I am trying to export tables from stata to word using putexcel, where the row and column variables have value labels. However, after using the matlist command, the frequencies are not reported correctly
I have used the following commands to proceed towards using putexcel to transfer the results to stata
As I have shown from the output above, the tabulate option frequencies are not rendered after I use the matlist code. The string variables for sect and edulevel are ordered alphabetically rather than what the initial table says. Thus, the frequencies are jumbled up. For instance in the first row, for agriculture, the tabulate option shows that frequency for primary educated is 1383, while after the matlist command, it shows that frequency for Higher educated is 1383 instead.
I require some help regarding correct alignment of the data in the tables. Any help would be really appreciated.
Code:
. tab sect edulevel, matcell(cellcounts)
| edulevel
sect | Below Pri Primary middle | Total
----------------------+---------------------------------+----------
Agriculture | 4,213 1,383 1,545 | 8,555
Forestry and Fishing | 84 77 104 | 403
Mining | 118 64 131 | 551
Food manuf | 207 144 292 | 1,125
Textile and leather m | 350 386 618 | 2,359
Wood Manuf | 107 71 178 | 523
Media Printing and Re | 6 19 58 | 199
Chemicals Manuf | 91 82 203 | 1,078
Non-metal Manuf | 193 77 168 | 693
Basic Metal Manuf | 65 64 119 | 661
Machinery manuf | 87 91 171 | 640
Electronics Manuf | 5 7 19 | 148
Equipt Manuf | 68 102 235 | 1,197
Furniture Manuf | 59 75 150 | 432
Manuf Others | 47 77 149 | 458
Repair & install mach | 25 38 103 | 354
Elec & Gas Supply | 38 29 115 | 594
Sanitation Services | 61 44 88 | 338
Construction | 2,877 1,612 2,544 | 9,253
Civil Engineering | 219 148 199 | 940
Special Construction | 246 268 570 | 1,676
Wholesale | 180 177 420 | 1,877
Retail | 253 329 1,014 | 3,689
Land & Pipeline Trans | 456 470 1,109 | 3,495
Transportation & post | 62 46 144 | 742
Tourism | 240 208 378 | 1,418
Media, Telecom & IT | 15 10 73 | 1,290
Finance Legal & Mktg | 33 59 277 | 3,081
Real Estate | 7 3 12 | 69
Architect. & Engineer | 0 2 1 | 95
R&D | 3 2 4 | 49
Veterinary | 0 2 5 | 53
Employment | 6 3 15 | 63
Security and Building | 92 72 201 | 677
Public Admin | 181 167 617 | 4,111
Education | 188 180 421 | 6,591
Health svs | 69 55 251 | 1,843
Resid & social Worker | 48 37 75 | 444
Art & entertain. | 32 26 58 | 226
Organizations | 33 32 82 | 335
Repair and personal s | 221 114 159 | 802
Domestic Personnel | 816 335 460 | 1,966
----------------------+---------------------------------+----------
Total | 12,101 7,187 13,535 | 65,093
| edulevel
sect | secondary Higher Se Higher | Total
----------------------+---------------------------------+----------
Agriculture | 875 391 148 | 8,555
Forestry and Fishing | 62 47 29 | 403
Mining | 81 64 93 | 551
Food manuf | 195 140 147 | 1,125
Textile and leather m | 499 281 225 | 2,359
Wood Manuf | 74 46 47 | 523
Media Printing and Re | 48 17 51 | 199
Chemicals Manuf | 182 179 341 | 1,078
Non-metal Manuf | 99 68 88 | 693
Basic Metal Manuf | 135 110 168 | 661
Machinery manuf | 121 78 92 | 640
Electronics Manuf | 16 19 82 | 148
Equipt Manuf | 191 184 417 | 1,197
Furniture Manuf | 94 41 13 | 432
Manuf Others | 89 56 40 | 458
Repair & install mach | 80 45 63 | 354
Elec & Gas Supply | 106 104 202 | 594
Sanitation Services | 61 39 45 | 338
Construction | 1,395 571 254 | 9,253
Civil Engineering | 137 66 171 | 940
Special Construction | 315 159 118 | 1,676
Wholesale | 317 284 499 | 1,877
Retail | 772 658 663 | 3,689
Land & Pipeline Trans | 737 387 336 | 3,495
Transportation & post | 135 156 199 | 742
Tourism | 225 167 200 | 1,418
Media, Telecom & IT | 89 120 983 | 1,290
Finance Legal & Mktg | 303 425 1,984 | 3,081
Real Estate | 8 11 28 | 69
Architect. & Engineer | 2 2 88 | 95
R&D | 4 2 34 | 49
Veterinary | 10 7 29 | 53
Employment | 5 8 26 | 63
Security and Building | 138 98 76 | 677
Public Admin | 716 890 1,540 | 4,111
Education | 537 844 4,421 | 6,591
Health svs | 241 328 899 | 1,843
Resid & social Worker | 97 78 109 | 444
Art & entertain. | 29 27 54 | 226
Organizations | 60 46 82 | 335
Repair and personal s | 133 97 78 | 802
Domestic Personnel | 184 86 85 | 1,966
----------------------+---------------------------------+----------
Total | 9,597 7,426 15,247 | 65,093
Code:
decode sect, g(sect_s)
levelsof sect_s, local(namesec)
"Agriculture"' `"Architect. & Engineer"' `"Art & entertain."' `"Basic Metal Manuf"' `"Chemicals Manuf"' `"Civil Engineering"' `"Construction"' `"Domestic Personnel"' `"Education"' `"Elec & Gas Supply"' `"Electronics Manuf"' `"Employment"' `"Equipt Manuf"' `"Finance Legal & Mktg Corp Svs"' `"Food manuf"' `"Forestry and Fishing"' `"Furniture Manuf"' `"Health svs"' `"Land & Pipeline Transport"' `"Machinery manuf"' `"Manuf Others"' `"Media Printing and Records"' `"Media, Telecom & IT"' `"Mining"' `"Non-metal Manuf"' `"Organizations "' `"Public Admin"' `"R&D"' `"Real Estate"' `"Repair & install machinery"' "Repair and personal svs"' `"Resid & social Workers"' `"Retail"' `"Sanitation Services"' `"Security and Building svs"' `"Special Construction"' `"Textile and leather manuf"' `"Tourism"' `"Transportation & postal"' `"Veterinary"' `"Wholesale"' `"Wood Manuf"'
matrix rownames cellcounts = `namesec'
.decode edulevel, g(edu_s)
. levelsof edu_s, local(edu)
`"Below Primary"' `"Higher"' `"Higher Secondary"' `"Primary"' `"middle"' `"sec
> ondary"'
. matrix colnames cellcounts = `edu'
. matlist cellcounts
| Below P~y Higher Higher ~y Primary middle
-------------+-------------------------------------------------------
Agriculture | 4213 1383 1545 875 391
Architect.~r | 84 77 104 62 47
Art & ente~. | 118 64 131 81 64
Basic Meta~f | 207 144 292 195 140
Chemicals ~f | 350 386 618 499 281
Civil Engi~g | 107 71 178 74 46
Construction | 6 19 58 48 17
Domestic P~l | 91 82 203 182 179
Education | 193 77 168 99 68
Elec & Gas~y | 65 64 119 135 110
Electronic~f | 87 91 171 121 78
Employment | 5 7 19 16 19
Equipt Manuf | 68 102 235 191 184
Finance Le~s | 59 75 150 94 41
Food manuf | 47 77 149 89 56
Forestry a~g | 25 38 103 80 45
Furniture ~f | 38 29 115 106 104
Health svs | 61 44 88 61 39
Land & Pip~t | 2877 1612 2544 1395 571
Machinery ~f | 219 148 199 137 66
Manuf Others | 246 268 570 315 159
Media Prin~s | 180 177 420 317 284
Media, Tel~T | 253 329 1014 772 658
Mining | 456 470 1109 737 387
Non-metal ~f | 62 46 144 135 156
Organizati~s | 240 208 378 225 167
Public Admin | 15 10 73 89 120
R&D | 33 59 277 303 425
Real Estate | 7 3 12 8 11
Repair & i~y | 0 2 1 2 2
Repair and~s | 3 2 4 4 2
Resid & so~s | 0 2 5 10 7
Retail | 6 3 15 5 8
Sanitation~s | 92 72 201 138 98
Security a~s | 181 167 617 716 890
Special Co~n | 188 180 421 537 844
Textile an~f | 69 55 251 241 328
Tourism | 48 37 75 97 78
Transporta~l | 32 26 58 29 27
Veterinary | 33 32 82 60 46
Wholesale | 221 114 159 133 97
Wood Manuf | 816 335 460 184 86
| secondary
-------------+-----------
Agriculture | 148
Architect.~r | 29
Art & ente~. | 93
Basic Meta~f | 147
Chemicals ~f | 225
Civil Engi~g | 47
Construction | 51
Domestic P~l | 341
Education | 88
Elec & Gas~y | 168
Electronic~f | 92
Employment | 82
Equipt Manuf | 417
Finance Le~s | 13
Food manuf | 40
Forestry a~g | 63
Furniture ~f | 202
Health svs | 45
Land & Pip~t | 254
Machinery ~f | 171
Manuf Others | 118
Media Prin~s | 499
Media, Tel~T | 663
Mining | 336
Non-metal ~f | 199
Organizati~s | 200
Public Admin | 983
R&D | 1984
Real Estate | 28
Repair & i~y | 88
Repair and~s | 34
Resid & so~s | 29
Retail | 26
Sanitation~s | 76
Security a~s | 1540
Special Co~n | 4421
Textile an~f | 899
Tourism | 109
Transporta~l | 54
Veterinary | 82
Wholesale | 78
Wood Manuf | 85
.
I require some help regarding correct alignment of the data in the tables. Any help would be really appreciated.
