Rename the variables of the pension register.
Arguments
- IND_YEARLY_RR
A data frame containing the data of the pension register subsetted for one year only.
- list
List of input data frames.
Value
a tidylist containing the following tidy data frames:
RR_OASI: A data frame containing all OASI data, whose variables are: variables:year: Year of the pension register extract.age: Age of the individual.age_retire: Effective retirement age.sex: Sex, female if female, 0 if male.nat: Nationality, foreign if 1, 0 if Swiss.resid: Residence, foreign if 1, 0 if Swiss.benef_type1: Old-age type of benefit if 1, 0 otherwise (dummy).benef_type2: Widow type of benefit if 1, 0 otherwise (dummy).benef_type3: Father's orphan type of benefit if 1, 0 otherwise (dummy).benef_type4: Mother's orphan type of benefit if 1, 0 otherwise (dummy).benef_type5: Twice orphan type of benefit if 1, 0 otherwise (dummy).benef_type6: Spouse's compl. type of benefit if 1, 0 otherwise (dummy).benef_type7: Father's child rent type of benefit if 1, 0 otherwise (dummy).benef_type8: Mother's child rent type of benefit if 1, 0 otherwise (dummy).benef_type: Types of benefits type of benefit (categorical).marital_stat1: Divorced marital status if 1, 0 otherwise (dummy).marital_stat2: Single as reference category marital status if 1, 0 otherwise (dummy).marital_stat3: Married marital status if 1, 0 otherwise (dummy).marital_stat4: Widowed marital status if 1, 0 otherwise (dummy).marital_stat: Marital status.splitting: If 1, splitting of the revenues, 0 otherwise.capping: If 1, the pension is capped, 0 otherwise.contrib_m_ind: Total number of OASI contribution months per individual.contrib_y_ageclass: Total number of contribution years per age group.bonus_m_edu: Number of months paid with a bonus for educative tasks.bonus_m_assist: Number of months paid with a bonus for assistance/care tasks.
Examples
#' @examples
#' IND_YEARLY_RR <- structure(
#' list(
#' alt = c(
#' 46L, 38L, 14L, 75L, 30L
#' ),
#' sex = c(
#' "f", "f", "f", "f", "m"
#' ),
#' nat = c(
#' "ch", "ch", "ch", "au", "ch"
#' ),
#' dom = c(
#' "ch", "au", "ch", "ch", "ch"
#' ),
#' gpr = c(
#' "rveuve", "renfant_pere_simple",
#' "rorphelin_pere_simple", "rvieillesse_simple", "renfant_pere_simple"
#' ),
#' zv = c(
#' "geschieden", "geschieden", "ledig", "ledig", "geschieden"
#' ),
#' csplit = c(NA, NA, NA, 0L, NA),
#' cplaf = c(
#' NA, NA, NA, 0L,
#' NA
#' ), jahr = c(2023L, 2023L, 2023L, 2023L, 2023L),
#' ram = c(
#' 879274L, 2988594L, 5111279L, 8900743L, 1322875L
#' ),
#' monatliche_rente = c(
#' 3399L, 2298L, 541L, 2496L, 3894L
#' ),
#' age_ret = c(
#' NA, NA, NA, 68L, NA
#' ),
#' eprc = c(
#' 0.0526315789473684, 0.0294117647058824, 0.024390243902439, 0.1, 0.125
#' ),
#' lcot = c(
#' 7L, 494L, 209L, 128L, 323L
#' ),
#' lcotg = c(
#' 38L,
#' 22L, 13L, 20L, 44L
#' ),
#' lbedu = c(
#' NA, NA, NA, 394L, NA
#' ),
#' lbass = c(
#' NA, NA, NA, 333L, NA
#' )
#' ),
#' class = c("tbl_df", "tbl", "data.frame"),
#' row.names = c(NA, -5L)
#' )
#'
#' mod_prepa_rr(IND_YEARLY_RR = IND_YEARLY_RR)