cleanUp {waterData}

Cleans up hydrologic time series data

Description

Function to identify and fix common problems with hydrologic data

Usage

cleanUp(dataset, task = "view", replace = 0.1)

Arguments

dataset

is a data frame in format of the data frame returned by importDVs.

task

is either "view" or "fix." View will return a list containing rows with negative values and rows with missing values. Fix will replace negative values with NA and replace zeroes with the value specified by the replace argument.

replace

is the value used to replace 0 values. The default is 0.1. For streamflow in small streams, one might want to use 0.01. For daily data other than streamflow, such as turbidity, users may not want to replace 0 values with a nonzero value. In those cases, replace can be set to 0.

Value

A list showing days with negative values and days with values of 0 when task is "view." When task is "fix" the fixed dataset is returned. When a negative value is replaced with NA, an "N" is added to the qualcode field to indicate that there had been a negative number. When a zero value is replaced, an "R" is added to the qualcode field to indicate that a zero value was replaced.

Note

If calculating anomalies (see compAnom), the user may need to replace isolated missing values with with a value; however, if there are larger periods with missing values, streamflow anomalies may not be an appropriate use of the data. The substitution of some missing data with values may be done using the function fillMiss that is part of this package. However, care needs to be taken when filling in missing data.

See Also

fillMiss

Examples

data(exampleWaterData)
head(badDataSet)
cleanUp(badDataSet, task="view")
q05054000Fix <- cleanUp(badDataSet, task="fix")
# replace 0s with NA, then one could use the fillMiss function
# to estimate values
q05054000Fix2 <- cleanUp(badDataSet, task="fix", replace=NA)
summary(badDataSet)
summary(q05054000Fix)
summary(q05054000Fix2)