Fills gaps in a variable based on changes in a proxy variable, using ratios between the filled variable and the proxy variable, and labels output accordingly.
Arguments
- df
A tibble data frame containing one observation per row.
- var
The variable of df containing gaps to be filled.
- proxy_var
The variable to be used as proxy.
- time_index
The time index variable (usually year).
- ...
Optionally, additional arguments that will be passed to
linear_fill()with the ratios. See that function to know the accepted arguments.
Value
A tibble dataframe (ungrouped) where gaps in var have been filled, a new proxy_ratio variable has been created, and a new "source" variable has been created indicating if the value is original or, in case it has been estimated, the gapfilling method that has been used.
Examples
sample_tibble <- tibble::tibble(
category = c("a", "a", "a", "a", "a", "a", "b", "b", "b", "b", "b", "b"),
year = c(
"2015", "2016", "2017", "2018", "2019", "2020",
"2015", "2016", "2017", "2018", "2019", "2020"
),
value = c(NA, 3, NA, NA, 0, NA, 1, NA, NA, NA, 5, NA),
proxy_variable = c(1, 2, 2, 2, 2, 2, 1, 2, 3, 4, 5, 6)
)
proxy_fill(sample_tibble, value, proxy_variable, year, .by = c("category"))
#> # A tibble: 12 × 7
#> category year value proxy_variable proxy_ratio source_proxy_ratio
#> <chr> <chr> <dbl> <dbl> <dbl> <chr>
#> 1 a 2015 1.5 1 1.5 First value carried backwards
#> 2 a 2016 3 2 1.5 Original
#> 3 a 2017 2 2 1 Linear interpolation
#> 4 a 2018 1 2 0.5 Linear interpolation
#> 5 a 2019 0 2 0 Original
#> 6 a 2020 0 2 0 Last value carried forward
#> 7 b 2015 1 1 1 Original
#> 8 b 2016 2 2 1 Linear interpolation
#> 9 b 2017 3 3 1 Linear interpolation
#> 10 b 2018 4 4 1 Linear interpolation
#> 11 b 2019 5 5 1 Original
#> 12 b 2020 6 6 1 Last value carried forward
#> # ℹ 1 more variable: source_value <chr>