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summarize_weighted_over_time.R
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summarize_weighted_over_time.R
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library(data.table)
library(stringr)
library(travelSurveyTools)
library(psrcelmer)
library(dplyr)
library(psrcplot)
library(tidyverse)
# testing a local version of the package so I can debug
devtools::load_all('C:/GitHub/travelSurveyTools/R')
### Load in Data --------
#hh<- get_table(schema= 'HHSurvey', tbl_name='v_households_labels')
#person<- get_table(schema= 'HHSurvey', tbl_name='v_persons_labels')
#day<- get_table(schema= 'HHSurvey', tbl_name='v_days_labels')
# only get the columns needed for the analysis to reduce table size
trip<- get_query(sql= "select trip_id, household_id as hh_id, day_id,
person_id, mode_1, survey_year, trip_weight
from HHSurvey.v_trips_labels")
#setDT(hh)
#setDT(person)
#setDT(day)
setDT(trip)
# load in codebook
cb_path = str_glue("J:/Projects/Surveys/HHTravel/Survey2023/Data/codebook/PSRC_Combined_Codebook_2023_packagable.xlsx")
variable_list = readxl::read_xlsx(cb_path, sheet = 'variable_list')
value_labels = readxl::read_xlsx(cb_path, sheet = 'value_labels')
setDT(variable_list)
setDT(value_labels)
# make all ids characters
#hh[, hhid := as.character(household_id)]
#person[, person_id := as.character(person_id)]
#person[, hhid := as.character(household_id)]
#day[, day_id := as.character(day_id)]
#day[, hhid := as.character(household_id)]
#day[, person_id := as.character(person_id)]
trip[, trip_id := as.character(trip_id)]
trip[, hh_id := as.character(hh_id)]
trip[, person_id := as.character(person_id)]
trip[, day_id := as.character(day_id)]
#hh = hh[!is.na(hh_weight)]
#person = person[hhid %in% hh$hhid]
#day = day[hhid %in% hh$hhid]
#trip = trip[hhid %in% hh$hhid]
trip <- trip%>%mutate(survey_year=as.character(survey_year))
#
# # mode_simple is an aggregation of mode_1; we need to add it to the value_labels
# # as it's own items
#
# # it already exists on the variables table somehow but not the values table;
#
#
variable_list<-rbind(
variable_list,
data.table(
variable = "mode_simple",
is_checkbox = 0,
hh = 0,
person = 0,
day = 0,
trip = 1,
vehicle = 0,
location = 0,
description = "mode aggregation",
logic = 'mode aggregation',
data_type = "integer/categorical",
shared_name = "mode_simple"
)
)
#
#
mode_simple_labels = value_labels[group_1_title == 'mode_simple',
c('label', 'group_1_value')]
# #
# #
# # # add mode_simple to the trip table with the correct label
trip <-merge(trip, mode_simple_labels, by.x = 'mode_1', by.y = 'label')
setnames(trip, 'group_1_value', 'mode_simple')
# #
# #
# # # only select the necessary columns
value_labels<-value_labels%>%select(variable, value, label, val_order)
# #
mode_simple_value_labels<-mode_simple_labels%>%
mutate(variable='mode_simple')%>%
distinct(group_1_value, .keep_all=TRUE)%>%
rowid_to_column(var='value')%>%
rowid_to_column(var='val_order')%>%
select(variable, value, group_1_value, val_order)%>%
rename(label=group_1_value)
value_labels<-rbind(value_labels,
data.table(mode_simple_value_labels))
### Data Updates -------
# make hts_data a list
hts_data = list(#hh = hh,
#person = person,
#day = day,
trip = trip)
variable_list<-setDT(variable_list)
value_labels<-setDT(value_labels)
value_labels[, val_order := seq_len(nrow(value_labels))]
devtools::load_all('C:/GitHub/travelSurveyTools/R')
### Example data summaries-----
### Use package for summary -----
prepped_dt = hts_prep_variable(summarize_var = 'mode_simple',
summarize_by = 'survey_year',
data = hts_data,
id_cols = 'trip_id',
wt_cols = 'trip_weight',
weighted = TRUE,
missing_values = '')
mode_summary<-hts_summary(prepped_dt = prepped_dt$cat,
summarize_var = 'mode_simple',
summarize_by = 'survey_year',
id_cols = 'trip_id',
wtname='trip_weight',
weighted = TRUE)
common_modes<-mode_summary$summary$wtd%>%filter(prop>.005)
static_bar_chart(common_modes, y='mode_simple', x='prop', fill='survey_year')