I am using Stata 17/SE on Mac and I am having trouble generating a variable using another observation within a group.
For context, I am working with tennis data. I have two rank variables, one a singles ranking (single_rank) and a doubles_ranking.
The singles rank is reflective of the player's rank, while the doubles rank is the average of the team's double ranking: egen var = mean(var), by(i).
i refers to match number, j is player number 1-2 is team 1 and 3-4 is team 2, Ranking_* variables refer to the original ranking data (MS = men's singles, MD = men's doubles, etc.).
My question is, is there a way that I can generate the opposing player/team's ranking in this long dataset (I have this for the tournament seed variable, where t_ refers to the player and o_ refers to the opponent).
Code:
input float i byte(j team p_pos t_tourn_seed o_tourn_seed) int(Ranking_MS Ranking_MD Ranking_WS Ranking_WD) float(rank_single rank_dbls) 367 1 1 1 4 . . . . . . . 367 3 2 1 . 4 . . 1326 . 1326 . 368 1 1 1 . 5 . . 1028 638 1028 638 368 3 2 1 5 . . . 536 626 536 626 369 1 1 1 . 3 . . . . . . 369 3 2 1 3 . . . 484 587 484 587 370 1 1 1 . 5 . . . . . . 370 3 2 1 5 . . . 536 626 536 626 371 1 1 1 . . . . . . . . 371 3 2 1 . . . . . . . . 372 1 1 1 4 . . . . . . . 372 3 2 1 . 4 . . . . . . 373 1 1 1 . . . . 692 . 692 . 373 3 2 1 . . . . . . . . 374 1 1 1 . 7 . . 1326 . 1326 . 374 3 2 1 7 . . . 612 620 612 620 375 1 1 1 4 2 . . . . . . 375 3 2 1 2 4 . . 326 324 326 324 376 1 1 1 . . . . 1326 . 1326 . 376 3 2 1 . . . . 986 . 986 . 377 1 1 1 . 3 . . 999 . 999 . 377 3 2 1 3 . . . 484 587 484 587 378 1 1 1 6 . . . 631 454 631 454 378 3 2 1 . 6 . . 938 . 938 . 379 1 1 1 1 4 . . 187 405 187 405 379 3 2 1 4 1 . . . . . . 380 1 1 1 . . . . 1168 . 1168 . 380 3 2 1 . . . . 825 852 825 852 381 1 1 1 1 5 . . 187 405 187 405 381 3 2 1 5 1 . . 536 626 536 626 382 1 1 1 . 3 . . . . . . 382 3 2 1 3 . . . 484 587 484 587 383 1 1 1 6 . . . 631 454 631 454 383 3 2 1 . 6 . . 915 891 915 891 384 1 1 1 1 . . . 187 405 187 405 384 3 2 1 . 1 . . . . . . 385 1 1 1 . . . . 692 . 692 . 385 3 2 1 . . . . 999 . 999 . 386 1 1 1 . . . . . . . . 386 3 2 1 . . . . 1307 . 1307 . 387 1 1 1 1 . . . 187 405 187 405 387 3 2 1 . 1 . . 925 . 925 . 388 1 1 1 . 2 . . . . . . 388 3 2 1 2 . . . 326 324 326 324 389 1 1 1 . 2 . . 1168 . 1168 . 389 3 2 1 2 . . . 326 324 326 324 390 1 1 1 . 2 . . 999 . 999 . 390 3 2 1 2 . . . 326 324 326 324 391 1 1 1 6 2 . . 631 454 631 454 391 3 2 1 2 6 . . 326 324 326 324 392 1 1 1 . . . . 1028 638 1028 638 392 3 2 1 . . . . 752 . 752 . 393 1 1 1 . 7 . . 1153 . 1153 . 393 3 2 1 7 . . . 612 620 612 620 394 1 1 1 . . . . . . . . 394 3 2 1 . . . . 1119 744 1119 744 395 1 1 1 . . . . 999 . 999 . 395 3 2 1 . . . . . . . . 396 1 1 1 4 . . . . . . . 396 3 2 1 . 4 . . . . . . 397 1 1 1 . . . . 1294 . 1294 . 397 3 2 1 . . . . 938 . 938 . 398 1 1 1 . . . . . . . 907 398 2 1 2 . . . . 1383 907 1383 907 398 3 2 1 . . . . 1101 927 1101 927 398 4 2 2 . . . . . . . 927 399 1 1 1 . . . . . . . . 399 2 1 2 . . . . . . . . 399 3 2 1 . . . . . . . . 399 4 2 2 . . . . . . . . 400 1 1 1 . 2 . . 915 891 915 891 400 2 1 2 . 2 . . 1168 . 1168 891 400 3 2 1 2 . . . . . . . 400 4 2 2 2 . . . . . . . 401 1 1 1 1 3 . . 187 405 187 364.5 401 2 1 2 1 3 . . 326 324 326 364.5 401 3 2 1 3 1 . . . . . 620 401 4 2 2 3 1 . . 612 620 612 620 402 1 1 1 3 . . . . . . 620 402 2 1 2 3 . . . 612 620 612 620 402 3 2 1 . 3 . . . . . . 402 4 2 2 . 3 . . . . . . 403 1 1 1 1 . . . 187 405 187 364.5 403 2 1 2 1 . . . 326 324 326 364.5 403 3 2 1 . 1 . . 536 626 536 626 403 4 2 2 . 1 . . 752 . 752 626 404 1 1 1 4 2 . . 1028 638 1028 638 404 2 1 2 4 2 . . . . . 638 404 3 2 1 2 4 . . . . . . 404 4 2 2 2 4 . . . . . . 405 1 1 1 1 . . . 187 405 187 364.5 405 2 1 2 1 . . . 326 324 326 364.5 405 3 2 1 . 1 . . 999 . 999 . 405 4 2 2 . 1 . . 986 . 986 . 406 1 1 1 . . . . . . . 454 406 2 1 2 . . . . 631 454 631 454 406 3 2 1 . . . . 536 626 536 626 406 4 2 2 . . . . 752 . 752 626 407 1 1 1 . 4 . . 1119 744 1119 665.5 407 2 1 2 . 4 . . 484 587 484 665.5 end
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