Filter datasets to only keep specified ids
Arguments
- hts_data
List of containing household travel data tables.
- ids
List of ids to keep in all of the tables
- id_name
Name of id being used for filtering (e.g., hh_id, person_id)
Examples
require(data.table)
data(test_data)
hts_filter_data(
hts_data = test_data,
ids = hh[num_people > 5, hh_id],
id_name = "hh_id"
)
#> $hh
#> hh_id sample_segment income_detailed income_followup home_lon home_lat
#> 1: 28 13 999 1 -93.87387 35.32633
#> 2: 176 21 2 995 -89.80981 37.44244
#> 3: 194 18 6 995 -96.11612 36.69269
#> 4: 216 16 6 995 -86.66667 36.93093
#> 5: 243 16 9 995 -86.54655 37.57558
#> 6: 270 18 2 995 -90.09009 35.20721
#> 7: 277 17 999 3 -97.33734 35.52252
#> 8: 462 17 2 995 -82.76276 34.18418
#> 9: 562 10 7 995 -87.44745 36.51752
#> 10: 664 17 7 995 -98.81882 35.70470
#> 11: 724 9 3 995 -92.25225 38.58458
#> 12: 745 19 7 995 -98.13814 37.12012
#> 13: 820 16 999 999 -89.16917 34.84284
#> 14: 845 18 6 995 -99.37938 38.29029
#> 15: 981 2 7 995 -83.22322 34.74474
#> home_county residence_type num_people num_trips hh_weight
#> 1: 2 997 6 37 522
#> 2: 1 4 6 35 90
#> 3: 2 4 6 28 591
#> 4: 2 4 6 43 972
#> 5: 1 1 6 35 523
#> 6: 1 4 6 48 355
#> 7: 3 4 6 56 486
#> 8: 1 4 6 47 15
#> 9: 1 1 6 44 154
#> 10: 3 1 6 43 524
#> 11: 2 4 6 31 664
#> 12: 2 1 9 86 678
#> 13: 1 1 6 40 107
#> 14: 1 4 7 66 498
#> 15: 1 4 6 45 414
#>
#> $person
#> person_id ethnicity_1 ethnicity_2 ethnicity_3 ethnicity_4 ethnicity_997
#> 1: 2 1 0 0 1 1
#> 2: 13 1 0 1 0 1
#> 3: 20 0 0 0 0 0
#> 4: 56 0 0 0 0 0
#> 5: 60 0 0 0 1 0
#> 6: 101 0 0 0 0 0
#> 7: 120 0 0 0 0 0
#> 8: 121 1 0 1 0 0
#> 9: 149 0 0 0 0 0
#> 10: 150 0 0 0 0 0
#> 11: 153 1 1 0 1 1
#> 12: 164 0 0 0 0 0
#> 13: 169 0 0 0 0 0
#> 14: 198 0 0 0 0 0
#> 15: 238 0 0 0 0 0
#> 16: 253 1 1 0 0 1
#> 17: 299 0 0 0 0 0
#> 18: 307 0 0 0 0 0
#> 19: 354 0 0 0 0 0
#> 20: 365 0 0 0 0 0
#> 21: 383 1 1 1 1 0
#> 22: 388 0 0 1 1 1
#> 23: 397 0 0 0 0 0
#> 24: 410 0 0 0 0 0
#> 25: 461 0 0 1 1 1
#> 26: 469 1 0 0 0 0
#> 27: 474 1 0 0 1 1
#> 28: 480 0 0 0 0 0
#> 29: 519 0 1 1 1 1
#> 30: 525 0 0 0 0 0
#> 31: 542 1 0 0 1 1
#> 32: 557 1 0 1 1 0
#> 33: 578 0 0 0 0 0
#> 34: 588 0 1 1 0 1
#> 35: 616 0 0 0 0 0
#> 36: 668 0 1 1 0 0
#> 37: 701 0 0 1 1 0
#> 38: 713 0 0 0 0 0
#> 39: 728 0 0 0 0 0
#> 40: 734 0 1 1 1 0
#> 41: 784 0 0 0 0 0
#> 42: 819 0 1 1 1 0
#> 43: 821 0 0 1 1 1
#> 44: 826 0 0 0 0 0
#> 45: 867 0 1 0 0 1
#> 46: 885 0 0 0 0 0
#> 47: 888 0 0 0 0 0
#> 48: 914 0 0 0 0 0
#> 49: 942 0 0 1 1 1
#> 50: 944 1 0 1 0 0
#> 51: 985 1 1 0 1 0
#> 52: 996 0 0 0 0 0
#> 53: 1028 0 0 0 0 0
#> 54: 1064 0 1 1 1 0
#> 55: 1153 0 0 0 0 0
#> 56: 1163 0 0 0 0 0
#> 57: 1169 0 0 0 0 0
#> 58: 1205 0 0 0 0 0
#> 59: 1231 0 0 0 0 0
#> 60: 1236 0 0 0 0 0
#> 61: 1238 0 0 0 0 0
#> 62: 1250 1 1 0 0 0
#> 63: 1255 0 0 0 0 0
#> 64: 1266 1 1 1 1 1
#> 65: 1292 1 0 1 1 1
#> 66: 1303 1 0 0 1 1
#> 67: 1313 1 1 1 0 1
#> 68: 1344 0 1 0 0 0
#> 69: 1409 0 1 1 0 1
#> 70: 1418 1 0 0 0 1
#> 71: 1435 0 1 0 0 1
#> 72: 1438 1 1 0 1 1
#> 73: 1441 0 0 0 0 0
#> 74: 1452 0 1 1 1 1
#> 75: 1455 0 0 0 0 0
#> 76: 1554 0 0 0 0 0
#> 77: 1566 0 0 0 0 0
#> 78: 1578 0 0 0 0 0
#> 79: 1586 0 0 0 0 0
#> 80: 1629 0 0 0 0 0
#> 81: 1664 0 0 0 0 0
#> 82: 1681 0 0 0 0 0
#> 83: 1699 0 1 0 0 0
#> 84: 1735 0 0 1 1 1
#> 85: 1738 0 0 0 0 0
#> 86: 1757 0 1 1 1 1
#> 87: 1763 0 0 0 0 0
#> 88: 1821 1 1 0 0 0
#> 89: 1864 0 0 1 0 1
#> 90: 1901 0 0 0 0 0
#> 91: 1924 0 0 0 0 0
#> 92: 1974 0 0 0 0 0
#> 93: 2013 1 1 1 1 0
#> 94: 2029 0 1 1 1 1
#> person_id ethnicity_1 ethnicity_2 ethnicity_3 ethnicity_4 ethnicity_997
#> ethnicity_999 race_1 race_2 race_3 race_4 race_5 race_997 race_999 hh_id
#> 1: 0 1 1 0 0 0 1 0 724
#> 2: 0 1 0 0 0 1 0 0 28
#> 3: 1 0 0 0 0 0 0 1 724
#> 4: 1 1 1 1 1 1 1 0 462
#> 5: 0 0 0 0 0 0 0 1 664
#> 6: 1 0 0 0 0 0 0 1 820
#> 7: 1 1 0 1 0 1 0 0 845
#> 8: 0 0 0 0 0 0 0 1 243
#> 9: 1 0 0 0 0 0 0 1 724
#> 10: 1 0 0 0 0 0 0 1 981
#> 11: 0 0 0 0 0 0 0 1 845
#> 12: 1 1 0 0 1 1 0 0 462
#> 13: 1 1 1 0 1 1 0 0 664
#> 14: 1 0 0 0 0 0 0 1 216
#> 15: 1 1 0 1 1 1 0 0 216
#> 16: 0 1 0 0 1 1 0 0 28
#> 17: 1 0 1 1 1 0 1 0 194
#> 18: 1 0 0 0 0 0 0 1 270
#> 19: 1 0 0 0 0 0 0 1 820
#> 20: 1 0 0 0 0 0 0 1 981
#> 21: 0 1 0 1 0 0 0 0 176
#> 22: 0 0 0 0 0 0 0 1 820
#> 23: 0 0 0 0 0 0 0 1 270
#> 24: 1 0 0 0 0 0 0 1 28
#> 25: 0 1 1 0 0 1 0 0 745
#> 26: 0 0 1 0 1 0 0 0 562
#> 27: 0 1 1 1 0 1 1 0 745
#> 28: 1 0 0 0 0 0 0 1 28
#> 29: 0 1 0 0 0 1 0 0 216
#> 30: 1 1 1 1 0 1 0 0 820
#> 31: 0 0 1 0 1 0 1 0 277
#> 32: 0 1 0 1 0 0 1 0 277
#> 33: 1 0 0 0 0 0 0 1 176
#> 34: 0 0 0 0 0 0 0 1 820
#> 35: 1 0 0 0 0 0 0 1 270
#> 36: 0 0 0 0 0 0 0 1 270
#> 37: 0 0 0 1 0 0 0 0 216
#> 38: 1 0 1 0 1 1 0 0 745
#> 39: 1 0 1 0 0 0 0 0 277
#> 40: 0 1 0 0 0 1 1 0 243
#> 41: 1 0 0 0 1 0 1 0 845
#> 42: 0 1 1 1 1 1 0 0 216
#> 43: 0 1 0 0 1 0 1 0 243
#> 44: 1 0 0 0 0 0 0 1 845
#> 45: 0 0 0 1 0 1 1 0 981
#> 46: 1 0 0 0 1 0 0 0 243
#> 47: 1 0 0 0 0 0 0 1 277
#> 48: 1 0 0 0 0 0 0 1 176
#> 49: 0 0 1 0 1 0 1 0 194
#> 50: 0 1 1 0 0 1 0 0 724
#> 51: 0 0 0 1 0 1 1 0 981
#> 52: 1 0 0 0 0 0 0 1 462
#> 53: 1 0 0 0 0 0 0 1 845
#> 54: 0 0 0 0 0 0 0 1 176
#> 55: 1 0 0 0 0 0 0 1 745
#> 56: 1 0 0 0 0 0 0 1 745
#> 57: 1 0 0 1 1 0 1 0 664
#> 58: 1 0 0 0 0 0 0 1 981
#> 59: 1 0 1 1 0 0 0 0 981
#> 60: 1 0 0 1 1 1 1 0 270
#> 61: 1 0 0 0 0 0 0 1 745
#> 62: 0 0 0 0 0 0 0 1 277
#> 63: 1 0 0 0 0 0 0 1 664
#> 64: 0 1 0 1 1 0 0 0 176
#> 65: 0 0 0 0 0 0 0 1 562
#> 66: 0 1 0 0 0 0 0 0 194
#> 67: 0 1 0 1 0 0 0 0 562
#> 68: 0 0 0 0 0 0 0 1 745
#> 69: 0 0 0 0 0 0 0 1 28
#> 70: 0 0 0 0 0 0 0 1 724
#> 71: 0 0 1 0 1 1 1 0 664
#> 72: 0 0 1 1 0 0 0 0 194
#> 73: 1 0 0 0 0 0 0 1 462
#> 74: 0 0 1 0 1 1 1 0 562
#> 75: 1 0 0 0 0 0 0 1 462
#> 76: 1 0 0 0 0 0 0 1 216
#> 77: 1 0 0 0 0 0 0 1 724
#> 78: 1 1 0 1 0 1 1 0 28
#> 79: 1 0 1 0 1 1 1 0 243
#> 80: 1 0 0 0 0 0 0 1 462
#> 81: 1 1 0 0 0 0 1 0 745
#> 82: 1 0 0 0 0 0 0 1 194
#> 83: 0 0 0 1 0 1 1 0 845
#> 84: 0 0 0 0 0 0 0 1 277
#> 85: 1 0 0 0 0 0 0 1 243
#> 86: 0 0 0 0 0 0 0 1 562
#> 87: 1 0 1 0 1 0 0 0 176
#> 88: 0 0 0 0 0 0 0 1 194
#> 89: 0 0 0 0 0 0 0 1 270
#> 90: 1 0 0 0 0 0 1 0 845
#> 91: 1 0 0 0 0 0 0 1 664
#> 92: 1 0 0 0 0 0 0 1 745
#> 93: 0 1 0 0 0 1 1 0 562
#> 94: 0 1 1 0 0 0 1 0 820
#> ethnicity_999 race_1 race_2 race_3 race_4 race_5 race_997 race_999 hh_id
#> age gender employment job_type education num_trips person_weight
#> 1: 11 2 3 995 2 0 128
#> 2: 5 2 1 5 7 8 492
#> 3: 7 995 1 5 6 0 871
#> 4: 3 2 5 995 4 11 683
#> 5: 10 995 2 1 995 4 736
#> 6: 11 999 1 1 6 0 608
#> 7: 6 2 1 1 1 8 59
#> 8: 1 1 5 995 995 0 572
#> 9: 1 999 1 2 4 8 394
#> 10: 9 4 6 995 6 2 15
#> 11: 3 999 1 1 3 6 850
#> 12: 9 999 1 1 4 5 890
#> 13: 10 2 1 1 2 8 163
#> 14: 11 4 5 995 999 10 791
#> 15: 1 2 1 5 7 5 553
#> 16: 1 2 2 2 999 0 895
#> 17: 5 995 5 995 6 9 978
#> 18: 10 2 5 995 999 10 688
#> 19: 5 1 5 995 7 20 427
#> 20: 2 2 5 995 999 1 682
#> 21: 9 999 1 3 3 10 179
#> 22: 6 4 5 995 6 3 951
#> 23: 3 2 995 995 2 8 446
#> 24: 11 2 3 3 3 8 989
#> 25: 1 999 5 995 6 10 884
#> 26: 5 1 5 995 4 11 194
#> 27: 11 4 3 3 5 5 730
#> 28: 8 2 6 995 7 3 375
#> 29: 1 4 995 995 7 14 128
#> 30: 10 2 1 1 2 7 646
#> 31: 4 999 995 995 1 15 804
#> 32: 4 1 2 1 4 0 648
#> 33: 6 999 1 1 999 5 31
#> 34: 11 995 5 995 5 0 825
#> 35: 3 1 1 5 999 20 337
#> 36: 11 4 1 3 2 4 954
#> 37: 12 999 995 995 2 0 479
#> 38: 6 2 5 995 995 4 386
#> 39: 1 995 6 995 995 4 831
#> 40: 12 4 1 1 2 11 544
#> 41: 6 995 995 995 1 3 577
#> 42: 7 2 1 5 6 9 856
#> 43: 11 1 1 5 2 6 556
#> 44: 3 995 3 995 1 16 52
#> 45: 3 995 995 995 6 10 690
#> 46: 12 1 995 995 3 17 227
#> 47: 9 2 1 5 995 4 649
#> 48: 3 4 1 5 3 3 426
#> 49: 11 4 1 1 995 5 33
#> 50: 9 995 995 995 1 8 389
#> 51: 9 1 995 995 2 0 21
#> 52: 2 2 1 995 1 19 618
#> 53: 3 2 1 1 7 9 223
#> 54: 11 4 1 995 1 4 967
#> 55: 1 1 995 995 1 13 166
#> 56: 1 999 1 5 5 24 788
#> 57: 11 2 5 995 4 4 92
#> 58: 1 4 995 995 5 6 540
#> 59: 7 2 1 995 2 26 586
#> 60: 1 999 1 5 4 0 336
#> 61: 5 1 995 995 7 8 321
#> 62: 5 999 3 2 5 15 566
#> 63: 2 999 1 5 4 9 537
#> 64: 6 1 1 1 6 9 376
#> 65: 12 995 2 1 999 7 432
#> 66: 1 999 5 995 1 3 860
#> 67: 1 2 1 2 4 0 113
#> 68: 3 4 995 995 7 3 962
#> 69: 7 1 5 995 5 5 586
#> 70: 11 4 1 5 7 9 594
#> 71: 12 4 1 2 995 5 604
#> 72: 4 999 2 3 7 0 884
#> 73: 11 2 1 5 7 0 590
#> 74: 11 4 3 3 995 8 171
#> 75: 10 2 1 1 995 12 678
#> 76: 4 4 1 995 4 5 51
#> 77: 3 999 1 1 4 6 620
#> 78: 12 2 1 5 1 13 339
#> 79: 3 1 1 1 2 0 474
#> 80: 12 999 2 995 2 0 222
#> 81: 9 4 5 995 6 5 396
#> 82: 12 999 5 995 2 3 287
#> 83: 1 1 5 995 2 18 100
#> 84: 5 2 1 5 1 18 89
#> 85: 9 2 1 2 999 1 578
#> 86: 1 995 5 995 999 0 872
#> 87: 10 995 1 5 999 4 937
#> 88: 12 4 3 5 999 8 321
#> 89: 9 4 5 995 4 6 409
#> 90: 3 995 6 995 6 6 51
#> 91: 7 1 1 1 4 13 294
#> 92: 12 995 1 1 4 14 94
#> 93: 8 2 995 995 3 18 659
#> 94: 2 4 1 3 4 10 438
#> age gender employment job_type education num_trips person_weight
#>
#> $day
#> day_id person_id delivery_2 delivery_3 delivery_4 delivery_5 delivery_6
#> 1: 39 153 0 0 0 1 0
#> 2: 66 578 0 0 0 0 0
#> 3: 99 885 0 0 0 0 0
#> 4: 108 519 0 0 0 0 0
#> 5: 117 56 995 995 995 995 995
#> ---
#> 179: 4085 153 0 0 0 0 0
#> 180: 4098 1231 0 0 0 0 0
#> 181: 4099 525 0 0 0 1 0
#> 182: 4116 1664 995 995 995 995 995
#> 183: 4117 2013 995 995 995 995 995
#> delivery_7 delivery_8 delivery_996 travel_date begin_day end_day hh_id
#> 1: 0 0 0 2023-05-16 1 1 845
#> 2: 0 0 1 2023-04-29 1 1 176
#> 3: 0 0 1 2023-04-26 1 1 243
#> 4: 0 0 1 2023-06-07 1 1 216
#> 5: 995 995 995 2023-05-28 1 1 462
#> ---
#> 179: 0 0 1 2023-05-15 1 1 845
#> 180: 0 0 1 2023-06-08 1 1 981
#> 181: 0 0 0 2023-05-08 1 1 820
#> 182: 995 995 995 2023-04-21 1 1 745
#> 183: 995 995 995 2023-04-20 1 995 562
#> num_trips day_weight
#> 1: 4 519
#> 2: 3 199
#> 3: 3 614
#> 4: 2 381
#> 5: 6 66
#> ---
#> 179: 2 449
#> 180: 2 454
#> 181: 4 272
#> 182: 1 390
#> 183: 7 44
#>
#> $trip
#> day_id trip_id speed_mph distance_miles mode_type mode_1 mode_2
#> 1: 39 7123 9.505124 1.5841873 8 6 995
#> 2: 39 2763 26.539083 4.7328031 8 34 995
#> 3: 39 9715 16.791985 6.9966606 8 6 995
#> 4: 39 5116 14.918498 1.7404915 8 6 995
#> 5: 66 11311 42.142684 9.1309148 8 49 22
#> ---
#> 680: 4117 13390 79.054610 0.1537173 1 1 995
#> 681: 4117 5446 2.317582 0.4557911 1 1 995
#> 682: 4117 5688 31.027928 3.4044533 8 7 995
#> 683: 4117 9478 31.305647 46.9584709 8 7 995
#> 684: 4117 14114 8.680979 1.4468299 8 6 995
#> num_travelers d_purpose_category hh_id person_id travel_date trip_weight
#> 1: 1 7 845 153 2023-05-16 903
#> 2: 2 12 845 153 2023-05-16 308
#> 3: 4 7 845 153 2023-05-16 903
#> 4: 1 7 845 153 2023-05-16 383
#> 5: 5 8 176 578 2023-04-29 459
#> ---
#> 680: 4 8 562 2013 2023-04-20 888
#> 681: 2 7 562 2013 2023-04-20 613
#> 682: 2 1 562 2013 2023-04-20 909
#> 683: 1 2 562 2013 2023-04-20 937
#> 684: 3 9 562 2013 2023-04-20 443
#>
#> $vehicle
#> hh_id vehicle_id fuel_type hh_weight
#> 1: 28 427 1 522
#> 2: 194 101 1 591
#> 3: 216 1321 1 972
#> 4: 270 378 5 355
#> 5: 270 418 1 355
#> 6: 462 1422 1 15
#> 7: 664 1334 1 524
#> 8: 664 85 5 524
#> 9: 724 321 2 664
#> 10: 745 46 1 678
#> 11: 845 1425 1 498
#> 12: 981 142 1 414
#> 13: 981 446 1 414
#> 14: 981 644 1 414
#>