timestamp_output_formatter <- function(x, precision, suppOutput) {

    # x: properly formatted ISO 8601 string (in UTC) which we receive as output from date_ingest_checker()
    # precision is a required argument for this function; may be automatically detected or specified when date_ingest_checker() is called, but either way we should have value(s) for precison before calling timestamp_output_formatter()
    # suppOutput ("supplemental output"): optional argument allowing user to request timestamp output in "local" or other time zone in addition to UTC; no localities allowed

  # Description:
      1. Additional QA/QC or timestamps updates based on some sort of input/tracking log (see Celeste's code snippet for incidents)
      2. Format date for output in ISO 8601 UTC as text string and user specified "local" or other time with appropriate level of precision from input
      3. Remove extra columns from dataframe for input timezone and precision and create an output similar to the ingest provenance output

  (fail or fix?).


}

sorted_by_timestamp <- function(x) {

  # Description: Check that data are sorted by timestamp

  # Returns

  # Team members: John

}

spaces_checker <- function(x) {

  # Description: Check character data for inadvertant spaces

  # Returns:

  # Team members: Kristin

}

complete_cases_checker <- function(x) {

  # Description: row by row check that all value columns

  # Returns:

  # Team members: Patrick, Amanda

}

col_type_checker <- function(x) {

  # Description:

  # Returns:

  # Team members: Patrick, Amanda

}

fuzzy_duplicate_checker <- function(x) {

  # Description: checks for near duplicate rows: 1) rows with the same keys but

  different values, 2) rows with the same values but (slightly) different keys, 3)

  other cases TBD

  # Returns:

  # Team members: Patrick, Amanda, Jamie

}

encoding_standard_checker <- function(x) {

  # Description:

  # Returns:

  # Team members:

}

gap_checker <- function(x) {

  # Description:

  # Returns:

  # Team members: Celeste, stevan

}

numberic_step_checker()
  # Description: Flags time series (e.g., sensor data) for unexpected
  # jumps (increase or decrease) in a single time step.

  # Methods:
  #  "simpleThreshold": manually set a threshold step value to flag observations
  # that exceed the limit
  #  "movingAverageDeviants": take the diffs based moving average and flag
  # 'large' changes
  #  "movingMedianDeviants": take the diffs based moving medians and flag
  # 'large' changes

  # Dependencies:

  # Inputs: datetime and value

  # Returns: original dataframe as

  # Tests:

  # Examples:

  # Team members:

numeric_range_checker <- function(x) {

  # Description:

  # Returns:

  # Team members: Celeste

}

acceptable_string_checker <- function(x) {

  # Description:

  # Returns:

  # Team members:

}

numeric_spike_checker <- function(x) {

  # Description:

  # Returns:

  # Team members: Patrick, kaelin

}

numeric_slope_checker <- function(x) {

  # Description:

  # Returns:

  # Team members: Celeste Patrick

}

check_timeseries_anomalies <- function(x) {

  # Description:

  # Returns:

  # Team members: Patrick

}

check_number_bensford <- function(x) {

  # Description: For numeric values that span multiple orders of magnitude, Benfords Law predicts a distribution rich in low values with few high values for the first digit.  The result applies to an entire series of data values, not to any particular data value.

  # Returns: T - fits Benford distribution, F - does not fit, NA - data not suitable (not >2 orders of magnitude)

  # Team members: John

}

anomaly_outlier_vis_tool <- function(args) {

  # Description:

  # Inputs:

  # Returns:

  # Tests:

 # Examples:

  # Team members: Patrick

}