v0.0.1.18

NAMESPACE

@@ -39,6 +39,7 @@ export(overcrowding)
 export(poverty)
 export(read_microdata)
 export(set_design)
+export(underemployment)
 export(unlabelled)
 export(unsatisfied_basic_needs)
 export(years_of_schooling)

R/add_geom.R

@@ -1,5 +1,5 @@
 #' add_geom
-#' This function allows you to add geom variable to a with a code varible of "zona", "barrio", "localidad", "segmentos", "secciones" or "departamentos".
+#' @description This function allows you to add a geom variable with a code variable of "zona", "barrio", "localidad", "segmentos", "secciones" or "departamentos".
 #'
 #' @param data data.frame
 #' @param unit spatial unit of data, may be: "Departamentos", "Secciones", "Secc MVD 2004", "Segmentos", "Segm MVD 2004", "Segm URB INT 2004", "Zonas", "Zonas MVD 2004", "Zonas URB INT 2004", "Localidades pg", "Municipios" o "Barrios".

R/dwelling.R

@@ -1,5 +1,5 @@
 #' housing_deprivation
-#' This function allows you to calculate the housing status
+#' @description This function allows you to calculate the housing status
 #'
 #' @param data data.frame
 #' @param n number of deprivations to consider. Default 1
@@ -117,13 +117,15 @@ housing_deprivation <- function(data = ech::toy_ech_2018,
          electricity (Carencia: Red electrica);
          housing_deprivation_q (Suma de carencias habitacionales) &
          housing_deprivation (Carencia habitacional en relacion al parametro n)")
-  data
+
+  return(data)
+
 }
 
 
 
 #' housing_situation
-#' A function to calculate the housing situation
+#' @description This function allows you to calculate the housing situation
 #'
 #' @param data data.frame
 #' @param c5_1 Variable name of roof condensation
@@ -197,7 +199,7 @@ housing_situation <- function(data = ech::toy_ech_2018,
 
 
 #' housing_conditions
-#' A function to calculate the housing conditions
+#' @description This function allows you to calculate the housing conditions
 #'
 #' @param data data.frame
 #' @param c2 Variable name of predominant material on external walls
@@ -242,7 +244,7 @@ housing_conditions <- function(data = ech::toy_ech_2018,
 }
 
 #' overcrowding
-#' A function to calculate overcrowding in the household
+#' @description This function allows you to calculate overcrowding in the household
 #'
 #' @param data data.frame
 #' @param ht19 Variable name of umber of individuals in the household
@@ -275,12 +277,14 @@ overcrowding <- function(data = ech::toy_ech_2018,
                   overcrowding = haven::labelled(overcrowding, labels = c("Hacinado" = 1, "No hacinado" = 0),
                                          label = "Hacinamiento"))
   message("A variable has been created: \n \t overcrowding (hacinamiento)")
-  data
+
+  return(data)
+
 }
 
 
 #' housing_tenure
-#' A function to calculate the housing tenure
+#' @description This function allows you to calculate the housing tenure
 #'
 #' @param data data.frame
 #' @param d8_1 Variable name of housing_tenure (owner, renter, rent-free occupancy, etc.)
@@ -315,5 +319,7 @@ housing_tenure <- function(data = ech::toy_ech_2018,
 
 
   message("A variable has been created: \n \t housing_tenure (tenencia de la vivienda)")
-  data
+
+  return(data)
+
 }

R/education.R

@@ -1,7 +1,6 @@
 #' enrolled_school
-#' A function to calculate people enrolled in school
+#' @description This function allows you to calculate the people enrolled in school
 #'
-#' This function allows you to calculate people enrolled in school
 #' @param data data.frame with necessary variables Defaults to ech.
 #' @param e193 Variable name of attendance school
 #' @param e197 Variable name of attendance primary
@@ -70,7 +69,7 @@ enrolled_school <- function(data = ech::toy_ech_2018,
 }
 
 #' years_of_schooling
-#' A function to calculate the years of schooling
+#' @description This function allows you to calculate the years of schooling
 #'
 #' @param data data.frame
 #' @param e193  Variable name of attendance school
@@ -177,13 +176,13 @@ years_of_schooling <- function(data = ech::toy_ech_2018,
   }
 
   message("A variable has been created: \n \t  years_schooling (anios de escolaridad)")
-  data
+  return(data)
 }
 
 
 
 #' level_education
-#' A function to calculate the highest level of education achieved
+#' @description This function allows you to calculate the highest level of education achieved
 #'
 #' @param data data.frame
 #' @param e51_2 Variable name of years passed in primary
@@ -262,14 +261,14 @@ level_education <- function(data = ech::toy_ech_2018,
                                         labels = c("Sin instruccion" = 0, "Primaria" = 1, "Secundaria" = 2, "UTU" = 3, "Magisterio o profesorado" = 4, "Universidad o similar" = 5),
                                         label = "Nivel educativo")
     )
-  message("A variable has been created: \n \t level_education (nivel educativo)")
-  data
+  message("A variable has been created: \n \t highest level_education achieved (maximo nivel educativo alcanzado)")
+  return(data)
 }
 
 
 
 #' level_completion
-#' A function to calculate the level of school completion
+#' @description This function allows you to calculate the level of school completion
 #'
 #' @param data data.frame
 #' @param e197 Variable name of attends primary school
@@ -333,5 +332,5 @@ level_completion <- function(data = ech::toy_ech_2018,
   message("Variables have been created: \n \t primary_completion (primaria completa);
          lower_secondary_completion (ciclo basico completo) &
          upper_secondary_completion (bachillerato completo)")
-  data
+  return(data)
 }

R/employment.R

@@ -1,5 +1,5 @@
 #' employment
-#' This function allows you to calculate the variables: PEA, PET, PO, PD
+#' @description This function allows you to calculate the variables: PEA, PET, PO, PD
 #'
 #' @param data data.frame with microdata
 #' @param pobpcoac Variable name of definition of population by activity status. Default: "pobpcoac"
@@ -28,7 +28,15 @@ employment <- function(data = ech::toy_ech_2018,
   data <- data %>% dplyr::mutate(pea = ifelse(pobpcoac %in% 2:5, 1, 0),
                           pet = ifelse(pobpcoac != 1, 1, 0),
                           po  = ifelse(pobpcoac == 2, 1, 0),
-                          pd  = ifelse(pobpcoac %in% 3:5, 1, 0)
+                          pd  = ifelse(pobpcoac %in% 3:5, 1, 0),
+                          pea = haven::labelled(pea, labels = c("Si" = 1, "No" = 0),
+                                                label = "Poblacion economicamente activa"),
+                          pet = haven::labelled(pet, labels = c("Si" = 1, "No" = 0),
+                                                label = "Poblacion en edad de trabajar"),
+                          po = haven::labelled(po, labels = c("Si" = 1, "No" = 0),
+                                               label = "Poblacion ocupada"),
+                          pd = haven::labelled(pd, labels = c("Si" = 1, "No" = 0),
+                                               label = "Poblacion desocupada")
   )
 
   message("Variables have been created: \n \t pea (Poblacion economicamente activa);
@@ -38,15 +46,63 @@ employment <- function(data = ech::toy_ech_2018,
   return(data)
 }
 
+#' underemployment
+#' @description This function allows you to identify underemployed people
+#'
+#' @param data data.frame
+#' @param pobpcoac Variable name of definition of population by activity status. Default: "pobpcoac"
+#' @param f85 Variable name of number of hours worked in the main job
+#' @param f98 Variable name of Number of hours worked at the secondary job
+#' @param f101 Variable name of reasons why you want another job
+#' @param f102 Variable name of want to work more hours
+#' @param f103 Variable name of are available to work more hours at this time
+#' @param f104 Variable name of reasons why you dont work more hours
+#'
+#' @importFrom dplyr mutate case_when
+#' @importFrom haven labelled
+#' @return data.frame
+#' @export
+#' @details
+#' Disclaimer: This script is not an official INE product.
+#' Aviso: El script no es un producto oficial de INE.
+#'
+#' @examples
+#' toy_ech_2018 <- underemployment(data = ech::toy_ech_2018)
+#'
+underemployment <- function(data = ech::toy_ech_2018,
+                         pobpcoac = "pobpcoac",
+                         f85 = "f85",
+                         f98 = "f98",
+                         f101 = "f101",
+                         f102 = "f102",
+                         f103 = "f103",
+                         f104 = "f104"){
+
+    data <- data %>% dplyr::mutate(underemployment = dplyr::case_when(pobpcoac == 2 & (f85 +  f98 < 40) & (f101 == 1 | f102 == 1) & (f103 == 1) & (f104 == 5) ~ 1,
+                                                                   TRUE ~ 0),
+                                   underemployment = haven::labelled(underemployment, labels = c("Si" = 1, "No" = 0), label = "Poblacion subempleada")
+      )
+
+    message("A variable has been created: \n \t underemployment (Poblacion subempleada)")
+    return(data)
+
+}
 #' employment_restrictions
 #'
+#' @description This function allows you to identify workers with employment restrictions
 #' @param data data.frame
-#' @param f82 Variable name of
-#' @param underemployment Variable name of
+#' @param f82 Variable name of contribution to the pension fund
+#' @param underemployment Variable name of underemployment
 #'
 #' @return data.frame
 #' @export
+#' @details
+#' Disclaimer: This script is not an official INE product.
+#' Aviso: El script no es un producto oficial de INE.
 #'
+#' @examples
+#' toy_ech_2018 <- underemployment(data = ech::toy_ech_2018)
+#' toy_ech_2018 <- employment_restrictions(data = toy_ech_2018)
 #'
 employment_restrictions  <- function(data = ech::toy_ech_2018,
                                      f82 = "f82",
@@ -67,6 +123,7 @@ employment_restrictions  <- function(data = ech::toy_ech_2018,
 
 #' branch_ciuu
 #'
+#' @description This function allows you to identify activity branches
 #' @param data data.frame
 #' @param f72_2 Variable name of ciiu code rev.4
 #' @param group logical to define 12 or 18 categories, if FALSE code 18. Default: TRUE

R/get_dictionary.R

@@ -1,5 +1,5 @@
 #' get_dictionary
-#' Download ECH dictionaries from INE website
+#' @description This function allows you to download ECH dictionaries from INE website
 #'
 #' @param year allows download data from 2011 to 2019. Default the last year
 #' @param folder Folder where are the files or be download

R/get_estimation.R

@@ -1,6 +1,7 @@
 #' get_estimation_mean
 #'
-#' This function allows you to estimate mean variable at universe level.
+#' @description This function allows you to estimate mean variable at universe level.
+#'
 #' @param data data frame with ECH microdata
 #' @param variable data frame column to estimate
 #' @param by.x data frame column
@@ -139,7 +140,7 @@ get_estimation_mean <- function(data = ech::toy_ech_2018,
 
 #' get_estimation_total
 #'
-#' This function allows you to estimate total variable at universe level.
+#' @description This function allows you to estimate total variable at universe level.
 #' @param data data frame with ECH microdata
 #' @param variable data frame column to estimate
 #' @param by.x data frame column
@@ -278,7 +279,7 @@ get_estimation_total <- function(data = ech::toy_ech_2018,
 
 #' get_estimation_ratio
 #'
-#' This function allows you to estimate ratio variable at universe level.
+#' @description This function allows you to estimate ratio variables at universe level.
 #' @param data data frame with ECH microdata
 #' @param variable.x data frame column to estimate
 #' @param variable.y data frame column to estimate
@@ -388,6 +389,7 @@ get_estimation_ratio <- function(data = ech::toy_ech_2018,
 
 #' get_estimation_gini
 #'
+#' @description  This function allows you to estimate the Gini coeficient
 #' @param data ech data frame
 #' @param variable income without rental value per capita deflated
 #' @param by variable
@@ -406,9 +408,9 @@ get_estimation_ratio <- function(data = ech::toy_ech_2018,
 #'
 #' @examples
 #' \donttest{
-#' df <- income_constant_prices(data = ech::toy_ech_2018, ipc = "R",
+#' toy_ech_2018 <- income_constant_prices(data = ech::toy_ech_2018, ipc = "R",
 #'  base_month = "01", base_year = "2005")
-#' get_estimation_gini(data = df, level = "h")
+#' get_estimation_gini(data = toy_ech_2018, level = "h")
 #' }
 #
 get_estimation_gini <- function(data = ech::toy_ech_2018,
@@ -421,7 +423,6 @@ get_estimation_gini <- function(data = ech::toy_ech_2018,
                                 pesoano = "pesoano",
                                 name = "estimacion"){
 
-
   # design ---
   design_ech <- ech::set_design(data = data, level = level)
 

R/get_microdata.R

@@ -129,7 +129,7 @@ archive_extract <- function(archive.path = NULL, dest.path = NULL) {
 #### get_microdata ####
 
 #' get_microdata
-#' Download and read ECH from INE website
+#' @description This function allows you to download and read ECH from INE website
 #'
 #' @param year allows download data from 2011 to 2019. Default the last year
 #' @param folder Folder where are the files or be download
@@ -286,7 +286,7 @@ get_microdata <- function(year = NULL,
 #### read_microdata ####
 
 #' read_microdata
-#' Read ECH from a local folder
+#' @description This function allows you to read ECH from a local folder
 #'
 #' @param path Folder where are the files or be download
 #' @importFrom fs path
@@ -340,7 +340,7 @@ read_microdata <- function(path = NULL){
 
 #' get_marco
 #'
-#' Download and read ECH from INE website
+#' @description This unction allows you to download and read the sampling frame from INE website
 #' @param year allows download data of 2011, 2004, 1996 or 1985. Default 2011
 #' @param folder Folder where are the files or be download
 #' @param toR write data frame in R format and delete download file and unpack files

R/household_type.R

@@ -1,5 +1,5 @@
 #' household_type
-#' This function allows you to calculate the household type for each household in the survey. A household is composed of one or more people who occupy a housing unit.
+#' @description This function allows you to calculate the household type for each household in the survey. A household is composed of one or more people who occupy a housing unit.
 #'
 #' @param data data frame with ECH microdata
 #' @param numero Variable name of household id
@@ -75,5 +75,6 @@ household_type <- function(data = ech::toy_ech_2018,
 
   data <- data %>% dplyr::select(everything(), -.data$sex_householder:-.data$no_rel)
   message("A variable has been created: \n \t household_type (Tipo de hogar)")
-  data
+  return(data)
+
  }

R/income.R

@@ -148,6 +148,7 @@ income_quantiles <- function(data = ech::toy_ech_2018,
 
 #' labor_income_per_capita
 #'
+#' @description This function allows you to calculate the labor income per capita
 #' @param data data frame
 #' @param numero Variable name of household id
 #' @param pobpcoac Variable name of definition of population by activity status
@@ -293,13 +294,14 @@ labor_income_per_capita <- function(data = ech::toy_ech_2018,
 
   message("Variables have been created: \n \t labor_income (Ingresos laborales) &
             labor_income_h (Ingresos laborales del hogar) &
-            labor_income_h_percapita (Ingresos laborales del hogar per capita)")
+            labor_income_h_percapita (Ingresos laborales per capita)")
   return(data)
 }
 
 
 #' labor_income_per_hour
 #'
+#' @description This function allows you to calculate the labor income per hour
 #' @param data data frame
 #' @param numero Variable name of household id
 #' @param f85 Variable name of hours worked per week
@@ -319,8 +321,8 @@ labor_income_per_capita <- function(data = ech::toy_ech_2018,
 #'
 #' @examples
 #' \donttest{
-#' df <- ech::toy_ech_2018
-#' toy_ech_2018 <- labor_income_per_hour(data = df, base_month = "06", base_year = "2018")
+#' toy_ech_2018 <- ech::toy_ech_2018
+#' toy_ech_2018 <- labor_income_per_hour(data = toy_ech_2018, base_month = "06", base_year = "2018")
 #' }
 #'
 labor_income_per_hour <- function(data = ech::toy_ech_2018,