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var_SOC.R
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##### Script para generar variables intermedias - SCL/SCL
# 1. Censos
if (tipo == "censos") {
# creating a vector with initial column names
initial_column_names <- names(data_filt)
is_haven_labelled <- function(x) {
inherits(x, "haven_labelled")
}
# Convert all haven_labelled columns to numeric
data_filt <- data_filt %>%
mutate(across(where(is_haven_labelled), as.numeric))
num_cores <- as.integer((detectCores() - 1)/2) # number of cores to use, often set to one less than the total available
cluster <- new_cluster(num_cores)
cluster_library(cluster, "dplyr")
initial_column_names <- names(data_filt)
data_filt <- data_filt %>% group_by(geolev1) %>%partition(cluster) %>%
mutate(jefa_ci = if_else(jefe_ci == 1, as.numeric(sexo_ci == 2), NA_real_),
ylm_ci=as.double(ylm_ci), ynlm_ci=as.double(ynlm_ci),
urbano_ci = case_when(zona_c == 1 ~ 1,
is.na(zona_c) ~NA_real_,
TRUE ~ 0),
pob_sfd = if_else(sexo_ci == 2 | afroind_ci == 1 | afroind_ci == 2 | dis_ci == 1, 1, 0), # variable requested for SFD - GDI
pob18_ci = as.numeric(edad_ci <= 18),
pob65_ci = as.numeric(edad_ci >= 65),
single_member = miembros_ci == 1,
age_scl = case_when(edad_ci>=0 & edad_ci<5 ~"00_04",
edad_ci>=5 & edad_ci<15 ~"05_14",
edad_ci>=15 & edad_ci<25 ~"15_24",
edad_ci>=25 & edad_ci<65 ~"25_64",
edad_ci>=65 & edad_ci<99 ~"65+",
TRUE ~NA_character_)) %>%
mutate(ytot_ci = pmax(0, rowSums(cbind(ylm_ci, ylnm_ci, ynlm_ci, ynlnm_ci), na.rm=TRUE)),
ytot_ci = ifelse(is.na(ylm_ci) & is.na(ylnm_ci) & is.na(ynlm_ci) & is.na(ynlnm_ci),NA_real_, ytot_ci),
yallsr18 = ifelse(edad_ci>=18, ytot_ci, NA)) %>%
group_by(anio_c, pais_c, idh_ch) %>%
mutate(ytot_ch = ifelse(single_member, sum(ytot_ci,na.rm=TRUE), NA),
ytot_ch = pmax(0, ytot_ch),
hhyallsr = ifelse(single_member, sum(yallsr18,na.rm=TRUE), NA),
hhyallsr = pmax(0, hhyallsr),
ywomen = sum(yallsr18[sexo_ci == 2], na.rm = TRUE),
hhywomen = max(ywomen, na.rm = TRUE),
jefa_ch = if_else(jefe_ci == 1, sum(jefa_ci, na.rm = TRUE), 0),
miembro6_ch = as.numeric(sum(edad_ci < 6 & relacion_ci > 0 & relacion_ci <= 5) > 0),
miembro65_ch = as.numeric(sum(edad_ci >= 65 & relacion_ci > 0 & relacion_ci <= 5) > 0),
miembro6y16_ch = as.numeric(sum(edad_ci >=6 & edad_ci <=16 & relacion_ci > 0 & relacion_ci <= 5) > 0),
shareylmfem_ch = hhywomen / hhyallsr,
perceptor_ci = if_else(ytot_ci > 0, sum(miembros_ci, na.rm = TRUE), NA_real_),
dis_ch = as.numeric(sum(dis_ci) > 0),
perceptor_ch = suppressWarnings(max(perceptor_ci, na.rm = TRUE))) %>%
ungroup() %>%
# Mutate to compute additional variables
mutate(
# Income per capita definition
pc_ytot_ch = ifelse(nmiembros_ch > 0, ytot_ch / nmiembros_ch, NA),
pc_ytot_ch = ifelse(pc_ytot_ch <= 0, NA, pc_ytot_ch),
# Define area and sex based on zona_c and sexo_ci respectively,
income_category = case_when(
(pc_ytot_ch < lp31_ci ~ "extreme"), # extreme poverty
(pc_ytot_ch >= lp31_ci) & (pc_ytot_ch < lp5_ci) ~ "poverty", # poverty
(pc_ytot_ch >= lp5_ci) & (pc_ytot_ch < lp31_ci*4) ~ "vulnerable", # vulnerable
(pc_ytot_ch >= lp31_ci*4) & (pc_ytot_ch < lp31_ci*20) ~ "middle", # middle class
(pc_ytot_ch >= lp31_ci*20) ~ "rich",
TRUE ~ NA_character_), # rich,
area = case_when(
zona_c == 1 ~ "urban",
zona_c == 0 ~ "rural",
TRUE ~ NA_character_
),
sex = case_when(
sexo_ci == 2 ~ "women",
sexo_ci == 1 ~ "men",
TRUE ~ NA_character_
),
disability_ch = dplyr::case_when(dis_ch == 1 ~ "household_with_disability",
dis_ch == 0 ~"household_with_no_disability",
TRUE ~ NA_character_),
# Calculate hhfem_ch
hhfem_ch = ifelse(hhywomen >= .5, 1, ifelse(is.na(yallsr18), NA, 0)),
# remesas
#indexrem = ifelse(jefe_ci == 1 & !is.na(remesas_ch) & remesas_ch > 0, 1, NA),
#ylmprixh = ylmpri_ci / (horaspri_ci * 4.34),
#vivienda
hacinamiento_ch = nmiembros_ch / cuartos_ch,
#demografia dependencia
depen_ch = nmiembros_ch / perceptor_ch
)%>%
collect()
# creating an if to see if pc_ytot_ch has a value%>%
if (length(unique(data_filt$pc_ytot_ch))>5){
data_filt <- data_filt %>%
arrange(pc_ytot_ch) %>%
mutate(
quintile = cut(pc_ytot_ch,
breaks = quantile(pc_ytot_ch,
probs = seq(0, 1, by = 0.2),
na.rm = TRUE,
names = FALSE),
labels = c("quintile_1", "quintile_2", "quintile_3", "quintile_4", "quintile_5"))
)
} else{
data_filt <- data_filt %>% mutate(quintile = NA_character_)
}
data_filt <- data_filt %>% rename(isoalpha3 = pais_c,
year = anio_c)
}
# 2. Encuestas
start_time <- Sys.time()
if (tipo == "encuestas") {
# creating a vector with initial column names
data_filt <- data_filt %>%
mutate(jefa_ci = if_else(jefe_ci == 1, as.numeric(sexo_ci == 2), NA_real_),
ylm_ci = as.double(ylm_ci),
ynlm_ci = as.double(ynlm_ci),
pob_sfd = if_else(sexo_ci == 2 | afroind_ci == 1 | afroind_ci == 2 | dis_ci == 1, 1, 0),
pob18_ci = as.numeric(edad_ci <= 18),
pob65_ci = as.numeric(edad_ci >= 65),
miembros_ci = as.numeric(miembros_ci == 1),
ytot_ci = pmax(0, rowSums(cbind(ylm_ci, ylnm_ci, ynlm_ci, ynlnm_ci), na.rm = TRUE)),
ytot_ci = ifelse(is.na(ylm_ci) & is.na(ylnm_ci) & is.na(ynlm_ci) & is.na(ynlnm_ci),NA_real_, ytot_ci),
yallsr18 = if_else(edad_ci >= 18, ytot_ci, NA_real_),
age_scl = case_when(edad_ci>=0 & edad_ci<5 ~"00_04",
edad_ci>=5 & edad_ci<15 ~"05_14",
edad_ci>=15 & edad_ci<25 ~"15_24",
edad_ci>=25 & edad_ci<65 ~"25_64",
edad_ci>=65 & edad_ci<99 ~"65+",
TRUE ~NA_character_)) %>%
group_by(idh_ch) %>%
mutate(ytot_ch = if_else(miembros_ci == 1, sum(ytot_ci, na.rm = TRUE), NA_real_),
ytot_ch = pmax(0, ytot_ch),
hhyallsr = if_else(miembros_ci == 1, sum(yallsr18, na.rm = TRUE), NA_real_),
hhyallsr = pmax(0, hhyallsr),
ywomen = sum(yallsr18[sexo_ci == 2], na.rm = TRUE),
hhywomen = max(ywomen, na.rm = TRUE),
shareylmfem_ch = hhywomen / hhyallsr,
jefa_ch = if_else(jefe_ci == 1, sum(jefa_ci, na.rm = TRUE), 0),
miembro6_ch = as.numeric(sum(edad_ci < 6 & relacion_ci > 0 & relacion_ci <= 5) > 0),
miembro65_ch = as.numeric(sum(edad_ci >= 65 & relacion_ci > 0 & relacion_ci <= 5) > 0),
miembro6y16_ch = as.numeric(sum(edad_ci >=6 & edad_ci <=16 & relacion_ci > 0 & relacion_ci <= 5) > 0),
perceptor_ci = if_else(ytot_ci > 0, sum(miembros_ci, na.rm = TRUE), NA_real_),
perceptor_ch = suppressWarnings(max(perceptor_ci, na.rm = TRUE))) %>%
ungroup() %>%
# Mutate to compute additional variables
mutate(
# Income per capita definition
pc_ytot_ch = ifelse(nmiembros_ch > 0, ytot_ch / nmiembros_ch, NA),
pc_ytot_ch = ifelse(pc_ytot_ch <= 0, NA, pc_ytot_ch),
# Define area and sex based on zona_c and sexo_ci respectively,
income_category = case_when(
(pc_ytot_ch < lp31_ci ~ "extreme"), # extreme poverty
(pc_ytot_ch >= lp31_ci) & (pc_ytot_ch < lp5_ci) ~ "poverty", # poverty
(pc_ytot_ch >= lp5_ci) & (pc_ytot_ch < lp31_ci*4) ~ "vulnerable", # vulnerable
(pc_ytot_ch >= lp31_ci*4) & (pc_ytot_ch < lp31_ci*20) ~ "middle", # middle class
(pc_ytot_ch >= lp31_ci*20) ~ "rich",
TRUE ~ NA_character_), # rich,
area = case_when(
zona_c == 1 ~ "urban",
zona_c == 0 ~ "rural",
TRUE ~ NA_character_
),
sex = case_when(
sexo_ci == 2 ~ "women",
sexo_ci == 1 ~ "men",
TRUE ~ NA_character_
),
# Calculate hhfem_ch
hhfem_ch = ifelse(hhywomen >= .5, 1, ifelse(is.na(yallsr18), NA, 0)),
# remesas
indexrem = ifelse(jefe_ci == 1 & !is.na(remesas_ch) & remesas_ch > 0, 1, ifelse(is.na(remesas_ch),NA_real_,0)),
ylmprixh = ylmpri_ci / (horaspri_ci * 4.34),
#vivienda
hacinamiento_ch = nmiembros_ch / cuartos_ch,
#demografia dependencia
depen_ch = nmiembros_ch / perceptor_ch
)
# Calculate quintiles
# sum all the values of factor ci where ytot"
data_filt <- data_filt %>%
arrange(pc_ytot_ch,idh_ch) %>%
mutate(suma1 = ifelse(!is.na(ytot_ch),factor_ci,0),
suma2 = cumsum(suma1),
cumulative_weight_prop = suma2 / sum(suma1)) %>%
mutate(
quintile = case_when(
cumulative_weight_prop < 0.20 ~ "quintile_1",
cumulative_weight_prop < 0.40 ~ "quintile_2",
cumulative_weight_prop < 0.60 ~ "quintile_3",
cumulative_weight_prop < 0.80 ~ "quintile_4",
cumulative_weight_prop >= 0.80 ~ "quintile_5",
TRUE ~ NA_character_
)
)
# Quintile_ci urban
data_filt <- data_filt %>%
arrange(pc_ytot_ch,idh_ch) %>%
mutate(suma1 = ifelse(zona_c==1 & !is.na(ytot_ch),factor_ci,0),
suma2 = cumsum(suma1),
cumulative_weight_prop = suma2 / sum(suma1)) %>%
mutate(
quintile_ci_urban = case_when(
cumulative_weight_prop < 0.20 & zona_c==1 ~ "quintile_1_urban",
cumulative_weight_prop < 0.40 & zona_c==1 ~ "quintile_2_urban",
cumulative_weight_prop < 0.60 & zona_c==1 ~ "quintile_3_urban",
cumulative_weight_prop < 0.80 & zona_c==1 ~ "quintile_4_urban",
cumulative_weight_prop >= 0.80 & zona_c==1 ~ "quintile_5_urban",
TRUE ~ NA_character_
)
)
# Quintile rural
data_filt <- data_filt %>%
arrange(pc_ytot_ch,idh_ch) %>%
mutate(suma1 = ifelse(zona_c==0 & !is.na(ytot_ch),factor_ci,0),
suma2 = cumsum(suma1),
cumulative_weight_prop_rural = suma2 / sum(suma1)) %>%
mutate(
quintile_ci_rural = case_when(
cumulative_weight_prop_rural < 0.20 & zona_c==0 ~ "quintile_1_rural",
cumulative_weight_prop_rural < 0.40 & zona_c==0 ~ "quintile_2_rural",
cumulative_weight_prop_rural < 0.60 & zona_c==0 ~ "quintile_3_rural",
cumulative_weight_prop_rural < 0.80 & zona_c==0 ~ "quintile_4_rural",
cumulative_weight_prop_rural >= 0.80 & zona_c==0 ~ "quintile_5_rural",
TRUE ~ NA_character_
)
)
# quintile_ch
data_filt <- data_filt %>%
arrange(pc_ytot_ch,idh_ch) %>%
mutate(suma1 = ifelse(jefe_ci==1 & !is.na(ytot_ch),factor_ci,0),
suma2 = cumsum(suma1),
cumulative_weight_prop2 = (suma2 / sum(suma1))) %>%
mutate(
quintile_ch = case_when(
cumulative_weight_prop2 < 0.20 & jefe_ci==1 ~ "quintile_1_ch",
cumulative_weight_prop2 < 0.40 & jefe_ci==1 ~ "quintile_2_ch",
cumulative_weight_prop2 < 0.60 & jefe_ci==1 ~ "quintile_3_ch",
cumulative_weight_prop2 < 0.80 & jefe_ci==1 ~ "quintile_4_ch",
cumulative_weight_prop2 >= 0.80 & jefe_ci==1 ~ "quintile_5_ch",
TRUE ~ NA_character_
)
)
# quintile_ch_urban
data_filt <- data_filt %>%
arrange(pc_ytot_ch,idh_ch) %>%
mutate(suma1 = ifelse(jefe_ci==1 & !is.na(ytot_ch) & zona_c==1,factor_ci,0),
suma2 = cumsum(suma1),
cumulative_weight_prop2 = (suma2 / sum(suma1))) %>%
mutate(
quintile_ch_urban = case_when(
cumulative_weight_prop2 < 0.20 & jefe_ci==1 & zona_c==1 ~ "quintile_1_ch_urban",
cumulative_weight_prop2 < 0.40 & jefe_ci==1 & zona_c==1 ~ "quintile_2_ch_urban",
cumulative_weight_prop2 < 0.60 & jefe_ci==1 & zona_c==1 ~ "quintile_3_ch_urban",
cumulative_weight_prop2 < 0.80 & jefe_ci==1 & zona_c==1 ~ "quintile_4_ch_urban",
cumulative_weight_prop2 >= 0.80 & jefe_ci==1 & zona_c==1~ "quintile_5_ch_urban",
TRUE ~ NA_character_
)
)
# quintile_ch_rural
data_filt <- data_filt %>%
arrange(pc_ytot_ch,idh_ch) %>%
mutate(suma1 = ifelse(jefe_ci==1 & !is.na(ytot_ch) & zona_c==0,factor_ci,0),
suma2 = cumsum(suma1),
cumulative_weight_prop2 = (suma2 / sum(suma1))) %>%
mutate(
quintile_ch_rural = case_when(
cumulative_weight_prop2 < 0.20 & jefe_ci==1 & zona_c==0 ~ "quintile_1_ch_rural",
cumulative_weight_prop2 < 0.40 & jefe_ci==1 & zona_c==0 ~ "quintile_2_ch_rural",
cumulative_weight_prop2 < 0.60 & jefe_ci==1 & zona_c==0 ~ "quintile_3_ch_rural",
cumulative_weight_prop2 < 0.80 & jefe_ci==1 & zona_c==0 ~ "quintile_4_ch_rural",
cumulative_weight_prop2 >= 0.80 & jefe_ci==1 & zona_c==0~ "quintile_5_ch_rural",
TRUE ~ NA_character_
)
)
# Calculate decile points
# sum all the values of factor ci where ytot"
# decile
data_filt <- data_filt %>%
arrange(pc_ytot_ch,idh_ch) %>%
mutate(suma1 = ifelse(!is.na(pc_ytot_ch),factor_ci,0),
suma2 = cumsum(suma1),
cumulative_weight_prop = suma2 / sum(factor_ci)) %>%
mutate(
decile_ci = case_when(
cumulative_weight_prop < 0.10 ~ 1,
cumulative_weight_prop < 0.20 ~ 2,
cumulative_weight_prop < 0.30 ~ 3,
cumulative_weight_prop < 0.40 ~ 4,
cumulative_weight_prop < 0.50 ~ 5,
cumulative_weight_prop < 0.60 ~ 6,
cumulative_weight_prop < 0.70 ~ 7,
cumulative_weight_prop < 0.80 ~ 8,
cumulative_weight_prop >=0.90 ~ 9,
TRUE ~ NA_real_
)
)
data_filt <- data_filt %>% group_by(decile_ci) %>%
mutate(
isminv2 = min(pc_ytot_ch, na.rm = T)
) %>%
ungroup() %>%
mutate(percentil_points = case_when(
decile_ci == 2 & (isminv2 == pc_ytot_ch) ~ 10,
decile_ci == 6 & (isminv2 == pc_ytot_ch) ~ 50,
decile_ci == 9 & (isminv2 == pc_ytot_ch) ~ 90,
TRUE ~ NA_real_
))
data_filt <- data_filt %>% rename(isoalpha3 = pais_c,
year = anio_c)
}