In paezha/discrtr: A Companion Package for the Book "Discrete Choice Analysis with 'R'"

Introduction

In this notebook we prepare a data object with the table used in Páez, Antonio. "Mapping travelers’ attitudes: does space matter?." Journal of Transport Geography 26 (2013): 117-125.

Preliminaries

Clear environment:

rm(list = ls())

Load packages used in the notebook:

library(readxl)
library(tidyverse)
#library(MASS)
#library(reshape2)
#library(plyr)
#library(kableExtra)
#library(gridExtra)

Load dataframe:

mc_attitudes <- read_excel("input-data-files/Individual+DA Data+Built Environment.xls")

Select and pre-process variables:

mc_attitudes <- mc_attitudes %>%
  transmute(id = ID,
            choice = factor(MODE, 
                            levels = c( "Car", "HSR","Walk")),
            LAT,
            LONG,
            license = factor(case_when(License == "YES" ~ "Yes",
                                       License == "NO" ~ "No")),
            vehicle = factor(case_when(Vehicle == "YES" ~ "Yes",
                                       Vehicle == "NO" ~ "No")),
            gender = factor(case_when(Gender == "FEMALE" ~ "Woman",
                                      Gender == "MALE" ~ "Man")),
            age = Age,
            visa = factor(case_when(Visa == "CANADIAN" ~ "Domestic",
                                    Visa == "INTERNATIONAL" ~ "International")),
            living_arrangements = factor(case_when(Living == 1 ~ "With family/relatives",
                                                  Living == 3 ~ "By myself",
                                                  Living == 4 ~ "Off-campus shared accommodations")),
            level = factor(case_when(Level == 1 ~ "UG I",
                                     Level == 2 ~ "UG II",
                                     Level == 3 ~ "UG III",
                                     Level == 4 ~ "UG IV",
                                     Level == 5 ~ "UG V",
                                     Level == 6 ~ "Masters",
                                     Level == 7 ~ "PhD",
                                     Level == 8 ~ "Other",
                                     Level == 9 ~ "Other")),
            Active_Neighborhood = factor(ACTIVE_NEIGHBORHOOD, 
                                 levels = c("STRONGLY DISAGREE", "DISAGREE", "NEUTRAL", "AGREE", "STRONGLY AGREE"), 
                                 ordered = TRUE),
            Community = factor(COMMUNITY, 
                               levels = c("STRONGLY DISAGREE", "DISAGREE", "NEUTRAL", "AGREE", "STRONGLY AGREE"), 
                               ordered = TRUE),
            Neighbors = factor(NEIGHBORS, 
                               levels = c("STRONGLY DISAGREE", "DISAGREE", "NEUTRAL", "AGREE", "STRONGLY AGREE"),
                               ordered = TRUE),
            Safe_Walk= factor(SAFE_WALK, 
                              levels = c("STRONGLY DISAGREE", "DISAGREE", "NEUTRAL", "AGREE", "STRONGLY AGREE"),
                              ordered = TRUE),
            Shops_Important = factor(SHOPS_IMPORTANT, 
                                     levels = c("STRONGLY DISAGREE", "DISAGREE", "NEUTRAL", "AGREE", "STRONGLY AGREE"), 
                                     ordered = TRUE),
            Travel_Alone = factor(TRAVEL_ALONE, 
                                 levels = c("STRONGLY DISAGREE", "DISAGREE", "NEUTRAL", "AGREE", "STRONGLY AGREE"), 
                                 ordered = TRUE),
            DAUID,
            Rate_Couple_Child = RateCoupleChild,
            Rate_SW_Child = RateFemLone,
            Rate_SM_Child = RateMaleLone,
            Mean_Children = AvgChild,
            Rate_Non_Canadian = RateNonCanadian,
            Median_HH_Income = MedIncome,
            Mean_HH_Income = MHI,
            Rate_Unemployment = UnempRate,
            Rate_1yr_Move = RateMob1Mover,
            Rate_5yr_Move = RateMob5Mover,
            Rate_Public = RatePublic,
            Rate_Walk = RateWalk,
            Rate_Cycle = RateCycle,
            AREA = AREA_SQKM,
            LUM,
            SIDEWALK_DENSITY = SIDEWALK_D,
            STREET_DENSITY = STREET_DEN,
            INTERSECTION_DENSITY = INTER_DENS,
            SF_P_RATIO,
            EMPLOYMENT_DENSITY = EMPLOY_DEN,
            POPULATION_DENSITY = POP_DENSIT) 

Scale income variables:

mc_attitudes <- mc_attitudes %>% mutate(Median_HH_Income = Median_HH_Income/10000, 
                                        Mean_HH_Income = Mean_HH_Income/10000)

Scale density variables:

mc_attitudes <- mc_attitudes %>% mutate(EMPLOYMENT_DENSITY = EMPLOYMENT_DENSITY/10000, 
                                        POPULATION_DENSITY = POPULATION_DENSITY/10000)

Save data:

usethis::use_data(mc_attitudes,
                  overwrite = TRUE)

paezha/discrtr documentation built on March 1, 2023, 5:25 p.m.