Remove missing data for summary variables
Source:R/hts_remove_missing_data.R
hts_remove_missing_data.RdRemove missing data for summary variables
Arguments
- hts_data
List containing household, person, day, trip, and vehicle datasets in data.table format.
- variables_dt
A variable list with descriptions and table locations of variables.
- summarize_var
Variable to be summarized that has it's missing data removed.
- ids
names of unique identifiers for each table in hts_data
- summarize_by
Variable being summarized by that has it's missing data removed. Default is NULL.
- missing_values
Missing values that will be removed. Defaults are 995 and 'Missing Response'.
- not_imputable
Value meaning not_imputable that will be removed. Default is -1.
Examples
require(data.table)
hts_remove_missing_data(
hts_data = list(
"hh" = hh,
"person" = person,
"day" = day,
"trip" = trip,
"vehicle" = vehicle
),
variables_dt = variable_list,
summarize_var = "speed_mph",
summarize_by = "mode_type"
)
#> $hh
#> hh_id sample_segment income_detailed income_followup home_lon home_lat
#> 1: 1 13 7 995 -86.30631 38.48649
#> 2: 2 16 5 995 -85.72573 39.71972
#> 3: 3 2 9 995 -83.36336 39.12412
#> 4: 4 4 8 995 -98.43844 36.68569
#> 5: 5 1 8 995 -92.89289 34.96897
#> ---
#> 996: 996 10 9 995 -86.72673 34.93393
#> 997: 997 16 5 995 -84.30430 39.09610
#> 998: 998 13 2 995 -85.66567 35.24224
#> 999: 999 6 9 995 -98.75876 37.89089
#> 1000: 1000 2 5 995 -90.67067 36.41942
#> home_county residence_type num_people num_trips hh_weight
#> 1: 1 4 0 0 54
#> 2: 3 995 4 21 478
#> 3: 3 995 2 17 760
#> 4: 3 1 1 0 754
#> 5: 3 2 1 4 839
#> ---
#> 996: 1 5 2 18 118
#> 997: 3 4 3 11 14
#> 998: 1 995 2 15 546
#> 999: 2 4 1 16 745
#> 1000: 3 4 1 10 133
#>
#> $person
#> person_id ethnicity_1 ethnicity_2 ethnicity_3 ethnicity_4 ethnicity_997
#> 1: 1 0 1 1 0 1
#> 2: 2 1 0 0 1 1
#> 3: 3 0 0 0 0 0
#> 4: 4 0 0 0 0 0
#> 5: 5 0 1 1 0 1
#> ---
#> 2043: 2043 1 0 0 0 1
#> 2044: 2044 1 0 0 1 1
#> 2045: 2045 0 0 0 0 0
#> 2046: 2046 0 0 0 0 1
#> 2047: 2047 0 0 0 0 0
#> ethnicity_999 race_1 race_2 race_3 race_4 race_5 race_997 race_999 hh_id
#> 1: 0 0 0 0 0 0 0 1 356
#> 2: 0 1 1 0 0 0 1 0 724
#> 3: 1 0 1 0 1 1 1 0 681
#> 4: 1 0 0 0 0 0 0 1 114
#> 5: 0 0 0 0 0 0 0 1 165
#> ---
#> 2043: 0 0 0 1 1 0 0 0 931
#> 2044: 0 0 0 0 0 0 0 1 667
#> 2045: 1 0 0 0 0 0 0 1 543
#> 2046: 0 0 0 0 0 0 0 1 749
#> 2047: 1 0 0 0 0 0 0 1 364
#> age gender employment job_type education num_trips person_weight
#> 1: 10 2 1 1 6 12 229
#> 2: 11 2 3 995 2 0 128
#> 3: 7 2 995 995 3 10 888
#> 4: 2 999 995 995 6 8 350
#> 5: 10 995 2 1 4 4 825
#> ---
#> 2043: 7 995 1 1 995 8 116
#> 2044: 1 995 1 1 995 12 122
#> 2045: 3 1 1 5 3 0 494
#> 2046: 11 2 1 5 6 11 874
#> 2047: 11 995 5 995 3 0 393
#>
#> $day
#> day_id person_id delivery_2 delivery_3 delivery_4 delivery_5 delivery_6
#> 1: 1 820 995 995 995 995 995
#> 2: 2 24 0 0 0 0 0
#> 3: 3 1866 0 0 0 0 0
#> 4: 4 1915 995 995 995 995 995
#> 5: 5 415 0 0 0 1 0
#> ---
#> 4121: 4121 1321 0 1 0 1 0
#> 4122: 4122 1619 0 0 0 0 0
#> 4123: 4123 886 995 995 995 995 995
#> 4124: 4124 964 0 0 0 0 0
#> 4125: 4125 1684 0 0 0 0 0
#> delivery_7 delivery_8 delivery_996 travel_date begin_day end_day hh_id
#> 1: 995 995 995 2023-05-28 1 1 642
#> 2: 0 0 1 2023-05-23 1 1 24
#> 3: 0 0 1 2023-05-25 1 1 888
#> 4: 995 995 995 2023-04-17 1 1 875
#> 5: 0 0 0 2023-05-26 1 1 976
#> ---
#> 4121: 0 0 0 2023-05-20 1 1 595
#> 4122: 0 0 1 2023-04-09 1 1 47
#> 4123: 995 995 995 2023-04-12 995 995 474
#> 4124: 0 0 1 2023-04-22 1 1 764
#> 4125: 0 0 1 2023-05-30 1 1 876
#> num_trips day_weight
#> 1: 5 583
#> 2: 5 220
#> 3: 2 73
#> 4: 6 63
#> 5: 3 139
#> ---
#> 4121: 1 755
#> 4122: 5 996
#> 4123: 5 818
#> 4124: 2 460
#> 4125: 5 762
#>
#> $trip
#> day_id trip_id speed_mph distance_miles mode_type mode_1 mode_2
#> 1: 1 6848 0.3570582 0.07736261 8 6 995
#> 2: 1 6099 3.8030030 0.31691692 8 34 995
#> 3: 1 15759 9.2827577 0.16244826 1 1 995
#> 4: 1 13883 10.7289440 10.72894403 13 2 23
#> 5: 1 9240 1.3936891 0.47308002 2 2 995
#> ---
#> 15449: 4125 4505 16.3377147 3.23577517 8 6 995
#> 15450: 4125 7897 42.9297111 22.65734754 8 6 995
#> 15451: 4125 719 1.5648402 7.77203953 1 1 995
#> 15452: 4125 14260 10.5795319 1.76325532 8 7 995
#> 15453: 4125 4397 8.5320851 1.42201419 8 34 995
#> num_travelers d_purpose_category hh_id person_id travel_date trip_weight
#> 1: 1 7 642 820 2023-05-28 957
#> 2: 2 7 642 820 2023-05-28 237
#> 3: 1 9 642 820 2023-05-28 287
#> 4: 1 11 642 820 2023-05-28 361
#> 5: 1 1 642 820 2023-05-28 578
#> ---
#> 15449: 1 12 876 1684 2023-05-30 999
#> 15450: 1 2 876 1684 2023-05-30 167
#> 15451: 1 12 876 1684 2023-05-30 954
#> 15452: 1 2 876 1684 2023-05-30 841
#> 15453: 2 7 876 1684 2023-05-30 977
#>
#> $vehicle
#> hh_id vehicle_id fuel_type hh_weight
#> 1: 1 1103 1 54
#> 2: 2 521 1 478
#> 3: 2 1356 1 478
#> 4: 3 1210 1 760
#> 5: 6 1394 1 987
#> ---
#> 1430: 998 520 1 546
#> 1431: 998 1141 1 546
#> 1432: 999 659 1 745
#> 1433: 999 108 1 745
#> 1434: 1000 1031 1 133
#>