Calculate year-over-year growth for a numeric column

Computes the year-over-year growth rate for a numeric column in a dataset. The function ensures a complete sequence of years between the minimum and maximum in the date column, fills in any missing years, and calculates the growth rate using lagged values.

Usage

compute_fastchange(data, col, date_col)

Arguments

data: A dataset.
col: A numeric column (either unquoted or as a string) for which the year-over-year growth rate will be calculated.
date_col: A date or numeric column (either unquoted or as a string) used to order the data and define the time sequence (typically a year column).

Value

A dataset with:

The completed `date_col` sequence.
A new column named `"growth_<col>"` containing the year-over-year growth rates.

The returned object will match the class of the input `data`.

Details

- Missing years in the sequence are added automatically. - Missing values in `col` result in `NA` for the corresponding growth rate. - The first observation (or any row where the lag is missing) will have `NA`. - The function can accept both unquoted column names or strings. - To compute growth rates by group (e.g., country), use `group_by()` from `dplyr`.

Examples

library(data.table)
#> 
#> Attaching package: ‘data.table’
#> The following object is masked from ‘package:base’:
#> 
#>     %notin%

dt <- data.table::data.table(
  year = c(2020, 2021, 2023),
  gdp = c(100, 110, 130)
)

# Using strings
compute_fastchange(dt, "gdp", "year")
#> Key: <year>
#>     year   gdp gdp_growth
#>    <int> <num>      <num>
#> 1:  2020   100         NA
#> 2:  2021   110        0.1
#> 3:  2022    NA         NA
#> 4:  2023   130         NA