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02_mcv_micrositio_genero_script.R
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02_mcv_micrositio_genero_script.R
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# 0. Configuración inicial ----
Sys.setlocale("LC_TIME", "es_ES")
## 0.1. Paquetes ----
if(!require("lubridate")) install.packages("lubridate") & require("lubridate")
if(!require("hot.deck")) install.packages("hot.deck") & require("hot.deck")
if(!require("zoo")) install.packages("zoo") & require("zoo")
if(!require("stringi")) install.packages("stringi") & require("stringi")
if(!require("gganimate")) install.packages("gganimate") & require("gganimate")
if(!require("gridExtra")) install.packages("gridExtra") & require("gridExtra")
if(!require("ggthemes")) install.packages("ggthemes") & require("ggthemes")
if(!require("hrbrthemes")) install.packages("hrbrthemes") & require("hrbrthemes")
if(!require("magick")) install.packages("magick") & require("magick")
if(!require("scales")) install.packages("scales") & require("scales")
if(!require("RColorBrewer")) install.packages("RColorBrewer") & require("RColorBrewer")
if(!require("foreign")) install.packages("foreign") & require("foreign")
if(!require("srvyr")) install.packages("srvyr") & require("srvyr")
if(!require("openxlsx")) install.packages("openxlsx") & require("openxlsx")
if(!require("googledrive")) install.packages("googledrive") & require("googledrive")
if(!require("googlesheets4")) install.packages("googlesheets4") & require("googlesheets4")
require("DatawRappr")
# Tidyverse <3
require(tidyverse)
# Desactiva notación científica
options(scipen=999)
## 0.2. Colores MCV -----
mcv_discrete <- c(
"#6950d8", "#3CEAFA", "#00b783",
"#ff6260", "#ffaf84", "#ffbd41"
)
mcv_semaforo <- c(
"#00b783", # verde
"#E8D92E", # amarillo
"#ffbd41", # naranja
"#ff6260" # rojo
)
mcv_blacks <- c("black", "#D2D0CD", "#777777")
## 0.3. Tokens ----
dw_token <- "XXXXXXXXXXX"
google_token <- "XXXXXXXXXXX"
## 0.4. Directorios ----
paste_inp <- function(x){paste0("~/mcv/enoe/01_datos/", x)}
paste_out <- function(x){paste0("~/mcv/mcv_micrositio_genero/03_productos/", x)}
## 0.5. Línea de pobreza ----
lps <- tribble(
~rururb,~year_vector,~trim_vector,~lp,
0,"05","1",712.54,
1,"05","1",492.78,
0,"05","2",741.49,
1,"05","2",520.05,
0,"05","3",740.37,
1,"05","3",516.84,
0,"05","4",736.48,
1,"05","4",510.03,
0,"06","1",754.24,
1,"06","1",526.53,
0,"06","2",748.55,
1,"06","2",518.10,
0,"06","3",764.24,
1,"06","3",533.35,
0,"06","4",797.76,
1,"06","4",565.69,
0,"07","1",814.70,
1,"07","1",576.48,
0,"07","2",803.92,
1,"07","2",563.70,
0,"07","3",810.79,
1,"07","3",567.70,
0,"07","4",830.52,
1,"07","4",582.76,
0,"08","1",839.18,
1,"08","1",585.91,
0,"08","2",858.24,
1,"08","2",601.28,
0,"08","3",875.52,
1,"08","3",614.67,
0,"08","4",906.75,
1,"08","4",640.53,
0,"09","1",922.00,
1,"09","1",649.04,
0,"09","2",949.84,
1,"09","2",672.97,
0,"09","3",967.60,
1,"09","3",686.43,
0,"09","4",968.90,
1,"09","4",686.09,
0,"10","1",992.68,
1,"10","1",705.17,
0,"10","2",987.38,
1,"10","2",695.86,
0,"10","3",979.48,
1,"10","3",684.99,
0,"10","4",1003.59,
1,"10","4",705.71,
0,"11","1",1021.50,
1,"11","1",717.40,
0,"11","2",1022.29,
1,"11","2",717.83,
0,"11","3",1023.17,
1,"11","3",716.93,
0,"11","4",1049.22,
1,"11","4",740.43,
0,"12","1",1079.48,
1,"12","1",765.39,
0,"12","2",1089.02,
1,"12","2",770.93,
0,"12","3",1130.09,
1,"12","3",805.72,
0,"12","4",1151.75,
1,"12","4",820.38,
0,"13","1",1166.22,
1,"13","1",828.61,
0,"13","2",1177.4,
1,"13","2",837.18,
0,"13","3",1177.99,
1,"13","3",833.37,
0,"13","4",1201.99,
1,"13","4",853.72,
0,"14","1",1234.8,
1,"14","1",870.81,
0,"14","2",1223.42,
1,"14","2",854.08,
0,"14","3",1243.83,
1,"14","3",869.82,
0,"14","4",1276.56,
1,"14","4",899.27,
0,"15","1",1264.53,
1,"15","1",896.17,
0,"15","2",1268.44,
1,"15","2",901.34,
0,"15","3",1282.51,
1,"15","3",911.38,
0,"15","4",1302.59,
1,"15","4",926.32,
0,"16","1",1338.64,
1,"16","1",959.7,
0,"16","2",1329.36,
1,"16","2",947.13,
0,"16","3",1323.88,
1,"16","3",942.81,
0,"16","4",1357.24,
1,"16","4",970.61,
0,"17","1",1376.88,
1,"17","1",975.82,
0,"17","2",1410.21,
1,"17","2",1003.88,
0,"17","3",1469.65,
1,"17","3",1053.26,
0,"17","4",1479.05,
1,"17","4",1054.84,
0,"18","1",1482.13,
1,"18","1",1052.56,
0,"18","2",1477.31,
1,"18","2",1046.25,
0,"18","3",1510.45,
1,"18","3",1068.21,
0,"18","4",1533.46,
1,"18","4",1091.92,
0,"19","1",1561.64,
1,"19","1",1111.31,
0,"19","2",1561.34,
1,"19","2",1109.07,
0,"19","3",1563.49,
1,"19","3",1109.45,
0,"19","4",1578.72,
1,"19","4",1119.41,
0,"20","1",1625.16,
1,"20","1",1158.80,
0,"20","3",1660.28,
1,"20","3",1191.07,
0,"20","4",1674.65,
1,"20","4",1204.29,
0,"21","1",1684.84,
1,"21","1",1205.15
)
# 1. Procesamiento de última ENOE_n ----
# Ingresos imputados de última ENOE_n
ingresos_imputados <- readRDS("~/mcv/enoe/01_datos/00_ingresos_imputados/01_ingresos_laborales_imputados/2021T1_ingresos_imputados.rds") %>%
mutate(sector = ifelse(str_detect(sector, "serviciosde"), "Restaurantes y servicios de alojamiento", sector))
# Últimos dígitos del año
i = "21"
# Trimestre
x = "1"
d_coe1 <- read.dbf(
paste_inp(
# ENOE
# paste0("20", i, "trim", x, "_dbf/COE1T", x, i, ".dbf")
# ENOE_N
paste0("enoe_n_20", i, "_trim", x, "_dbf/ENOEN_COE1T", x, i, ".dbf")
), as.is = T
) %>%
rename_all(tolower) %>%
mutate(
llave = paste0(
# cd_a, ent, con, v_sel, n_hog, h_mud, n_ren
cd_a, ent, con, v_sel, n_hog, h_mud, n_ren, tipo, ca, mes_cal
)
)%>%
mutate_at(
vars(-starts_with("llave")),
as.numeric
)
d_coe2 <- read.dbf(
paste_inp(
# paste0("20", i, "trim", x, "_dbf/COE2T", x, i, ".dbf")
paste0("enoe_n_20", i, "_trim", x, "_dbf/ENOEN_COE2T", x, i, ".dbf")
), as.is = T
) %>%
rename_all(tolower) %>%
mutate(
llave = paste0(
# cd_a, ent, con, v_sel, n_hog, h_mud, n_ren
cd_a, ent, con, v_sel, n_hog, h_mud, n_ren, tipo, ca, mes_cal
)
) %>%
mutate_at(
vars(-starts_with("llave")),
as.numeric
)
d_sdem <- read.dbf(
paste_inp(
# paste0("20", i, "trim", x, "_dbf/SDEMT", x, i, ".dbf")
paste0("enoe_n_20", i, "_trim", x, "_dbf/ENOEN_SDEMT", x, i, ".dbf")
), as.is = T
) %>%
rename_all(tolower) %>%
mutate(
llaveh = paste0(
# cd_a, ent, con, v_sel, n_hog, h_mud
cd_a, ent, con, v_sel, n_hog, h_mud, tipo, ca, mes_cal
),
llave = paste0(
# cd_a, ent, con, v_sel, n_hog, h_mud, n_ren
cd_a, ent, con, v_sel, n_hog, h_mud, n_ren, tipo, ca, mes_cal
)
) %>%
mutate_at(
vars(-c(starts_with("llave"),r_def,c_res)),
as.numeric
) %>%
filter(
as.character(r_def)=="00" & (as.character(c_res)=="1" | as.character(c_res)=="3")
)
tempo <- d_sdem %>%
rename(fac = fac_tri, t_loc = t_loc_tri) %>%
select(-c(cd_a, con, upm, d_sem, n_pro_viv, v_sel,fac_men,
n_hog, h_mud, n_ent, per, ur)) %>%
# left_join(d_coe1 %>% select(-c(fac, ent)), by = c("llave")) %>%
left_join(d_coe1 %>% select(-c(fac_tri, fac_men, ent)), by = c("llave")) %>%
# left_join(d_coe2 %>% select(-c(fac, ent)), by = c("llave")) %>%
left_join(d_coe2 %>% select(-c(fac_tri, fac_men, ent)), by = c("llave")) %>%
select(-c(ends_with(".y"),ends_with(".x"))) %>%
filter(eda>14) %>%
select(llaveh, llave, n_ren, everything())%>%
mutate(sexo = ifelse(sex==1,"Hombres",ifelse(sex==2,"Mujeres",NA)))
tempo_todo <- d_sdem %>%
rename(fac = fac_tri, t_loc = t_loc_tri) %>%
select(-c(cd_a, con, upm, d_sem, n_pro_viv, v_sel,fac_men,
n_hog, h_mud, n_ent, per, n_ren, ur)) %>%
# left_join(d_coe1 %>% select(-c(fac, ent)), by = c("llave")) %>%
left_join(d_coe1 %>% select(-c(fac_tri, fac_men, ent)), by = c("llave")) %>%
# left_join(d_coe2 %>% select(-c(fac, ent)), by = c("llave")) %>%
left_join(d_coe2 %>% select(-c(fac_tri, fac_men, ent)), by = c("llave")) %>%
select(-c(ends_with(".y"),ends_with(".x")))%>%
mutate(sexo = ifelse(sex==1,"Hombres",ifelse(sex==2,"Mujeres",NA)))
# 2. General ----
## 2.1. Población total ----
gen <- tempo_todo %>%
as_survey_design(weights = fac) %>%
srvyr::summarise(tot = survey_total(na.rm = T)) %>%
select(-ends_with("se")) %>%
mutate(ord = "1", tipo = "Población total") %>%
## 2.2. Población en edad de trabajar ----
bind_rows(
tempo %>%
as_survey_design(weights = fac) %>%
srvyr::summarise(tot = survey_total(na.rm = T)) %>%
select(-ends_with("se")) %>%
mutate(ord = "2", tipo = "Población en edad de trabajar (15 años o más)")
) %>%
## 2.3. y 2.7. PEA y PNEA ----
bind_rows(
tempo %>%
mutate(
tipo = case_when(
clase1 == 1 ~ "Población económicamente activa",
clase1 == 2 ~ "Población no económicamente activa",
T ~ NA_character_
),
ord = ifelse(clase1==1, "3","7")
) %>%
as_survey_design(weights = fac) %>%
group_by(ord, tipo) %>%
srvyr::summarise(tot = survey_total(na.rm = T)) %>%
left_join(
tempo %>%
mutate(
tipo = case_when(
clase1 == 1 ~ "Población económicamente activa",
clase1 == 2 ~ "Población no económicamente activa",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(tipo) %>%
srvyr::summarise(prop = survey_mean(na.rm = T)) %>%
mutate(ord = ifelse(str_starts(tipo, "Población no"), "7", "3"))
) %>%
select(-ends_with("se"))
) %>%
## 2.4. y 2.5. Población ocupada y desocupada ----
bind_rows(
tempo %>%
filter(clase1==1) %>%
mutate(
tipo = case_when(
clase2 == 1 ~ "Ocupada",
clase2 == 2 ~ "Desocupada",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(tipo) %>%
srvyr::summarise(tot = survey_total(na.rm = T)) %>%
left_join(
tempo %>%
filter(clase1==1) %>%
mutate(
tipo = case_when(
clase2 == 1 ~ "Ocupada",
clase2 == 2 ~ "Desocupada",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(tipo) %>%
srvyr::summarise(prop = survey_mean(na.rm = T))
) %>%
select(-ends_with("se")) %>%
mutate(ord = ifelse(tipo == "Ocupada", "4", "5"))
) %>%
## 2.6. Población subocupada ----
bind_rows(
tempo %>%
filter(clase1 == 1) %>%
mutate(
tipo = case_when(
sub_o == 1 ~ "Población subocupada",
sub_o == 0 ~ "Población no subocupada",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(tipo) %>%
srvyr::summarise(tot = survey_total(na.rm = T)) %>%
left_join(
tempo %>%
filter(clase1 == 1) %>%
mutate(
tipo = case_when(
sub_o == 1 ~ "Población subocupada",
sub_o == 0 ~ "Población no subocupada",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(tipo) %>%
srvyr::summarise(prop = survey_mean(na.rm = T))
) %>%
select(-ends_with("se")) %>%
drop_na(tipo) %>%
mutate(ord = "6")
) %>%
## 2.8. y 2.9. Población disponible y no disponible ----
bind_rows(
tempo %>%
filter(clase1==2) %>%
mutate(
tipo = case_when(
clase2 == 3 ~ "Disponibles",
clase2 == 4 ~ "No disponibles",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(tipo) %>%
srvyr::summarise(tot = survey_total(na.rm = T)) %>%
left_join(
tempo %>%
filter(clase1==2) %>%
mutate(
tipo = case_when(
clase2 == 3 ~ "Disponibles",
clase2 == 4 ~ "No disponibles",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(tipo) %>%
srvyr::summarise(prop = survey_mean(na.rm = T))
) %>%
select(-ends_with("se")) %>%
mutate(ord = ifelse(tipo == "Disponibles", "8", "9"))
) %>%
## 2.10. Tasa de participación laboral ----
bind_rows(
tempo %>%
mutate(
tipo = case_when(
clase1 == 1 ~ "Tasa de participación laboral",
clase1 == 2 ~ "drop",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(tipo) %>%
srvyr::summarise(tot = survey_mean(na.rm = T)) %>%
mutate(prop = tot,
tot = tot*100,
ord = "10") %>%
select(-ends_with("se")) %>%
filter(!tipo=="drop")
) %>%
## 2.11. Empleo informal ----
bind_rows(
tempo %>%
filter(clase1 == 1 & clase2 == 1) %>%
mutate(
tipo = case_when(
emp_ppal == 1 ~ "Empleo informal",
emp_ppal == 2 ~ "Empleo formal",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(tipo) %>%
srvyr::summarise(tot = survey_total(na.rm = T)) %>%
left_join(
tempo %>%
filter(clase1 == 1 & clase2 == 1) %>%
mutate(
tipo = case_when(
emp_ppal == 1 ~ "Empleo informal",
emp_ppal == 2 ~ "Empleo formal",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(tipo) %>%
srvyr::summarise(prop = survey_mean(na.rm = T))
) %>%
select(-ends_with("se")) %>%
drop_na(tipo) %>%
filter(tipo == "Empleo informal") %>%
mutate(ord = "11")
## 2.12. Pobreza laboral ----
) %>%
bind_rows(
tempo_todo %>%
mutate(
ocupado = ifelse(clase1 == 1 & clase2 == 1, 1, 0),
ingreso = case_when(
ocupado == 0 | (is.na(p6b2) & (p6_9==9 | p6a3==3)) ~ 0,
is.na(p6b2) & p6c == 1 ~ salario*0.5,
is.na(p6b2) & p6c == 2 ~ salario*1,
is.na(p6b2) & p6c == 3 ~ salario*1.5,
is.na(p6b2) & p6c == 4 ~ salario*2.5,
is.na(p6b2) & p6c == 5 ~ salario*4,
is.na(p6b2) & p6c == 6 ~ salario*7.5,
is.na(p6b2) & p6c == 7 ~ salario*10,
T ~ p6b2
),
tamh = 1,
rururb = if_else((t_loc>=1 & t_loc<=3), 0, 1),
mv = if_else(is.na(ingreso) & ocupado == 1, 1, 0)
) %>%
select(
llaveh, tamh, ingreso, mv, ocupado, rururb, factor = fac, ent, mun
) %>%
group_by(llaveh) %>%
summarise(
tamh = sum(tamh, na.rm = T),
ingreso = sum(ingreso, na.rm = T),
mv = sum(mv, na.rm = T),
ocupado = sum(ocupado, na.rm = T),
rururb = mean(rururb, na.rm = T),
factor = mean(factor, na.rm = T),
ent = mean(ent, na.rm = T),
mun=first(mun)
) %>%
select(llaveh, tamh, ingreso, mv, ocupado, rururb, factor, ent, mun) %>%
mutate(
mv = if_else(!is.na(mv) & mv > 0, 1, 0),
year_vector = as.character(str_pad(i,2,"l","0")),
trim_vector = as.character(x)
) %>%
filter(
mv != 1
) %>%
ungroup() %>%
left_join(
lps
) %>%
select(-c(year_vector, trim_vector)) %>%
mutate(
factorp = factor*tamh,
ingreso_per_cap = ingreso/tamh,
pob_lab = case_when(
ingreso_per_cap<lp ~ "1",
T ~ "0"
)
) %>%
as_survey_design(weights = factorp) %>%
srvyr::group_by(pob_lab) %>%
srvyr::summarise(tlp = survey_total(na.rm = T)) %>%
select(-contains("se")) %>%
filter(pob_lab == "1") %>%
mutate(tipo="Pobreza laboral") %>%
select(tipo, tot = tlp) %>%
left_join(
tempo_todo %>%
mutate(
ocupado = ifelse(clase1 == 1 & clase2 == 1, 1, 0),
ingreso = case_when(
ocupado == 0 | (is.na(p6b2) & (p6_9==9 | p6a3==3)) ~ 0,
is.na(p6b2) & p6c == 1 ~ salario*0.5,
is.na(p6b2) & p6c == 2 ~ salario*1,
is.na(p6b2) & p6c == 3 ~ salario*1.5,
is.na(p6b2) & p6c == 4 ~ salario*2.5,
is.na(p6b2) & p6c == 5 ~ salario*4,
is.na(p6b2) & p6c == 6 ~ salario*7.5,
is.na(p6b2) & p6c == 7 ~ salario*10,
T ~ p6b2
),
tamh = 1,
rururb = if_else((t_loc>=1 & t_loc<=3), 0, 1),
mv = if_else(is.na(ingreso) & ocupado == 1, 1, 0)
) %>%
select(
llaveh, tamh, ingreso, mv, ocupado, rururb, factor = fac, ent, mun
) %>%
group_by(llaveh) %>%
summarise(
tamh = sum(tamh, na.rm = T),
ingreso = sum(ingreso, na.rm = T),
mv = sum(mv, na.rm = T),
ocupado = sum(ocupado, na.rm = T),
rururb = mean(rururb, na.rm = T),
factor = mean(factor, na.rm = T),
ent = mean(ent, na.rm = T),
mun=first(mun)
) %>%
select(llaveh, tamh, ingreso, mv, ocupado, rururb, factor, ent, mun) %>%
mutate(
mv = if_else(!is.na(mv) & mv > 0, 1, 0),
year_vector = as.character(str_pad(i,2,"l","0")),
trim_vector = as.character(x)
) %>%
filter(
mv != 1
) %>%
ungroup() %>%
left_join(
lps
) %>%
select(-c(year_vector, trim_vector)) %>%
mutate(
factorp = factor*tamh,
ingreso_per_cap = ingreso/tamh,
pob_lab = case_when(
ingreso_per_cap<lp ~ "1",
T ~ "0"
)
) %>%
as_survey_design(weights = factorp) %>%
srvyr::group_by(pob_lab) %>%
srvyr::summarise(tlp = survey_mean(na.rm = T)) %>%
select(-contains("se")) %>%
filter(pob_lab == "1") %>%
mutate(tipo="Pobreza laboral") %>%
select(tipo, prop = tlp)
)%>%
mutate(ord = "12")
) %>%
## 2.13-15. Ingresos promedio ----
bind_rows(
ingresos_imputados %>%
as_survey_design(weights = factor) %>%
summarise(
tot = survey_mean(ingreso_sm, na.rm = T)
) %>%
select(-ends_with("se")) %>%
mutate(tipo="Ingreso laboral promedio",
ord = "13") %>%
select(tipo, tot, ord)%>%
bind_rows(
ingresos_imputados %>%
mutate(
tipo = tipo_formalidad
) %>%
as_survey_design(weights = factor) %>%
group_by(tipo) %>%
summarise(
tot = survey_mean(ingreso_sm, na.rm = T)
) %>%
select(-ends_with("se")) %>%
mutate(
ord = ifelse(tipo == "Empleo formal", "14", "15"),
tipo=paste0("Ingreso laboral promedio (",tipo,")")
) %>%
select(tipo, tot, ord)
)
) %>%
## 2.16. Horas trabajadas a la semana ----
bind_rows(
tempo %>%
filter(clase1==1) %>%
as_survey_design(weights = fac) %>%
summarise(
tot = survey_mean(hrsocup, na.rm = T)
) %>%
mutate(tipo = "Horas trabajadas a la semana (promedio)") %>%
select(-contains("_se")) %>%
mutate(ord = "16")
) %>%
## 2.17. Horas trabajadas en el tareas del hogar ----
# El nombre de las variables cambia entre trimestres 1 y el resto
bind_rows(
tempo %>%
mutate(
p11_h2 = ifelse(p11_h2>97,NA,p11_h2),
horas_semana_cuidados = p11_h2 + (p11_m2/60),
p11_h7 = ifelse(p11_h7>97,NA,p11_h7),
horas_semana_quehaceres = p11_h7 + (p11_m7/60),
horas_semana_quehaceres_cuidados = horas_semana_cuidados+horas_semana_quehaceres
) %>%
as_survey_design(weights = fac) %>%
summarise(tot = survey_mean(horas_semana_quehaceres_cuidados, na.rm = T)) %>%
select(-ends_with("_se")) %>%
mutate(tipo = "Horas trabajadas en tareas del hogar y de cuidados (no remunerado)",
ord = "17")
) %>%
mutate(sexo="General")
# 3. Por sexo ----
## 3.1. Población total ----
sex <-
tempo_todo %>%
as_survey_design(weights = fac) %>%
group_by(sexo) %>%
srvyr::summarise(tot = survey_total(na.rm = T)) %>%
select(-ends_with("se")) %>%
left_join(
tempo_todo %>%
as_survey_design(weights = fac) %>%
group_by(sexo) %>%
srvyr::summarise(prop = survey_mean(na.rm = T)) %>%
select(-ends_with("se"))
) %>%
mutate(ord = "1", tipo = "Población total") %>%
## 3.2. Población en edad de trabajar ----
bind_rows(
tempo %>%
as_survey_design(weights = fac) %>%
group_by(sexo) %>%
srvyr::summarise(tot = survey_total(na.rm = T)) %>%
select(-ends_with("se")) %>%
left_join(
tempo %>%
as_survey_design(weights = fac) %>%
group_by(sexo) %>%
srvyr::summarise(prop = survey_mean(na.rm = T)) %>%
select(-ends_with("se"))
) %>%
mutate(ord = "2", tipo = "Población en edad de trabajar (15 años o más)")
) %>%
## 3.3. y 3.7. PEA y PNEA ----
bind_rows(
tempo %>%
mutate(
tipo = case_when(
clase1 == 1 ~ "Población económicamente activa",
clase1 == 2 ~ "Población no económicamente activa",
T ~ NA_character_
),
ord = ifelse(clase1==1, "3","7")
) %>%
as_survey_design(weights = fac) %>%
group_by(sexo, ord, tipo) %>%
srvyr::summarise(tot = survey_total(na.rm = T)) %>%
left_join(
tempo %>%
mutate(
tipo = case_when(
clase1 == 1 ~ "Población económicamente activa",
clase1 == 2 ~ "Población no económicamente activa",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(sexo, tipo) %>%
srvyr::summarise(prop = survey_mean(na.rm = T)) %>%
mutate(ord = ifelse(str_starts(tipo, "Población no"), "7", "3"))
) %>%
select(-ends_with("se"))
) %>%
## 3.4. y 3.5. Población ocupada y desocupada ----
bind_rows(
tempo %>%
filter(clase1==1) %>%
mutate(
tipo = case_when(
clase2 == 1 ~ "Ocupada",
clase2 == 2 ~ "Desocupada",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(sexo, tipo) %>%
srvyr::summarise(tot = survey_total(na.rm = T)) %>%
left_join(
tempo %>%
filter(clase1==1) %>%
mutate(
tipo = case_when(
clase2 == 1 ~ "Ocupada",
clase2 == 2 ~ "Desocupada",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(sexo, tipo) %>%
srvyr::summarise(prop = survey_mean(na.rm = T))
) %>%
select(-ends_with("se")) %>%
mutate(ord = ifelse(tipo == "Ocupada", "4", "5"))
) %>%
## 3.6. Población subocupada ----
bind_rows(
tempo %>%
filter(clase1 == 1) %>%
mutate(
tipo = case_when(
sub_o == 1 ~ "Población subocupada",
sub_o == 0 ~ "Población no subocupada",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(sexo, tipo) %>%
srvyr::summarise(tot = survey_total(na.rm = T)) %>%
left_join(
tempo %>%
filter(clase1 == 1) %>%
mutate(
tipo = case_when(
sub_o == 1 ~ "Población subocupada",
sub_o == 0 ~ "Población no subocupada",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(sexo, tipo) %>%
srvyr::summarise(prop = survey_mean(na.rm = T))
) %>%
select(-ends_with("se")) %>%
drop_na(tipo) %>%
mutate(ord = "6")
) %>%
## 3.8. y 3.9. Población disponible y no disponible ----
bind_rows(
tempo %>%
filter(clase1==2) %>%
mutate(
tipo = case_when(
clase2 == 3 ~ "Disponibles",
clase2 == 4 ~ "No disponibles",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(sexo, tipo) %>%
srvyr::summarise(tot = survey_total(na.rm = T)) %>%
left_join(
tempo %>%
filter(clase1==2) %>%
mutate(
tipo = case_when(
clase2 == 3 ~ "Disponibles",
clase2 == 4 ~ "No disponibles",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(sexo, tipo) %>%
srvyr::summarise(prop = survey_mean(na.rm = T))
) %>%
select(-ends_with("se")) %>%
mutate(ord = ifelse(tipo == "Disponibles", "8", "9"))
) %>%
## 3.10. Tasa de participación laboral ----
bind_rows(
tempo %>%
mutate(
tipo = case_when(
clase1 == 1 ~ "Tasa de participación laboral",
clase1 == 2 ~ "drop",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(sexo, tipo) %>%
srvyr::summarise(tot = survey_mean(na.rm = T)) %>%
mutate(prop = tot,
tot = tot*100,
ord = "10") %>%
select(-ends_with("se")) %>%
filter(!tipo=="drop")
) %>%
## 3.11. Empleo informal ----
bind_rows(
tempo %>%
filter(clase1 == 1 & clase2 == 1) %>%
mutate(
tipo = case_when(
emp_ppal == 1 ~ "Empleo informal",
emp_ppal == 2 ~ "Empleo formal",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(sexo, tipo) %>%
srvyr::summarise(tot = survey_total(na.rm = T)) %>%
left_join(
tempo %>%
filter(clase1 == 1 & clase2 == 1) %>%
mutate(
tipo = case_when(
emp_ppal == 1 ~ "Empleo informal",
emp_ppal == 2 ~ "Empleo formal",
T ~ NA_character_
)
) %>%
as_survey_design(weights = fac) %>%
group_by(sexo, tipo) %>%
srvyr::summarise(prop = survey_mean(na.rm = T))
) %>%
select(-ends_with("se")) %>%
drop_na(tipo) %>%
filter(tipo == "Empleo informal") %>%
mutate(ord = "11")
) %>%
## 3.12. Pobreza laboral ----
bind_rows(
tempo_todo %>%
mutate(
ocupado = ifelse(clase1 == 1 & clase2 == 1, 1, 0),
ingreso = case_when(
ocupado == 0 | (is.na(p6b2) & (p6_9==9 | p6a3==3)) ~ 0,
is.na(p6b2) & p6c == 1 ~ salario*0.5,
is.na(p6b2) & p6c == 2 ~ salario*1,
is.na(p6b2) & p6c == 3 ~ salario*1.5,
is.na(p6b2) & p6c == 4 ~ salario*2.5,
is.na(p6b2) & p6c == 5 ~ salario*4,
is.na(p6b2) & p6c == 6 ~ salario*7.5,
is.na(p6b2) & p6c == 7 ~ salario*10,
T ~ p6b2
),
tamh = 1,
rururb = if_else((t_loc>=1 & t_loc<=3), 0, 1),
mv = if_else(is.na(ingreso) & ocupado == 1, 1, 0)
) %>%
select(
llaveh, tamh, ingreso, mv, ocupado, rururb, factor = fac, ent, mun
) %>%
group_by(llaveh) %>%
summarise(
tamh = sum(tamh, na.rm = T),
ingreso = sum(ingreso, na.rm = T),
mv = sum(mv, na.rm = T),
ocupado = sum(ocupado, na.rm = T),
rururb = mean(rururb, na.rm = T),
factor = mean(factor, na.rm = T),
ent = mean(ent, na.rm = T),
mun=first(mun)
) %>%
select(llaveh, tamh, ingreso, mv, ocupado, rururb, factor, ent, mun) %>%
mutate(
mv = if_else(!is.na(mv) & mv > 0, 1, 0),
year_vector = as.character(str_pad(i,2,"l","0")),
trim_vector = as.character(x)
) %>%
filter(
mv != 1
) %>%
ungroup() %>%
left_join(
lps
) %>%
select(-c(year_vector, trim_vector)) %>%
mutate(
factorp = factor*tamh,
ingreso_per_cap = ingreso/tamh,
pob_lab = case_when(
ingreso_per_cap<lp ~ "1",
T ~ "0"
)
) %>%
left_join(
tempo_todo %>%
mutate(sexo = ifelse(sex==1,"Hombres",ifelse(sex==2,"Mujeres",NA))) %>%
select(llaveh, sexo, fac_original = fac)
) %>%
as_survey_design(weights = fac_original) %>%
srvyr::group_by(sexo, pob_lab) %>%
srvyr::summarise(tot = survey_total(na.rm = T)) %>%
select(-contains("_se")) %>%
filter(pob_lab == "1") %>%
mutate(tipo="Pobreza laboral") %>%
select(sexo, tipo, tot) %>%
left_join(
tempo_todo %>%
mutate(
ocupado = ifelse(clase1 == 1 & clase2 == 1, 1, 0),
ingreso = case_when(
ocupado == 0 | (is.na(p6b2) & (p6_9==9 | p6a3==3)) ~ 0,
is.na(p6b2) & p6c == 1 ~ salario*0.5,
is.na(p6b2) & p6c == 2 ~ salario*1,
is.na(p6b2) & p6c == 3 ~ salario*1.5,
is.na(p6b2) & p6c == 4 ~ salario*2.5,
is.na(p6b2) & p6c == 5 ~ salario*4,
is.na(p6b2) & p6c == 6 ~ salario*7.5,
is.na(p6b2) & p6c == 7 ~ salario*10,
T ~ p6b2
),
tamh = 1,
rururb = if_else((t_loc>=1 & t_loc<=3), 0, 1),
mv = if_else(is.na(ingreso) & ocupado == 1, 1, 0)
) %>%
select(
llaveh, tamh, ingreso, mv, ocupado, rururb, factor = fac, ent, mun
) %>%
group_by(llaveh) %>%
summarise(
tamh = sum(tamh, na.rm = T),
ingreso = sum(ingreso, na.rm = T),
mv = sum(mv, na.rm = T),
ocupado = sum(ocupado, na.rm = T),
rururb = mean(rururb, na.rm = T),