Add Standard Groupings • psrc.travelsurvey

Agency standard category schemes

In light of sample sizes, breakdowns from PSRC’s Household Travel Survey instrument often involve category groupings with less than the most granular reported level of detail. psrc.travelsurvey functions provide the most commonly utilized groupings, which can be considered agency standard for the variables of interest. Although each new variable is specific to one unit of analysis (i.e. hh, person, day, trip, vehicle), the first and only argument to each grouping function is the entire hts_data object, in order to facilitate piping. When more than one standard grouping exists for a variable, the function returns them all, for convenience; they can be distinguished by a suffix denoting the number of categories.

library(psrc.travelsurvey)
library(magrittr)
library(data.table)

# Specify which variables to retrieve
vars <- c("age", "hhsize")

# Retrieve the data
hts_data <- get_psrc_hts(survey_year=2023, survey_vars = vars) 

# Add standard groupings for age and household size
# -- Notice age and hhsize use different tables (person, hh)
# -- but you don't need to worry about that; the package knows
hts_data <- hts_data %>% 
  hts_bin_age() %>%
  hts_bin_hhsize()

hts_data$person[, head(.SD), .SDcols=patterns("^age")]
#>            age       age_bin5       age_bin3
#>          <ord>         <fctr>         <fctr>
#> 1: 45-54 years    45-64 Years    18-64 Years
#> 2: 16-17 years Under 18 Years Under 18 Years
#> 3: 12-15 years Under 18 Years Under 18 Years
#> 4: 35-44 years    25-44 Years    18-64 Years
#> 5: 25-34 years    25-44 Years    18-64 Years
#> 6: 25-34 years    25-44 Years    18-64 Years

These functions each require a disaggregate input variable to be requested in the earlier get_psrc_hts() call. Both the input variable and the output variable are included in the resulting hts_data object.

Currently, the following functions exist, along with the required input variable.

Function	Input variable	Output variable(s)	Breakpoints
`hts_bin_income()`	hhincome_broad	hhincome_bin3 / hhincome_bin5	$50K ]( $100K or $25K ]( $50K ]( $75K ]( $100K
`hts_bin_hhsize()`	hhsize	hhsize_bin4	1 • 2 • 3 • 4+
`hts_bin_vehicle_count()`	vehicle_count	vehicle_count_bin4	1 • 2 • 3 • 4+
`hts_bin_rent_own()`	rent_own	rent_own_bin2	rent • own
`hts_bin_age()`	age	adult / age_bin3 / age_bin5	18[ or 18 )[ 64 )[ 65 or 18 )[ 25 )[ 45 )[ 65
`hts_bin_worker()`	employment	worker	worker • non-worker
`hts_bin_edu()`	education	edu_bin2	Less )[ Bachelors+
`hts_bin_gender()`	gender	gender_bin3	m • f • nb/oth
`hts_bin_sexuality()`	sexuality	sexuality_bin3	hetero • LGBTQ+ • Missing
`hts_bin_commute_freq()`	commute_freq	commute_freq_bin6	Less • 1 • 2 • 3 • 4 • 5+ days/week
`hts_bin_commute_mode()`	commute_mode	commute_mode_bin5	SOV • HOV • trnst • w/b • oth
`hts_bin_lum_sector()`	industry	lum_sector	(13 sectors)
`hts_bin_industry_sector()`	industry	industry_sector	(8 sectors)
`hts_bin_telecommute_freq()`	telecommute_freq	telecommute_freq_bin4	Less )[ 1 )[ 3 )[ 5+
`hts_bin_telework_time()`	telework_time	telework_time_bin3	Less )[ 3 )[ 6 hrs
`hts_bin_dest_purpose()`	dest_purpose	dest_purpose_bin4 / dest_purpose_bin9	Home • Work • Social/Rec/Eat • Errand
`hts_bin_mode()`	mode_characterization	mode_basic	Walk/Bike/Micromobility • Transit • Carpool • Drive alone
`hts_bin_transit_acc_mode()`	mode_acc, mode_characterization	transit_mode_acc	Walked or jogged • Bike/Micromobility • Vehicular