857144  .3571429 2.0476189 1 3
 6  7 "KESK"   0 .14285707  2.428571 2.2380955 1 3
 6  7 "KD"     0  .8571429 2.2857141 2.4358976 1 3
 6  7 "PS"     0 1.5714288  2.785714  6.452381 1 3
 6  7 "KOK"    1  1.714286  4.357143  1.095238 1 3
 6  7 "VAS"    0       4.5 1.4285715 1.1190476 1 3
 7  9 "PS"     1         1      2.25 .58333325 1 3
 7  9 "LR"     0     5.875     7.375  4.541667 1 3
 7  9 "LREM"   0 4.3333335  5.833333 2.3333333 1 3
 7  9 "MoDem"  0     4.125  5.857143 2.0416667 1 3
 7  9 "FI"     0       .75      .625  .6666667 1 3
 7  9 "EELV"   0        .5     2.375 1.7083333 1 3
 7  9 "RN"     0      7.75         4  6.541667 1 3
 8 10 "LIBDEM" 0 .23529434  .9705882  .7450979 1 1
 8 10 "UKIP"   0    4.6875 4.5666666  6.137256 1 1
 8 10 "LAB"    0 2.0588236  .9117647 .19607854 1 1
 8 10 "SNP"    0        .5      .625         1 1 1
 8 10 "GREEN"  0         2      .625  1.627451 1 1
 8 10 "CONS"   1  3.117647  4.147059  4.254902 1 1
12 11 "VVD"    0 2.6153846       .75 4.5897436 0 1
12 11 "D66"    1 .15384626      1.75 .33333325 0 1
12 11 "SP"     0 3.6153846      6.75 1.9166667 0 1
12 11 "PVV"    0 3.6923075 1.6666665  6.589744 0 1
12 11 "PvdA"   0 1.3846154  4.666667  .8333333 0 1
12 11 "CDA"    0 1.8461537       1.5  3.666667 0 1
12 11 "CU"     0  .0769229  3.318182 1.1282051 0 1
12 11 "GL"     0  2.692308  5.318182 1.7179487 0 1
 1 13 "SPO"    0         5       2.4 1.3666666 1 1
 1 13 "OVP"    0       2.1       6.2  5.266667 1 1
 1 13 "Grune"  0       6.5       2.5  .6333332 1 1
 1 13 "FPO"    1 .10000038       5.6  6.466667 1 1
 1 13 "NEOS"   0       3.1       6.9  .9666669 1 1
11 14 "FdI"    0 2.0526314  3.029412 3.1754384 1 0
11 14 "LN"     0 1.7894735   4.81579  3.280702 1 0
11 14 "FI"     1 .05263138  4.388889  .3333335 1 0
11 14 "PD"     0  3.789474 .22222233  3.614035 1 0
11 14 "M5S"    0 2.2222223  .6052631 .11111116 1 0
11 14 "SI"     0  5.555555       1.5  5.979167 1 0
 7 17 "RN"     0      2.75         1 3.2083335 0 0
 7 17 "PS"     0         4      2.75 3.9166665 0 0
 7 17 "EELV"   0       4.5     2.625  5.041667 0 0
 7 17 "FI"     0      5.75     4.375 2.6666665 0 0
 7 17 "LREM"   0  .6666665  .8333335         1 0 0
 7 17 "MoDem"  0      .875  .8571429 1.2916665 0 0
 7 17 "LR"     1      .875     2.375 1.2083335 0 0
 6 21 "VIHR"   0 1.2857144  .3571429  5.380952 1 0
 6 21 "KOK"    1  2.714286  4.357143 2.2380953 1 0
 6 21 "PS"     0  2.571429  2.785714 3.1190476 1 0
 6 21 "SFP"    0         1 2.2142859  4.380952 1 0
 6 21 "VAS"    0       3.5 1.4285715  4.452381 1 0
 6 21 "SDP"    0  1.857143  .7857143  2.952381 1 0
 6 21 "KESK"   0  .8571429  2.428571 1.0952377 1 0
 6 21 "KD"     0  1.857143 2.2857141  .8974357 1 0
11 22 "FdI"    0  7.052631  3.029412  9.842105 0 0
11 22 "LN"     0  6.789474   4.81579  9.947369 0 0
11 22 "SI"     0  .5555556       1.5     .6875 0 0
11 22 "M5S"    0  2.777778  .6052631  6.555555 0 0
11 22 "PD"     1 1.2105262 .22222233 3.0526316 0 0
11 22 "FI"     0  4.947369  4.388889         7 0 0
 3 23 "CDU"    0  2.857143  5.947369         6 0 3
 3 23 "LINKE"  0 1.5714285  .8947368       2.7 0 3
 3 23 "AfD"    0  6.238095         6       9.9 0 3
 3 23 "GRUNEN" 0  .2380953  3.421053       1.7 0 3
 3 23 "SPD"    1  .6190476 3.0526316      4.15 0 3
 3 23 "FDP"    0  3.428571  8.578947      5.95 0 3
 3 24 "SPD"    1 1.6190476  .5526316  .8166668 1 2
 3 24 "GRUNEN" 0 1.2380953  .9210527 1.6333332 1 2
 3 24 "AfD"    0  7.238095       3.5  6.566667 1 2
 3 24 "FDP"    0  4.428571  6.078947 2.6166666 1 2
 3 24 "LINKE"  0 .57142854 1.6052632  .6333332 1 2
 3 24 "CDU"    0  3.857143 3.4473686  2.666667 1 2
10 25 "GP"     0  3.142857         3  3.333333 0 1
10 25 "SF"     0 2.5714285 2.2857144  2.333333 0 1
10 25 "FG"     0  5.857143  5.857143         2 0 1
10 25 "FF"     1  5.428571  4.857143  1.833333 0 1
10 25 "Lab"    0 3.5714285 3.2857144  2.833333 0 1
10 26 "FG"     0  .8571429  1.642857  5.333333 1 2
10 26 "Lab"    0 1.4285715  4.214286  6.166667 1 2
10 26 "FF"     1  .4285712  2.642857  5.166667 1 2
10 26 "GP"     0  1.857143       4.5  6.666667 1 2
10 26 "SF"     0 2.4285715  5.214286  5.666667 1 2
 5 27 "Cs"     0       7.2  6.533333  6.428571 1 2
 5 27 "Vox"    0  9.714286  8.333333       9.8 1 2
 5 27 "Pais"   0  2.733333 2.3333333 2.2857144 1 2
 5 27 "IU"     0 1.8666667       1.4  2.533333 1 2
 5 27 "PSOE"   1       3.6       2.8 3.9333334 1 2
 5 27 "PPP"    0  8.066667  7.266667       7.6 1 2
end
label values country country_label
label def country_label 1 "1. Austria", modify
label def country_label 3 "3. Germany", modify
label def country_label 5 "5. Spain", modify
label def country_label 6 "6. Finland", modify
label def country_label 7 "7. France", modify
label def country_label 8 "8. Great Britain", modify
label def country_label 10 "10. Ireland", modify
label def country_label 11 "11. Italy", modify
label def country_label 12 "12. Netherlands", modify
label values gender gender
label def gender 0 "0. Female", modify
label def gender 1 "1. Male", modify
label values age age_groups
label def age_groups 0 "0. 30 and Under", modify
label def age_groups 1 "1. 31-50", modify
label def age_groups 2 "2. 51-69", modify
label def age_groups 3 "3. 70 and over", modify

I am not finding a way (or an alternative) to compute means for different groups of socio-demographics. Indeed, I am forced to create multiple tables for each group, making then very sloppy putting all tables together in a nice format.

Here is the code I am using now (example for one groupvar):

asdoc tabstat dist_lr dist_red dist_immpoor dist_immdfrace dist_eu dist_gendiss if vote==1, by(gender) save(Gender_descri.doc) format(%9.2f)

Hopefully, I would like something like this:

asdoc tabstat dist_lr dist_red dist_immpoor dist_immdfrace dist_eu dist_gendiss if vote==1, by(gender age education) save(Gender_descri.doc) format(%9.2f)

Is there a way to include multiple group variables?

Sincerely
Mattia

[Jeff Pitblado (StataCorp)]

If you have Stata 17 or newer, you can use the new collect
features with the updated table command to build your table and
export it to Word (docx).

Using the dataset your provided, here is how I accomplished this.

* variables to compute means from
local vlist dist_lr dist_red dist_immpoor
* group variables
local gvars gender age

collect clear

* compute means from the total sample
table if vote==1,        ///
    statistic(mean `vlist')    ///
    nformat(%9.2f mean)    ///
    name(Total)
collect addtags Total[_hide], fortags(result[mean])

foreach gvar of local gvars {
    * compute group-level means
    table (`gvar') (var result)    ///
        if vote==1        ///
        ,            ///
        statistic(mean `vlist')    ///
        nformat(%9.2f mean)    ///
        nototals        ///
        name(`gvar')
}

* combine the collections into a new collection named 'all'
collect combine all = `gvars' Total

* arrange the items into a table
collect layout (`gvars' Total) (var#result)

Here is the resulting table.

------------------------------------------------------
                  |  dist_lr   dist_red   dist_immpoor
------------------+-----------------------------------
gender            |
  0. Female       |     1.52       3.03           2.76
  1. Male         |     1.52       3.45           2.69
age               |
  0. 30 and Under |     1.21       2.84           1.71
  1. 31-50        |     1.93       3.94           2.98
  2. 51-69        |     1.63       2.99           3.98
  3. 70 and over  |     1.11       3.22           1.94
Total             |     1.52       3.29           2.72
------------------------------------------------------

See the documentation for collect export for details on how to
export this table to Word.

[Mattia Gatti]

Sorry Jeff, I just saw the reply now. Thanks a lot, I tried it and it works perfectly!