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Fill missing data for the number of hours worked and employed persons

fill_ilo_data.Rd

Fill missing values from the ILO for the number of employed persons and yearly working hours by adding years absent from the raw data, removing groups of data for which there are no values at all, then interpolating and extrapolating groups of data for which there is at least one value.

Usage

fill_ilo_data(
  .df,
  country_col = MWTools::conc_cols$country_col,
  hmw_region_code_col = MWTools::conc_cols$hmw_region_code_col,
  sex_ilo_col = MWTools::ilo_cols$sex_ilo_col,
  sector_col = MWTools::mw_constants$sector_col,
  year = MWTools::mw_cols$year,
  yearly_working_hours_ilo_col = MWTools::ilo_cols$yearly_working_hours_ilo_col,
  employed_persons_ilo_col = MWTools::ilo_cols$employed_persons_ilo_col,
  hours_count = MWTools::ilo_cols$hours_count,
  employed_count = MWTools::ilo_cols$employed_count,
  col_1960 = MWTools::hmw_analysis_constants$col_1960,
  col_2020 = MWTools::hmw_analysis_constants$col_2020
)

Arguments

.df

The ILO labor data with added region codes. Usually produced by calling the add_hmw_region_codes function in sequence on the raw FAO data.

country_col, hmw_region_code_col

See MWTools::conc_cols.

sex_ilo_col, yearly_working_hours_ilo_col, employed_persons_ilo_col, employed_count, hours_count

See MWTools::ilo_cols.

sector_col

See MWTools::mw_constants.

year

See MWTools::mw_cols.

col_1960, col_2020

See MWTools::hmw_analysis_constants.

Examples

ilo_working_hours_data <- read.csv(file = MWTools::ilo_working_hours_test_data_path())
ilo_employment_data <- read.csv(file = MWTools::ilo_employment_test_data_path())
hmw_data <- prepareRawILOData(ilo_working_hours_data = ilo_working_hours_data,
                              ilo_employment_data = ilo_employment_data)
working_humans_data <- hmw_data |>
  add_hmw_region_codes() |>
  fill_ilo_data()