Validate one day of data, returning a vector of error strings if needed

Provides ability to skip a poorly-formatted day for calculating metabolism, without breaking the whole loop. Rather than producing errors, quietly collects problems/errors as a list of strings for the calling function to handle.

Usage

mm_is_valid_day(
  data_ply,
  day_start = 4,
  day_end = 27.99,
  day_tests = c("full_day", "even_timesteps", "complete_data", "pos_discharge",
    "pos_depth"),
  required_timestep = NA,
  ply_date = as.Date(format(data_ply[max(1, nrow(data_ply)/2), "solar.time"],
    "%Y-%m-%d")),
  timestep_days = NA
)

Arguments

data_ply: a data.frame containing all relevant, validated modeling data for a single ply of data. (1 ply ~= 1 date, although the day length has been specified by day_start and day_end and may not be exactly 24 hours)
day_start: start time (inclusive) of a day's data in number of hours from the midnight that begins the date. For example, day_start=-1.5 indicates that data describing 2006-06-26 begin at 2006-06-25 22:30, or at the first observation time that occurs after that time if day_start doesn't fall exactly on an observation time.
day_end: end time (exclusive) of a day's data in number of hours from the midnight that begins the date. For example, day_end=30 indicates that data describing 2006-06-26 end at the last observation time that occurs before 2006-06-27 06:00.
day_tests: list of tests to conduct to determine whether each date worth of data is valid for modeling. full_day: Do the data span the full expected period (e.g., from 10:30pm on preceding day to 6am on following day)? even_timesteps: are all of the timesteps within the day the same length, to within a tolerance of 0.2% of the timestep length? complete_data: are all columns of input data available at every timestep? pos_discharge: is discharge greater than 0 at every timestep? pos_depth: is depth greater than 0 at every timestep? A further test is implied if required_timestep is a non-NA numeric.
required_timestep: NA or numeric (length 1). If numeric, the timestep length in days that a date must have to pass the validity check (to within a tolerance of 0.2% of the value of required_timestep)
ply_date: the Date this data_ply is intended to match. May be NA
timestep_days: the expected timestep length in fraction of a day; for example, a 1-hour timestep is 1/24 is 0.0416667. This is calculated within the function if timestep_days is NA. May be supplied as an argument to (1) pre-calculate the value for efficiency, or (2) require a specific timestep.

Value

character vector of errors if day is invalid, or TRUE if it's valid

Details

Assumes that the data have already been validated as in mm_validate_data

Examples

mm_is_valid_day(data_metab('1'))
#> [1] TRUE
mm_is_valid_day(data_metab('1', flaws='missing middle'))
#> [1] "uneven timesteps"
mm_is_valid_day(data_metab('1', flaws='missorted'))
#> [1] "uneven timesteps"
mm_is_valid_day(data_metab('1', flaws='duplicated'))
#> [1] "uneven timesteps"
mm_is_valid_day(data_metab('1', flaws=c('duplicated','missing end')))
#> [1] "data don't end when expected" "uneven timesteps"            
mm_is_valid_day(data_metab('3'))
#> [1] "data don't start when expected" "data don't end when expected"