## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----message=FALSE, warning=FALSE--------------------------------------------- library(eph) library(dplyr) library(tidyr) library(purrr) library(ggplot2) ## ----------------------------------------------------------------------------- ind_3_18 <- get_microdata( year = 2018, period = 3, type = "individual" ) ## ----------------------------------------------------------------------------- ind_3_18 <- organize_labels( df = ind_3_18, type = "individual" ) ## ----------------------------------------------------------------------------- hog_3_18 <- get_microdata( year = 2018, period = 3, type = "hogar" ) %>% organize_labels(., type = "hogar" ) ## ----------------------------------------------------------------------------- calculate_tabulates( base = ind_3_18, x = "NIVEL_ED", y = "CH04", weights = "PONDERA", add.totals = "row", add.percentage = "col" ) ## ----------------------------------------------------------------------------- calculate_tabulates( base = ind_3_18, x = "NIVEL_ED", y = "CH04", add.totals = "row", add.percentage = "col" ) ## ----------------------------------------------------------------------------- ### Armo vector con el nombre de las variables de interĂ©s incluyendo # -variables necesarias para hacer el panel # -variables que nos interesan en nuestro anĂ¡lisis variables <- c( "CODUSU", "NRO_HOGAR", "COMPONENTE", "ANO4", "TRIMESTRE", "CH04", "CH06", "ESTADO", "PONDERA" ) ### Descargo la base individual para el 2018_t1 base_2018t1 <- get_microdata( year = 2018, period = 1, type = "individual", vars = variables ) ### Descargo la base individual para el 2018_t2 base_2018t2 <- get_microdata( year = 2018, period = 2, type = "individual", vars = variables ) ### Armo el panel pool <- organize_panels( bases = list(base_2018t1, base_2018t2), variables = c("ESTADO", "PONDERA"), window = "trimestral" ) ## ----------------------------------------------------------------------------- pool ## ----message=FALSE, warning=FALSE--------------------------------------------- pool %>% organize_labels(.) %>% calculate_tabulates( x = "ESTADO", y = "ESTADO_t1", weights = "PONDERA", add.percentage = "row" ) ## ----message=FALSE, warning=FALSE--------------------------------------------- df <- get_microdata( year = 2017:2019, period = 1:4, type = "individual", vars = c("ANO4", "TRIMESTRE", "PONDERA", "ESTADO", "CAT_OCUP") ) df %>% sample_n(5) ## ----------------------------------------------------------------------------- df <- df %>% group_by(ANO4, TRIMESTRE) %>% summarise(indicador = sum(PONDERA[CAT_OCUP == 3 & ESTADO == 1], na.rm = T) / sum(PONDERA[ESTADO == 1], na.rm = T)) df ## ----------------------------------------------------------------------------- lineas <- get_poverty_lines() lineas ## ----fig.width=7, fig.height=5------------------------------------------------ lineas %>% select(-ICE) %>% pivot_longer(cols = c("CBA", "CBT"), names_to = "canasta", values_to = "valor") %>% ggplot() + geom_line(aes(x = periodo, y = valor, col = canasta)) ## ----------------------------------------------------------------------------- canastas_reg_example ## ----------------------------------------------------------------------------- adulto_equivalente %>% head() ## ----warning=FALSE------------------------------------------------------------ bases <- bind_rows(toybase_individual_2016_03, toybase_individual_2016_04) base_pobreza <- calculate_poverty( base = bases, basket = canastas_reg_example, print_summary = TRUE ) ## ----------------------------------------------------------------------------- base_pobreza %>% select(CODUSU, ITF, region, adequi_hogar, CBA_hogar, CBT_hogar, situacion) %>% sample_n(10)