A wrapper around dplyr::mutate() that creates a correction factor for each observation.
dr_factor(.data, corrFactor, dateVar, timeVar, tz = NULL, format = c("MDY", "YMD"), keepDateTime = TRUE)
| .data | A tbl |
|---|---|
| corrFactor | New variable name for correction factor data |
| dateVar | Name of variable containing date data |
| timeVar | Name of variable containing time data |
| tz | String name of timezone, defaults to system's timezone |
| format | Either "MDY" or "YMD" for |
| keepDateTime | A logical statement to keep an intermediate dateTime variable |
An object of the same class as .data with the new correction factor variable added
to the other data in .data as well as a dateTime variable if keepDateTime = TRUE.
Correction factors are calculated based on the time of the observation and the total amount of time that the instrument had been deployed. They are used in the equations for both the one-point and two-point drift corrections.
dr_correctOne for correction factor creation,
dr_correctTwo for the two-point drift correction
testData <- data.frame( Date = c("9/18/2015", "9/18/2015", "9/18/2015", "9/18/2015", "9/18/2015", "9/18/2015"), Time = c("12:10:49", "12:15:50", "12:20:51", "12:25:51", "12:30:51", "12:35:51"), Temp = c(14.76, 14.64, 14.57, 14.51, 14.50, 14.63), SpCond = c(0.754, 0.750, 0.750, 0.749, 0.749, 0.749), stringsAsFactors = FALSE ) dr_factor(testData, corrFactor = corrFac, dateVar = Date, timeVar = Time, keepDateTime = TRUE)#> Date Time Temp SpCond dateTime corrFac #> 1 9/18/2015 12:10:49 14.76 0.754 9/18/2015 12:10:49 0.0000000 #> 2 9/18/2015 12:15:50 14.64 0.750 9/18/2015 12:15:50 0.2003995 #> 3 9/18/2015 12:20:51 14.57 0.750 9/18/2015 12:20:51 0.4007989 #> 4 9/18/2015 12:25:51 14.51 0.749 9/18/2015 12:25:51 0.6005326 #> 5 9/18/2015 12:30:51 14.50 0.749 9/18/2015 12:30:51 0.8002663 #> 6 9/18/2015 12:35:51 14.63 0.749 9/18/2015 12:35:51 1.0000000