Bind a column from one table to another
Arguments
- lhs_table
Table to bind a column to in data.table format
- rhs_var
Variable to bind to the lhs_table.
- hts_data
List of household, person, vehicle, day, and trip tables in data.table format.
- variable_list
A variable list with descriptions and table locations of variables.
- return_weight_cols
If true binds weight variable along with rhs_var to lhs_table. Default is FALSE.
- cbind_ids
list of unique identifiers for each table in hts_data
- cbind_wts
list of weight for each table in hts_data
Examples
require(data.table)
hts_cbind_var(
lhs_table = trip,
rhs_var = "speed_mph",
hts_data = test_data,
variable_list = variable_list
)
#> Joining speed_mph to table on day_id, trip_id, speed_mph, hh_id, person_id, trip_weight
#> day_id trip_id speed_mph hh_id person_id trip_weight distance_miles
#> 1: 1 6099 3.8030030 642 820 237 0.31691692
#> 2: 1 6848 0.3570582 642 820 957 0.07736261
#> 3: 1 9240 1.3936891 642 820 578 0.47308002
#> 4: 1 13883 10.7289440 642 820 361 10.72894403
#> 5: 1 15759 9.2827577 642 820 287 0.16244826
#> ---
#> 15870: 4125 719 1.5648402 876 1684 954 7.77203953
#> 15871: 4125 4397 8.5320851 876 1684 977 1.42201419
#> 15872: 4125 4505 16.3377147 876 1684 999 3.23577517
#> 15873: 4125 7897 42.9297111 876 1684 167 22.65734754
#> 15874: 4125 14260 10.5795319 876 1684 841 1.76325532
#> mode_type mode_1 mode_2 num_travelers d_purpose_category travel_date
#> 1: 8 34 995 2 7 2023-05-28
#> 2: 8 6 995 1 7 2023-05-28
#> 3: 2 2 995 1 1 2023-05-28
#> 4: 13 2 23 1 11 2023-05-28
#> 5: 1 1 995 1 9 2023-05-28
#> ---
#> 15870: 1 1 995 1 12 2023-05-30
#> 15871: 8 34 995 2 7 2023-05-30
#> 15872: 8 6 995 1 12 2023-05-30
#> 15873: 8 6 995 1 2 2023-05-30
#> 15874: 8 7 995 1 2 2023-05-30
hts_cbind_var(
lhs_table = trip,
rhs_var = "speed_mph",
hts_data = test_data,
variable_list = variable_list,
return_weight_cols = TRUE
)
#> Joining speed_mph to table on day_id, trip_id, speed_mph, hh_id, person_id, trip_weight
#> day_id trip_id speed_mph hh_id person_id trip_weight distance_miles
#> 1: 1 6099 3.8030030 642 820 237 0.31691692
#> 2: 1 6848 0.3570582 642 820 957 0.07736261
#> 3: 1 9240 1.3936891 642 820 578 0.47308002
#> 4: 1 13883 10.7289440 642 820 361 10.72894403
#> 5: 1 15759 9.2827577 642 820 287 0.16244826
#> ---
#> 15870: 4125 719 1.5648402 876 1684 954 7.77203953
#> 15871: 4125 4397 8.5320851 876 1684 977 1.42201419
#> 15872: 4125 4505 16.3377147 876 1684 999 3.23577517
#> 15873: 4125 7897 42.9297111 876 1684 167 22.65734754
#> 15874: 4125 14260 10.5795319 876 1684 841 1.76325532
#> mode_type mode_1 mode_2 num_travelers d_purpose_category travel_date
#> 1: 8 34 995 2 7 2023-05-28
#> 2: 8 6 995 1 7 2023-05-28
#> 3: 2 2 995 1 1 2023-05-28
#> 4: 13 2 23 1 11 2023-05-28
#> 5: 1 1 995 1 9 2023-05-28
#> ---
#> 15870: 1 1 995 1 12 2023-05-30
#> 15871: 8 34 995 2 7 2023-05-30
#> 15872: 8 6 995 1 12 2023-05-30
#> 15873: 8 6 995 1 2 2023-05-30
#> 15874: 8 7 995 1 2 2023-05-30