Fill missing rows in an even time series — even

Builds a complete timestamp sequence for logger data and joins the original observations onto it. Missing timestamps become explicit rows with NA values in the measured columns.

Usage

even_timesteps(
  logger_data,
  datetime_col = NULL,
  site_col = NULL,
  loggerData = lifecycle::deprecated()
)

Arguments

logger_data: Data frame or tibble containing timestamped logger data.
datetime_col: Optional character string naming the POSIXct datetime column. If NULL (default), the single POSIXct column in logger_data is detected automatically; when logger_data contains more than one POSIXct column, datetime_col must be supplied.
site_col: Optional character string naming a site column. When supplied, each site is completed independently.
loggerData: Use logger_data instead.

Value

A data frame or tibble, matching the input class, with all original rows plus inserted NA rows for missing time steps.

Details

The time step is inferred from the sorted unique timestamps for each site. Use this after removing obvious duplicate or invalid timestamps.

Examples

df <- data.frame(
  DateTime_UTC = as.POSIXct(
    c("2024-01-01 00:00", "2024-01-01 01:00", "2024-01-01 03:00"),
    tz = "UTC"
  ),
  temp_C = c(10.1, 10.4, 10.5)
)

even_timesteps(df)
#>          DateTime_UTC temp_C
#> 1 2024-01-01 00:00:00   10.1
#> 2 2024-01-01 01:00:00   10.4
#> 3 2024-01-01 02:00:00     NA
#> 4 2024-01-01 03:00:00   10.5

df_multi <- data.frame(
  DateTime_UTC = c(
    as.POSIXct(
      c("2024-01-01 00:00", "2024-01-01 01:00", "2024-01-01 03:00"),
      tz = "UTC"
    ),
    as.POSIXct(c("2024-01-01 00:00", "2024-01-01 00:30"), tz = "UTC")
  ),
  Site = c("A", "A", "A", "B", "B")
)

even_timesteps(df_multi, site_col = "Site")
#>          DateTime_UTC Site
#> 1 2024-01-01 00:00:00    A
#> 2 2024-01-01 01:00:00    A
#> 3 2024-01-01 02:00:00    A
#> 4 2024-01-01 03:00:00    A
#> 5 2024-01-01 00:00:00    B
#> 6 2024-01-01 00:30:00    B