Data management question - figuring out a span of ordered dummy variables

[Pete Ware]

Hi all,

Per the synthetic -dataex- included below, I am working with a sample of Pre-Kindergarten through 12th-grade schools in the United States. For each school, I have a set of dummy variables for whether the school in question offers that particular grade. I am trying to create three variables based on the information encoded in these dummies: (1) the lowest grade offered at the school, (2) the highest grade offered at the school, and (3) a single variable indicating the span of the lowest grade to the highest grade (e.g., Pre-K - 6, 9 - 12, etc.). I know that once I get variables (1) and (2), I can trivially create variable (3) with -egen group-, but I am completely flummoxed on how to create variables (1) and (2) from the dummies I have, other than through some inelegant brute-forcing of an -egen group- on the dummies. Any help would be much appreciated!

Thanks,
Peter

* Example generated by -dataex-. For more info, type help dataex
clear
input float id byte(prek kindergarten grade1 grade2 grade3 grade4 grade5 grade6 grade7 grade8 grade9 grade10 grade11 grade12)
  1 . . . . . . . 1 1 1 1 1 1 1
  2 . 1 1 1 1 1 1 1 1 1 . . . .
  3 . . . . . . . . . . 1 1 1 1
  4 1 1 1 1 1 1 1 1 1 1 . . . .
  5 . 1 1 1 1 1 1 1 . . . . . .
  6 . . . . . . . 1 1 1 . . . .
  7 . . . . . . . 1 1 1 . . . .
  8 1 1 1 1 1 1 1 . . . . . . .
  9 . . . . . . . . . . 1 1 1 1
 10 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 11 . . . . . . . 1 1 1 1 1 1 1
 12 1 1 1 1 1 1 1 1 . . . . . .
 13 1 1 1 1 1 1 1 . . . . . . .
 14 . . . . . . . 1 1 1 1 1 1 1
 15 1 . . . . . . 1 1 1 . . . .
 16 . 1 1 1 1 1 . . . . . . . .
 17 . . . . . . . . . . 1 1 1 1
 18 . . . . . . . . . . 1 1 1 1
 19 . . . . . . . 1 1 1 . . . .
 20 . . . . . . . . . . 1 1 1 1
 21 . 1 1 1 1 1 1 . . . . . . .
 22 . . . . . . . . 1 1 1 1 1 1
 23 . . . . . . . 1 1 1 . . . .
 24 . 1 1 1 1 1 1 1 1 1 1 1 1 1
 25 . . . . . 1 1 . . . . . . .
 26 . 1 1 1 1 1 1 1 1 1 . . . .
 27 . 1 1 1 1 1 1 1 . . . . . .
 28 1 1 1 1 1 1 1 1 . . . . . .
 29 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 30 . . . . . . . . . . 1 1 1 1
 31 . 1 1 1 1 1 1 . . . . . . .
 32 . 1 1 1 1 1 1 . . . . . . .
 33 1 1 1 1 1 1 1 1 1 1 . . . .
 34 . . . 1 1 1 1 1 1 1 1 1 1 1
 35 . . . . . . . 1 1 1 . . . .
 36 . . . . . . . . . . 1 1 1 1
 37 . 1 1 1 1 1 1 . . . . . . .
 38 . 1 1 1 1 1 1 . . . . . . .
 39 1 1 1 1 1 1 1 1 . . . . . .
 40 . 1 1 1 1 1 1 1 1 1 . . . .
 41 . . . . . . . 1 1 1 1 1 1 1
 42 . 1 1 1 1 1 1 1 1 1 . . . .
 43 1 1 1 1 1 1 1 1 1 1 . . . .
 44 . . . . . . . . . . 1 1 1 1
 45 . . . . . . . . . . 1 1 1 1
 46 1 1 1 1 1 1 1 1 1 1 . . . .
 47 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 48 . 1 1 1 1 . . 1 1 1 1 1 1 1
 49 . . . . . . . 1 1 1 . . . .
 50 . 1 1 1 1 1 1 1 1 1 . . . .
 51 . . . . . . . . . . 1 1 1 1
 52 . 1 1 1 1 1 1 . . . . . . .
 53 1 1 1 1 1 1 1 1 1 1 . . . .
 54 . 1 1 1 1 1 1 1 1 1 1 1 1 1
 55 . 1 1 1 1 1 1 1 1 1 1 1 1 1
 56 1 1 1 1 1 1 1 1 . . . . . .
 57 . . . . . . . . . . 1 1 1 1
 58 . 1 1 1 1 1 . . . . . . . .
 59 . 1 1 1 1 1 1 . . . . . . .
 60 . . . . . . . 1 1 1 . . . .
 61 . . . . . . . . . . 1 1 1 1
 62 1 1 1 1 1 1 1 . . . . . . .
 63 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 64 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 65 . . . . . . . 1 1 1 . . . .
 66 . . . . . . . . . . 1 1 1 1
 67 . . . . . . . . . . . . . .
 68 1 1 1 1 1 1 1 1 . . . . . .
 69 1 1 1 1 1 1 1 . . . . . . .
 70 . 1 1 1 1 1 1 . . . . . . .
 71 1 1 1 1 1 1 1 1 . . . . . .
 72 . 1 1 1 1 1 . . . . . . . .
 73 . . . . . . . 1 1 1 . . . .
 74 1 1 1 1 1 1 1 1 1 1 . . . .
 75 1 1 1 1 1 1 1 . . . . . . .
 76 1 1 1 1 1 1 1 1 . . . . . .
 77 . . . . . . . 1 1 1 . . . .
 78 . . . . . . . . 1 1 1 . . .
 79 1 1 1 1 1 1 1 . . . . . . .
 80 . . . . . . . 1 1 1 . . . .
 81 . . . . . . . . . 1 1 1 1 1
 82 . 1 1 1 1 1 1 1 . . . . . .
 83 . 1 1 1 1 1 1 1 . . . . . .
 84 . . . . . . . 1 1 1 . . . .
 85 1 1 1 1 1 1 1 1 . . . . . .
 86 . 1 1 1 1 1 1 1 1 1 1 1 1 1
 87 . 1 1 1 1 1 1 . . . . . . .
 88 1 1 1 1 1 1 1 1 1 1 . . . .
 89 . . . . . 1 1 . . . . . . .
 90 . 1 1 1 1 1 1 . . . . . . .
 91 . 1 1 1 1 1 1 1 1 1 1 1 1 1
 92 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 93 . 1 1 1 . . 1 1 1 1 1 1 1 1
 94 1 1 1 1 1 1 1 1 . . . . . .
 95 . 1 1 1 1 1 1 . . . . . . .
 96 1 1 1 1 1 1 1 . . . . . . .
 97 . . . . . . . 1 1 1 . . . .
 98 . 1 1 1 1 1 1 . . . . . . .
 99 1 1 1 1 1 1 1 . . . . . . .
100 . . . . . . . . 1 1 . . . .
end
label values prek prek
label def prek 1 "Pre-K", modify
label values kindergarten k
label def k 1 "K", modify
label values grade1 first
label def first 1 "1st", modify
label values grade2 second
label def second 1 "2nd", modify
label values grade3 third
label def third 1 "3rd", modify
label values grade4 fourth
label def fourth 1 "4th", modify
label values grade5 fifth
label def fifth 1 "5th", modify
label values grade6 sixth
label def sixth 1 "6th", modify
label values grade7 seventh
label def seventh 1 "7th", modify
label values grade8 eighth
label def eighth 1 "8th", modify
label values grade9 ninth
label def ninth 1 "9th", modify
label values grade10 tenth
label def tenth 1 "10th", modify
label values grade11 eleventh
label def eleventh 1 "11th", modify
label values grade12 twelfth
label def twelfth 1 "12th", modify

[Ali Atia]

gen wanted1 = ""
gen wanted2 = ""
foreach v of varlist prek-grade12{
    replace wanted1 = "`v'" if `v'==1 & wanted1==""
    replace wanted2 = "`v'" if `v'==1
}
gen wanted3 = wanted1+"-"+wanted2

[Pete Ware]

Perfect. Thanks!

[Nick Cox]

As a footnote, I flag the small virtues of concatenation:

. egen all = concat(prek-grade12)

. gen first = strpos(all, "1")

. gen last = strrpos(all, "1")

. su first last

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       first |        100        4.25    3.814843          0         11
        last |        100       10.19    3.054042          0         14

. tab all

           all |      Freq.     Percent        Cum.
---------------+-----------------------------------
.............. |          1        1.00        1.00
..........1111 |         13       13.00       14.00
.........11111 |          1        1.00       15.00
........11.... |          1        1.00       16.00
........111... |          1        1.00       17.00
........111111 |          1        1.00       18.00
.......111.... |         13       13.00       31.00
.......1111111 |          4        4.00       35.00
.....11....... |          2        2.00       37.00
...11111111111 |          1        1.00       38.00
.111..11111111 |          1        1.00       39.00
.1111..1111111 |          1        1.00       40.00
.11111........ |          3        3.00       43.00
.111111....... |         12       12.00       55.00
.1111111...... |          4        4.00       59.00
.111111111.... |          5        5.00       64.00
.1111111111111 |          5        5.00       69.00
1......111.... |          1        1.00       70.00
1111111....... |          8        8.00       78.00
11111111...... |          9        9.00       87.00
1111111111.... |          7        7.00       94.00
11111111111111 |          6        6.00      100.00
---------------+-----------------------------------
         Total |        100      100.00