About TravelSurveyTools
The travelSurveyTools package provides tools for R users
to aid use of data from household travel surveys. Some possible uses
include creating custom cross tabs, labeling data, and calculating trip
rates.
Data Assumptions
travelSurveyTools assumes the the data have the
structure shown below.
hts_data
hts_data is a list of five core tables:
hh
Household dataset
- hh_id: household ID
- survey variables asked on a household level
- hh_weight: household weight
person
Person dataset
- hh_id: household ID
- person_id: person ID
- survey variables asked on a person level
- person_weight: person weight
day
Day dataset
- hh_id: household ID
- person_id: person ID
- day_id: day ID
- survey variable asked on a day level
- day_weight: day weight
Codebook
In addition to data from the household travel survey. The codebook is also required. The codebook is assumed to be in two parts:
variable_list
A dataset containing information about all variables existing in the hh, person, day, trip, and vehicle tables. The variables are as follows:
- variable: Name of the variable
- is_checkbox: The variable is a multiple response categorical variable question (multiple or mrcv variable)
- hh: The variable exists in the hh table
- person: The variable exists in the person table
- day: The variable exists in the day table
- trip: The variable exists in the trip table
- vehicle: The variable exists in the vehicle table
- location: The variable exists in the location table
- data_type: Data type of the variable
- description: A description of the variable
- logic: Conditions where the variable should have a value
- shared_name: the shared name of a multiple response categorical variable variable or the variable name for non-multiple response categorical variable variables
value_labels
A dataset containing the values for all variables found in variable_list The variables are as follows:
- variable: Name of the variable
- value: The numeric value of the variable
- label: What the numeric value of the variable represents
- label_value: value concatenated with the label (e.g., 11 85 or older)
- val_order: order for each variable label to appear in
Using travelSurveyTools
Prepping the Data
In order to create summaries of our data we first need to prepare our
data. We can do this by using hts_prep_variable. This will
return a categorical (cat) and numeric (num) (if applicable) prepped
data table that can be used to create summaries.
library(travelSurveyTools)
library(data.table)
# Load data
data("test_data")
data("variable_list")
data("value_labels")
speed_list = hts_prep_variable(
summarize_var = "speed_mph",
variables_dt = variable_list,
data = test_data
)## Warning in hts_remove_outliers(var_dt, numvar = summarize_var, threshold =
## threshold): 378 outliers were removed based on the threshold of 0.975.Numeric variables will be automatically binned in
hts_prep_variable to create categorical summaries. Here we
can make a categorical summary of a numeric variable using
hts_summary.
speed_cat_summary = hts_summary(
prepped_dt = speed_list$cat,
summarize_var = "speed_mph",
summarize_by = NULL,
summarize_vartype = "categorical",
weighted = FALSE
)
speed_cat_summary$summary## $unwtd
## speed_mph count prop
## 1: 1 or less 635 0.04313273
## 2: 1-10 7289 0.49510936
## 3: 10-19 3618 0.24575465
## 4: 19-28 1819 0.12355658
## 5: 28-37 726 0.04931395
## 6: 37-44 285 0.01935878
## 7: 44 or more 350 0.02377394Additionally, for numeric variables we can create numeric summaries.
speed_num_summary = hts_summary(
prepped_dt = speed_list$num,
summarize_var = "speed_mph",
summarize_by = NULL,
summarize_vartype = "numeric",
weighted = FALSE
)
speed_num_summary$summary## $unwtd
## count min max mean median
## 1: 14722 0 112.5371 12.27017 8.914392Using Weighted Data
Additionally, we can use weighted data by setting
weighted = TRUE and specifying the name of the weight to be
used (wtname).
speed_cat_summary = hts_summary(
prepped_dt = speed_list$cat,
summarize_var = "speed_mph",
summarize_by = NULL,
summarize_vartype = "categorical",
weighted = TRUE,
wtname = "trip_weight"
)
speed_cat_summary$summary## $unwtd
## speed_mph count prop
## 1: 1 or less 635 0.04313273
## 2: 1-10 7289 0.49510936
## 3: 10-19 3618 0.24575465
## 4: 19-28 1819 0.12355658
## 5: 28-37 726 0.04931395
## 6: 37-44 285 0.01935878
## 7: 44 or more 350 0.02377394
##
## $wtd
## speed_mph count prop est
## 1: 1 or less 635 0.04225006 314952
## 2: 1-10 7289 0.49882701 3718493
## 3: 10-19 3618 0.24538136 1829189
## 4: 19-28 1819 0.12194475 909034
## 5: 28-37 726 0.04891599 364643
## 6: 37-44 285 0.02049065 152747
## 7: 44 or more 350 0.02219016 165416
##
## $weight_name
## [1] "trip_weight"Calculating Standard Errors
Additionally, by specifying se = TRUE we can calculate
standard errors.
speed_cat_summary = hts_summary(
prepped_dt = speed_list$cat,
summarize_var = "speed_mph",
summarize_by = NULL,
summarize_vartype = "categorical",
weighted = TRUE,
wtname = "trip_weight",
se = TRUE
)
speed_cat_summary$summary## $unwtd
## speed_mph count prop
## 1: 1 or less 635 0.04313273
## 2: 1-10 7289 0.49510936
## 3: 10-19 3618 0.24575465
## 4: 19-28 1819 0.12355658
## 5: 28-37 726 0.04931395
## 6: 37-44 285 0.01935878
## 7: 44 or more 350 0.02377394
##
## $wtd
## speed_mph count prop prop_se est est_se
## 1: 1 or less 635 0.04225006 0.001886624 314952 14128.27
## 2: 1-10 7289 0.49882701 0.004730868 3718493 39396.65
## 3: 10-19 3618 0.24538136 0.004073123 1829189 31541.65
## 4: 19-28 1819 0.12194475 0.003087738 909034 23382.12
## 5: 28-37 726 0.04891599 0.002026164 364643 15186.92
## 6: 37-44 285 0.02049065 0.001361407 152747 10179.97
## 7: 44 or more 350 0.02219016 0.001373240 165416 10258.47
##
## $weight_name
## [1] "trip_weight"Summarizing Two Variables
If we want to summarize a variable by another variable (e.g., mode
type by a person’s race, mode_type by a person’s ethnicity, age by study
year) we can use the summarize_by argument.
mode_type_list = hts_prep_variable(
summarize_var = "mode_type",
summarize_by = "race",
variables_dt = variable_list,
data = test_data
)
mode_by_race_summary = hts_summary(
prepped_dt = mode_type_list$cat,
summarize_var = "mode_type",
summarize_by = "race",
summarize_vartype = "categorical",
weighted = TRUE,
wtname = "trip_weight",
se = TRUE
)
mode_by_race_summary$summary## $unwtd
## race mode_type count prop
## 1: African American or Black 1 22 0.2222222222
## 2: African American or Black 2 3 0.0303030303
## 3: African American or Black 8 68 0.6868686869
## 4: African American or Black 13 6 0.0606060606
## 5: American Indian or Alaska Native 1 23 0.2555555556
## 6: American Indian or Alaska Native 2 3 0.0333333333
## 7: American Indian or Alaska Native 5 1 0.0111111111
## 8: American Indian or Alaska Native 6 1 0.0111111111
## 9: American Indian or Alaska Native 8 57 0.6333333333
## 10: American Indian or Alaska Native 10 2 0.0222222222
## 11: American Indian or Alaska Native 11 1 0.0111111111
## 12: American Indian or Alaska Native 13 2 0.0222222222
## 13: Asian 1 46 0.2705882353
## 14: Asian 2 4 0.0235294118
## 15: Asian 6 1 0.0058823529
## 16: Asian 7 1 0.0058823529
## 17: Asian 8 104 0.6117647059
## 18: Asian 10 3 0.0176470588
## 19: Asian 11 1 0.0058823529
## 20: Asian 13 10 0.0588235294
## 21: Native Hawaiian or other Pacific Islander 1 43 0.2544378698
## 22: Native Hawaiian or other Pacific Islander 2 7 0.0414201183
## 23: Native Hawaiian or other Pacific Islander 6 2 0.0118343195
## 24: Native Hawaiian or other Pacific Islander 7 1 0.0059171598
## 25: Native Hawaiian or other Pacific Islander 8 106 0.6272189349
## 26: Native Hawaiian or other Pacific Islander 11 1 0.0059171598
## 27: Native Hawaiian or other Pacific Islander 13 9 0.0532544379
## 28: White 1 45 0.2941176471
## 29: White 2 4 0.0261437908
## 30: White 4 1 0.0065359477
## 31: White 6 2 0.0130718954
## 32: White 8 90 0.5882352941
## 33: White 13 11 0.0718954248
## 34: Two or more 1 2058 0.2920391656
## 35: Two or more 2 148 0.0210018448
## 36: Two or more 3 3 0.0004257131
## 37: Two or more 4 20 0.0028380871
## 38: Two or more 5 4 0.0005676174
## 39: Two or more 6 36 0.0051085568
## 40: Two or more 7 40 0.0056761743
## 41: Two or more 8 4299 0.6100468284
## 42: Two or more 10 36 0.0051085568
## 43: Two or more 11 28 0.0039733220
## 44: Two or more 12 6 0.0008514261
## 45: Two or more 13 355 0.0503760465
## 46: Two or more 14 14 0.0019866610
## 47: Other race 1 28 0.3146067416
## 48: Other race 6 1 0.0112359551
## 49: Other race 8 53 0.5955056180
## 50: Other race 10 2 0.0224719101
## 51: Other race 13 5 0.0561797753
## 52: Prefer not to answer 1 2176 0.2918846412
## 53: Prefer not to answer 2 183 0.0245472837
## 54: Prefer not to answer 3 9 0.0012072435
## 55: Prefer not to answer 4 22 0.0029510396
## 56: Prefer not to answer 5 3 0.0004024145
## 57: Prefer not to answer 6 43 0.0057679410
## 58: Prefer not to answer 7 43 0.0057679410
## 59: Prefer not to answer 8 4456 0.5977196512
## 60: Prefer not to answer 10 51 0.0068410463
## 61: Prefer not to answer 11 22 0.0029510396
## 62: Prefer not to answer 12 6 0.0008048290
## 63: Prefer not to answer 13 425 0.0570087190
## 64: Prefer not to answer 14 16 0.0021462106
## race mode_type count prop
##
## $wtd
## race mode_type count prop
## 1: African American or Black 1 22 0.2425302553
## 2: African American or Black 2 3 0.0322180413
## 3: African American or Black 8 68 0.6657378216
## 4: African American or Black 13 6 0.0595138818
## 5: American Indian or Alaska Native 1 23 0.2377237162
## 6: American Indian or Alaska Native 2 3 0.0464969871
## 7: American Indian or Alaska Native 5 1 0.0064529184
## 8: American Indian or Alaska Native 6 1 0.0122088317
## 9: American Indian or Alaska Native 8 57 0.6401879665
## 10: American Indian or Alaska Native 10 2 0.0240129508
## 11: American Indian or Alaska Native 11 1 0.0215621908
## 12: American Indian or Alaska Native 13 2 0.0113544383
## 13: Asian 1 46 0.2677774299
## 14: Asian 2 4 0.0178672881
## 15: Asian 6 1 0.0057161409
## 16: Asian 7 1 0.0007536494
## 17: Asian 8 104 0.6057138219
## 18: Asian 10 3 0.0213688592
## 19: Asian 11 1 0.0104699294
## 20: Asian 13 10 0.0703328811
## 21: Native Hawaiian or other Pacific Islander 1 43 0.2519546682
## 22: Native Hawaiian or other Pacific Islander 2 7 0.0575492058
## 23: Native Hawaiian or other Pacific Islander 6 2 0.0097055320
## 24: Native Hawaiian or other Pacific Islander 7 1 0.0047996981
## 25: Native Hawaiian or other Pacific Islander 8 106 0.6093847660
## 26: Native Hawaiian or other Pacific Islander 11 1 0.0097998750
## 27: Native Hawaiian or other Pacific Islander 13 9 0.0568062549
## 28: White 1 45 0.2736028717
## 29: White 2 4 0.0297990108
## 30: White 4 1 0.0128238502
## 31: White 6 2 0.0193727822
## 32: White 8 90 0.5994327912
## 33: White 13 11 0.0649686939
## 34: Two or more 1 2058 0.2915903665
## 35: Two or more 2 148 0.0193177986
## 36: Two or more 3 3 0.0002755179
## 37: Two or more 4 20 0.0034360184
## 38: Two or more 5 4 0.0009396083
## 39: Two or more 6 36 0.0053118852
## 40: Two or more 7 40 0.0052803416
## 41: Two or more 8 4299 0.6102537891
## 42: Two or more 10 36 0.0057560078
## 43: Two or more 11 28 0.0039066600
## 44: Two or more 12 6 0.0008086884
## 45: Two or more 13 355 0.0509043799
## 46: Two or more 14 14 0.0022189382
## 47: Other race 1 28 0.3300569182
## 48: Other race 6 1 0.0121151165
## 49: Other race 8 53 0.5754451734
## 50: Other race 10 2 0.0096463758
## 51: Other race 13 5 0.0727364162
## 52: Prefer not to answer 1 2176 0.2901116906
## 53: Prefer not to answer 2 183 0.0250127937
## 54: Prefer not to answer 3 9 0.0008927077
## 55: Prefer not to answer 4 22 0.0023674661
## 56: Prefer not to answer 5 3 0.0004499353
## 57: Prefer not to answer 6 43 0.0059550102
## 58: Prefer not to answer 7 43 0.0054772192
## 59: Prefer not to answer 8 4456 0.5990344431
## 60: Prefer not to answer 10 51 0.0066169138
## 61: Prefer not to answer 11 22 0.0030776528
## 62: Prefer not to answer 12 6 0.0008958912
## 63: Prefer not to answer 13 425 0.0587054120
## 64: Prefer not to answer 14 16 0.0014028643
## race mode_type count prop
## prop_se est est_se
## 1: 0.0488793701 11924 2760.2835
## 2: 0.0192747891 1584 961.7070
## 3: 0.0538431192 32731 4557.6808
## 4: 0.0278595569 2926 1415.2034
## 5: 0.0489623011 10573 2431.3038
## 6: 0.0265355469 2068 1210.9162
## 7: 0.0064587476 287 287.0000
## 8: 0.0121494434 543 543.0000
## 9: 0.0568590617 28473 4415.2742
## 10: 0.0171042374 1068 768.2942
## 11: 0.0212536599 959 959.0000
## 12: 0.0089016245 505 395.6596
## 13: 0.0390288610 23095 3939.9840
## 14: 0.0108784324 1541 945.4068
## 15: 0.0057056370 493 493.0000
## 16: 0.0007560023 65 65.0000
## 17: 0.0430582623 52241 5824.9319
## 18: 0.0128200078 1843 1117.8535
## 19: 0.0104007037 903 903.0000
## 20: 0.0233047946 6066 2090.2978
## 21: 0.0388570716 21365 3803.6897
## 22: 0.0228735703 4880 2008.4349
## 23: 0.0071925215 823 611.3568
## 24: 0.0047960813 407 407.0000
## 25: 0.0440034185 51674 5845.4095
## 26: 0.0097433256 831 831.0000
## 27: 0.0217247910 4817 1901.8419
## 28: 0.0409308580 19970 3464.8052
## 29: 0.0159829589 2175 1184.4750
## 30: 0.0127152614 936 936.0000
## 31: 0.0136901767 1414 1010.1692
## 32: 0.0457651407 43752 5263.5573
## 33: 0.0239756794 4742 1814.6243
## 34: 0.0062024440 1044577 25013.0886
## 35: 0.0018149697 69203 6536.9674
## 36: 0.0001992965 987 713.9738
## 37: 0.0008360485 12309 2998.9750
## 38: 0.0004717023 3366 1690.5458
## 39: 0.0009889333 19029 3548.7518
## 40: 0.0009940850 18916 3567.4116
## 41: 0.0066548054 2186139 33829.8317
## 42: 0.0010634803 20620 3817.7647
## 43: 0.0008631699 13995 3096.3278
## 44: 0.0003923923 2897 1406.0707
## 45: 0.0030039411 182357 10947.9882
## 46: 0.0006557321 7949 2350.8972
## 47: 0.0573116049 14439 3049.7214
## 48: 0.0120592582 530 530.0000
## 49: 0.0604560934 25174 4056.9946
## 50: 0.0088957777 422 389.4846
## 51: 0.0338891184 3182 1547.2956
## 52: 0.0060416540 1093556 25527.6793
## 53: 0.0020680881 94284 7856.6878
## 54: 0.0003637299 3365 1371.2763
## 55: 0.0005670664 8924 2138.0610
## 56: 0.0003045156 1696 1148.0627
## 57: 0.0010522269 22447 3974.6248
## 58: 0.0009766971 20646 3687.7976
## 59: 0.0065250441 2258019 34349.4918
## 60: 0.0010559060 24942 3987.6756
## 61: 0.0007423448 11601 2801.0127
## 62: 0.0004094685 3377 1543.9496
## 63: 0.0031475192 221286 12101.0995
## 64: 0.0004001287 5288 1508.1227
## prop_se est est_se
##
## $weight_name
## [1] "trip_weight"
if (FALSE) {
age_study_year_list = hts_prep_variable(
summarize_var = "age",
summarize_by = "study_year",
variables_dt = variable_list,
data = test_data
)
mode_by_race_summary = hts_summary(
prepped_dt = age_study_year_list$cat,
summarize_var = "age",
summarize_by = "study_year",
summarize_vartype = "categorical",
weighted = TRUE,
wtname = "trip_weight",
se = TRUE
)
}If we want to summarize a multiple response categorical variable
(mrcv or multiple), we can set summarize_vartype to
checkbox.
race_list = hts_prep_variable(
summarize_var = "race",
summarize_by = "mode_type",
variables_dt = variable_list,
data = test_data
)
mode_by_race_summary = hts_summary(
prepped_dt = race_list$cat,
summarize_var = "race",
summarize_by = "mode_type",
summarize_vartype = "checkbox",
weighted = TRUE,
wtname = "trip_weight",
se = TRUE
)
mode_by_race_summary$summary## $unwtd
## mode_type race count prop
## 1: 1 African American or Black 1173 0.26072461
## 2: 1 American Indian or Alaska Native 1149 0.25539009
## 3: 1 Asian 1095 0.24338742
## 4: 1 Native Hawaiian or other Pacific Islander 1145 0.25450100
## 5: 1 White 1188 0.26405868
## 6: 1 Other race 1150 0.25561236
## 7: 1 Prefer not to answer 2176 0.48366304
## 8: 2 African American or Black 79 0.22005571
## 9: 2 American Indian or Alaska Native 78 0.21727019
## 10: 2 Asian 68 0.18941504
## 11: 2 Native Hawaiian or other Pacific Islander 94 0.26183844
## 12: 2 White 87 0.24233983
## 13: 2 Other race 85 0.23676880
## 14: 2 Prefer not to answer 183 0.50974930
## 15: 3 American Indian or Alaska Native 2 0.16666667
## 16: 3 Asian 1 0.08333333
## 17: 3 Native Hawaiian or other Pacific Islander 1 0.08333333
## 18: 3 White 2 0.16666667
## 19: 3 Other race 1 0.08333333
## 20: 3 Prefer not to answer 9 0.75000000
## 21: 4 African American or Black 9 0.20454545
## 22: 4 American Indian or Alaska Native 11 0.25000000
## 23: 4 Asian 11 0.25000000
## 24: 4 Native Hawaiian or other Pacific Islander 9 0.20454545
## 25: 4 White 10 0.22727273
## 26: 4 Other race 10 0.22727273
## 27: 4 Prefer not to answer 22 0.50000000
## 28: 5 African American or Black 2 0.25000000
## 29: 5 American Indian or Alaska Native 2 0.25000000
## 30: 5 Asian 2 0.25000000
## 31: 5 Native Hawaiian or other Pacific Islander 3 0.37500000
## 32: 5 White 1 0.12500000
## 33: 5 Other race 2 0.25000000
## 34: 5 Prefer not to answer 3 0.37500000
## 35: 6 African American or Black 24 0.27586207
## 36: 6 American Indian or Alaska Native 21 0.24137931
## 37: 6 Asian 17 0.19540230
## 38: 6 Native Hawaiian or other Pacific Islander 18 0.20689655
## 39: 6 White 16 0.18390805
## 40: 6 Other race 27 0.31034483
## 41: 6 Prefer not to answer 43 0.49425287
## 42: 7 African American or Black 21 0.24418605
## 43: 7 American Indian or Alaska Native 21 0.24418605
## 44: 7 Asian 15 0.17441860
## 45: 7 Native Hawaiian or other Pacific Islander 24 0.27906977
## 46: 7 White 20 0.23255814
## 47: 7 Other race 21 0.24418605
## 48: 7 Prefer not to answer 43 0.50000000
## 49: 8 African American or Black 2422 0.25956489
## 50: 8 American Indian or Alaska Native 2341 0.25088415
## 51: 8 Asian 2333 0.25002679
## 52: 8 Native Hawaiian or other Pacific Islander 2457 0.26331583
## 53: 8 White 2503 0.26824563
## 54: 8 Other race 2375 0.25452792
## 55: 8 Prefer not to answer 4456 0.47754796
## 56: 10 African American or Black 19 0.19587629
## 57: 10 American Indian or Alaska Native 26 0.26804124
## 58: 10 Asian 24 0.24742268
## 59: 10 Native Hawaiian or other Pacific Islander 20 0.20618557
## 60: 10 White 17 0.17525773
## 61: 10 Other race 21 0.21649485
## 62: 10 Prefer not to answer 51 0.52577320
## 63: 11 African American or Black 18 0.33962264
## 64: 11 American Indian or Alaska Native 14 0.26415094
## 65: 11 Asian 18 0.33962264
## 66: 11 Native Hawaiian or other Pacific Islander 16 0.30188679
## 67: 11 White 15 0.28301887
## 68: 11 Other race 16 0.30188679
## 69: 11 Prefer not to answer 22 0.41509434
## 70: 12 African American or Black 4 0.33333333
## 71: 12 American Indian or Alaska Native 3 0.25000000
## 72: 12 Asian 1 0.08333333
## 73: 12 Native Hawaiian or other Pacific Islander 4 0.33333333
## 74: 12 White 5 0.41666667
## 75: 12 Other race 2 0.16666667
## 76: 12 Prefer not to answer 6 0.50000000
## 77: 13 African American or Black 206 0.24700240
## 78: 13 American Indian or Alaska Native 189 0.22661871
## 79: 13 Asian 184 0.22062350
## 80: 13 Native Hawaiian or other Pacific Islander 210 0.25179856
## 81: 13 White 220 0.26378897
## 82: 13 Other race 196 0.23501199
## 83: 13 Prefer not to answer 425 0.50959233
## 84: 14 African American or Black 5 0.16129032
## 85: 14 American Indian or Alaska Native 8 0.25806452
## 86: 14 Asian 8 0.25806452
## 87: 14 Native Hawaiian or other Pacific Islander 7 0.22580645
## 88: 14 White 5 0.16129032
## 89: 14 Other race 6 0.19354839
## 90: 14 Prefer not to answer 16 0.51612903
## mode_type race count prop
##
## $wtd
## mode_type race count prop
## 1: 1 African American or Black 1173 0.26371389
## 2: 1 American Indian or Alaska Native 1149 0.26062794
## 3: 1 Asian 1095 0.25433099
## 4: 1 Native Hawaiian or other Pacific Islander 1145 0.25516511
## 5: 1 White 1188 0.26543213
## 6: 1 Other race 1150 0.25876904
## 7: 1 Prefer not to answer 2176 0.48830386
## 8: 2 African American or Black 79 0.19397957
## 9: 2 American Indian or Alaska Native 78 0.21172220
## 10: 2 Asian 68 0.17892850
## 11: 2 Native Hawaiian or other Pacific Islander 94 0.25340427
## 12: 2 White 87 0.22521979
## 13: 2 Other race 85 0.21375366
## 14: 2 Prefer not to answer 183 0.53651236
## 15: 3 American Indian or Alaska Native 2 0.07054228
## 16: 3 Asian 1 0.15625000
## 17: 3 Native Hawaiian or other Pacific Islander 1 0.03791360
## 18: 3 White 2 0.18887868
## 19: 3 Other race 1 0.03791360
## 20: 3 Prefer not to answer 9 0.77320772
## 21: 4 African American or Black 9 0.28639091
## 22: 4 American Indian or Alaska Native 11 0.26956561
## 23: 4 Asian 11 0.29396906
## 24: 4 Native Hawaiian or other Pacific Islander 9 0.24547792
## 25: 4 White 10 0.32080834
## 26: 4 Other race 10 0.27299382
## 27: 4 Prefer not to answer 22 0.40254409
## 28: 5 African American or Black 2 0.33090297
## 29: 5 American Indian or Alaska Native 2 0.18508132
## 30: 5 Asian 2 0.32978127
## 31: 5 Native Hawaiian or other Pacific Islander 3 0.46120770
## 32: 5 White 1 0.16806880
## 33: 5 Other race 2 0.33501589
## 34: 5 Prefer not to answer 3 0.31706861
## 35: 6 African American or Black 24 0.29466198
## 36: 6 American Indian or Alaska Native 21 0.22747852
## 37: 6 Asian 17 0.19717750
## 38: 6 Native Hawaiian or other Pacific Islander 18 0.19435058
## 39: 6 White 16 0.19828176
## 40: 6 Other race 27 0.33450385
## 41: 6 Prefer not to answer 43 0.49574858
## 42: 7 African American or Black 21 0.26624869
## 43: 7 American Indian or Alaska Native 21 0.23157816
## 44: 7 Asian 15 0.20025478
## 45: 7 Native Hawaiian or other Pacific Islander 24 0.26315132
## 46: 7 White 20 0.26202728
## 47: 7 Other race 21 0.28235999
## 48: 7 Prefer not to answer 43 0.51571165
## 49: 8 African American or Black 2422 0.26321795
## 50: 8 American Indian or Alaska Native 2341 0.25013558
## 51: 8 Asian 2333 0.25196235
## 52: 8 Native Hawaiian or other Pacific Islander 2457 0.26776841
## 53: 8 White 2503 0.27305121
## 54: 8 Other race 2375 0.25516272
## 55: 8 Prefer not to answer 4456 0.48266802
## 56: 10 African American or Black 19 0.23781573
## 57: 10 American Indian or Alaska Native 26 0.28499847
## 58: 10 Asian 24 0.31129972
## 59: 10 Native Hawaiian or other Pacific Islander 20 0.23196646
## 60: 10 White 17 0.18989672
## 61: 10 Other race 21 0.26256263
## 62: 10 Prefer not to answer 51 0.51011351
## 63: 11 African American or Black 18 0.31075683
## 64: 11 American Indian or Alaska Native 14 0.25281912
## 65: 11 Asian 18 0.37686026
## 66: 11 Native Hawaiian or other Pacific Islander 16 0.29131465
## 67: 11 White 15 0.22994804
## 68: 11 Other race 16 0.29364771
## 69: 11 Prefer not to answer 22 0.41008873
## 70: 12 African American or Black 4 0.30108384
## 71: 12 American Indian or Alaska Native 3 0.29693975
## 72: 12 Asian 1 0.06391457
## 73: 12 Native Hawaiian or other Pacific Islander 4 0.33854001
## 74: 12 White 5 0.30554670
## 75: 12 Other race 2 0.12320689
## 76: 12 Prefer not to answer 6 0.53825311
## 77: 13 African American or Black 206 0.26177265
## 78: 13 American Indian or Alaska Native 189 0.22290264
## 79: 13 Asian 184 0.22623925
## 80: 13 Native Hawaiian or other Pacific Islander 210 0.25100439
## 81: 13 White 220 0.25915925
## 82: 13 Other race 196 0.24087949
## 83: 13 Prefer not to answer 425 0.51959585
## 84: 14 African American or Black 5 0.21787414
## 85: 14 American Indian or Alaska Native 8 0.32953086
## 86: 14 Asian 8 0.34955050
## 87: 14 Native Hawaiian or other Pacific Islander 7 0.25783788
## 88: 14 White 5 0.25043439
## 89: 14 Other race 6 0.25542041
## 90: 14 Prefer not to answer 16 0.39948629
## mode_type race count prop
## prop_se est est_se wtd_group_n
## 1: 0.007521060 590587 19751.7986 2239499
## 2: 0.007495255 583676 19648.7332 2239499
## 3: 0.007466454 569574 19535.6370 2239499
## 4: 0.007414976 571442 19331.1747 2239499
## 5: 0.007526441 594435 19772.6450 2239499
## 6: 0.007461661 579513 19514.5648 2239499
## 7: 0.008563971 1093556 26811.8530 2239499
## 8: 0.022599934 34089 4414.1432 175735
## 9: 0.024164798 37207 4823.3924 175735
## 10: 0.022551372 31444 4410.9500 175735
## 11: 0.026088794 44532 5374.6399 175735
## 12: 0.024342203 39579 4866.3344 175735
## 13: 0.023920166 37564 4755.1810 175735
## 14: 0.030021051 94284 7888.2174 175735
## 15: 0.052699591 307 217.6892 4352
## 16: 0.140949165 680 680.0000 4352
## 17: 0.038857534 165 165.0000 4352
## 18: 0.142679479 822 694.6670 4352
## 19: 0.038857534 165 165.0000 4352
## 20: 0.145603070 3365 1371.4699 4352
## 21: 0.080437905 6349 2240.6010 22169
## 22: 0.076052484 5976 2049.5226 22169
## 23: 0.077637475 6517 2123.5988 22169
## 24: 0.075599347 5442 2022.4376 22169
## 25: 0.082213793 7112 2351.4577 22169
## 26: 0.077765448 6052 2120.6468 22169
## 27: 0.079192007 8924 2139.0479 22169
## 28: 0.186600411 1770 1251.7298 5349
## 29: 0.133384954 990 759.3249 5349
## 30: 0.186290950 1764 1247.4276 5349
## 31: 0.192933642 2467 1431.8735 5349
## 32: 0.151516165 899 899.0000 5349
## 33: 0.187681926 1792 1267.1366 5349
## 34: 0.178382865 1696 1148.1009 5349
## 35: 0.055114087 13342 2984.7356 45279
## 36: 0.049043617 10300 2506.0621 45279
## 37: 0.048517999 8928 2474.9189 45279
## 38: 0.046308723 8800 2326.6173 45279
## 39: 0.049260333 8978 2524.8655 45279
## 40: 0.057426192 15146 3218.0346 45279
## 41: 0.060813165 22447 3978.0761 45279
## 42: 0.056861230 10659 2706.3282 40034
## 43: 0.053446701 9271 2467.7153 40034
## 44: 0.051527293 8017 2345.8605 40034
## 45: 0.056127613 10535 2652.5818 40034
## 46: 0.055797849 10490 2628.6720 40034
## 47: 0.058773581 11304 2854.9687 40034
## 48: 0.063825811 20646 3690.9426 40034
## 49: 0.005203138 1231387 28332.1942 4678203
## 50: 0.005116724 1170185 27637.5572 4678203
## 51: 0.005134471 1178731 27779.5760 4678203
## 52: 0.005248280 1252675 28712.6809 4678203
## 53: 0.005278611 1277389 28969.5310 4678203
## 54: 0.005139851 1193703 27816.7752 4678203
## 55: 0.005936967 2258019 38295.4940 4678203
## 56: 0.051116600 11628 2965.5116 48895
## 57: 0.051754394 13935 3038.6851 48895
## 58: 0.054519915 15221 3324.7134 48895
## 59: 0.049258980 11342 2814.1460 48895
## 60: 0.045780759 9285 2543.1649 48895
## 61: 0.052387775 12838 3088.3310 48895
## 62: 0.057635458 24942 3991.9477 48895
## 63: 0.072982647 8791 2487.8448 28289
## 64: 0.068914514 7152 2262.1340 28289
## 65: 0.076902249 10661 2784.2457 28289
## 66: 0.070817274 8241 2360.7733 28289
## 67: 0.063881177 6505 2024.8278 28289
## 68: 0.071296301 8307 2387.9966 28289
## 69: 0.077086742 11601 2802.2847 28289
## 70: 0.142535861 1889 1008.0910 6274
## 71: 0.154063036 1863 1163.4025 6274
## 72: 0.063368644 401 401.0000 6274
## 73: 0.156085712 2124 1192.3160 6274
## 74: 0.142806765 1917 1008.4793 6274
## 75: 0.111008499 773 745.5257 6274
## 76: 0.165739202 3377 1544.1139 6274
## 77: 0.017327203 111484 8690.1437 425881
## 78: 0.016211618 94930 7891.0872 425881
## 79: 0.016475113 96351 8071.0462 425881
## 80: 0.017035182 106898 8469.7402 425881
## 81: 0.017218169 110371 8606.6809 425881
## 82: 0.016808579 102586 8305.0090 425881
## 83: 0.019747128 221286 12214.0113 425881
## 84: 0.089745276 2884 1418.4716 13237
## 85: 0.099541462 4362 1704.0613 13237
## 86: 0.102184473 4627 1800.9467 13237
## 87: 0.093495068 3413 1514.9622 13237
## 88: 0.095464165 3315 1556.3302 13237
## 89: 0.095448565 3381 1558.7443 13237
## 90: 0.095344414 5288 1508.6015 13237
## prop_se est est_se wtd_group_n
##
## $weight_name
## [1] "trip_weight"summarize_by can be used with an unlimited amount of
variables. To use more than one summarize_by variable pass
a vector to the argument.
mode_type_race_ethnicity_list = hts_prep_variable(
summarize_var = "mode_type",
summarize_by = c("race", "ethnicity"),
variables_dt = variable_list,
data = list(
"hh" = hh,
"person" = person,
"day" = day,
"trip" = trip,
"vehicle" = vehicle
)
)
mode_by_race_ethnicity_summary = hts_summary(
prepped_dt = mode_type_race_ethnicity_list$cat,
summarize_var = "mode_type",
summarize_by = c("race", "ethnicity"),
wtname = "trip_weight"
)
head(mode_by_race_ethnicity_summary$summary$wtd, 10)## race ethnicity
## 1: African American or Black Cuban
## 2: African American or Black Cuban
## 3: African American or Black Two or more
## 4: African American or Black Two or more
## 5: African American or Black Two or more
## 6: African American or Black Prefer not to answer
## 7: African American or Black Prefer not to answer
## 8: African American or Black Prefer not to answer
## 9: African American or Black Prefer not to answer
## 10: American Indian or Alaska Native Not of Hispanic, Latino, or Spanish origin
## mode_type count prop est
## 1: 8 20 0.85193115 9948
## 2: 1 4 0.14806885 1729
## 3: 8 19 0.61909727 8312
## 4: 1 6 0.28318189 3802
## 5: 13 2 0.09772084 1312
## 6: 8 29 0.60140470 14471
## 7: 1 12 0.26568864 6393
## 8: 13 4 0.06707672 1614
## 9: 2 3 0.06582994 1584
## 10: 1 4 0.18561680 1768Calculating trip rates
hts_summary can also be used to calculate trip
rates.
employment_triprate_list = hts_prep_triprate(
summarize_by = "employment",
variables_dt = variable_list,
trip_name = "trip",
day_name = "day",
hts_data = list(
"hh" = hh,
"person" = person,
"day" = day,
"trip" = trip,
"vehicle" = vehicle
)
)
trip_rate_by_employment_summary = hts_summary(
prepped_dt = employment_triprate_list$num,
summarize_var = "num_trips_wtd",
summarize_by = "employment",
summarize_vartype = "numeric",
weighted = TRUE,
wtname = "day_weight",
se = TRUE
)
head(trip_rate_by_employment_summary$summary$wtd, 10)## employment count min max mean mean_se median
## 1: 1 1798 0.0000000 391.81818 3.810070 0.07239468 2.745520
## 2: 2 298 0.0000000 130.57143 4.064631 0.18903924 3.023569
## 3: 3 246 0.0000000 186.50000 3.685040 0.18526271 2.721311
## 4: 5 982 0.0000000 246.71429 3.855805 0.10134559 2.697699
## 5: 6 182 0.0000000 163.23529 4.033846 0.23908385 2.775264
## 6: 7 28 0.2647059 165.04545 3.851056 0.66241644 2.773455
## 7: 8 30 0.7719101 26.95098 4.065571 0.58654865 3.592087
## 8: 995 561 0.0000000 299.10000 4.031359 0.13624559 2.922734Labeling Values
To label values we can use factorize_column.
trip_rate_by_employment_summary$summary$wtd$employment = factorize_column(
trip_rate_by_employment_summary$summary$wtd$employment,
"employment",
value_labels,
variable_colname = "variable",
value_colname = "value",
value_label_colname = "label",
value_order_colname = "val_order"
)
trip_rate_by_employment_summary$summary$wtd## employment
## 1: Employed full-time (35+ hours/week, paid)
## 2: Employed part-time (fewer than 35 hours/week, paid)
## 3: Self-employed
## 4: Not employed and not looking for work (e.g., retired, stay-at-home parent, student)
## 5: Unemployed and looking for work
## 6: Unpaid volunteer or intern
## 7: Employed, but not currently working (e.g., on leave, furloughed 100%)
## 8: Missing Response
## count min max mean mean_se median
## 1: 1798 0.0000000 391.81818 3.810070 0.07239468 2.745520
## 2: 298 0.0000000 130.57143 4.064631 0.18903924 3.023569
## 3: 246 0.0000000 186.50000 3.685040 0.18526271 2.721311
## 4: 982 0.0000000 246.71429 3.855805 0.10134559 2.697699
## 5: 182 0.0000000 163.23529 4.033846 0.23908385 2.775264
## 6: 28 0.2647059 165.04545 3.851056 0.66241644 2.773455
## 7: 30 0.7719101 26.95098 4.065571 0.58654865 3.592087
## 8: 561 0.0000000 299.10000 4.031359 0.13624559 2.922734Creating Visuals using hts_summary output
hts_summary creates outputs that can easily be used to
create visuals.
## Warning: package 'ggplot2' was built under R version 4.3.2
p = ggplot(
trip_rate_by_employment_summary$summary$wtd,
aes(x = mean, y = employment, label = count)
) +
geom_bar(stat = "identity") +
geom_errorbar(
aes(
xmin = (mean - mean_se),
xmax = (mean + mean_se),
width = .2
)
) +
labs(
x = "Mean Trip Rate",
y = "Employment"
) +
scale_y_discrete(
labels = function(x) stringr::str_wrap(x, width = 50),
limits = rev
)
print(p)
Summarizing a new variable
To summarize a new variable with hts_summary it must
first be added to the variable_list and
value_labels. In this example we are creating a new
variable called hh_size that we want to summarize.
test_data$hh[, hh_size := ifelse(num_people < 4, 0, 1)]
variable_list = rbind(
variable_list,
data.table(
variable = "hh_size",
is_checkbox = 0,
hh = 1,
person = 0,
day = 0,
trip = 0,
vehicle = 0,
description = "Household size",
data_type = "integer/categorical",
shared_name = "hh_size"
)
)
value_labels = rbind(
value_labels,
data.table(
variable = rep("hh_size", 2),
value = c(0, 1),
label = c("Small household", "Large household"),
val_order = c(214:215)
)
)
hh_size_list = hts_prep_variable(
summarize_var = "hh_size",
variables_dt = variable_list,
data = test_data
)
hh_size_summary = hts_summary(
prepped_dt = hh_size_list$cat,
summarize_var = "hh_size",
summarize_vartype = "categorical",
weighted = TRUE,
wtname = "hh_weight"
)
factorize_df(df = hh_size_summary$summary$wtd, value_labels, value_label_colname = "label")## hh_size count prop est
## 1: Small household 842 0.8400047 436054
## 2: Large household 158 0.1599953 83055