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Script_CleanCars.R
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# Project : Clean vehicle ownership in Spain
# Creation date : 08/04/2024
# Last update : 08/11/2024
# Author : Mercè Amich (merce.amich@ehu.eus)
# Institution : Euskal Herriko Unibertsitatea / Universidad del País Vasco
# Last run time : ~86h
# [1] Objective
# Download ECEPOV-21 data & process it
# Descriptive statistics and frequency tables
# Error-Component Mixed Logit model (Apollo)
# Post-estimation: benchmark individual, plots
# [2] Preliminaries ----
# Clear workspace
rm(list = ls(all = TRUE))
# Set language
Sys.setenv(LANG = "en")
# Set start time
start.time = Sys.time()
# Install and load any required R-package
packages.loaded <- installed.packages()
packages.needed <- c ("dplyr" ,
"openxlsx" ,
"httr" ,
"downloader" ,
"stringr" ,
"here" ,
"tidyr" ,
"DataExplorer",
"descstat" ,
"summarytools",
"tibble" ,
"weights" ,
"TAM" ,
"apollo" ,
"rsample" ,
"ggplot2" ,
"dplyr" ,
"MASS" ,
"forplo" ,
"patchwork" ,
"forcats"
)
for (p in packages.needed) {
if (!p %in% row.names(packages.loaded)) install.packages(p)
eval(bquote(library(.(p))))
}
# Define main path
path <- here()
# Set working directory
setwd(paste0(path))
################################################################################
####### DOWNLOAD DATA & PROCESS IT #############################################
################################################################################
# [3] Download survey from the Spanish Statistical Office ----
# Define structural part of the url
base.url = 'https://www.ine.es/ftp/microdatos/ecepov/'
# Delete the folder generated in previous runs (if it exists)
if(dir.exists(paste0(path, "/DATA/"))){unlink(paste0(path, "/DATA/"), recursive = TRUE)}
# Create raw data folder
dir.create(paste0(path, "/DATA/"))
# Define zip file
eval(parse(text = paste0("file = 'datos_2021.zip'")))
# Define url
url <- paste0(base.url, file)
# Set the destination file
destination <- paste0(file)
# Set working directory
setwd(paste0(path, "/DATA/"))
# Download microdata
download(url, destination, mode = 'wb')
# Unzip microdata
unzip(destination)
# Delete unzipped folder
unlink(destination, recursive = TRUE)
# Create list of folders in the directory
list.folders = list.files(pattern = '.zip',
ignore.case = TRUE)
# By folder
for (f in 1:length(list.folders)) {
# Define parameters
folder = list.folders[f]
# Unzip microdata
unzip(folder)
# Delete useless folder(s)
unlink(folder, recursive = TRUE)
}
# Function to load and rename datasets
load_and_rename <- function(filename, new_name) {
load(paste0(path, "/DATA/R/", filename))
assign(new_name, Microdatos,
envir = .GlobalEnv)
remove(list = c("Metadatos", "Microdatos"))
}
# Load and rename datasets
load_and_rename("ECEPOVvivienda_2021.RData", "dataV")
load_and_rename("ECEPOVhogar_2021.RData" , "dataH")
load_and_rename("ECEPOVadultos_2021.RData" , "dataA")
# [4] Create intermediate files ----
## [4.1] From "dataH" ("hogar") to "mergeFileH" ####
# Create new dummy variables and select final variables of interest
# "NACIM" · 1: "Individual born outside from Spain"
dataH$NACIM <- ifelse(dataH$NACIM == 2, 1, 0)
# HOUSEHOLD TYPE (uniperson, monoparen, dosadsinhij, dosadconhij, otrostiphogar)
dataH$UNIPERSON <- ifelse(dataH$TIPOHOGAR == 1, 1, 0)
dataH$MONOPAREN <- ifelse(dataH$TIPOHOGAR == 2, 1, 0)
dataH$DOSADSINHIJ <- ifelse(dataH$TIPOHOGAR == 3, 1, 0)
dataH$DOSADCONHIJ <- ifelse(dataH$TIPOHOGAR == 4, 1, 0)
dataH$OTROSTIPOSHOG <- ifelse(dataH$TIPOHOGAR %in% c(5, 6, 7), 1, 0)
# HH EMPLOYMENT STATUS
dataH$TODOSTRABAJAN <- ifelse(dataH$SITLABHOGAR == 4, 1, 0)
dataH$NINGUNOTRABAJA <- ifelse(dataH$SITLABHOGAR == 1, 1, 0)
dataH$OTRASSITLAB <- ifelse(dataH$SITLABHOGAR %in% c(2, 3, 5, 6, 7), 1, 0)
# HH HIGH EDUCATION
dataH$NINGUNOHOGEESS <- ifelse(dataH$NIVEDUCAHOGAR == 1, 1, 0)
dataH$ALGUNOHOGEESS <- ifelse(dataH$NIVEDUCAHOGAR == 2, 1, 0)
dataH$TODOSHOGEESS <- ifelse(dataH$NIVEDUCAHOGAR == 3, 1, 0)
# MALE PROPORTION >18y / HH
dataH$MALE <- ifelse(dataH$SEXO == 1, 1, 0)
### Create intermediate "mergeFileH"
### Select only variables of interest
mergeFileH <- dataH %>%
dplyr::select(
IDEN, FACTOR,
# select numerical variables
HIJOS_NUCLEO_MENORES, EDAD,
# select newly created dummy variables
NACIM:MALE
)
### Collapse / Group observations by "IDEN"
### "mergeFileH" will contain the 36 vars of interest + has the size of dataV
mergeFileH <- mergeFileH %>%
# Transform NA's to 0
dplyr::mutate(HIJOS_NUCLEO_MENORES = replace_na(HIJOS_NUCLEO_MENORES, 0)) %>%
# Group by "IDEN"
group_by(IDEN) %>%
# Indications on how to collapse variables
dplyr::summarize(
# Deliver value of the first observation (repeated in all IDEN)
FACTOR = first(FACTOR),
HIJOS_NUCLEO_MENORES = first(HIJOS_NUCLEO_MENORES),
# Deliver 1 if any of the dummies is 1
NACIM = ifelse(any(NACIM == 1), 1, 0),
UNIPERSON = ifelse(any(UNIPERSON == 1), 1, 0),
DOSADSINHIJ = ifelse(any(DOSADSINHIJ == 1), 1, 0),
DOSADCONHIJ = ifelse(any(DOSADCONHIJ == 1), 1, 0),
MONOPAREN = ifelse(any(MONOPAREN == 1), 1, 0),
OTROSTIPOSHOG = ifelse(any(OTROSTIPOSHOG == 1), 1, 0),
TODOSTRABAJAN = ifelse(any(TODOSTRABAJAN == 1), 1, 0),
NINGUNOTRABAJA = ifelse(any(NINGUNOTRABAJA == 1), 1, 0),
OTRASSITLAB = ifelse(any(OTRASSITLAB == 1), 1, 0),
NINGUNOHOGEESS = ifelse(any(NINGUNOHOGEESS == 1), 1, 0),
ALGUNOHOGEESS = ifelse(any(ALGUNOHOGEESS == 1), 1, 0),
TODOSHOGEESS = ifelse(any(TODOSHOGEESS == 1), 1, 0),
#Build new variable PROPFEM18 (%female per HH =>18y)
# "ifelse" used to avoid NA's for those HH with no male: 0/0 = NA
PROPMALE18 = ifelse(sum(EDAD >= 18) == 0, 0,
sum(MALE == 1 & EDAD >= 18) / sum(EDAD >= 18)),
#Build new variable MEANAGE18 (mean age >=18y)
# IDEN(=008233, 058919, 117255, 119309) were unipersonal HH <18y
# Not to have NA's introduced, "ifelse" to put 0 instead
MEANAGE18 = ifelse(sum(EDAD >= 18) == 0, 0, mean(EDAD[EDAD >= 18]))
)
## [4.2] From "dataA" ("adultos") to "mergeFileA" ####
# Recode EC (Estado Civil)
dataA$SOLTERO <- ifelse(dataA$EC == 1, 1, 0)
dataA$CASADO <- ifelse(dataA$EC %in% c(2, 3), 1, 0)
dataA$VIDUO <- ifelse(dataA$EC == 4, 1, 0)
dataA$SEPARADO <- ifelse(dataA$EC == 5, 1, 0)
dataA$DIVORCI <- ifelse(dataA$EC == 6, 1, 0)
# Recode LUGTRAB
dataA$MIDOMICILIO <- ifelse(dataA$LUGTRAB == 1, 1, 0)
dataA$MIMUNICIPIO <- ifelse(dataA$LUGTRAB == 3, 1, 0)
dataA$MIPROVINCIA <- ifelse(dataA$LUGTRAB == 4, 1, 0)
dataA$OTROSSITIOS <- ifelse(dataA$LUGTRAB %in% c(2, 5, 6), 1, 0)
# Recode TIEMDESPLA
dataA$MENOSDE20MIN <- ifelse(dataA$TIEMDESPLA == 1, 1, 0)
dataA$ENTRE20Y39MIN <- ifelse(dataA$TIEMDESPLA == 2, 1, 0)
dataA$ENTRE40Y59MIN <- ifelse(dataA$TIEMDESPLA == 3, 1, 0)
dataA$MASDE60 <- ifelse(dataA$TIEMDESPLA %in% c(4, 5, 6, 7), 1, 0)
dataA$ENTRE60Y89MIN <- ifelse(dataA$TIEMDESPLA == 4, 1, 0)
dataA$MASDE90 <- ifelse(dataA$TIEMDESPLA %in% c(5, 6, 7), 1, 0)
# Transform NDESPLA (handling NAs)
dataA$NDESPLA <- as.numeric(dataA$NDESPLA)
dataA$NDESPLA[is.na(dataA$NDESPLA)] <- 0
# Recode MEDIO TRANSPORTE_1
dataA$MTRANSPOR_1 <- as.numeric(dataA$MTRANSPOR_1)
dataA$MTRANSPOR_1[is.na(dataA$MTRANSPOR_1)] <- 0
dataA$PMT_COCHEPART <- ifelse(dataA$MTRANSPOR_1 == 1, 1, 0)
dataA$PMT_TPUBLICO <- ifelse(dataA$MTRANSPOR_1 %in% c(11, 13, 14, 16), 1, 0)
dataA$PMT_MOTO <- ifelse(dataA$MTRANSPOR_1 == 7, 1, 0)
dataA$PMT_OTROS <- ifelse(dataA$MTRANSPOR_1 %in% c(0, 2:6, 8:10, 12, 15, 17), 1, 0)
# Recode "personas dependientes"
dataA$TIPODEPDH <- as.numeric(dataA$TIPODEPDH)
dataA$ENFECRONIC <- ifelse(dataA$TIPODEPDH == 1, 1, 0)
dataA$DISCAP <- ifelse(dataA$TIPODEPDH == 2, 1, 0)
dataA$MAYOR70DEP <- ifelse(dataA$TIPODEPDH == 4, 1, 0)
# Create intermediate "mergeFileA"
# Select only variables of interest
mergeFileA <- dataA %>%
dplyr::select(
IDEN, FACTOR,
# select the numerical variables
NDESPLA,
# select the newly created dummy variables
SOLTERO:MAYOR70DEP,
# remove the transformed variables
-SITLAB, -ESTUDIOS, -EC, -LUGTRAB, -MTRANSPOR_1, -MTRANSPOR_2, -TIEMDESPLA
)
## Collapse / Group observations by "IDEN"
## Now "mergeFileA" contains the 30 vars of interest + has the size of dataV
mergeFileA <- mergeFileA %>%
# group by "IDEN"
dplyr::group_by(IDEN) %>%
# indicate how to collapse each variable
dplyr::summarize(
# Deliver value of the first observation (repeated in all IDEN)
FACTOR = first(FACTOR),
# Sum numerical variables
NDESPLA = ifelse(is.na(max(NDESPLA)) || max(NDESPLA) == 0, 0,
max(NDESPLA)),
# Recode EC (Estado Civil)
SOLTERO = ifelse(any(SOLTERO == 1), 1, 0),
CASADO = ifelse(any(CASADO == 1), 1, 0),
VIDUO = ifelse(any(VIDUO == 1), 1, 0),
SEPARADO = ifelse(any(SEPARADO == 1), 1, 0),
DIVORCI = ifelse(any(DIVORCI == 1), 1, 0),
# Recode LUGTRAB
MIDOMICILIO = ifelse(any(MIDOMICILIO == 1), 1, 0),
MIMUNICIPIO = ifelse(any(MIMUNICIPIO == 1), 1, 0),
MIPROVINCIA = ifelse(any(MIPROVINCIA == 1), 1, 0),
OTROSSITIOS = ifelse(any(OTROSSITIOS == 1), 1, 0),
# Recode TIEMPDESPLA
MENOSDE20MIN = ifelse(any(MENOSDE20MIN == 1), 1, 0),
ENTRE20Y39MIN = ifelse(any(ENTRE20Y39MIN == 1), 1, 0),
ENTRE40Y59MIN = ifelse(any(ENTRE40Y59MIN == 1), 1, 0),
MASDE60 = ifelse(any(MASDE60 == 1), 1, 0),
ENTRE60Y89MIN = ifelse(any(ENTRE60Y89MIN == 1), 1, 0),
MASDE90 = ifelse(any(MASDE90 == 1), 1, 0),
# Recode MTRANSPOR_1
PMT_COCHEPART = ifelse(any(PMT_COCHEPART == 1), 1, 0),
PMT_TPUBLICO = ifelse(any(PMT_TPUBLICO == 1), 1, 0),
PMT_MOTO = ifelse(any(PMT_MOTO == 1), 1, 0),
PMT_OTROS = ifelse(any(PMT_OTROS == 1), 1, 0),
# Recode TIPODEPH
ENFECRONIC = ifelse(any(ENFECRONIC == 1), 1, 0),
DISCAP = ifelse(any(DISCAP == 1), 1, 0),
MAYOR70DEP = ifelse(any(MAYOR70DEP == 1), 1, 0)
)
## [4.3] Merge "mergeFileH" and "mergeFileA" with "dataV" ----
data <- left_join(dataV, mergeFileH, by = c("IDEN", "FACTOR"))
data <- left_join(data, mergeFileA, by = c("IDEN", "FACTOR"))
# The resulting dataframe has 172444 observations and 121 variables
# Create final "data.Rdata" object
save(data, file = "data.Rdata")
remove("dataA", "dataH", "dataV", "mergeFileA", "mergeFileH")
# [5] Create new variables ----
# Clean-Car Ownership
# 1: No car / 2: Fuel car / 3: Clean car
# 1. Optimize CARTYPE assignment
data$CARTYPE <- NA # Initialize with NA
data$CARTYPE[data$VEHICULO == 6] <- 1
data$CARTYPE[data$VEHICULO == 1 & (data$VEHIELECTR != 1 | data$VEHIHIBRI != 1)] <- 2
data$CARTYPE[data$VEHIELECTR == 1 | data$VEHIHIBRI == 1] <- 3
# 2. Optimize TYPEOF assignment
data$TYPEOF <- NA # Initialize with NA
data$TYPEOF[data$VEHICULO == 6] <- 1
data$TYPEOF[data$VEHICULO == 1 & (data$VEHIELECTR != 1 | data$VEHIHIBRI != 1)] <- 2
data$TYPEOF[data$VEHIELECTR == 1] <- 3
data$TYPEOF[data$VEHIHIBRI == 1] <- 4
# 3. Set NVEHICULOS missing values and create OTHERCARS
data$NVEHICULOS[is.na(data$NVEHICULOS)] <- 0
data$OTHERCARS <- ifelse(data$NVEHICULOS > 1, 1, 0)
# 4. Housing Tenure variables
data$PROPIEDAD <- ifelse(data$REGVI %in% 1:3, 1, 0)
data$ALQUILER <- ifelse(data$REGVI == 4, 1, 0)
data$OTROSREGVI <- ifelse(data$REGVI %in% 5:6, 1, 0)
# 5. Simplified Regional code assignment using matrix assignment (for multiple regions)
region_codes <- c("01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12",
"13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24",
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36",
"37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48",
"49", "50", "51", "52")
region_names <- c("ALAVA", "ALBACETE", "ALACANT", "ALMERIA", "AVILA", "BADAJOZ", "BALEARS",
"BARCELONA", "BURGOS", "CACERES", "CADIZ", "CASTELLO", "CIUDADREAL",
"CORDOBA", "ACORUNA", "CUENCA", "GIRONA", "GRANADA", "GUADALAJARA",
"GIPUZKOA", "HUELVA", "HUESCA", "JAEN", "LEON", "LLEIDA", "LARIOJA",
"LUGO", "MADRID", "MALAGA", "MURCIA", "NAVARRA", "OURENSE", "ASTURIAS",
"PALENCIA", "LASPALMAS", "PONTEVEDRA", "SALAMANCA", "SCTENERIFE",
"CANTABRIA", "SEGOVIA", "SEVILLA", "SORIA", "TARRAGONA", "TERUEL",
"TOLEDO", "VALENCIA", "VALLADOLID", "BIZKAIA", "ZAMORA", "ZARAGOZA",
"CEUTA", "MELILLA")
for (i in seq_along(region_codes)) {
data[[region_names[i]]] <- ifelse(data$IDQ_PV == region_codes[i], 1, 0)
}
# 6. Assign OTRASPROV based on exclusion
data$OTRASPROV <- ifelse(data$IDQ_PV != "08" & data$IDQ_PV != "28", 1, 0)
# 7. Municipality-based calculations
data$MENOS50MILHAB <- ifelse(data$IDQ_MUN == "00000", 1, 0)
# 8. Capital cities (5 largest)
capital_codes <- c("08019", "28079", "46250", "41091", "50297")
capital_names <- c("c_barcelona", "c_madrid", "c_valencia", "c_sevilla", "c_zaragoza")
for (i in seq_along(capital_codes)) {
data[[capital_names[i]]] <- ifelse(data$IDQ_MUN == capital_codes[i], 1, 0)
}
# 9. Municipality size
data$MENOS50MILHAB <- ifelse(data$TAM_MUNI == 1, 1, 0)
data$DE50A100MIL <- ifelse(data$TAM_MUNI == 2, 1, 0)
data$DE100A500MIL <- ifelse(data$TAM_MUNI == 3, 1, 0)
data$MASDE500MIL <- ifelse(data$TAM_MUNI == 4, 1, 0)
# 10. Income brackets
data$INGREHOG <- as.numeric(data$INGREHOG)
data$MENOSDEMIL <- ifelse(data$INGREHOG %in% 1:2, 1, 0)
data$DEMILADOSMIL <- ifelse(data$INGREHOG %in% 3:4, 1, 0)
data$DEDOSATRESMIL <- ifelse(data$INGREHOG %in% 5:6, 1, 0)
data$DETRESACINCOMIL <- ifelse(data$INGREHOG == 7, 1, 0)
data$MASDECINCOMIL <- ifelse(data$INGREHOG %in% 8:9, 1, 0)
# 11. Recycling behavior
recycling_items <- c("PAPEL", "VIDRIO", "ENVASES", "ORGANICO")
for (item in recycling_items) {
data[[item]] <- ifelse(data[[item]] == 1, 1, 0)
}
data$RECYCLE <- rowSums(data[, recycling_items], na.rm = TRUE)
# 12. Services in area
services_items <- c("COLEGIO", "CSALUD", "SUPER", "FARMACIA", "BARES")
for (item in services_items) {
data[[item]] <- ifelse(data[[item]] == 1, 1, 0)
}
data$SERVICES <- rowSums(data[, services_items], na.rm = TRUE)
# 13. Perception of bad communication
data$MALCOMUNIC <- ifelse(data$MALCOMUNIC == 1, 1, 0)
# 14. Vacation home ownership, location, and days used
data$SEGUNRESI <- ifelse(data$SEGUNRESI == 1, 1, 0)
segun_resi_vars <- c("LUGSEGUNRESI", "DIASUSA")
segun_resi_labels <- c("SEGUNRESIMISMOMUNI", "SEGUNRESIOTROMUNI", "SEGUNROTRAPROVINMISMACCAA", "SEGUNRESIOTRACCAA", "SEGUNRESIEXTRANJERO", "SEGUNRESIMENOS15", "SEGUNRESI15A29", "SEGUNRESIENTRE30Y59", "SEGUNRESIMAS60D")
# Handle the vacation home conditions
for (i in seq_along(segun_resi_labels)) {
data[[segun_resi_labels[i]]] <- ifelse(data[[segun_resi_vars[[i %/% 5 + 1]]]] == (i %% 5 + 1), 1, 0)
}
# 15. Garage ownership
data$GARAJE <- ifelse(data$GARAJE == 1, 1, 0)
# 16. Renewable energy use
data$ENERENOV <- ifelse(data$ENERENOV == 1, 1, 0)
# 17. Building type
data$UNIFAMILIAR <- ifelse(data$TIPOEDIF == 1, 1, 0)
data$MULTIFAMILIAR <- ifelse(data$TIPOEDIF == 2, 1, 0)
save(data, file = "data.Rdata")
# [6] Rename vars in english ----
data <- data %>%
rename(
# Family type
hh_foreign = NACIM,
hh_oneperson = UNIPERSON,
hh_singleparent = MONOPAREN,
hh_twoadultsandchild = DOSADCONHIJ,
hh_twoadultsalone = DOSADSINHIJ,
hh_num_members = NRESI,
hh_num_minors = HIJOS_NUCLEO_MENORES,
# Socio-dems household
hh_propmale18 = PROPMALE18,
hh_meanage18 = MEANAGE18,
hh_some_higheduc = ALGUNOHOGEESS,
hh_all_higheduc = TODOSHOGEESS,
# Income
inc_1_to_2_thous = DEMILADOSMIL,
inc_2_to_3_thous = DEDOSATRESMIL,
inc_3_to_5_thous = DETRESACINCOMIL,
inc_more_5_thous = MASDECINCOMIL,
# Working status
ws_all_work = TODOSTRABAJAN,
ws_none_work = NINGUNOTRABAJA,
# Working place
wp_ownhome = MIDOMICILIO,
wp_myprovince = MIPROVINCIA,
wp_otherplace = OTROSSITIOS,
# Commutement
com_20_to_39_min = ENTRE20Y39MIN,
com_40_to_59_min = ENTRE40Y59MIN,
com_more_1_hour = MASDE60,
com_num_trips = NDESPLA,
# Housing location
geo_50_100_thous = DE50A100MIL,
geo_100_500_thous = DE100A500MIL,
geo_more_500_thous = MASDE500MIL,
geo_madrid = MADRID,
geo_barcelona = BARCELONA,
geo_c_madrid = c_madrid,
geo_c_barcelona = c_barcelona,
geo_c_valencia = c_valencia,
geo_c_zaragoza = c_zaragoza,
geo_c_sevilla = c_sevilla,
geo_services = SERVICES,
# Environmental attitude
ea_recycle = RECYCLE,
ea_renew_energy = ENERENOV,
# Housing
h_secondhome = SEGUNRESI,
h_ownership = PROPIEDAD,
h_rental = ALQUILER,
h_park_slot = GARAJE,
h_detached = UNIFAMILIAR,
# Mobility
mob_other_vehi = OTHERCARS,
mob_pmt_priv_car = PMT_COCHEPART,
mob_pmt_pub_trans = PMT_TPUBLICO,
mob_pmt_moto = PMT_MOTO,
# Dependent people at hh
dep_enfecronic = ENFECRONIC,
dep_disability = DISCAP,
dep_mayor70y = MAYOR70DEP,
# Malcomunicado subjetivo
mob_malcomunicado = MALCOMUNIC,
# dependent:
vehitype = CARTYPE
)
save(data, file = "data.Rdata")
################################################################################
####### TABLES #################################################################
################################################################################
## [7.1] "Table 2: Vehicle ownership" ----
### a) "No vehicle", "Yes vehicle": Type of vehicles (4 types) ----
table2_a <- data %>%
dplyr::mutate(
VEHICLETYPE = case_when(
VEHIELECTR == 1 ~ "Electric",
VEHIHIBRI == 1 ~ "Hybrid",
VEHICULO == 1 & (VEHIELECTR != 1 & VEHIHIBRI != 1) ~ "Fuel",
TRUE ~ "No car"
)
) %>%
dplyr::group_by(VEHICLETYPE) %>%
dplyr::summarize(
count = n(),
weight = sum(FACTOR)
) %>%
mutate(
prevalence = (count / sum(count)) * 100,
prevalence_percent = (weight / sum(weight)) * 100
) %>%
filter(VEHICLETYPE != "No car") %>%
print()
### b) "No vehicle", "Yes vehicle": Type of vehicles (3 types) ----
table2_b <- data %>%
dplyr::mutate(
CARTYPE = case_when(
VEHICULO == 6 ~ "No car",
VEHICULO == 1 & (VEHIELECTR != 1 & VEHIHIBRI != 1) ~ "Fuel car",
VEHIELECTR == 1 | VEHIHIBRI == 1 ~ "Clean car"
)
) %>%
dplyr::group_by(CARTYPE) %>%
dplyr::summarize(
count = n(),
weight = sum(FACTOR)
) %>%
mutate(
prevalence = (count / sum(count)) * 100,
prevalence_percent = (weight / sum(weight)) * 100
) %>%
arrange(match(CARTYPE, c("No car", "Fuel car", "Clean car"))) %>%
print()
## [7.2] "Table B.1. Numerical variables: descriptive statistics" ----
### a) Socio-demographic ----
tableb1_socdem <- data %>%
summarise(
variable = c("hh_num_members", "hh_num_minors", "hh_propmale18", "hh_meanage18"),
weighted_mean = c(
weighted_mean(hh_num_members, w = FACTOR),
weighted_mean(hh_num_minors, w = FACTOR),
weighted_mean(hh_propmale18, w = FACTOR),
weighted_mean(hh_meanage18, w = FACTOR)
),
weighted_median = c(
weighted_quantile(hh_num_members, w = FACTOR, probs = 0.5),
weighted_quantile(hh_num_minors, w = FACTOR, probs = 0.5),
weighted_quantile(hh_propmale18, w = FACTOR, probs = 0.5),
weighted_quantile(hh_meanage18, w = FACTOR, probs = 0.5)
),
weighted_sd = c(
weighted_sd(hh_num_members, w = FACTOR),
weighted_sd(hh_num_minors, w = FACTOR),
weighted_sd(hh_propmale18, w = FACTOR),
weighted_sd(hh_meanage18, w = FACTOR)
),
weighted_min = c(
weighted_quantile(hh_num_members, w = FACTOR, probs = 0),
weighted_quantile(hh_num_minors, w = FACTOR, probs = 0),
weighted_quantile(hh_propmale18, w = FACTOR, probs = 0),
weighted_quantile(hh_meanage18, w = FACTOR, probs = 0)
),
weighted_max = c(
weighted_quantile(hh_num_members, w = FACTOR, probs = 1),
weighted_quantile(hh_num_minors, w = FACTOR, probs = 1),
weighted_quantile(hh_propmale18, w = FACTOR, probs = 1),
weighted_quantile(hh_meanage18, w = FACTOR, probs = 1)
)
) %>%
tibble() %>%
print()
### b) Mobility-related ----
tableb1_mob <- data %>%
dplyr::filter(!is.na(com_num_trips)) %>%
dplyr::summarise(
variable = "com_num_trips",
weighted_mean = weighted_mean(com_num_trips, w = FACTOR),
weighted_median = weighted_quantile(com_num_trips, w = FACTOR, probs = 0.5),
weighted_sd = weighted_sd(com_num_trips, w = FACTOR),
weighted_min = weighted_quantile(com_num_trips, w = FACTOR, probs = 0),
weighted_max = weighted_quantile(com_num_trips, w = FACTOR, probs = 1)
) %>%
tibble() %>%
print()
## [7.3] "Table C.1. Categorical variables: frequency distributions" ----
### a) Socio-demographic ----
tablec1_socdem <- data %>%
summarise(
across(
c(hh_foreign,
hh_oneperson,
hh_singleparent,
hh_twoadultsalone,
hh_twoadultsandchild,
hh_some_higheduc,
hh_all_higheduc,
inc_1_to_2_thous,
inc_2_to_3_thous,
inc_3_to_5_thous,
inc_more_5_thous,
ws_all_work,
ws_none_work
),
~ weighted.mean(.x,
w = FACTOR,
na.rm = TRUE),
.names = "proportion_of_1_{.col}"
)
) %>%
pivot_longer(
cols = everything(),
names_to = "variable",
values_to = "proportion_of_1"
) %>%
mutate(variable = sub("proportion_of_1_", "", variable)) %>%
print()
### b) Mobility-related ----
tablec1_mob <- data %>%
summarise(
across(
c(wp_ownhome,
wp_myprovince,
wp_otherplace,
com_20_to_39_min,
com_40_to_59_min,
com_more_1_hour,
geo_50_100_thous,
geo_100_500_thous,
geo_more_500_thous,
geo_barcelona,
geo_madrid,
mob_other_vehi,
mob_pmt_priv_car,
mob_pmt_pub_trans,
mob_pmt_moto
),
~ weighted.mean(.x,
w = FACTOR,
na.rm = TRUE),
.names = "proportion_of_1_{.col}"
)
) %>%
pivot_longer(
cols = everything(),
names_to = "variable",
values_to = "proportion_of_1"
) %>%
mutate(variable = sub("proportion_of_1_", "", variable)) %>%
print()
### c) Services ----
tablec1_services <- data %>%
dplyr::group_by(geo_services) %>%
dplyr::summarise(weighted_count = sum(FACTOR)) %>%
dplyr::mutate(weighted_percentage = (weighted_count / sum(weighted_count)) * 100)
### d) Dwelling-related ----
tablec1_dwell <- data %>%
summarise(
across(
c(h_secondhome,
h_ownership,
h_rental,
h_park_slot,
h_detached),
~ weighted.mean(.x,
w = FACTOR,
na.rm = TRUE),
.names = "proportion_of_1_{.col}"
)
) %>%
pivot_longer(
cols = everything(),
names_to = "variable",
values_to = "proportion_of_1"
) %>%
mutate(variable = sub("proportion_of_1_", "", variable)) %>%
print()
################################################################################
####### ERROR-COMPONENT MIXED LOGIT MODEL (APOLLO) #############################
################################################################################
# [8] Set Apollo controls ----
apollo_control = list(
modelName = "EC",
modelDescr = "Weighted_EC_vehitype",
indivID = "IDEN",
weights = "FACTOR",
nCores = 10
)
# [9] Load data ----
# Read data
load("data.Rdata")
set.seed(123)
database <- data
remove(list = "data")
# Divide FACTOR/100 in order for the Log-Likelihood algorithm to converge
database$FACTOR <- database$FACTOR/100
# [10] Define model parameters ----
#ASC_2 + Betas of Alt:2 [baseline: Fuel Car] are fixed to zero
# [11] Define model parameters (initial values) ----
apollo_beta = c(
asc_1 = -0.067885,
asc_2 = 0.0,
asc_3 = -4.144465,
b1_hh_foreign = 1.007025,
b2_hh_foreign = 0.0,
b3_hh_foreign = 0.144441,
b1_hh_oneperson = 0.518905,
b2_hh_oneperson = 0.0,
b3_hh_oneperson = 0.074264,
b1_hh_singleparent = -0.039404,
b2_hh_singleparent = 0.0,
b3_hh_singleparent = 0.022402,
b1_hh_twoadultsalone = -0.790167,
b2_hh_twoadultsalone = 0.0,
b3_hh_twoadultsalone = 0.152213,
b1_hh_twoadultsandchild = -1.241126,
b2_hh_twoadultsandchild = 0.0,
b3_hh_twoadultsandchild = -0.086202,
b1_hh_num_members = -0.192716,
b2_hh_num_members = 0.0,
b3_hh_num_members = -0.038092,
b1_hh_num_minors = 0.066684,
b2_hh_num_minors = 0.0,
b3_hh_num_minors = 0.186956,
b1_hh_propmale18 = 0.0,
b2_hh_propmale18 = 0.0,
b3_hh_propmale18 = 0.0,
b1_hh_meanage18 = 0.0,
b2_hh_meanage18 = 0.0,
b3_hh_meanage18 = 0.0,
b1_hh_some_higheduc = -0.468874,
b2_hh_some_higheduc = 0.0,
b3_hh_some_higheduc = 0.526702,
b1_hh_all_higheduc = -0.562453,
b2_hh_all_higheduc = 0.0,
b3_hh_all_higheduc = 0.713541,
b1_inc_1_to_2_thous = -0.360102,
b2_inc_1_to_2_thous = 0.0,
b3_inc_1_to_2_thous = -0.052324,
b1_inc_2_to_3_thous = -0.980756,
b2_inc_2_to_3_thous = 0.0,
b3_inc_2_to_3_thous = 0.303296,
b1_inc_3_to_5_thous = -1.104291,
b2_inc_3_to_5_thous = 0.0,
b3_inc_3_to_5_thous = 0.748509,
b1_inc_more_5_thous = -0.656055,
b2_inc_more_5_thous = 0.0,
b3_inc_more_5_thous = 1.158671,
b1_ws_all_work = -0.511006,
b2_ws_all_work = 0.0,
b3_ws_all_work = 0.072424,
b1_ws_none_work = 0.275080,
b2_ws_none_work = 0.0,
b3_ws_none_work = -0.712666,
b1_wp_ownhome = -0.083514,
b2_wp_ownhome = 0.0,
b3_wp_ownhome = 0.362673,
b1_wp_myprovince = -0.467923,
b2_wp_myprovince = 0.0,
b3_wp_myprovince = 0.235941,
b1_wp_otherplace = -0.590603,
b2_wp_otherplace = 0.0,
b3_wp_otherplace = -0.083879,
b1_com_20_to_39_min = -0.189068,
b2_com_20_to_39_min = 0.0,
b3_com_20_to_39_min = -0.083177,
b1_com_40_to_59_min = 0.077159,
b2_com_40_to_59_min = 0.0,
b3_com_40_to_59_min = 0.003872,
b1_com_more_1_hour = 0.077159,
b2_com_more_1_hour = 0.0,
b3_com_more_1_hour = 0.003872,
b1_com_num_trips = -0.155225,
b2_com_num_trips = 0.0,
b3_com_num_trips = -0.003803,
b1_geo_50_100_thous = 0.176347,
b2_geo_50_100_thous = 0.0,
b3_geo_50_100_thous = 0.227539,
b1_geo_100_500_thous = 0.516286,
b2_geo_100_500_thous = 0.0,
b3_geo_100_500_thous = 0.179566,
b1_geo_more_500_thous = 1.138612,
b2_geo_more_500_thous = 0.0,
b3_geo_more_500_thous = 0.168840,
b1_geo_barcelona = 0.450697,
b2_geo_barcelona = 0.0,
b3_geo_barcelona = 0.133745,
b1_geo_madrid = 0.0,
b2_geo_madrid = 0.0,
b3_geo_madrid = 0.0,
b1_geo_services = 0.0,
b2_geo_services = 0.0,
b3_geo_services = 0.0,
b1_h_secondhome = 0.0,
b2_h_secondhome = 0.0,
b3_h_secondhome = 0.0,
b1_h_ownership = 0.0,
b2_h_ownership = 0.0,
b3_h_ownership = 0.0,
b1_h_rental = 0.0,
b2_h_rental = 0.0,
b3_h_rental = 0.0,
b1_h_park_slot = 0.0,
b2_h_park_slot = 0.0,
b3_h_park_slot = 0.0,
b1_h_detached = 0.0,
b2_h_detached = 0.0,
b3_h_detached = 0.0,
#b1: No
b2_mob_other_vehi = 0.0,
b3_mob_other_vehi = 0.0,
#b1: No
b2_mob_pmt_priv_car = 0.0,
b3_mob_pmt_priv_car = 0.0,
b1_mob_pmt_pub_trans = 0.0,
b2_mob_pmt_pub_trans = 0.0,
b3_mob_pmt_pub_trans = 0.0,
#b1: No
b2_mob_pmt_moto = 0.0,
b3_mob_pmt_moto = 0.0,
# ERROR TERMS
sd_2 = 0.1,
sd_3 = 0.2
)
## Apollo fixed (alt2) ####
### Vector with names (in quotes) of parameters to be kept fixed at their starting value in apollo_beta
### Use apollo_beta_fixed = c() if none
apollo_fixed = c("asc_2",
"b2_hh_foreign",
"b2_hh_oneperson",
"b2_hh_singleparent",
"b2_hh_twoadultsalone",
"b2_hh_twoadultsandchild",
"b2_hh_num_members",
"b2_hh_num_minors",
"b2_hh_propmale18",
"b2_hh_meanage18",
"b2_hh_some_higheduc",
"b2_hh_all_higheduc",
"b2_inc_1_to_2_thous",
"b2_inc_2_to_3_thous",
"b2_inc_3_to_5_thous",
"b2_inc_more_5_thous",
"b2_ws_all_work",
"b2_ws_none_work",
"b2_wp_ownhome",
"b2_wp_myprovince",
"b2_wp_otherplace",
"b2_com_20_to_39_min",
"b2_com_40_to_59_min",
"b2_com_more_1_hour",
"b2_com_num_trips",
"b2_geo_50_100_thous",
"b2_geo_100_500_thous",
"b2_geo_more_500_thous",
"b2_geo_barcelona",
"b2_geo_madrid",
"b2_geo_services",
"b2_h_secondhome",
"b2_h_ownership",
"b2_h_rental",
"b2_h_park_slot",
"b2_h_detached",