How to re-shape a wide large data set to a long data with multiple id variables?

[Jose Williams]

Dear All,

I am working with a wide data set consisting of 391 and four ID variables. Since the hyphen and star do not work when re-shaping it to long, I have tried using the greshape command with @ option. Still, the code seems too long.

I can put in all the variables names in the command but is there an efficient way to do this?

The code that I can run is:

reshape long m1_q19_age m1_q23_edu m1_q29_no_children m1_q30_no_children_school m1_q32_month_expenditure m1_q22_rw_lang, i(HHID m1_q8_state_code m1_q9_district_code m1_q11_village_code) j(year)

I have attached a few variables from the data set along with the ids. The year variable in time takes two values: 2015 and 2018.

Example of the data:

* Example generated by -dataex-. For more info, type help dataex
clear
input byte(m1_q19_age2015 m1_q22_rw_lang2015 m1_q23_edu2015 m1_q19_age2018 m1_q22_rw_lang2018 m1_q23_edu2018) long HHID byte m1_q8_state_code int m1_q9_district_code double m1_q11_village_code
35 0 1 25 0 1   1 9 133 110499
22 1 1 25 1 2   2 9 133 110499
70 0 1 75 0 1   3 9 133 110499
40 1 2 46 1 2   4 9 133 110499
60 1 3 63 1 2   5 9 133 110499
65 1 3 64 1 3   6 9 133 110499
40 1 5 54 1 5   7 9 133 110499
74 1 3 33 1 2   8 9 133 110499
50 1 2 19 1 2   9 9 133 110499
60 0 1 29 1 4  10 9 133 110499
38 1 3 43 1 2  11 9 133 110499
40 1 2 45 1 2  12 9 133 110499
22 1 5 25 1 5  13 9 133 110615
25 0 1 25 1 2  14 9 133 110615
80 0 1 34 1 4  15 9 133 110615
20 1 1 65 1 2  16 9 133 110615
65 1 2 38 1 5  17 9 133 110615
23 1 1 65 0 1  18 9 133 110615
41 1 4 38 0 1  19 9 133 110615
32 1 2 30 1 2  20 9 133 110615
45 0 1 48 0 1  21 9 133 110615
28 1 3 45 0 1  22 9 133 110615
32 1 2 30 1 2  23 9 133 110615
30 1 3 34 1 2  24 9 133 110615
32 1 3 55 1 2  25 9 133 110907
80 1 2 80 1 2  26 9 133 110907
27 1 3 30 1 4  27 9 133 110907
40 1 2 40 1 2  28 9 133 110907
29 1 2 42 1 1  29 9 133 110907
63 1 2 75 0 2  30 9 133 110907
58 1 2 64 1 2  31 9 133 110907
38 1 4 35 0 1  32 9 133 110907
46 1 2 45 1 2  33 9 133 110907
35 0 1 45 0 1  34 9 133 110907
45 0 2 25 0 1  35 9 133 110907
45 0 1 48 0 1  36 9 133 110907
47 1 2 46 1 2  37 9 133 110913
85 0 1 49 0 1  38 9 133 110913
44 1 4 38 1 4  39 9 133 110913
24 1 3 55 1 3  40 9 133 110913
50 1 1 54 1 2  41 9 133 110913
55 1 2 19 1 4  42 9 133 110913
25 1 4 24 1 3  43 9 133 110913
48 1 2 51 1 2  44 9 133 110913
55 1 4 26 1 3  45 9 133 110913
55 1 4 61 1 1  46 9 133 110913
75 1 3 21 1 3  47 9 133 110913
70 1 2 18 1 4  48 9 133 110913
81 1 3 82 1 3  49 9 133 110934
55 0 1 65 0 1  50 9 133 110934
60 0 2 60 0 1  51 9 133 110934
45 0 1 24 1 4  52 9 133 110934
61 1 3 65 1 2  53 9 133 110934
32 1 1 36 1 5  54 9 133 110934
20 1 1 22 1 5  55 9 133 110934
65 0 2 70 0 1  56 9 133 110934
55 1 3 65 1 3  57 9 133 110934
30 1 2 28 1 4  58 9 133 110934
68 1 2 71 1 1  59 9 133 110934
65 1 3 68 1 2  60 9 133 110934
50 0 2 32 1 5  61 9 133 110972
32 1 3 35 1 3  62 9 133 110972
38 1 2 18 1 2  63 9 133 110972
50 0 1 54 0 1  64 9 133 110972
45 0 1 52 0 1  65 9 133 110972
26 1 3 29 0 1  66 9 133 110972
38 1 1 42 1 2  67 9 133 110972
60 0 1 63 0 1  68 9 133 110972
40 0 1 48 0 1  69 9 133 110972
60 1 2 35 1 3  70 9 133 110972
23 1 1 65 0 1  71 9 133 110972
25 1 2 35 0 1  72 9 133 110972
42 1 4 44 0 2  73 9 133 111006
36 1 2 65 1 2  74 9 133 111006
40 0 1 70 0 1  75 9 133 111006
42 1 3 18 1 3  76 9 133 111006
40 0 2 43 0 2  77 9 133 111006
60 0 1 40 1 1  78 9 133 111006
38 1 3 42 1 2  79 9 133 111006
45 1 1 65 0 1  80 9 133 111006
80 1 3 86 1 2  81 9 133 111006
43 1 3 44 1 3  82 9 133 111006
43 1 3 40 1 5  83 9 133 111006
19 1 4 50 0 1  84 9 133 111006
35 0 2 29 1 3  85 9 133 111079
48 1 2 50 1 2  86 9 133 111079
45 0 1 30 1 3  87 9 133 111079
50 1 3 60 1 1  88 9 133 111079
67 1 2 70 1 2  89 9 133 111079
45 1 2 38 1 4  90 9 133 111079
34 1 5 37 1 5  91 9 133 111079
40 1 4 42 1 3  92 9 133 111079
44 1 4 50 1 4  93 9 133 111079
28 1 3 41 1 3  94 9 133 111079
70 1 1 40 0 1  95 9 133 111079
42 0 1 45 0 1  96 9 133 111079
32 1 2 35 1 4  97 9 133 111096
22 1 4 66 1 2  98 9 133 111096
65 1 4 21 1 5  99 9 133 111096
52 0 1 22 1 5 100 9 133 111096
end

[Clyde Schechter]

ds *2015 *2018
local stubs `r(varlist)'
local stubs: subinstr local stubs "2015" "", all
local stubs: subinstr local stubs "2018" "", all
local stubs: list uniq stubs

reshape long `stubs', i(HHID m1_q8_state_code m1_q9_district_code m1_q11_village_code) ///
    j(year)

Note: This code contemplates the possibility that there are some variables that occur in 2015 but not 2018, or vice versa. If, however, every variable occurs in both years, you can simplify the code to:

ds *2015
local stubs `r(varlist)'
local stubs: subinstr local stubs "2015" "", all

reshape long `stubs', i(HHID m1_q8_state_code m1_q9_district_code m1_q11_village_code) ///
    j(year)