Summarize a categorical variable
Usage
hts_summary_cat(
prepped_dt,
summarize_var = NULL,
summarize_by = NULL,
weighted = TRUE,
se = FALSE,
wtname = NULL,
strataname = NULL,
checkbox_valname = "value",
checkbox_yesval = 1,
summarize_vartype = "categorical",
id_cols = c("hh_id", "person_id", "day_id", "trip_id", "vehicle_id")
)Arguments
- prepped_dt
Dataset containing the summary variables and key columns in data.table format.
- summarize_var
Name of the categorical variable to summarize. Default is NULL.
- summarize_by
Name of the variable to summarize the summarize_var by. Default is NULL.
- weighted
Whether the data is weighted. Default is TRUE.
- se
Whether to calculate standard error. Default is FALSE.
- wtname
Name of the weight column to use. Default is NULL.
- strataname
Name of strata name to bring in. Default is NULL.
- checkbox_valname
Name of the column with the checkbox value. Default is 'value'. Must be provided if summarize_var is a checkbox variable.
- checkbox_yesval
Value of checkbox_valname that indicates it was selected. Default is 1. Must be provided if summarize_var is a checkbox variable.
- summarize_vartype
String; one of either 'categorical' (when the variable being summarized is categorical) or 'checkbox' (when the variable being summarized is derived from a multiple response, aka select-all-that-apply question).
- id_cols
names of possible ids in prepped_dt to return unique counts of
Examples
require(data.table)
require(stringr)
require(dplyr)
require(srvyr)
DT = hts_prep_variable(
summarize_var = "age",
variables_dt = variable_list,
data = list(
"hh" = hh,
"person" = person,
"day" = day,
"trip" = trip,
"vehicle" = vehicle
)
)$cat
hts_summary_cat(
prepped_dt = DT,
summarize_var = "age",
wtname = "person_weight"
)
#> $unwtd
#> age count prop
#> 1: 1 183 0.08939912
#> 2: 2 158 0.07718613
#> 3: 3 172 0.08402540
#> 4: 4 172 0.08402540
#> 5: 5 176 0.08597948
#> 6: 6 162 0.07914021
#> 7: 7 178 0.08695652
#> 8: 8 180 0.08793356
#> 9: 9 169 0.08255984
#> 10: 10 149 0.07278945
#> 11: 11 177 0.08646800
#> 12: 12 171 0.08353688
#>
#> $wtd
#> age count prop est
#> 1: 1 183 0.09068116 93964
#> 2: 2 158 0.07077481 73337
#> 3: 3 172 0.08828491 91481
#> 4: 4 172 0.08196664 84934
#> 5: 5 176 0.09268849 96044
#> 6: 6 162 0.07615986 78917
#> 7: 7 178 0.08525751 88344
#> 8: 8 180 0.07853874 81382
#> 9: 9 169 0.08182574 84788
#> 10: 10 149 0.07724652 80043
#> 11: 11 177 0.09292107 96285
#> 12: 12 171 0.08365454 86683
#>
#> $weight_name
#> [1] "person_weight"
#>
DT = hts_prep_variable(
summarize_var = "race",
summarize_by = "employment",
variables_dt = variable_list,
data = list(
"hh" = hh,
"person" = person,
"day" = day,
"trip" = trip,
"vehicle" = vehicle
)
)$cat
hts_summary_cat(
prepped_dt = DT,
summarize_var = "race",
summarize_by = "employment",
wtname = "person_weight",
summarize_vartype = "checkbox"
)
#> $unwtd
#> employment race count prop
#> 1: 1 African American or Black 232 0.25863991
#> 2: 1 American Indian or Alaska Native 240 0.26755853
#> 3: 1 Asian 214 0.23857302
#> 4: 1 Native Hawaiian or other Pacific Islander 242 0.26978818
#> 5: 1 White 245 0.27313266
#> 6: 1 Other race 220 0.24526198
#> 7: 1 Prefer not to answer 442 0.49275362
#> 8: 2 African American or Black 38 0.25165563
#> 9: 2 American Indian or Alaska Native 29 0.19205298
#> 10: 2 Asian 37 0.24503311
#> 11: 2 Native Hawaiian or other Pacific Islander 41 0.27152318
#> 12: 2 White 27 0.17880795
#> 13: 2 Other race 46 0.30463576
#> 14: 2 Prefer not to answer 75 0.49668874
#> 15: 3 African American or Black 24 0.21818182
#> 16: 3 American Indian or Alaska Native 29 0.26363636
#> 17: 3 Asian 34 0.30909091
#> 18: 3 Native Hawaiian or other Pacific Islander 28 0.25454545
#> 19: 3 White 24 0.21818182
#> 20: 3 Other race 28 0.25454545
#> 21: 3 Prefer not to answer 57 0.51818182
#> 22: 5 African American or Black 122 0.24351297
#> 23: 5 American Indian or Alaska Native 130 0.25948104
#> 24: 5 Asian 122 0.24351297
#> 25: 5 Native Hawaiian or other Pacific Islander 118 0.23552894
#> 26: 5 White 127 0.25349301
#> 27: 5 Other race 118 0.23552894
#> 28: 5 Prefer not to answer 256 0.51097804
#> 29: 6 African American or Black 22 0.27160494
#> 30: 6 American Indian or Alaska Native 19 0.23456790
#> 31: 6 Asian 17 0.20987654
#> 32: 6 Native Hawaiian or other Pacific Islander 25 0.30864198
#> 33: 6 White 24 0.29629630
#> 34: 6 Other race 15 0.18518519
#> 35: 6 Prefer not to answer 36 0.44444444
#> 36: 7 African American or Black 1 0.07142857
#> 37: 7 Native Hawaiian or other Pacific Islander 2 0.14285714
#> 38: 7 White 3 0.21428571
#> 39: 7 Other race 2 0.14285714
#> 40: 7 Prefer not to answer 11 0.78571429
#> 41: 8 African American or Black 2 0.12500000
#> 42: 8 American Indian or Alaska Native 1 0.06250000
#> 43: 8 Asian 2 0.12500000
#> 44: 8 Native Hawaiian or other Pacific Islander 2 0.12500000
#> 45: 8 White 3 0.18750000
#> 46: 8 Other race 4 0.25000000
#> 47: 8 Prefer not to answer 11 0.68750000
#> employment race count prop
#>
#> $wtd
#> employment race count prop
#> 1: 1 African American or Black 232 0.27073716
#> 2: 1 American Indian or Alaska Native 240 0.28655208
#> 3: 1 Asian 214 0.24514797
#> 4: 1 Native Hawaiian or other Pacific Islander 242 0.27775413
#> 5: 1 White 245 0.28411353
#> 6: 1 Other race 220 0.25628640
#> 7: 1 Prefer not to answer 442 0.48754533
#> 8: 2 African American or Black 38 0.26319327
#> 9: 2 American Indian or Alaska Native 29 0.17631616
#> 10: 2 Asian 37 0.22820145
#> 11: 2 Native Hawaiian or other Pacific Islander 41 0.26018745
#> 12: 2 White 27 0.16369678
#> 13: 2 Other race 46 0.31722196
#> 14: 2 Prefer not to answer 75 0.50837698
#> 15: 3 African American or Black 24 0.24917350
#> 16: 3 American Indian or Alaska Native 29 0.29843295
#> 17: 3 Asian 34 0.33304681
#> 18: 3 Native Hawaiian or other Pacific Islander 28 0.28963898
#> 19: 3 White 24 0.22072534
#> 20: 3 Other race 28 0.24761968
#> 21: 3 Prefer not to answer 57 0.47690756
#> 22: 5 African American or Black 122 0.25345189
#> 23: 5 American Indian or Alaska Native 130 0.26013795
#> 24: 5 Asian 122 0.24647690
#> 25: 5 Native Hawaiian or other Pacific Islander 118 0.24306672
#> 26: 5 White 127 0.24809246
#> 27: 5 Other race 118 0.22771685
#> 28: 5 Prefer not to answer 256 0.52309602
#> 29: 6 African American or Black 22 0.29014733
#> 30: 6 American Indian or Alaska Native 19 0.22133978
#> 31: 6 Asian 17 0.19500460
#> 32: 6 Native Hawaiian or other Pacific Islander 25 0.30255525
#> 33: 6 White 24 0.27992634
#> 34: 6 Other race 15 0.12228361
#> 35: 6 Prefer not to answer 36 0.49417587
#> 36: 7 African American or Black 1 0.10779358
#> 37: 7 Native Hawaiian or other Pacific Islander 2 0.21159045
#> 38: 7 White 3 0.22087692
#> 39: 7 Other race 2 0.21159045
#> 40: 7 Prefer not to answer 11 0.77912308
#> 41: 8 African American or Black 2 0.15386297
#> 42: 8 American Indian or Alaska Native 1 0.04491312
#> 43: 8 Asian 2 0.09682002
#> 44: 8 Native Hawaiian or other Pacific Islander 2 0.17265873
#> 45: 8 White 3 0.12621571
#> 46: 8 Other race 4 0.23516556
#> 47: 8 Prefer not to answer 11 0.70112556
#> employment race count prop
#> est wtd_group_n
#> 1: 121460 448627
#> 2: 128555 448627
#> 3: 109980 448627
#> 4: 124608 448627
#> 5: 127461 448627
#> 6: 114977 448627
#> 7: 218726 448627
#> 8: 20752 78847
#> 9: 13902 78847
#> 10: 17993 78847
#> 11: 20515 78847
#> 12: 12907 78847
#> 13: 25012 78847
#> 14: 40084 78847
#> 15: 15074 60496
#> 16: 18054 60496
#> 17: 20148 60496
#> 18: 17522 60496
#> 19: 13353 60496
#> 20: 14980 60496
#> 21: 28851 60496
#> 22: 62282 245735
#> 23: 63925 245735
#> 24: 60568 245735
#> 25: 59730 245735
#> 26: 60965 245735
#> 27: 55958 245735
#> 28: 128543 245735
#> 29: 12604 43440
#> 30: 9615 43440
#> 31: 8471 43440
#> 32: 13143 43440
#> 33: 12160 43440
#> 34: 5312 43440
#> 35: 21467 43440
#> 36: 917 8507
#> 37: 1800 8507
#> 38: 1879 8507
#> 39: 1800 8507
#> 40: 6628 8507
#> 41: 1408 9151
#> 42: 411 9151
#> 43: 886 9151
#> 44: 1580 9151
#> 45: 1155 9151
#> 46: 2152 9151
#> 47: 6416 9151
#> est wtd_group_n
#>
#> $weight_name
#> [1] "person_weight"
#>
DT = hts_prep_variable(
summarize_var = "employment",
summarize_by = c("race", "income_detailed", "gender"),
variables_dt = variable_list,
data = list(
"hh" = hh,
"person" = person,
"day" = day,
"trip" = trip,
"vehicle" = vehicle
)
)$cat
hts_summary_cat(
prepped_dt = DT,
summarize_var = "employment",
summarize_by = c("race", "income_detailed", "gender"),
wtname = "person_weight"
)$unwtd
#> race income_detailed gender employment count
#> 1: African American or Black 1 1 6 1
#> 2: African American or Black 4 2 5 1
#> 3: African American or Black 5 4 5 1
#> 4: African American or Black 6 4 6 1
#> 5: African American or Black 6 999 1 2
#> ---
#> 394: Prefer not to answer 999 999 2 1
#> 395: Prefer not to answer 999 999 3 2
#> 396: Prefer not to answer 999 999 5 7
#> 397: Prefer not to answer 999 999 7 1
#> 398: Prefer not to answer 999 999 8 2
#> prop
#> 1: 1.00000000
#> 2: 1.00000000
#> 3: 1.00000000
#> 4: 1.00000000
#> 5: 0.66666667
#> ---
#> 394: 0.04347826
#> 395: 0.08695652
#> 396: 0.30434783
#> 397: 0.04347826
#> 398: 0.08695652