Change time resolution of a time-series data frame

Usage

change_timeseries_resolution(df, resolution_out, method)

Arguments

df: tibble, first column of name datetime being of class datetime and rest of columns being numeric
resolution_out: integer, desired interval of minutes between two consecutive datetime values
method: character, being interpolate, repeat or divide if the resolution has to be increased, or average, first or sum if the resolution has to be decreased. See Examples for more information.

Value

tibble

Examples

# Example time-series data set: time resolution 15 minutes
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 time resolution to 60 minutes
# It is decreasing time resolution, so we use the `average` method
head(change_timeseries_resolution(
  df = energy_profiles,
  resolution_out = 60,
  method = "average"
))
#> # 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     2.33       -0.00146          0.025        0     
#> 2 2023-01-01 01:00:00     0     1.74       -0.00152          0.025        0.0320
#> 3 2023-01-01 02:00:00     0     1.55       -0.005            0.025        0.0225
#> 4 2023-01-01 03:00:00     0     1.48       -0.0046           0.025        0     
#> 5 2023-01-01 04:00:00     0     1.39       -0.00405          0.025        0     
#> 6 2023-01-01 05:00:00     0     1.43       -0.0036           0.025        0     
#> # ℹ 1 more variable: price_turn_down <dbl>