Replacing problematic observations from the monitoring period

dr_replace() includes two approaches for identifying problematic observations for specific measurements that should be recoded as missing values (NA).

dr_replace(.data, sourceVar, cleanVar = NULL, overwrite = FALSE, dateVar = NULL,
    timeVar = NULL, from = NULL, to = NULL, tz = NULL, exp)

Arguments

.data	A tbl
sourceVar	Name of variable to replace missing values in
cleanVar	New variable name for cleaned data
overwrite	A logical scalar. Should the current variable be overwritten instead of creating a new variable?
dateVar	Name of variable containing date data
timeVar	Name of variable containing time data
from	Beginning date and (optionally) time to remove observations
to	End date and (optionally) time to remove observations
tz	String name of timezone, defaults to system's timezone
exp	Unquoted expression

Value

An object of the same class as .data with specified observations recoded as missing.

Details

During monitoring, a sensor malfunction may impact only a single element of a given reading. Removing the entire observation may therefore be imprudent. dr_replace() provides two methods for identifying these values and declaring them as missing. Values can be identified by specifying one or two timepoints in the data where problematic measurements begin, end, or fall between. Values can also be identified based on a problematic sensor value or range of values using an expression.

Examples

testData <- data.frame(
   Date = c("9/18/2015", "9/18/2015", "9/18/2015", "9/18/2015", "9/19/2015", "9/21/2015"),
   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_replace(testData, sourceVar = Temp, dateVar = Date, timeVar = Time,
     from = "2015-09-19 12:30:51", to = "2015-09-21 12:35:51")
#> Replacement approach - completed using the date/time arguments.
#>        Date     Time  Temp SpCond Temp_na
#> 1 9/18/2015 12:10:49 14.76  0.754   14.76
#> 2 9/18/2015 12:15:50 14.64  0.750   14.64
#> 3 9/18/2015 12:20:51 14.57  0.750   14.57
#> 4 9/18/2015 12:25:51 14.51  0.749   14.51
#> 5 9/19/2015 12:30:51 14.50  0.749      NA
#> 6 9/21/2015 12:35:51 14.63  0.749   14.63
 dr_replace(testData, sourceVar = Temp, dateVar = Date, timeVar = Time,
     from = "2015-09-19", to = "2015-09-21")
#> Replacement approach - completed using the date/time arguments.
#>        Date     Time  Temp SpCond Temp_na
#> 1 9/18/2015 12:10:49 14.76  0.754   14.76
#> 2 9/18/2015 12:15:50 14.64  0.750   14.64
#> 3 9/18/2015 12:20:51 14.57  0.750   14.57
#> 4 9/18/2015 12:25:51 14.51  0.749   14.51
#> 5 9/19/2015 12:30:51 14.50  0.749      NA
#> 6 9/21/2015 12:35:51 14.63  0.749   14.63
 dr_replace(testData, sourceVar = Temp, dateVar = Date, timeVar = Time, from = "2015-09-19")
#> Replacement approach - completed using the date/time arguments.
#>        Date     Time  Temp SpCond Temp_na
#> 1 9/18/2015 12:10:49 14.76  0.754   14.76
#> 2 9/18/2015 12:15:50 14.64  0.750   14.64
#> 3 9/18/2015 12:20:51 14.57  0.750   14.57
#> 4 9/18/2015 12:25:51 14.51  0.749   14.51
#> 5 9/19/2015 12:30:51 14.50  0.749      NA
#> 6 9/21/2015 12:35:51 14.63  0.749      NA
 dr_replace(testData, sourceVar = Temp, dateVar = Date, timeVar = Time, to = "2015-09-19")
#> Replacement approach - completed using the date/time arguments.
#>        Date     Time  Temp SpCond Temp_na
#> 1 9/18/2015 12:10:49 14.76  0.754      NA
#> 2 9/18/2015 12:15:50 14.64  0.750      NA
#> 3 9/18/2015 12:20:51 14.57  0.750      NA
#> 4 9/18/2015 12:25:51 14.51  0.749      NA
#> 5 9/19/2015 12:30:51 14.50  0.749   14.50
#> 6 9/21/2015 12:35:51 14.63  0.749   14.63
 dr_replace(testData, sourceVar = Temp, cleanVar = temp2, dateVar = Date, timeVar = Time,
     to = "09/19/2015 12:35:51")
#> Replacement approach - completed using the date/time arguments.
#>        Date     Time  Temp SpCond temp2
#> 1 9/18/2015 12:10:49 14.76  0.754    NA
#> 2 9/18/2015 12:15:50 14.64  0.750    NA
#> 3 9/18/2015 12:20:51 14.57  0.750    NA
#> 4 9/18/2015 12:25:51 14.51  0.749    NA
#> 5 9/19/2015 12:30:51 14.50  0.749    NA
#> 6 9/21/2015 12:35:51 14.63  0.749 14.63
 dr_replace(testData, sourceVar = Temp, overwrite = TRUE, exp = Temp > 14.75)
#> Replacement approach - completed using the expression.
#>        Date     Time  Temp SpCond
#> 1 9/18/2015 12:10:49    NA  0.754
#> 2 9/18/2015 12:15:50 14.64  0.750
#> 3 9/18/2015 12:20:51 14.57  0.750
#> 4 9/18/2015 12:25:51 14.51  0.749
#> 5 9/19/2015 12:30:51 14.50  0.749
#> 6 9/21/2015 12:35:51 14.63  0.749