Description Usage Arguments Value Author(s) References See Also Examples
View source: R/start_end_to_fill.R
This function allows to fill the start and end gaps of a time series by doing repetition of next (for the start) and previous values (for the end)
1 | start_end_to_fill(data, calendar, gap_variable, key_variable, time_variable)
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data |
a R data frame |
calendar |
a R data frame containing a complete empty calendar (as one can performs with |
gap_variable |
a character. This represents the name of the variable we want to fill the start and end gaps |
key_variable |
a character. This represents the variable name that refers to the key variable in the panel data (an ID, ...) |
time_variable |
a character. This represents the time variable name that permits to sort observation on a time scale |
a R data frame that contains the original columns and a new one:
gap_variable
_corrected_1: the gap variable with starts and ends filled
Simon CORDE
Link to the author's github package repository: https://github.com/Redcart/helda
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | library(dplyr)
# We take three countries from 2011 to 2018
fr_sp_ge_pop <- world_countries_pop %>%
filter(country_name %in% c('France', 'Spain', 'Germany')) %>%
filter(year > 2010) %>%
arrange(country_name, year)
# We artificially create some gaps in time series
fr_sp_ge_pop$population[c(1, 5, 11, 12, 24)] <- NA
fr_sp_ge_pop <- na.omit(fr_sp_ge_pop)
data_1 <- create_calendar(data = fr_sp_ge_pop, key_variable = "country_code",
time_variable = "year", start_year = 2011, end_year = 2018)
data_2 <- start_end_to_fill(data = fr_sp_ge_pop, calendar = data_1, gap_variable = "population",
key_variable = "country_code", time_variable = "year")
data_3 <- gap_to_fill(data = data_2, gap_variable = "population_corrected_1",
key_variable = "country_code", time_variable = "year", digits = 1)
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