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database.R
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# ============================================================================ #
#
# UFTM BioStat DataBase
#
# database.R
#
# By: Pedro Diniz Guglielmeli <[email protected]>
# https://github.com/paulk2jonas/UFTM-BioStat-DataBase
#
# TODO:
# Add friend easter eggs
# Remove city_index and other unused variables/files
# Get a new standard deviation for adults, and create a impacting score from sports
# Verify if the paths work elsewhere
# Add some relation between private schools and college prevalence
# Add relation between location and football team
# Set up the blood work part to respect normality always, except when
# a disease has already been chosen
# Add an influence of rbc count to ht
#
# * The cities:
# Mojuí dos Campos (PA)
# Balneário Rincão (SC)
# Boa Saúde (RN)
# Pescaria Brava (SC)
# Paraíso das Águas (MS)
# Pinto Bandeira (RS)
# Januário Cicco (RN)
# Were excluded because they weren't on both lists at the same time.
# ============================================================================ #
# --------------------------------- Libraries -------------------------------- #
if (!require(tidyverse)) {
install.packages("tidyverse")
library(tidyverse)
}
if (!require(readxl)) {
install.packages("readxl")
library(readxl)
}
if (!require(lubridate)) {
install.packages("lubridate")
library(lubridate)
}
if (!require(svDialogs)) {
install.packages("svDialogs")
library(svDialogs)
}
# ---------------------------------- Sources --------------------------------- #
source("./config.R")
source("./personal_info_engine.R")
source("./socio-economics_engine.R")
source("./health_engine.R")
source("./hemogram_engine.R")
source("./personality_preferences_engine.R")
source("./validation_functions.R")
source("./helper_functions.R")
source("./chance_models.R")
# --------------------------------- Variables -------------------------------- #
# n <- n_selector()
n <- 1000
# seed <- seed_selector()
seed <- 42
seed_list <- seed_generator(n, seed)
# language <- language_selector()
language <- "pt"
# minimum_wage <- wage_selector()
minimum_wage <- 121200
# generated_data <- data_selector() * To be implemented on RStudio
# ---------------------------------------------------------------------------- #
# Data Loading and Managing #
# ---------------------------------------------------------------------------- #
# --------------------------------- Residence -------------------------------- #
city_list <- read_excel(
"./Background_Data/cities.xlsx",
col_types = c("text", "text", "numeric")
)
# To save on processing power later on
city_list_total_pop <- sum(city_list$population)
state_initials <- c(
"Acre" = "AC",
"Alagoas" = "AL",
"Amapá" = "AP",
"Amazonas" = "AM",
"Bahia" = "BA",
"Ceará" = "CE",
"Espírito Santo" = "ES",
"Goiás" = "GO",
"Maranhão" = "MA",
"Mato Grosso" = "MT",
"Mato Grosso do Sul" = "MS",
"Minas Gerais" = "MG",
"Pará" = "PA",
"Paraíba" = "PB",
"Paraná" = "PR",
"Pernambuco" = "PE",
"Piauí" = "PI",
"Rio de Janeiro" = "RJ",
"Rio Grande do Norte" = "RN",
"Rio Grande do Sul" = "RS",
"Rondônia" = "RO",
"Roraima" = "RR",
"Santa Catarina" = "SC",
"São Paulo" = "SP",
"Sergipe" = "SE",
"Tocantins" = "TO",
"Distrito Federal" = "DF"
)
state_regions <- c(
"Acre" = "Norte",
"Alagoas" = "Nordeste",
"Amapá" = "Norte",
"Amazonas" = "Norte",
"Bahia" = "Nordeste",
"Ceará" = "Nordeste",
"Espírito Santo" = "Sudeste",
"Goiás" = "Centro-Oeste",
"Maranhão" = "Nordeste",
"Mato Grosso" = "Centro-Oeste",
"Mato Grosso do Sul" = "Centro-Oeste",
"Minas Gerais" = "Sudeste",
"Pará" = "Norte",
"Paraíba" = "Nordeste",
"Paraná" = "Sul",
"Pernambuco" = "Nordeste",
"Piauí" = "Nordeste",
"Rio de Janeiro" = "Sudeste",
"Rio Grande do Norte" = "Nordeste",
"Rio Grande do Sul" = "Sul",
"Rondônia" = "Norte",
"Roraima" = "Norte",
"Santa Catarina" = "Sul",
"São Paulo" = "Sudeste",
"Sergipe" = "Nordeste",
"Tocantins" = "Norte",
"Distrito Federal" = "Centro-Oeste"
)
# ------------------------- Sex, Gender and Sexuality ------------------------ #
# Made by me
sex_by_city <- read.csv(
"./Background_Data/sex_by_city.csv"
)
# ------------------------------- Age and Birth ------------------------------ #
study_date <- Sys.Date()
# Age
age_sex_by_city <- read_excel(
"./Background_Data/age.xlsx"
)
age_range <- 0:99
# Birth
load("./Background_Data/birth_frequency.RData")
# Zodiac Sign
zodiac_sign_list <- list(
"Áries" = dmy(21032023) %--% dmy(20042023),
"Touro" = dmy(21042023) %--% dmy(20052023),
"Gêmeos" = dmy(21052023) %--% dmy(20062023),
"Câncer" = dmy(21062023) %--% dmy(22072023),
"Leão" = dmy(23072023) %--% dmy(22082023),
"Virgem" = dmy(23082023) %--% dmy(22092023),
"Libra" = dmy(23092023) %--% dmy(22102023),
"Escorpião" = dmy(23102023) %--% dmy(21112023),
"Sagitário" = dmy(22112023) %--% dmy(21122023),
"Aquário" = dmy(21012023) %--% dmy(19022023),
"Peixes" = dmy(20022023) %--% dmy(20032023)
)
# ! BREAKS HERE TOO I HATE MYSELF
# Chinese sign
chinese_sign_list <- c(
"Macaco",
"Galo",
"Cão",
"Javali",
"Rato",
"Boi",
"Tigre",
"Coelho",
"Dragão",
"Serpente",
"Cavalo",
"Cabra"
)
# ----------------------------------- Name ----------------------------------- #
# First Name
forename_list <- read.csv(
"./Background_Data/first_name.csv"
) %>%
select(1:4)
name_filter <- FALSE
# Last Name
surname_list <- read_excel(
"./Background_Data/last_name.xlsx"
)
# ------------------------- Race, Hair and Eye Color ------------------------- #
race_list <- read_excel("./Background_Data/race.xlsx")
if (language == "pt") {
possible_races <- c("Branco", "Preto", "Amarelo", "Pardo", "Indígena")
} else if (language == "en") {
possible_races <- c("White", "Black", "Asian", "Multiracial", "Indigenous")
}
hair_color_list <- c(
"Loiro comum",
"Castanho claro",
"Castanho médio",
"Castanho escuro",
"Castanho-ruivo",
"Ruivo",
"Preto",
"Grisalho",
"Branco"
)
eye_color_list <- c("Castanho", "Azul", "Âmbar", "Verde")
eye_color_distribution <- c(45, 27, 18, 9)
# -------------------------------- Height (cm) ------------------------------- #
boys_height_table <- read_excel(
"./Background_Data/19_heights.xlsx",
sheet = 1
)
girls_height_table <- read_excel(
"./Background_Data/19_heights.xlsx",
sheet = 2
)
male_height <- c("mean" = 170.8, "sd" = 6.425)
female_height <- c("mean" = 158, "sd" = 6)
# ---------------------- Sports and Physical Activities ---------------------- #
# Sports
# Every 0, except when age doesn't make sense, was changed to 0.0005
sports_by_sex <- read_excel(
"./Background_Data/sports.xlsx",
sheet = 1
)
sports_by_age <- read_excel(
"./Background_Data/sports.xlsx",
sheet = 2
)
# Weekly sports time (h)
sports_time <- read_excel(
"./Background_Data/sports.xlsx",
sheet = 3
)
age_groups <- list(
"0_6" = 0:6,
"7_14" = 7:14,
"15_19" = 15:19,
"20_24" = 20:24,
"25_34" = 25:34,
"35_44" = 35:44,
"45_54" = 45:54,
"55_64" = 55:64,
"65_74" = 65:74,
"75_above" = 75:100
)
# ------------------------- Weight, BMI, BF% and MM% ------------------------- #
# Weight (kg)
boys_weight <- read_excel(
"./Background_Data/weight_by_age.xlsx",
sheet = 1
)
girls_weight <- read_excel(
"./Background_Data/weight_by_age.xlsx",
sheet = 2
)
# Gonna keep these values because it worked good enough
male_weight <- c("mean" = 50, "sd" = 4.5) # 74.6, 24.5 -> 50, 14.5
female_weight <- c("mean" = 43.6, "sd" = 3.1) # 65.1, 23.1 -> 43.6, 13.1
# BF%
male_bf <- read_excel(
"./Background_Data/body_fat_percent.xlsx",
sheet = 1
)
female_bf <- read_excel(
"./Background_Data/body_fat_percent.xlsx",
sheet = 2
)
bf_age_groups <- list(
"20_29" = 20:29,
"30_39" = 30:39,
"40_49" = 40:49,
"50_59" = 50:59,
"60_more" = 60:100
)
# ! BF% and MM% will be implemented after I fix the weight values,
# --------------------------------- Fractures -------------------------------- #
fracture_list <- read_excel(
"./Background_Data/fractures.xlsx"
)
# ---------------------------- Residence Situation --------------------------- #
situation_list <- read_excel(
"./Background_Data/housing_situation_by_state.xlsx"
)
# ----------------------- Alphabetization and Schooling ---------------------- #
# Alphabetization
reading_list <- read_excel(
"./Background_Data/reading_by_age_situation_state.xlsx"
)
# Schooling
age_14_24_sex <- read_excel(
"./Background_Data/instruction_level.xlsx",
sheet = "14_sex"
)
age_14_24_race <- read_excel(
"./Background_Data/instruction_level.xlsx",
sheet = "14_race"
)
age_25_sex <- read_excel(
"./Background_Data/instruction_level.xlsx",
sheet = "25_sex"
)
age_25_race <- read_excel(
"./Background_Data/instruction_level.xlsx",
sheet = "25_race"
)
instruction_level <- c(
"Sem instrução",
"Ensino fundamental incompleto",
"Ensino fundamental completo",
"Ensino médio incompleto",
"Ensino médio completo",
"Ensino superior incompleto",
"Ensino superior completo"
)
race_translation <- c(
"Branco" = "white",
"Pardo" = "black_multiracial",
"Amarelo" = "white",
"Preto" = "black_multiracial",
"Indígena" = "black_multiracial"
)
# --------------------------- Occupation and Income -------------------------- #
city_codes <- read_excel(
"./Background_Data/city_codes.xls"
)
load("./Background_Data/RAIS_data/AC2016.Rda")
load("./Background_Data/RAIS_data/AL2016.Rda")
load("./Background_Data/RAIS_data/AM2016.Rda")
load("./Background_Data/RAIS_data/AP2016.Rda")
load("./Background_Data/RAIS_data/BA2016.Rda")
load("./Background_Data/RAIS_data/CE2016.Rda")
load("./Background_Data/RAIS_data/DF2016.Rda")
load("./Background_Data/RAIS_data/ES2016.Rda")
load("./Background_Data/RAIS_data/GO2016.Rda")
load("./Background_Data/RAIS_data/MA2016.Rda")
load("./Background_Data/RAIS_data/MG2016.Rda")
load("./Background_Data/RAIS_data/MS2016.Rda")
load("./Background_Data/RAIS_data/MT2016.Rda")
load("./Background_Data/RAIS_data/PA2016.Rda")
load("./Background_Data/RAIS_data/PB2016.Rda")
load("./Background_Data/RAIS_data/PE2016.Rda")
load("./Background_Data/RAIS_data/PI2016.Rda")
load("./Background_Data/RAIS_data/PR2016.Rda")
load("./Background_Data/RAIS_data/RJ2016.Rda")
load("./Background_Data/RAIS_data/RN2016.Rda")
load("./Background_Data/RAIS_data/RO2016.Rda")
load("./Background_Data/RAIS_data/RR2016.Rda")
load("./Background_Data/RAIS_data/RS2016.Rda")
load("./Background_Data/RAIS_data/SC2016.Rda")
load("./Background_Data/RAIS_data/SE2016.Rda")
load("./Background_Data/RAIS_data/SP2016.Rda")
load("./Background_Data/RAIS_data/TO2016.Rda")
unemployment_rate <- c(
"Norte" = .089,
"Nordeste" = .127,
"Sudeste" = .093,
"Sul" = .056,
"Centro-Oeste" = .07
)
occupation_age_groups <- list(
"1" = 14,
"2" = 15:17,
"3" = 18:24,
"4" = 25:29,
"5" = 30:39,
"6" = 40:49,
"7" = 50:64,
"8" = 65:99
)
# Maybe this should be a list
occupation_database <- list(
"Acre" = AC2016,
"Alagoas" = AL2016,
"Amapá" = AP2016,
"Amazonas" = AM2016,
"Bahia" = BA2016,
"Ceará" = CE2016,
"Espírito Santo" = ES2016,
"Goiás" = GO2016,
"Maranhão" = MA2016,
"Mato Grosso" = MT2016,
"Mato Grosso do Sul" = MS2016,
"Minas Gerais" = MG2016,
"Pará" = PA2016,
"Paraíba" = PB2016,
"Paraná" = PR2016,
"Pernambuco" = PE2016,
"Piauí" = PI2016,
"Rio de Janeiro" = RJ2016,
"Rio Grande do Norte" = RN2016,
"Rio Grande do Sul" = RS2016,
"Rondônia" = RO2016,
"Roraima" = RR2016,
"Santa Catarina" = SC2016,
"São Paulo" = SP2016,
"Sergipe" = SE2016,
"Tocantins" = TO2016,
"Distrito Federal" = DF2016
)
sex_codes <- c("M" = 1, "F" = 2)
race_codes <- c(
"Indígena" = 1,
"Branco" = 2,
"Preto" = 4,
"Amarelo" = 6,
"Pardo" = 8
)
# Using the proportion of 7% (housewives) over the simple mean
# of unemplyment rates (7/((8.9+12.7+9.3+5.6+7)/5)) = .8
house_chance <- .8
nothing_chance <- 1 - house_chance
# ------------------------------ Marital Status ------------------------------ #
marital_distribution <- read_excel("./Background_Data/marital_status.xlsx")
# ------------------------- Internet Access and Speed ------------------------ #
internet_access_chances <- c("Urbana" = .9, "Rural" = .747)
internet_classes <- list(
"Até 1 Mbps" = c(.01, 1),
"1-2" = c(1.01, 2),
"2-5" = c(2.01, 5),
"5-10" = c(5.01, 10),
"10-30" = c(10.01, 30),
"30-50" = c(30.01, 50),
"50-100" = c(50.01, 100),
"100-150" = c(100.01, 150),
"150-200" = c(150.01, 200),
"200-300" = c(200.01, 300)
)
# ----------------------------- Health Insurance ----------------------------- #
health_insurance_chances <- c(
"Norte" = .0937,
"Nordeste" = .117,
"Centro-Oeste" = .199,
"Sudeste" = .3264,
"Sul" = .2325
)
# ------------------------------ Private School ------------------------------ #
private_public_schools <- read_excel(
"./Background_Data/private_public_schools.xlsx"
)
load("./Background_Data/school_data.RData")
school_age_groups <- list(
"6 a 10" = 6:10,
"11 a 14" = 11:14,
"15 a 17" = 15:17,
"18 ou mais" = 18:99
)
column_selectors <- list(
"F" = "QT_MAT_BAS_FEM",
"M" = "QT_MAT_BAS_MASC",
"Branco" = "QT_MAT_BAS_BRANCA",
"Preto" = "QT_MAT_BAS_PRETA",
"Pardo" = "QT_MAT_BAS_PARDA",
"Amarelo" = "QT_MAT_BAS_AMARELA",
"Indígena" = "QT_MAT_BAS_INDIGENA",
"6 a 10" = "QT_MAT_BAS_6_10",
"11 a 14" = "QT_MAT_BAS_11_14",
"15 a 17" = "QT_MAT_BAS_15_17",
"18 ou mais" = "QT_MAT_BAS_18_MAIS"
)
# -------------------------------- Blood Type -------------------------------- #
# ABO
abo_distribution <- c(A = .42, B = .1, AB = .03, O = .45)
# Rh
rh_distribution <- c(pos = .805, neg = .195)
# Personality traits
blood_type_personality_traits <- list(
"A" = c(
"Organizado",
"Meticuloso",
"Tímido",
"Sensível",
"Paciente",
"Ansioso",
"Intenso",
"Reservado",
"Educado",
"Atencioso",
"Responsável",
"Perfeccionista",
"Bondoso",
"Consciente"
),
"B" = c(
"Criativo",
"Curioso",
"Forte",
"Aventureiro",
"Pragmático",
"Animado",
"Disposto",
"Proativo",
"Inconvencional",
"Jovial"
),
"AB" = c(
"Dual",
"Reflexivo",
"Talentoso",
"Racional",
"Controlado",
"Espiritual",
"Centrado",
"Artístico",
"Misterioso",
"Diplomata",
"Confiável",
"Social" # was "Sociável"
),
"O" = c(
"Otimista",
"Flexível",
"Confiante",
"Determinado",
"Energético",
"Calmo",
"Social",
"Leal",
"Líder",
"Intuitivo",
"Trabalhador",
"Independente",
"Centrado",
"Devotado"
)
)
# ----------------------------------- Drugs ---------------------------------- #
drug_data_sex <- read_excel("./Background_Data/drugs.xlsx", sheet = 1)
drug_data_age <- read_excel("./Background_Data/drugs.xlsx", sheet = 2)
drug_data_schooling <- read_excel("./Background_Data/drugs.xlsx", sheet = 3)
# drug_data_illegal <- read_excel("./Background_Data/drugs.xlsx", sheet = 4)
# ! TO BE IMPLEMENTED
drug_age_groups <- list(
"12_17" = 12:17,
"18_24" = 18:24,
"25_34" = 25:34,
"35_44" = 35:44,
"45_54" = 45:54,
"55_65" = 55:100 # gonna use it straight up as "55 or more"
)
drug_schooling_groups <- c(
"Sem instrução" = "Sem instrução e fundamental incompleto",
"Ensino fundamental incompleto" = "Sem instrução e fundamental incompleto",
"Ensino fundamental completo" = "Fundamental completo e médio incompleto",
"Ensino médio incompleto" = "Fundamental completo e médio incompleto",
"Ensino médio completo" = "Médio completo e superior incompleto",
"Ensino superior incompleto" = "Médio completo e superior incompleto",
"Ensino superior completo" = "Superior completo ou mais"
)
# possible_illicity_drugs <- c(
# "Maconha/Haxixe/Skank",
# "Cocaína",
# "Crack ou similares",
# "Solventes",
# "Ecstasy/MDMA",
# "Injetáveis",
# "Heroína",
# "LSD",
# "Quetamina",
# "Chá de Ayahuasca"
# )
# ! TO BE IMPLEMENTED
# Hypertension
htension_schooling <- read_excel(
"./Background_Data/hypertension.xlsx",
sheet = 1
)
htension_nutrition <- read_excel(
"./Background_Data/hypertension.xlsx",
sheet = 2
)
htension_schooling_groups <- c(
"Sem instrução" = "Nenhuma",
"Ensino fundamental incompleto" = "Fundamental 1",
"Ensino fundamental completo" = "Fundamental 2",
"Ensino médio incompleto" = "Fundamental 2",
"Ensino médio completo" = "Médio",
"Ensino superior incompleto" = "Médio",
"Ensino superior completo" = "Superior"
)
htension_nutrition_groups <- c(
"Baixo peso",
"Eutrofia",
"Sobrepeso",
"Obesidade"
)
normal_systolic_data <- c(mean = 110, sd = 10)
normal_diastolic_data <- c(mean = 70, sd = 6.67)
htension_systolic_data <- c(mean = 160, sd = 13)
htension_diastolic_data <- c(mean = 100, sd = 10)
# Diabetes mellitus
dm_data <- read_excel("./Background_Data/diabetes_mellitus_prevalence.xlsx")
initial_dm_age <- 7
dm_subdiagnosis <- .5
dm1_prevalence <- .1
dm2_prevalence <- .9
dm_age_groups <- list(
"20 a 39 anos" = 0:39,
"40 a 64 anos" = 40:64,
"65 anos ou mais" = 65:100
)
dm_schooling_groups <- c(
"Sem instrução" = "Fundamental incompleto",
"Ensino fundamental incompleto" = "Fundamental incompleto",
"Ensino fundamental completo" = "Fundamental completo",
"Ensino médio incompleto" = "Fundamental completo",
"Ensino médio completo" = "Médio incompleto ou mais",
"Ensino superior incompleto" = "Médio incompleto ou mais",
"Ensino superior completo" = "Médio incompleto ou mais"
)
# Stroke
stroke_data <- read_excel("./Background_Data/stroke.xlsx", sheet = 1)
initial_stroke_age <- 18
stroke_age_groups <- list(
"18 a 29 anos" = 0:29,
"30 a 59 anos" = 30:59,
"60 a 64" = 60:64,
"65 a 74" = 65:74,
"75 ou mais" = 75:100
)
stroke_schooling_groups <- c(
"Sem instrução" = "Sem instrução e fundamental incompleto",
"Ensino fundamental incompleto" = "Sem instrução e fundamental incompleto",
"Ensino fundamental completo" = "Fundamental completo e médio incompleto",
"Ensino médio incompleto" = "Fundamental completo e médio incompleto",
"Ensino médio completo" = "Médio completo e superior incompleto",
"Ensino superior incompleto" = "Médio completo e superior incompleto",
"Ensino superior completo" = "Superior completo"
)
# Heart Attack
heart_attack_data <- read_excel(
"./Background_Data/heart_attack.xlsx",
sheet = 1
)
initial_heart_attack_age <- 18
heart_attack_age_groups <- list(
"18 a 29 anos" = 0:29,
"30 a 59 anos" = 30:59,
"60 a 64" = 60:64,
"65 a 74" = 65:74,
"75 ou mais" = 75:100
)
heart_attack_schooling_groups <- c(
"Sem instrução" = "Sem instrução e fundamental incompleto",
"Ensino fundamental incompleto" = "Sem instrução e fundamental incompleto",
"Ensino fundamental completo" = "Fundamental completo e médio incompleto",
"Ensino médio incompleto" = "Fundamental completo e médio incompleto",
"Ensino médio completo" = "Médio completo e superior incompleto",
"Ensino superior incompleto" = "Médio completo e superior incompleto",
"Ensino superior completo" = "Superior completo"
)
# Resting Heart Rate
heart_rate_data <- list(
"Até 1 mês" = c(mean = 130, sd = 20),
"Até 1 ano" = c(mean = 120, sd = 13.33),
"1 a 2 anos" = c(mean = 105, sd = 8.33),
"3 a 4 anos" = c(mean = 100, sd = 6.67),
"5 a 6 anos" = c(mean = 95, sd = 6.67),
"7 a 9 anos" = c(mean = 90, sd = 6.67),
"10 ou mais" = c(mean = 80, sd = 6.67), # Ain't this too hight?
"Adultos treinados" = c(mean = 50, sd = 3.33)
)
heart_rate_age_groups <- list(
"Até 1 ano" = 0,
"1 a 2 anos" = 1:2,
"3 a 4 anos" = 3:4,
"5 a 6 anos" = 5:6,
"7 a 9 anos" = 7:9,
"10 ou mais" = 10:100
)
# -------------------------------- Preferences ------------------------------- #
# Football Team
# TODO: Add city/state preference later
football_clubs <- read_excel("./Background_Data/football_clubs.xlsx")
# Travel Destiny
destinies <- c("Praia", "Campo", "Cidade")
beach_traits <- c(
"Intenso",
"Criativo",
"Forte",
"Aventureiro",
"Animado",
"Disposto",
"Proativo",
"Jovial",
"Espiritual",
"Otimista",
"Confiante",
"Determinado",
"Energético",
"Social",
"Líder"
)
country_traits <- c(
"Tímido",
"Sensível",
"Paciente",
"Ansioso",
"Reservado",
"Aventureiro",
"Disposto",
"Inconvencional",
"Dual",
"Reflexivo",
"Controlado",
"Espiritual",
"Centrado",
"Artístico",
"Misterioso",
"Calmo",
"Intuitivo",
"Trabalhador",
"Independente"
)
city_traits <- c(
"Organizado",
"Meticuloso",
"Educado",
"Responsável",
"Perfeccionista",
"Pragmático",
"Racional",
"Controlado",
"Centrado",
"Diplomata",
"Social",
"Energético",
"Líder",
"Trabalhador",
"Centrado"
)
# Period of the day
day_periods <- c("Manhã", "Tarde", "Noite")
morning_traits <- c(
"Organizado",
"Meticuloso",
"Educado",
"Atencioso",
"Responsável",
"Perfeccionista",
"Criativo",
"Pragmático",
"Animado",
"Disposto",
"Proativo",
"Inconvencional",
"Reflexivo",
"Talentoso",
"Racional",
"Controlado",
"Centrado",
"Confiável",
"Otimista",
"Flexível",
"Confiante",
"Determinado",
"Energético",
"Calmo",
"Líder",
"Intuitivo",
"Trabalhador",
"Independente",
"Centrado",
"Devotado"
)
afternoon_traits <- c("Sensível",
"Paciente",
"Ansioso",
"Intenso",
"Consciente",
"Criativo",
"Curioso",
"Forte",
"Jovial",
"Confiável",
"Social",
"Otimista",
"Flexível",
"Confiante",
"Leal",
"Intuitivo"
)
evening_traits <- c(
"Ansioso",
"Intenso",
"Criativo",
"Curioso",
"Forte",
"Aventureiro",
"Animado",
"Inconvencional",
"Jovial",
"Talentoso",
"Espiritual",
"Misterioso",
"Diplomata",
"Confiável",
"Social",
"Energético",
"Leal"
)
# Prefered meal
meals <- c("Café da manhã", "Almoço", "Jantar")
# Favorite food
foods <- c(
"Churrasco",
"Feijoada",
"Coxinha",
"Pastel",
"Bife acebolado",
"Moqueca",
"Acarajé",
"Baião de dois",
"Arroz carreteiro",
"Feijão tropeiro",
"Paçoca de carne seca",
"Galinhada",
"Lasanha",
"Macarrão",
"Pizza",
"Frango assado",
"Comida mexicana",
"Comida japonesa",
"Comida chinesa",
"Chocolate",
"Churros",
"Cachorro quente",
"Estrogonofe",
"Brigadeiro",
"Tapioca",
"Pão de queijo",
"Açaí",
"Sorvete",
"Risoto"
)
state_foods <- list(
"Amazônia" = "Tacacá",
"Goiás" = "Arroz com pequi",
"Minas Gerais" = "Frango ao molho pardo",
"Pará" = "Pato no Tucupi",
"Paraná" = "Barreado",
"São Paulo" = c("Virado à paulista", "Camarão na moranga")
)
# World View
world_views <- c("Negativa", "Realista", "Positiva")
situation_view <- c("Urbana" = "Negativa", "Rural" = "Positiva")
marital_view <- c(
"Solteiro(a)" = "Positiva",
"Casado(a)" = "Realista",
"Divorciado(a)" = "Negativa",
"Viúvo(a)" = "Realista",
"NA" = NA
)
employment_view <- c("TRUE" = "Positiva", "FALSE" = "Negativa")
reading_view <- c("S" = "Realista", "N" = "Negativa")
race_view <- c(
"Branco" = "Positiva",
"Pardo" = "Realista",
"Amarelo" = "Positiva",
"Preto" = "Negativa",
"Indígena" = "Negativa"
)
sex_view <- c("M" = "Positiva", "F" = "Negativa")
# Favorite Color
favorite_color_freq <- read_excel("./Background_Data/color.xlsx")
# --------------------------------- Hemogram --------------------------------- #
hemogram_data_1 <- read_excel("./Background_Data/hemogram.xlsx", sheet = 1)
hemogram_data_2 <- read_excel("./Background_Data/hemogram.xlsx", sheet = 2)
hemogram_age_groups <- list(
"1-2 years" = 1:2,
"3-5 years" = 3:5,
"6-10 years" = 6:10,
"male adult" = "M", # ! may not need from here on
"female adult" = "F",
"70 years or more" = 70:100
)
hemogram_baby_groups <- list(
# I think the month age thing is "up to" the month, so...
"1 month" = 0:1,
"3 months" = 2:3,
"6 months" = 4:6
)
# ---------------------------------------------------------------------------- #
# Data Generation #
# ---------------------------------------------------------------------------- #
# --------------------------------- Residence -------------------------------- #
city <- city_generator(n, seed, city_list)
state <- state_generator(n, seed, city_list)
city_state <- paste(city, " (", state_initials[state], ")", sep = "")
# ------------------------- Sex, Gender and Sexuality ------------------------ #
# Sex
sex <- mapply(sex_generator, seed_list, city_state)
# ------------------------------- Age and Birth ------------------------------ #
# Age
# This generator will skip february 29th for now
age <- mapply(age_generator, seed_list, city_state, sex)
birth <- mapply(birthday_generator, seed_list, SIMPLIFY = FALSE)
birth <- reduce(birth, c)
# Zodiac Sign
zodiac_sign <- mapply(zodiac_sign_generator, birth)
# Birth
# Remaking it because it seems I need to
# Maybe I'll find another way later
birth <- mapply(birth_date_generator, birth, age, SIMPLIFY = FALSE)
birth <- reduce(birth, c)
# Chinese Horoscope
# TODO: update it to be more precise (not only year, but day and month)