Apply linear drift correction to a continuous monitoring parameter

Corrects for instrument drift over a specified window using a linear interpolation approach. The correction at the start of the window is zero and grows linearly to cal_ref - cal_check at the end, where cal_check is inferred from the data as the sensor reading at drift_end_time.

Usage

utilASRdrift(
  cont,
  param,
  cal_ref,
  drift_start_time,
  drift_end_time,
  plot = FALSE
)

Arguments

cont: contdat data frame returned by readASRcont
param: character string naming the parameter column to correct
cal_ref: numeric; the true or accepted value measured by an independent calibrated instrument at the end of the deployment period
drift_start_time: start of the drift window (POSIXct or coercible)
drift_end_time: end of the drift window (POSIXct or coercible)
plot: logical; if FALSE (default) the corrected data frame is returned. If TRUE a plotly object is returned instead, showing the corrected time series (blue), the original values within the drift window (gray), and the reference value at the end of the window (red circle).

Value

If plot = FALSE, a copy of cont with corrected values for param in the drift window (values outside the window are unchanged). If plot = TRUE, a plotly object.

Details

The cal_check value (what the deployed sensor was actually reading at drift_end_time) is inferred directly from the data, so only the independent reference reading (cal_ref) needs to be supplied. The total drift cal_ref - cal_check is distributed linearly across the window: zero correction is applied at drift_start_time and the full correction is applied at drift_end_time.

This correction formula is as follows: $$ \mathrm{adj} = \mathrm{sensor} + (\mathrm{cal\_ref} - \mathrm{cal\_check}) \times \frac{i - i_{\min}}{i_{\max} - i_{\min}} $$

Examples

contpth <- system.file("extdata/ExampleCont1.xlsx", package = "AquaSensR")
contdat <- readASRcont(contpth, runchk = FALSE)
t1 <- min(contdat$DateTime)
t2 <- max(contdat$DateTime)
utilASRdrift(contdat, "Water_Temp_C", cal_ref = 26, t1, t2)
#> # A tibble: 927 × 8
#>    DateTime            Water_Temp_C DO_pctsat DO_mg_l Conductivity_uS_cm
#>    <dttm>                     <dbl>     <dbl>   <dbl>              <dbl>
#>  1 2024-08-14 13:56:33         24.2      76.9    6.44               410.
#>  2 2024-08-14 13:56:43         24.2      76.7    6.43               410.
#>  3 2024-08-14 13:56:53         24.2      76.6    6.42               410.
#>  4 2024-08-14 13:57:03         24.2      76.5    6.41               410.
#>  5 2024-08-14 13:57:13         24.2      76.3    6.4                409 
#>  6 2024-08-14 13:57:23         24.2      76.3    6.39               409.
#>  7 2024-08-14 13:57:33         24.2      76.2    6.39               409.
#>  8 2024-08-14 13:57:43         24.2      76.1    6.38               409.
#>  9 2024-08-14 13:57:53         24.2      76.5    6.41               404.
#> 10 2024-08-14 13:58:03         24.2      77.6    6.5                399.
#> # ℹ 917 more rows
#> # ℹ 3 more variables: TDS_mg_l <dbl>, Salinity_ppt <dbl>, pH_SU <dbl>