View source: R/calc_hmw_functions.R
fill_ilo_data | R Documentation |
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.
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
)
.df |
The ILO labor data with added region codes.
Usually produced by calling the
|
country_col , hmw_region_code_col |
See |
sex_ilo_col , yearly_working_hours_ilo_col , employed_persons_ilo_col , employed_count , hours_count |
See |
sector_col |
See |
year |
See |
col_1960 , col_2020 |
See |
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()
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