Improving data processing efficiency

Ok, sorry about the zip, then. :) Thanks for taking the trouble to clue 
me in as to the best posting procedure!

well, here's a dput-ed version of the small data subset you can use for 
testing. below that, an updated version of the function, with extra 
explanatory comments, and producing an extra column showing exactly what 
is matched to what.

to test, just run the function, with the dataset as sole argument.

Thanks again; i'd appreciate any input on this.

=========== begin dataset dput representation ============

structure(list(PERMNO = c(10001L, 10001L, 10298L, 10298L, 10484L,
10484L, 10515L, 10515L, 10634L, 10634L, 11048L, 11048L, 11237L,
11294L, 11294L, 11338L, 11338L, 11404L, 11404L, 11587L, 11587L,
11591L, 11591L, 11737L, 11737L, 11791L, 11809L, 11809L, 11858L,
11858L, 11955L, 11955L, 12003L, 12003L, 12016L, 12016L, 12223L,
12223L, 12758L, 12758L, 13688L, 13688L, 16117L, 16117L, 17770L,
17770L, 21514L, 21514L, 21792L, 21792L, 21821L, 21821L, 22437L,
22437L, 22947L, 22947L, 23027L, 23027L, 23182L, 23182L, 23536L,
23536L, 23712L, 23712L, 24053L, 24053L, 24117L, 24117L, 24256L,
24256L, 24299L, 24299L, 24352L, 24352L, 24379L, 24379L, 24467L,
24467L, 24679L, 24679L, 24870L, 24870L, 25056L, 25056L, 25208L,
25208L, 25232L, 25232L, 25241L, 25590L, 25590L, 26463L, 26463L,
26470L, 26470L, 26614L, 26614L, 27385L, 27385L, 29196L, 29196L,
30411L, 30411L, 32943L, 32943L, 38893L, 38893L, 40708L, 40708L,
41005L, 41005L, 42817L, 42817L, 42833L, 42833L, 43668L, 43668L,
45947L, 45947L, 46017L, 46017L, 48274L, 48274L, 49971L, 49971L,
53786L, 53786L, 53859L, 53859L, 54199L, 54199L, 56371L, 56952L,
56952L, 57277L, 57277L, 57381L, 57381L, 58202L, 58202L, 59395L,
59395L, 59935L, 60169L, 60169L, 61188L, 61188L, 61444L, 61444L,
62690L, 62690L, 62842L, 62842L, 64290L, 64290L, 64418L, 64418L,
64450L, 64450L, 64477L, 64477L, 64557L, 64557L, 64646L, 64646L,
64902L, 64902L, 67774L, 67774L, 68910L, 68910L, 70471L, 70471L,
74406L, 74406L, 75091L, 75091L, 75304L, 75304L, 75743L, 75964L,
75964L, 76026L, 76026L, 76162L, 76170L, 76173L, 78530L, 78530L,
78682L, 78682L, 81569L, 81569L, 82502L, 82502L, 83337L, 83337L,
83919L, 83919L, 88242L, 88242L, 90852L, 90852L, 91353L, 91353L
), DATE = c(19900331, 19900630, 19900630, 19900331, 19900331,
19900630, 19900331, 19900630, 19900331, 19900630, 19900331, 19900630,
19900630, 19900630, 19900331, 19900331, 19900630, 19900331, 19900630,
19900331, 19900630, 19900630, 19900331, 19900630, 19900331, 19900630,
19900630, 19900331, 19900630, 19900331, 19900331, 19900630, 19900630,
19900331, 19900630, 19900331, 19900331, 19900630, 19900331, 19900630,
19900331, 19900630, 19900331, 19900630, 19900331, 19900630, 19900630,
19900331, 19900630, 19900331, 19900630, 19900331, 19900630, 19900331,
19900331, 19900630, 19900630, 19900331, 19900331, 19900630, 19900630,
19900331, 19900331, 19900630, 19900331, 19900630, 19900630, 19900331,
19900331, 19900630, 19900331, 19900630, 19900630, 19900331, 19900331,
19900630, 19900331, 19900630, 19900331, 19900630, 19900331, 19900630,
19900331, 19900630, 19900331, 19900630, 19900630, 19900331, 19900331,
19900331, 19900630, 19900630, 19900331, 19900331, 19900630, 19900331,
19900630, 19900630, 19900331, 19900331, 19900630, 19900331, 19900630,
19900630, 19900331, 19900630, 19900331, 19900630, 19900331, 19900331,
19900630, 19900331, 19900630, 19900331, 19900630, 19900331, 19900630,
19900331, 19900630, 19900331, 19900630, 19900630, 19900331, 19900331,
19900630, 19900331, 19900630, 19900630, 19900331, 19900331, 19900630,
19900630, 19900331, 19900630, 19900630, 19900331, 19900630, 19900331,
19900630, 19900331, 19900331, 19900630, 19900331, 19900331, 19900630,
19900331, 19900630, 19900331, 19900630, 19900630, 19900331, 19900331,
19900630, 19900331, 19900630, 19900331, 19900630, 19900331, 19900630,
19900331, 19900630, 19900630, 19900331, 19900331, 19900630, 19900331,
19900630, 19900630, 19900331, 19900630, 19900331, 19900331, 19900630,
19900630, 19900331, 19900331, 19900630, 19900331, 19900630, 19900630,
19900331, 19900630, 19900331, 19900630, 19900630, 19900630, 19900630,
19900331, 19900630, 19900331, 19900630, 19900331, 19900630, 19900630,
19900331, 19900331, 19900630, 19900331, 19900630, 19900331, 19900630,
19900331, 19900630, 19900331, 19900630), Shares.Owned = c(50100,
50100, 250000, 293500, 3656629, 3827119, 4132439, 3566591, 2631193,
2500301, 775879, 816879, 38700, 1041600, 1070300, 533768, 558815,
61384492, 60466567, 194595, 196979, 359946, 314446, 106770, 107070,
20242, 1935098, 2099403, 1902125, 1766750, 41991, 41991, 34490,
36290, 589400, 596700, 1549395, 1759440, 854473, 762903, 156366785,
98780287, 2486389, 2635718, 122264, 122292, 25455916, 25458658,
71645490, 71855722, 30969596, 30409838, 2738576, 2814490, 20846605,
20930233, 1148299, 505415, 396388, 385714, 25239923, 24117950,
73465526, 73084616, 8096614, 7595742, 3937930, 3820215, 20884821,
19456342, 2127331, 2188276, 2334515, 2813347, 8267264, 8544084,
783277, 810742, 742048, 512956, 9659658, 9436873, 40107717, 41234384,
9111755, 9708782, 12815719, 13144148, 1146100, 8292392, 8271030,
282650, 281273, 4196126, 4273758, 2489363, 2734182, 1579681,
1369192, 51947585, 51941430, 54673, 52585, 317601, 314876, 62626258,
63341772, 8977553, 8940106, 4478872, 4315631, 1246339, 1227442,
68484747, 68041081, 22679902, 21775270, 927147, 936881, 2626449,
2245552, 14029366, 14304855, 2434123, 2184358, 77479654, 81754241,
333070, 282967, 241146, 256146, 11419, 819092, 798490, 1403179,
1326018, 238974451, 237684105, 1889699, 2317096, 4887641, 5972387,
3567239, 1024595, 993627, 5254732, 5459404, 413146, 432697, 5307595,
4813261, 7717872, 8689444, 2431341, 2372096, 909359, 868068,
2110670, 2055349, 23774859, 23573345, 4234466, 4143534, 1192314,
1255105, 3052000, 2605700, 5566270, 5972761, 1470173, 1448403,
28065345, 32961737, 1844441, 2247991, 651758, 655658, 65864806,
82392617, 1942906, 14800, 14657, 6600, 5534, 394064, 163000,
2499320, 1123624, 1227987, 198000, 241000, 3681688, 3409586,
2416988, 2407798, 55081, 48091, 480000, 785710, 1040147, 1171854,
1363994, 1555229, 199237, 192637), Shares.Outstanding.13f = c(1,
1, 7, 7, 8, 8, 8, 8, 6, 6, 8, 8, 4, 4, 4, 18, 19, 228, 228, 2,
2, 3, 3, 5, 5, 7, 9, 9, 6, 6, 2, 2, 3, 3, 7, 7, 14, 15, 3, 3,
429, 429, 17, 16, 2, 2, 43, 41, 127, 126, 86, 86, 15, 15, 51,
51, 7, 7, 3, 3, 67, 67, 211, 211, 35, 35, 14, 14, 49, 49, 12,
12, 22, 22, 31, 31, 4, 4, 4, 5, 34, 34, 64, 64, 56, 56, 27, 27,
47, 28, 28, 2, 2, 10, 10, 8, 8, 13, 13, 87, 87, 1, 1, 3, 3, 101,
101, 38, 36, 49, 56, 22, 22, 245, 247, 36, 35, 6, 6, 22, 22,
30, 30, 11, 11, 151, 151, 2, 2, 3, 3, 4, 4, 4, 10, 10, 468, 459,
10, 10, 16, 16, 27, 8, 8, 19, 19, 3, 3, 7, 7, 15, 15, 6, 6, 6,
6, 13, 13, 60, 60, 11, 11, 10, 10, 8, 8, 153, 152, 7, 7, 206,
206, 5, 5, 4, 4, 246, 299, 4, 0, 0, 13, 13, 7, 5, 10, 7, 7, 11,
11, 16, 16, 6, 6, 1, 1, 7, 7, 10, 10, 5, 5, 10, 10), Percent.Inst.Owned 
= c(0.0501,
0.0501, 0.0357142857142857, 0.0419285714285714, 0.457078625,
0.478389875, 0.516554875, 0.445823875, 0.438532166666667, 
0.416716833333333,
0.096984875, 0.102109875, 0.009675, 0.2604, 0.267575, 0.0296537777777778,
0.0294113157894737, 0.269230228070175, 0.26520424122807, 0.0972975,
0.0984895, 0.119982, 0.104815333333333, 0.021354, 0.021414, 
0.00289171428571429,
0.215010888888889, 0.233267, 0.317020833333333, 0.294458333333333,
0.0209955, 0.0209955, 0.0114966666666667, 0.0120966666666667,
0.0842, 0.0852428571428571, 0.110671071428571, 0.117296, 0.284824333333333,
0.254301, 0.36449134032634, 0.230257079254079, 0.146258176470588,
0.164732375, 0.061132, 0.061146, 0.591998046511628, 0.62094287804878,
0.564137716535433, 0.570283507936508, 0.360111581395349, 0.353602767441860,
0.182571733333333, 0.187632666666667, 0.408756960784314, 0.410396725490196,
0.164042714285714, 0.0722021428571429, 0.132129333333333, 
0.128571333333333,
0.376715268656716, 0.359969402985075, 0.348177848341232, 0.346372587677725,
0.231331828571429, 0.2170212, 0.281280714285714, 0.2728725, 
0.426220836734694,
0.397068204081633, 0.177277583333333, 0.182356333333333, 0.106114318181818,
0.127879409090909, 0.266685935483871, 0.275615612903226, 0.19581925,
0.2026855, 0.185512, 0.1025912, 0.284107588235294, 0.277555088235294,
0.626683078125, 0.64428725, 0.162709910714286, 0.173371107142857,
0.474656259259259, 0.486820296296296, 0.0243851063829787, 
0.296156857142857,
0.295393928571429, 0.141325, 0.1406365, 0.4196126, 0.4273758,
0.311170375, 0.34177275, 0.121513923076923, 0.105322461538462,
0.59709867816092, 0.597027931034483, 0.054673, 0.052585, 0.105867,
0.104958666666667, 0.62006196039604, 0.627146257425743, 0.236251394736842,
0.248336277777778, 0.0914055510204082, 0.0770648392857143, 
0.0566517727272727,
0.0557928181818182, 0.279529579591837, 0.275469963562753, 
0.629997277777778,
0.622150571428571, 0.1545245, 0.156146833333333, 0.119384045454545,
0.102070545454545, 0.467645533333333, 0.4768285, 0.221283909090909,
0.198578, 0.513110291390729, 0.541418814569536, 0.166535, 0.1414835,
0.080382, 0.085382, 0.00285475, 0.204773, 0.1996225, 0.1403179,
0.1326018, 0.510629168803419, 0.517830294117647, 0.1889699, 0.2317096,
0.3054775625, 0.3732741875, 0.132119962962963, 0.128074375, 0.124203375,
0.276564842105263, 0.287337052631579, 0.137715333333333, 0.144232333333333,
0.758227857142857, 0.687608714285714, 0.5145248, 0.579296266666667,
0.4052235, 0.395349333333333, 0.151559833333333, 0.144678, 
0.162359230769231,
0.158103769230769, 0.39624765, 0.392889083333333, 0.384951454545455,
0.376684909090909, 0.1192314, 0.1255105, 0.3815, 0.3257125, 
0.0363808496732026,
0.0392944802631579, 0.210024714285714, 0.206914714285714, 
0.136239538834951,
0.160008432038835, 0.3688882, 0.4495982, 0.1629395, 0.1639145,
0.267743113821138, 0.275560591973244, 0.4857265, Inf, Inf, 
0.000507692307692308,
0.000425692307692308, 0.0562948571428571, 0.0326, 0.249932, 
0.160517714285714,
0.175426714285714, 0.018, 0.0219090909090909, 0.2301055, 0.213099125,
0.402831333333333, 0.401299666666667, 0.055081, 0.048091, 
0.0685714285714286,
0.112244285714286, 0.1040147, 0.1171854, 0.2727988, 0.3110458,
0.0199237, 0.0192637), Latest.Issue.Date.ByPERMNO = c(19860108,
19860108, 19600101, 19600101, 19600101, 19600101, 19870728, 19870728,
19870501, 19870501, 19870805, 19870805, 19600101, 19600101, 19600101,
19600101, 19600101, 19730523, 19730523, 19600101, 19600101, 19870811,
19870811, 19870930, 19870930, 19600101, 19880729, 19880729, 19880225,
19880225, 19880602, 19880602, 19860610, 19860610, 19880802, 19880802,
19890629, 19890629, 19600101, 19600101, 19821109, 19821109, 19860619,
19860619, 19871117, 19871117, 19600101, 19600101, 19890308, 19890308,
19900208, 19900208, 19861120, 19861120, 19880803, 19880803, 19600101,
19600101, 19890216, 19890216, 19761202, 19761202, 19890919, 19890919,
19810623, 19810623, 19770615, 19770615, 19831004, 19831004, 19830616,
19830616, 19810519, 19810519, 19850311, 19850311, 19781130, 19781130,
19841016, 19900515, 19800904, 19800904, 19830825, 19830825, 19830601,
19830601, 19811110, 19811110, 19600101, 19890309, 19890309, 19850529,
19850529, 19881122, 19881122, 19840620, 19840620, 19740305, 19740305,
19860718, 19860718, 19600101, 19600101, 19860207, 19860207, 19891003,
19891003, 19870403, 19870403, 19600101, 19600101, 19790403, 19790403,
19850528, 19850528, 19830322, 19830322, 19761202, 19761202, 19841114,
19841114, 19800826, 19800826, 19880517, 19880517, 19860516, 19860516,
19891122, 19891122, 19600101, 19600101, 19600101, 19871119, 19871119,
19760624, 19760624, 19851206, 19851206, 19890615, 19890615, 19860805,
19860805, 19600101, 19890919, 19890919, 19860501, 19860501, 19600101,
19600101, 19890308, 19890308, 19900125, 19900125, 19890714, 19890714,
19880412, 19880412, 19890809, 19890809, 19870306, 19870306, 19751112,
19751112, 19870604, 19870604, 19810625, 19810625, 19600101, 19600101,
19860416, 19860416, 19891027, 19891027, 19890125, 19890125, 19860502,
19860502, 19600101, 19600101, 19900405, 19600101, 19600101, 19600101,
19600101, 19900412, 19900514, 19900518, 19890518, 19890518, 19600101,
19600101, 19900117, 19900117, 19891214, 19891214, 19600101, 19600101,
19600101, 19600101, 19851206, 19851206, 19851211, 19851211, 19600101,
19600101), Quarters.Since.19800101 = c(41L, 42L, 42L, 41L, 41L,
42L, 41L, 42L, 41L, 42L, 41L, 42L, 42L, 42L, 41L, 41L, 42L, 41L,
42L, 41L, 42L, 42L, 41L, 42L, 41L, 42L, 42L, 41L, 42L, 41L, 41L,
42L, 42L, 41L, 42L, 41L, 41L, 42L, 41L, 42L, 41L, 42L, 41L, 42L,
41L, 42L, 42L, 41L, 42L, 41L, 42L, 41L, 42L, 41L, 41L, 42L, 42L,
41L, 41L, 42L, 42L, 41L, 41L, 42L, 41L, 42L, 42L, 41L, 41L, 42L,
41L, 42L, 42L, 41L, 41L, 42L, 41L, 42L, 41L, 42L, 41L, 42L, 41L,
42L, 41L, 42L, 42L, 41L, 41L, 41L, 42L, 42L, 41L, 41L, 42L, 41L,
42L, 42L, 41L, 41L, 42L, 41L, 42L, 42L, 41L, 42L, 41L, 42L, 41L,
41L, 42L, 41L, 42L, 41L, 42L, 41L, 42L, 41L, 42L, 41L, 42L, 42L,
41L, 41L, 42L, 41L, 42L, 42L, 41L, 41L, 42L, 42L, 41L, 42L, 42L,
41L, 42L, 41L, 42L, 41L, 41L, 42L, 41L, 41L, 42L, 41L, 42L, 41L,
42L, 42L, 41L, 41L, 42L, 41L, 42L, 41L, 42L, 41L, 42L, 41L, 42L,
42L, 41L, 41L, 42L, 41L, 42L, 42L, 41L, 42L, 41L, 41L, 42L, 42L,
41L, 41L, 42L, 41L, 42L, 42L, 41L, 42L, 41L, 42L, 42L, 42L, 42L,
41L, 42L, 41L, 42L, 41L, 42L, 42L, 41L, 41L, 42L, 41L, 42L, 41L,
42L, 41L, 42L, 41L, 42L), Quarters.Since.Latest.Issue = c(17L,
18L, 122L, 121L, 121L, 122L, 11L, 12L, 12L, 13L, 11L, 12L, 122L,
122L, 121L, 121L, 122L, 68L, 69L, 121L, 122L, 12L, 11L, 11L,
10L, 122L, 8L, 7L, 10L, 9L, 8L, 9L, 17L, 16L, 8L, 7L, 4L, 5L,
121L, 122L, 30L, 31L, 16L, 17L, 10L, 11L, 122L, 121L, 6L, 5L,
2L, 1L, 15L, 14L, 7L, 8L, 122L, 121L, 5L, 6L, 55L, 54L, 3L, 4L,
36L, 37L, 53L, 52L, 26L, 27L, 28L, 29L, 37L, 36L, 21L, 22L, 46L,
47L, 22L, 1L, 39L, 40L, 27L, 28L, 28L, 29L, 35L, 34L, 121L, 5L,
6L, 21L, 20L, 6L, 7L, 24L, 25L, 66L, 65L, 15L, 16L, 121L, 122L,
18L, 17L, 3L, 2L, 13L, 12L, 121L, 122L, 44L, 45L, 20L, 21L, 29L,
30L, 54L, 55L, 22L, 23L, 40L, 39L, 8L, 9L, 16L, 17L, 3L, 2L,
121L, 122L, 122L, 10L, 11L, 57L, 56L, 19L, 18L, 5L, 4L, 15L,
16L, 121L, 3L, 4L, 16L, 17L, 121L, 122L, 6L, 5L, 1L, 2L, 3L,
4L, 8L, 9L, 3L, 4L, 13L, 14L, 59L, 58L, 12L, 13L, 36L, 37L, 122L,
121L, 17L, 16L, 2L, 3L, 6L, 5L, 16L, 17L, 121L, 122L, 1L, 121L,
122L, 121L, 122L, 1L, 1L, 1L, 4L, 5L, 121L, 122L, 1L, 2L, 3L,
2L, 121L, 122L, 121L, 122L, 18L, 19L, 18L, 19L, 121L, 122L),
     ALTPRC = c(9.9375, 9.875, 0.45313, 0.67188, 7.875, 10, 18,
     22, 14.75, 9.75, 0.375, 0.15625, 3.9375, 16, 14.25, 7, 7.125,
     27.25, 23.375, 10.75, 13, 3.125, 3.125, 2.6875, 3.4375, 0.5,
     8.75, 7, 16.875, 12.375, 2.40625, 3.96875, 4, 4.625, 4.5,
     5.125, 26.25, 28.75, 4.5, 5.5, 21.75, 23.25, 15, 14.375,
     16.625, 14, 50.5, 48.75, 31.875, 33.125, 41.5, 46, 21, 22.125,
     30.75, 30.125, 10.375, 5.5, 11.5, 11, 29, 28.875, 27.25,
     26.75, 22.375, 22.25, 33.375, 35, 21, 19.75, 29.875, 28.875,
     22.125, 20.125, 21, 18.875, 24.625, 26.75, 21.75, 22, 22.125,
     21.125, 24.75, 26.75, 42.75, 43.5, 13.375, 29.625, 0.07813,
     25.125, 23.75, 18, 20, 17.5, 18.125, 18.875, 19, 28.875,
     30, 23.875, 23.625, 15.5, 15.625, 17.5, 19.5, 34.75, 30.75,
     2, 2.25, 18.625, 17.5, 21.375, 19.875, 45.25, 20.125, 37.25,
     41.75, 32.25, 32.5, 23.125, 21.875, 35.25, 38.75, 27.875,
     27.375, 35.875, 42.125, 24.25, 24.5, 25.125, 23.875, 2.0625,
     16.75, 16.25, 34.625, 37.75, 40, 31.625, 19.375, 20, 30.875,
     29.375, 0.125, 17.625, 17, 16.625, 17.75, 12.625, 13.25,
     26, 19.75, 15.25, 18.625, 18.125, 18, 16.375, 15.625, 18.5,
     19, 12.875, 14.375, 32.375, 33.375, 16.375, 16.375, 1.625,
     2.8125, 13.875, 14.625, 4.625, 4.5, 18.5, 24.125, 6.375,
     5.875, 10.625, 11.625, 6.625, 7.375, 14.75, 0.8125, 0.6875,
     2.125, 2.375, 20.25, 7.625, 34, 15.25, 15, 2.09375, 2.375,
     19.5, 18.125, 38.5, 30.75, 36, 35.75, 9.375, 11.25, 21.25,
     18.625, 6, 5.25, 1.15625, 1.25), HSICIG = c(492, 492, 494,
     494, 495, 495, 495, 495, 495, 495, 493, 493, 495, 495, 495,
     495, 495, 493, 493, 492, 492, 495, 495, 495, 495, 495, 495,
     495, 495, 495, 495, 495, 495, 495, 495, 495, 495, 495, 495,
     495, 493, 493, 494, 494, 492, 492, 492, 492, 493, 493, 492,
     492, 493, 493, 493, 493, 495, 495, 492, 492, 493, 493, 493,
     493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493,
     493, 493, 493, 492, 492, 493, 493, 492, 492, 493, 493, 492,
     492, 495, 492, 492, 494, 494, 492, 492, 492, 492, 493, 493,
     492, 492, 494, 494, 492, 492, 492, 492, 492, 492, 492, 492,
     493, 493, 493, 493, 492, 492, 493, 493, 493, 493, 492, 492,
     492, 492, 495, 495, 494, 494, 494, 494, 495, 492, 492, 493,
     493, 495, 495, 492, 492, 492, 492, 495, 492, 492, 492, 492,
     492, 492, 492, 492, 494, 494, 492, 492, 492, 492, 492, 492,
     495, 495, 493, 493, 492, 492, 495, 495, 492, 492, 492, 492,
     495, 495, 495, 495, 495, 495, 492, 492, 495, 495, 495, 492,
     492, 495, 495, 495, 492, 492, 494, 494, 492, 492, 495, 495,
     492, 492, 493, 493, 494, 494, 495, 495, 495, 495), Market.Cap.13f = 
c(9937500,
     9875000, 3171910, 4703160, 6.3e+07, 8e+07, 1.44e+08, 1.76e+08,
     88500000, 58500000, 3e+06, 1250000, 15750000, 6.4e+07, 5.7e+07,
     1.26e+08, 135375000, 6.213e+09, 5329500000, 21500000, 2.6e+07,
     9375000, 9375000, 13437500, 17187500, 3500000, 78750000,
     6.3e+07, 101250000, 74250000, 4812500, 7937500, 1.2e+07,
     13875000, 31500000, 35875000, 367500000, 431250000, 13500000,
     16500000, 9330750000, 9974250000, 2.55e+08, 2.3e+08, 33250000,
     2.8e+07, 2171500000, 1998750000, 4048125000, 4173750000,
     3.569e+09, 3.956e+09, 3.15e+08, 331875000, 1568250000, 1536375000,
     72625000, 38500000, 34500000, 3.3e+07, 1.943e+09, 1934625000,
     5749750000, 5644250000, 783125000, 778750000, 467250000,
     4.9e+08, 1.029e+09, 967750000, 358500000, 346500000, 486750000,
     442750000, 6.51e+08, 585125000, 98500000, 1.07e+08, 8.7e+07,
     1.1e+08, 752250000, 718250000, 1.584e+09, 1.712e+09, 2.394e+09,
     2.436e+09, 361125000, 799875000, 3672110, 703500000, 6.65e+08,
     3.6e+07, 4e+07, 1.75e+08, 181250000, 1.51e+08, 1.52e+08,
     375375000, 3.9e+08, 2077125000, 2055375000, 15500000, 15625000,
     52500000, 58500000, 3509750000, 3105750000, 7.6e+07, 8.1e+07,
     912625000, 9.8e+08, 470250000, 437250000, 11086250000, 4970875000,
     1.341e+09, 1461250000, 193500000, 1.95e+08, 508750000, 481250000,
     1057500000, 1162500000, 306625000, 301125000, 5417125000,
     6360875000, 48500000, 4.9e+07, 75375000, 71625000, 8250000,
     6.7e+07, 6.5e+07, 346250000, 377500000, 1.872e+10, 14515875000,
     193750000, 2e+08, 4.94e+08, 4.7e+08, 3375000, 1.41e+08, 1.36e+08,
     315875000, 337250000, 37875000, 39750000, 1.82e+08, 138250000,
     228750000, 279375000, 108750000, 1.08e+08, 98250000, 93750000,
     240500000, 2.47e+08, 772500000, 862500000, 356125000, 367125000,
     163750000, 163750000, 1.3e+07, 22500000, 2122875000, 2.223e+09,
     32375000, 31500000, 3.811e+09, 4969750000, 31875000, 29375000,
     42500000, 46500000, 1629750000, 2205125000, 5.9e+07, 0, 0,
     27625000, 30875000, 141750000, 38125000, 3.4e+08, 106750000,
     1.05e+08, 23031250, 26125000, 3.12e+08, 2.9e+08, 2.31e+08,
     184500000, 3.6e+07, 35750000, 65625000, 78750000, 212500000,
     186250000, 3e+07, 26250000, 11562500, 12500000), IPO.Flag = c(0,
     0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
     0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0,
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
     0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
     1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0,
     0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0), IPO.Issue.Date = c(NA,
     NA, NA, NA, NA, NA, 19860724, 19860724, NA, NA, 19870127,
     19870127, NA, NA, NA, NA, NA, NA, NA, NA, NA, 19870811, 19870811,
     19870930, 19870930, NA, 19871124, 19871124, 19880225, 19880225,
     19880602, 19880602, NA, NA, 19880802, 19880802, NA, NA, NA,
     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 19710324, 19710324,
     NA, NA, NA, NA, NA, NA, NA, NA, NA, 19710617, 19710617, NA,
     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
     19831014, 19831014, 19861016, 19861016, NA, NA, 19860502,
     19860502, NA, NA, 19890419, NA, NA, NA, NA, 19900412, 19900514,
     19900518, NA, NA, NA, NA, NA, NA, 19830603, 19830603, NA,
     NA, NA, NA, 19851206, 19851206, 19851211, 19851211, NA, NA
     ), Quarters.Since.IPO.Issue = c(NA, NA, NA, NA, NA, NA, 15L,
     16L, NA, NA, 13L, 14L, NA, NA, NA, NA, NA, NA, NA, NA, NA,
     12L, 11L, 11L, 10L, NA, 11L, 10L, 10L, 9L, 8L, 9L, NA, NA,
     8L, 7L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
     NA, 77L, 78L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 77L, 76L,
     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
     NA, 27L, 26L, 14L, 15L, NA, NA, 16L, 17L, NA, NA, 5L, NA,
     NA, NA, NA, 1L, 1L, 1L, NA, NA, NA, NA, NA, NA, 29L, 28L,
     NA, NA, NA, NA, 18L, 19L, 18L, 19L, NA, NA)), .Names = c("PERMNO",
"DATE", "Shares.Owned", "Shares.Outstanding.13f", "Percent.Inst.Owned",
"Latest.Issue.Date.ByPERMNO", "Quarters.Since.19800101", 
"Quarters.Since.Latest.Issue",
"ALTPRC", "HSICIG", "Market.Cap.13f", "IPO.Flag", "IPO.Issue.Date",
"Quarters.Since.IPO.Issue"), row.names = c(79L, 85L, 9902L, 9908L,
15739L, 15758L, 16673L, 16675L, 20159L, 20160L, 32879L, 32889L,
38023L, 39404L, 39409L, 40405L, 40420L, 43114L, 43116L, 47939L,
47953L, 48091L, 48120L, 52828L, 52837L, 54612L, 55002L, 55048L,
56506L, 56508L, 59230L, 59247L, 60454L, 60461L, 60845L, 60852L,
66143L, 66147L, 69439L, 69454L, 72218L, 72232L, 81826L, 81840L,
87882L, 87883L, 105814L, 105832L, 106687L, 106709L, 106867L,
106875L, 110008L, 110081L, 113124L, 113125L, 113448L, 113460L,
114419L, 114431L, 116222L, 116234L, 117215L, 117310L, 119463L,
119477L, 119913L, 119927L, 120787L, 120799L, 121214L, 121215L,
121541L, 121548L, 121670L, 121680L, 122420L, 122421L, 123629L,
123679L, 124479L, 124485L, 125607L, 125608L, 126683L, 126716L,
126911L, 126954L, 126986L, 128941L, 128979L, 132991L, 133048L,
133090L, 133091L, 134227L, 134228L, 137449L, 137465L, 146656L,
146710L, 151717L, 151728L, 162724L, 162738L, 186344L, 186346L,
194239L, 194251L, 195124L, 195125L, 203411L, 203426L, 203486L,
203487L, 206821L, 206863L, 218733L, 218734L, 219083L, 219084L,
232389L, 232401L, 241221L, 241222L, 262518L, 262556L, 263151L,
263154L, 264783L, 264811L, 275743L, 278957L, 278958L, 281230L,
281242L, 281957L, 281962L, 286492L, 286504L, 294444L, 294445L,
297641L, 298974L, 298988L, 304628L, 304669L, 306326L, 306339L,
315987L, 316013L, 316939L, 316940L, 327003L, 327032L, 327976L,
327977L, 328372L, 328386L, 328621L, 328622L, 329277L, 329289L,
329983L, 329984L, 331735L, 331746L, 350849L, 350887L, 357747L,
357750L, 366913L, 366917L, 380680L, 380749L, 385635L, 385642L,
394280L, 394281L, 410203L, 417419L, 417420L, 418842L, 418851L,
423401L, 423687L, 423795L, 494497L, 494498L, 496519L, 496520L,
576735L, 576737L, 590042L, 590057L, 606077L, 606087L, 620736L,
620737L, 704834L, 704837L, 749540L, 749573L, 754161L, 754162L
), class = "data.frame")

======== end dataset dput representation =========

=========== begin function code ==========
fcn_create_nonissuing_match_by_quarterssinceissue = function(tfdata, 
quarters_since_issue=20) {
     # tfdata must not have NAs for market.cap
     result = matrix(nrow=0, ncol=(ncol(tfdata) + 1)) # rbind for matrix 
is cheaper, so typecast the result to matrix

     colnames = names(tfdata) #grab the colnames, which we will shove 
back to the result at the end when we reconvert to data.frame

     quarterends = sort(unique(tfdata$DATE)) # the data are quarterly, 
all dates are quarter ends

     # basic code logic:
     # grab each quarter's data, in each quarter get the ipo subset, and 
the eligible matching firm subset
     # for each ipo from the ipo subset, select a matching firm from the 
eligible matching firm subset
     # the said selection is done based on industry group (HSICIG), and 
market cap (Market.Cap.13f)
     # Industry group has to be the same, market cap has to be 'closest 
one from above', or if that is not available, then 'closest one from below'.

     for (aquarter in quarterends) {
         tfdata_quarter = tfdata[tfdata$DATE == aquarter, ]
         tfdata_quarter_fitting_nonissuers = tfdata_quarter[ 
(tfdata_quarter$Quarters.Since.Latest.Issue > quarters_since_issue) & 
(tfdata_quarter$IPO.Flag == 0), ]
         tfdata_quarter_ipoissuers = tfdata_quarter[ 
tfdata_quarter$IPO.Flag == 1, ]

         for (i in 1:nrow(tfdata_quarter_ipoissuers)) {
             arow = tfdata_quarter_ipoissuers[i,]
             industrypeers = tfdata_quarter_fitting_nonissuers[ 
tfdata_quarter_fitting_nonissuers$HSICIG == arow$HSICIG, ]
             industrypeers = industrypeers[ 
order(industrypeers$Market.Cap.13f), ]
             if ( nrow(industrypeers) > 0 ) {
                 if ( nrow(industrypeers[industrypeers$Market.Cap.13f >= 
arow$Market.Cap.13f, ]) > 0 ) {
                     bestpeer = 
industrypeers[industrypeers$Market.Cap.13f >= arow$Market.Cap.13f, ][1,]
                 }
                 else {
                     bestpeer = industrypeers[nrow(industrypeers),]
                 }
                 bestpeer$Quarters.Since.IPO.Issue = 
arow$Quarters.Since.IPO.Issue
                 bestpeer$Peer.To.PERMNO = arow$PERMNO
                 result = rbind(result, as.matrix(bestpeer))
             }
         }
         print (aquarter)
     }

     result = as.data.frame(result)
     names(result) = c(colnames, 'Peer.To.PERMNO')
     return(result)

}
============== end function code===========


on 06/06/2008 01:35 PM Gabor Grothendieck said the following:

I think the posting guide may not be clear enough and have suggested that
it be clarified.  Hopefully this better communicates what is required and why
in a shorter amount of space:

https://stat.ethz.ch/pipermail/r-devel/2008-June/049891.html


On Fri, Jun 6, 2008 at 1:25 PM, Daniel Folkinshteyn <dfolkins at gmail.com> wrote:

i thought since the function code (which i provided in full) was pretty
short, it would be reasonably easy to just read the code and see what it's
doing.

but ok, so... i am attaching a zip file, with a small sample of the data set
(tab delimited), and the function code, in a zip file (posting guidelines
claim that "some archive formats" are allowed, i assume zip is one of
them...

would appreciate your comments! :)

on 06/06/2008 12:05 PM Gabor Grothendieck said the following:

Its summarized in the last line to r-help.  Note reproducible and
minimal.

On Fri, Jun 6, 2008 at 12:03 PM, Daniel Folkinshteyn <dfolkins at gmail.com>
wrote:

i did! what did i miss?

on 06/06/2008 11:45 AM Gabor Grothendieck said the following:

Try reading the posting guide before posting.

On Fri, Jun 6, 2008 at 11:12 AM, Daniel Folkinshteyn
<dfolkins at gmail.com>
wrote:

Anybody have any thoughts on this? Please? :)

on 06/05/2008 02:09 PM Daniel Folkinshteyn said the following:

Hi everyone!

I have a question about data processing efficiency.

My data are as follows: I have a data set on quarterly institutional
ownership of equities; some of them have had recent IPOs, some have
not
(I
have a binary flag set). The total dataset size is 700k+ rows.

My goal is this: For every quarter since issue for each IPO, I need to
find a "matched" firm in the same industry, and close in market cap.
So,
e.g., for firm X, which had an IPO, i need to find a matched
non-issuing
firm in quarter 1 since IPO, then a (possibly different) non-issuing
firm in
quarter 2 since IPO, etc. Repeat for each issuing firm (there are
about
8300
of these).

Thus it seems to me that I need to be doing a lot of data selection
and
subsetting, and looping (yikes!), but the result appears to be highly
inefficient and takes ages (well, many hours). What I am doing, in
pseudocode, is this:

1. for each quarter of data, getting out all the IPOs and all the
eligible
non-issuing firms.
2. for each IPO in a quarter, grab all the non-issuers in the same
industry, sort them by size, and finally grab a matching firm closest
in
size (the exact procedure is to grab the closest bigger firm if one
exists,
and just the biggest available if all are smaller)
3. assign the matched firm-observation the same "quarters since issue"
as
the IPO being matched
4. rbind them all into the "matching" dataset.

The function I currently have is pasted below, for your reference. Is
there any way to make it produce the same result but much faster?
Specifically, I am guessing eliminating some loops would be very good,
but I
don't see how, since I need to do some fancy footwork for each IPO in
each
quarter to find the matching firm. I'll be doing a few things similar
to
this, so it's somewhat important to up the efficiency of this. Maybe
some of
you R-fu masters can clue me in? :)

I would appreciate any help, tips, tricks, tweaks, you name it! :)

========== my function below ===========

fcn_create_nonissuing_match_by_quarterssinceissue = function(tfdata,
quarters_since_issue=40) {

 result = matrix(nrow=0, ncol=ncol(tfdata)) # rbind for matrix is
cheaper, so typecast the result to matrix

 colnames = names(tfdata)

 quarterends = sort(unique(tfdata$DATE))

 for (aquarter in quarterends) {
     tfdata_quarter = tfdata[tfdata$DATE == aquarter, ]

     tfdata_quarter_fitting_nonissuers = tfdata_quarter[
(tfdata_quarter$Quarters.Since.Latest.Issue > quarters_since_issue) &
(tfdata_quarter$IPO.Flag == 0), ]
     tfdata_quarter_ipoissuers = tfdata_quarter[
tfdata_quarter$IPO.Flag
== 1, ]

     for (i in 1:nrow(tfdata_quarter_ipoissuers)) {
         arow = tfdata_quarter_ipoissuers[i,]
         industrypeers = tfdata_quarter_fitting_nonissuers[
tfdata_quarter_fitting_nonissuers$HSICIG == arow$HSICIG, ]
         industrypeers = industrypeers[
order(industrypeers$Market.Cap.13f), ]
         if ( nrow(industrypeers) > 0 ) {
             if ( nrow(industrypeers[industrypeers$Market.Cap.13f >=
arow$Market.Cap.13f, ]) > 0 ) {
                 bestpeer = industrypeers[industrypeers$Market.Cap.13f

= arow$Market.Cap.13f, ][1,]

             }
             else {
                 bestpeer = industrypeers[nrow(industrypeers),]
             }
             bestpeer$Quarters.Since.IPO.Issue =
arow$Quarters.Since.IPO.Issue

#tfdata_quarter$Match.Dummy.By.Quarter[tfdata_quarter$PERMNO ==
bestpeer$PERMNO] = 1
             result = rbind(result, as.matrix(bestpeer))
         }
     }
     #result = rbind(result, tfdata_quarter)
     print (aquarter)
 }

 result = as.data.frame(result)
 names(result) = colnames
 return(result)

}

========= end of my function =============

______________________________________________
R-help at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.