Change the year of a time series data frame keeping the original weekdays
Source:R/preprocessing.R
change_timeseries_year.RdThe input df must contain full-week time-series profiles in order to
arrange the data according to the day of the week. For example, if the first
day in the df is a Monday the last one must be a Sunday.
Examples
# Example time-series data set: year 2023
head(energy_profiles)
#> # A tibble: 6 × 7
#> datetime solar building price_imported price_exported price_turn_up
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2023-01-01 00:00:00 0 2.61 -0.00146 0.025 0
#> 2 2023-01-01 00:15:00 0 2.42 -0.00146 0.025 0
#> 3 2023-01-01 00:30:00 0 2.23 -0.00146 0.025 0
#> 4 2023-01-01 00:45:00 0 2.04 -0.00146 0.025 0
#> 5 2023-01-01 01:00:00 0 1.85 -0.00152 0.025 0.0328
#> 6 2023-01-01 01:15:00 0 1.78 -0.00152 0.025 0.0351
#> # ℹ 1 more variable: price_turn_down <dbl>
# Change year to 2025
# Note that the data from columns has changed according to the weekday
head(change_timeseries_year(
df = energy_profiles,
year_out = 2025
))
#> # A tibble: 6 × 7
#> datetime solar building price_imported price_exported price_turn_up
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2025-01-01 00:00:00 0 1.60 0.0684 0.025 0
#> 2 2025-01-01 00:15:00 0 1.56 0.0684 0.025 0
#> 3 2025-01-01 00:30:00 0 1.51 0.0684 0.025 0
#> 4 2025-01-01 00:45:00 0 1.47 0.0684 0.025 0
#> 5 2025-01-01 01:00:00 0 1.43 0.0379 0.025 0
#> 6 2025-01-01 01:15:00 0 1.41 0.0379 0.025 0
#> # ℹ 1 more variable: price_turn_down <dbl>