dtable summary

[Ali Abutaleb]

Hi there - for dtable, is it possible to specify non- missing values only to produce the summary table ?

I have a two-way summary table wherein most variable has missing value, but I do not need to count of those missing value to be included in summary statistic.

I tried to use :

dtable v1 v2 v3 , by(group,  nomissing ) sample(,  place(seplabels)) sformat("(N=%s)" frequency) nformat(%7.2f mean sd) title(Table 1. Descriptive statistics) note(Mean (Standard deviation)) export(table1.docx, replace

)
but this gave me error "option by not allowed
in option by()"



Thanks,

[Ken Chui]

Was not able to recreate that error. This example works fine:

sysuse auto, clear

dtable rep78 headroom price , by(foreign,  nomissing ) sample(,  place(seplabels)) sformat("(N=%s)" frequency) nformat(%7.2f mean sd) title(Table 1. Descriptive statistics) note(Mean (Standard deviation))

It would be great if you can provide a small sample of data using dataex that would produce the error you described.

[Ali Abutaleb]

Was not able to recreate that error. This example works fine:

sysuse auto, clear

dtable rep78 headroom price , by(foreign, nomissing ) sample(, place(seplabels)) sformat("(N=%s)" frequency) nformat(%7.2f mean sd) title(Table 1. Descriptive statistics) note(Mean (Standard deviation))

It would be great if you can provide a small sample of data using dataex that would produce the error you described.

Thanks Ken for your reply

here is the sample of my data :

* Example generated by -dataex-. For more info, type help dataex
clear
input double(hourpay hrrate lockdwon) byte(ages sex) long(industry1 full_time part_time temporary_job1) byte publicr
                 .    .    0 14 2  7 0 1 . 1
                 .    .    0 13 2 12 0 1 0 1
6.2700000000000005  998    0 10 2  8 1 0 0 1
                 .    .    0  9 1 14 1 0 . 1
                 .    .    0 10 2 12 0 1 0 2
                 .    .    0 10 2 14 1 0 0 1
             12.25    .    0  8 1 23 1 0 0 1
             15.88   16    0 10 2 12 0 1 0 2
                 .    .    0 12 2 18 0 1 . 1
             19.47    .    0  7 1 17 1 0 0 1
                 .    .    0 14 1 23 0 1 . 1
                 .    .    0  9 1  7 1 0 0 2
                 .  998    0 14 2 12 0 0 0 2
                 .    . 22.7  9 2 14 1 0 0 1
                 .    .    0 12 1  7 0 1 1 2
              8.33    8    0 14 1 23 0 1 0 1
                 .    .  6.3 14 1 14 1 0 0 1
                 .    .  4.1 11 1  5 1 0 0 1
                 .    .    0 14 1 19 0 1 0 2
             18.17    .    0 13 2  9 1 0 0 2
                 .    .  2.2 13 1 21 1 0 0 1
             30.23    .    0 10 1  2 1 0 0 1
                 .    .  8.3  6 2 14 1 0 0 1
                 .    .    0 13 1 14 1 0 0 1
              9.17 8.72 78.1 12 1  1 1 0 0 1
              9.23  998    0 11 2  7 1 0 0 1
                 .    .    0 12 2 17 1 0 . 1
                 .    .  1.8 10 2 23 1 0 0 1
                 .    .  3.5  6 2  7 1 0 0 2
                 .    . 29.4  8 2 23 0 1 . 1
                 .    .    0  9 2 12 1 0 . 1
                 .    . 17.4 14 1  5 1 0 . 1
             28.85    .    0 11 2  5 0 1 0 1
                 .    .    0 13 1 14 1 0 0 1
                 .    .    0 10 2 12 0 1 0 1
                 .    .    0 12 1 19 1 0 0 2
              9.28 9.85    0 13 1  2 1 0 0 1
                 .    .    0  7 1  5 1 0 . 1
                 .    .    0 13 2 12 0 1 . 1
 8.290000000000001    .    0 10 2  7 0 1 0 2
                 .    .  2.4 13 1  5 0 1 0 1
 9.870000000000001 10.9    0  9 2  2 1 0 1 1
                 .    .    0 12 1 19 1 0 0 2
                 .    .    0 12 2 18 0 1 0 1
                 .    . 37.9 11 1  1 1 0 0 1
              9.61  9.5    0 13 2 12 1 0 0 2
                 .    .    0  9 1 14 1 0 0 1
                 .    .    0 13 2 12 0 1 0 1
              19.2    .    0  8 2  7 0 1 0 2
                 .    .    0 12 2 19 1 0 0 1
                 .    .    0 12 1  7 1 0 0 2
                 .    .    0 12 1 14 1 0 0 1
                 .    .    0 12 2 17 1 0 0 1
10.290000000000001 9.55  3.3 12 2 14 0 1 0 1
             10.11    .    0  9 2 18 1 0 0 1
                 .    .    0 12 1 23 1 0 . 1
                 .    .    0 10 1 14 1 0 0 1
                 .    .    0 11 1 10 1 0 0 1
                 .    . 34.8 11 2  7 0 1 0 2
             21.38    .  7.5 11 1 18 1 0 0 1
                 .    .    0  7 2 23 0 1 0 1
             13.42  998    0 10 1 12 1 0 0 1
                 .    .    0 13 2 12 1 0 0 2
12.030000000000001    .    0  8 2 13 1 0 0 1
                 .    .    0  5 2 12 1 0 0 2
             15.58 15.8    0  8 2 12 1 0 0 2
                 .    .    0 12 1  5 1 0 0 1
                 .    . 24.2 13 2  4 0 1 1 1
                 .    .    0 10 2 12 1 0 0 2
                 .    .  6.6  9 1  4 1 0 1 2
                 .    .    0 11 2 18 0 1 0 1
             20.62    .    0  8 2  7 0 1 0 2
                10    9    0 13 1 23 0 1 0 1
                 .    .    0 11 2 12 1 0 0 2
                 .    .    0  8 1 13 1 0 . 1
                 .    .    0 11 2  7 1 0 0 2
             14.68   14    0  6 1 13 1 0 0 1
             32.89    .    0  7 2 14 1 0 0 1
                 .    .    0 11 2  7 1 0 0 2
                 .    .    0  6 1  5 1 0 0 1
             93.81    .    0  9 1 23 1 0 0 1
                 .    .    0 12 2 18 1 0 0 1
                 .    .    0 10 2 19 1 0 0 2
                 .    .    0  9 1 18 1 0 . 1
                 .    .    0 10 1 13 1 0 0 1
             15.32    .    0 10 2 19 1 0 0 2
                 .    .  2.4  8 1  5 1 0 0 1
                 .    .    0  9 1 12 1 0 0 2
                 .    .    0  9 2  7 0 1 0 2
                 .    .    0 10 1 14 1 0 0 1
                 .    .    0 14 1 17 0 1 . 1
              8.75    .    0 12 2 14 1 0 0 1
                 .    .    0 13 2 17 0 1 . 1
                 .    .   15 12 1 13 1 0 0 1
                 .    .    0  9 2 18 0 1 0 1
             21.26    .    0 13 1 14 1 0 0 1
                 .    .    0 10 1 21 1 0 0 1
                 .    .    0  7 1 14 1 0 0 1
             12.34    .    0 12 2  7 1 0 0 1
                 .    . 19.9  6 2 12 1 0 . 1
end
label values hourpay HOURPAY5
label values hrrate HRRATE5
label def HRRATE5 998 "Don't know", modify
label values ages AGES5
label def AGES5 5 "20-24yrs", modify
label def AGES5 6 "25-29yrs", modify
label def AGES5 7 "30-34yrs", modify
label def AGES5 8 "35-39yrs", modify
label def AGES5 9 "40-44yrs", modify
label def AGES5 10 "45-49yrs", modify
label def AGES5 11 "50-54yrs", modify
label def AGES5 12 "55-59yrs", modify
label def AGES5 13 "60-64yrs", modify
label def AGES5 14 "65-69yrs", modify
label values sex SEX
label def SEX 1 "Male", modify
label def SEX 2 "Female", modify
label values industry1 industry
label def industry 1 "Accommodation And Food Service Activities", modify
label def industry 2 "Administrative And Support Service Activities", modify
label def industry 4 "Arts, Entertainment And Recreation", modify
label def industry 5 "Construction", modify
label def industry 7 "Education", modify
label def industry 8 "Electricity, gas, air cond supply", modify
label def industry 9 "Extraterritorial organisations", modify
label def industry 10 "Financial and insurance activities", modify
label def industry 12 "Human Health And Social Work Activities", modify
label def industry 13 "Information And Communication", modify
label def industry 14 "Manufacturing", modify
label def industry 17 "Other service activities", modify
label def industry 18 "Professional, Scientific And Technical Activities", modify
label def industry 19 "Public admin and defence", modify
label def industry 21 "Transportation And Storage", modify
label def industry 23 "Wholesale And Retail Trade; Repair Of Motor Vehicles And Motorcycles", modify
label values publicr PUBLICR5
label def PUBLICR5 1 "Private", modify
label def PUBLICR5 2 "Public", modify

I tried to use this command :

dtable hourpay hrrate lockdwon i.ages i.sex i.industry1 full_time part_time temporary_job1 i.publicr , by(ZHC ) sample(,  place(seplabels)) sformat("(N=%s)" frequency) nformat(%7.2f mean sd) title(Table 1. Descriptive statistics) note(Mean (Standard deviation))

this should give me a summary with missing value. However when I add the nomissing in the command also it gave me the same result !!

dtable hourpay hrrate lockdwon i.ages i.sex i.industry1 full_time part_time temporary_job1 i.publicr , by(ZHC, nomissing ) sample(,  place(seplabels)) sformat("(N=%s)" frequency) nformat(%7.2f mean sd) title(Table 1. Descriptive statistics) note(Mean (Standard deviation)) export(table1.docx, replace)

so this mean all these command gave me the same summary table

[Ken Chui]

Thanks,

The ZHC variable is missing, so I had to create one like the followings.

gen temp = runiform()
gen ZHC = (temp > 0.4)
replace ZHC = . if temp > 0.8

The two commands are the same because "nomissing" is the default. If you want a table with an extra column that shows data where ZHC is a missing, then the option should be "by(ZHC, missing)":

However, that only manages the presentation of missing in ZHC, not the missing values in the row variables. If you do not want to count missing, then nothing needs to be done because they are already excluded. But if you want to only include cases with no missing in all values, use the listwise option, here is the explanation from the technical document:

listwise and nolistwise specify how to handle missing values across the continuous and factor variables.
listwise handles missing values through listwise deletion, meaning that the entire observation is omitted
from the sample if any continuous or factor variable is missing for that observation; nolistwise
specifies that dtable omit an observation only if all the continuous and factor variables are missing for
that observation.