# Applying Drift Corrections with driftR

## Overview

In situ water-quality monitoring instruments take continuous measurements of various chemical and physical parameters. The longer these instruments stay in the field, the further sensor readings drift from their true values. The purpose of this package is to correct water quality monitoring data sets for instrumental drift in a reliable, reproducible method.

## Getting Started with R

If you are new to R, welcome! You will need to download R. We also recommend downloading RStudio. Once you have those installed, you can install driftR:

install.packages("driftR")
library(driftR)

If you want, install the devtools package to install the development version of driftR:

install.packages("devtools")
devtools::install_github("shaughnessyar/driftR")

## Packages

In addition to driftR, you will also want to install and load at least two other packages:

Both of these packages can be installed with the install.packages() function. Since both are part of the tidyverse, you can also install both at once by using install.packages("tidyverse").

## Equations

The drift correction equations that are implemented in driftR were originally published as part of Dr. Elizabeth Hasenmueller’s dissertation research (see page 32).

### Correction Factor

The correction factor equation (implemented in dr_factor()) is as follows:

$$\textrm{Let:}$$

• $${f}_{t} = \textrm{correction factor}$$
• $$t = \textrm{time interval}$$
• $$\sum{t} = \textrm{total time}$$

${f}_{t} = \left( \frac { t }{ \sum { t } } \right)$

### One-Point Calibration

The one-point calibration equation (implemented in dr_correctOne()) is as follows:

$$\textrm{Let:}$$

• $$C = \textrm{drift corrected parameter}$$
• $$m = \textrm{uncorrected in-situ measurement parameter}$$
• $${f}_{t} = \textrm{correction factor}$$
• $${s}_{i} = \textrm{calibration standard value}$$
• $${s}_{f} = \textrm{calibration standard value after drift has occurred}$$

$C = m + {f}_{t} \cdot \left( { s }_{ i }-{ s }_{ f } \right)$

### Two-Point Calibration

The two-point calibration equation (implemented in dr_correctTwo()) is as follows:

$$\textrm{Let:}$$

• $$C = \textrm{drift corrected parameter}$$
• $$m = \textrm{uncorrected in-situ measurement parameter}$$
• $${f}_{t} = \textrm{correction factor}$$
• $${a}_{t} = \textrm{drift correction using lower concentration calibration standard}$$
• $${b}_{t} = \textrm{drift correction using higher concentration calibration standard}$$
• $${a}_{i} = \textrm{lower calibration standard value}$$
• $${b}_{i} = \textrm{higher calibration standard value}$$
• $${a}_{f} = \textrm{lower calibration standard value after drift has occurred}$$
• $${b}_{f} = \textrm{higher calibration standard value after drift has occurred}$$

${ a }_{ t } = { a }_{ i } + {f}_{t} \cdot \left( { a }_{ i }-{ a }_{ f } \right)$ ${ b }_{ t } = { b }_{ i } - {f}_{t} \cdot \left( { b }_{ i }-{ b }_{ f } \right)$ $C=\left( \frac { m-a_{ t } }{ { b }_{ t }-{ a }_{ t } } \right) \cdot \left( { b }_{ i }-{ a }_{ i } \right) + { a }_{ i }$

## Using driftR

There are five main functions in the driftR which each serve their own purpose in data cleaning process:

1. dr_readSonde() - import data from the sonde
2. dr_factor() - apply correction factors
3. dr_correctOne() - one point calibration
4. dr_correctTwo() - two point calibration
5. dr_drop() - drop observations at the start and finish of a data set

### Importing Data from a YSI Multiparameter V2 Sonde

To import a data set, the dr_readSonde() function is utilized. The argument file tells the function where the data is located. The argument defineVar is a logical statement where if FALSE, the data will be imported with no modifications, and if TRUE, the “units” observation will be removed and the data will be stored as numeric variables. Using defineVar = TRUE will format the date as Month/Day/Year.

waterTibble <- dr_readSonde(file = "sondeData.csv", defineVar = TRUE)

If you want to use the package and do not have your own data, you can load the sample data included in the package using the following syntax:

waterTibble <- dr_readSonde(file = system.file("extdata", "rawData.csv", package = "driftR"),
defineVar = TRUE)

If your data are from another instrument model or brand, please refer to our article on importing non-YSI Multiparameter V2 Sonde data.

### Creating Correction Factors

The next step in the data cleaning process is creating correction factors using dr_factor(). The correction factors that are generated are used to determine how much drift is experienced by each observation in the data set. The argument .data is the working data frame for the correction. corrFactor is the name of the variable that will contain the correction factors. The argument dateVar is the date variable for the data set and timeVar is the time variable. The argument format describes the way that the date data are stored in the data frame and can be either “YMD” or “DMY”. If the dr_readSonde() function is used to import the data, then format = "MDY". The argument keepDateTime is a logical term that, if TRUE, will keep an intermediate dateTime variable and export it with the correction factors.

waterTibble <- dr_factor(waterTibble, corrFactor = corfac, dateVar= Date,
timeVar = Time, format = "MDY", keepDateTime = TRUE)

### Correcting the Data

After creating the correction factors, you can correcting the data for drift. In order to correct the data, there needs to be some measurement taken that tells the user what the instrument should be reading compared to what the instrument is actually reading. This step can be done with either one or two standard measurements. If one standard measurement is taken, then the function dr_correctOne() is used. The argument .data is the working data frame for the correction. sourceVar is the variable name that you want to correct and cleanVar is the name of the variable that will contain the corrected data. The arguments calVal and calStd are what the instrument was reading and the standard value (i.e. what it should have been reading), respectively. The factorVar argument is the result generated from the dr_factor() function.

waterTibble <- dr_correctOne(waterTibble, sourceVar = SpCond, cleanVar = SpCond_corr,
calVal = 1.07, calStd = 1, factorVar = corfac)

If two standard measurements are taken, then the function dr_correctTwo() is utilized. The calValLow and calValHigh arguments are what the instrument was reading for the low and high concentration standard measurements respectively and the calStdLow and calStdHigh arguments are what the instrument should have been reading for the low and high concentration standard measurements respectively.

waterTibble <- dr_correctTwo(waterTibble, sourceVar = pH, cleanVar = pH_corr, calValLow = 7.01,
calStdLow = 7, calValHigh = 11.8, calStdHigh = 10, factorVar = corfac)

### Dropping Observations

After the data has been corrected, some of the data will likely need to be removed (i.e. dropped) in order to account for the equilibration of the sensors as well as time out of the water during preparation for calibration. It is important to do this step last because dropping data before using dr_correctOne() or dr_correctTwo() will result in the corrections being inconsistent. To do this, the dr_drop() function is used. The argument .data is the working data frame for the correction. The arguments head and tail are the number of observations to be dropped from the beginning and end of the data set respectively. Based on YSI Multiparameter V2 Sonde performance, it is recommended that at least 30 minutes are dropped from both the beginning and end of the data set (i.e. if an observation is taken every 5 minutes, then head = 6and tail = 6).

waterTibble <- dr_drop(waterTibble, head = 6, tail = 6)

## Tidying Data

### Renaming Variables

YSI’s product output sometimes contains special characters in variable names. For example, “Turbidity+” is a variable name created by the YSI Multiparameter V2 Sonde, which is imported into R as Turbidity.. Neither the original name or R’s attempt at clarifying it follow good variable naming practices. You can rename Turbidity. using the rename() function from dplyr to accomplish this:

waterTibble <- rename(Turbidity = Turbidity.)

Note how the back tick symbols are used to surround non-standard variable names.

### Reordering Variables

Variables will need to be re-ordered after using driftR to get similar variables next to each other in your data frame. You can use the select() function from dplyr to accomplish this:

waterTibble <- select(waterTibble, Date, Time, dateTime, SpCond, SpCond_Corr, pH, pH_Corr, pHmV,
Chloride, Chloride_Corr, AmmoniumN, AmmoniumN_Corr, NitrateN, NitrateN_Corr,
Turbidity, Turbidity_Corr, DO, DO_Corr, corfac)

Like all other dplyr functions, select() can be also included in a pipe.

### Removing Unnecessary Variables

If there are unnecessary variables left in your data set at the end of the post-processing stage, you can also use the select() function from dplyr to remove them. The function accepts the data frame name followed by a comma and negative sign in front of the variable to be removed. For example, the NitrateN variable does not contain any non-zero observations in our example, so it can be removed.

waterTibble <- select(waterTibble, -NitrateN)

If there are multiple variables to be removed, a list of the variables can be provided inside -c(varlist):

waterTibble <- select(waterTibble, -c(NitrateN, pHmV))

## Exporting Data

Finally, data can be exported to csv (our recommended file format because it is plain-text and non-proprietary) using the readr package’s write_csv() function:

write_csv(df, path = "waterData.csv", na = "NA")

## A Full Session

Below is an example of a full data set correction:

# load needed packages
library(driftR)
library(dplyr)

# import data exported from a Sonde
# example file located in the package
waterTibble <- dr_readSonde(file = system.file("extdata", "rawData.csv", package="driftR"),
define = TRUE)

# calculate correction factors
# results stored in new vector corrFac
waterTibble <- dr_factor(waterTibble, corrFactor = corrFac, dateVar = Date,
timeVar = Time, format = "MDY", keepDateTime = TRUE)

# apply one-point calibration to SpCond;
# results stored in new vector SpConde_Corr
waterTibble <- dr_correctOne(waterTibble, sourceVar = SpCond, cleanVar = SpCond_Corr,
calVal = 1.07, calStd = 1, factorVar = corrFac)

# apply one-point calibration to Turbidity.;
# results stored in new vector Turbidity_Corr
waterTibble <- rename(waterTibble, Turbidity = Turbidity.)
waterTibble <- dr_correctOne(waterTibble, sourceVar = Turbidity, cleanVar = Turbidity_Corr,
calVal = 1.3, calStd = 0, factorVar = corrFac)

# apply one-point calibration to DO;
# results stored in new vector DO_Corr
waterTibble <- dr_correctOne(waterTibble, sourceVar = DO, cleanVar = DO_Corr,
calVal = 97.6, calStd = 99, factorVar = corrFac)

# apply two-point calibration to pH;
# results stored in new vector ph_Corr
waterTibble <- dr_correctTwo(waterTibble, sourceVar = pH, cleanVar = pH_Corr,
calValLow = 7.01, calStdLow = 7, calValHigh = 11.8,
calStdHigh =  10, factorVar = corrFac)

# apply two-point calibration to Chloride;
# results stored in new vector Chloride_Corr
waterTibble <- dr_correctTwo(waterTibble, sourceVar = Chloride, cleanVar = Chloride_Corr,
calValLow = 11.6, calStdLow = 10, calValHigh = 1411,
calStdHigh =  1000, factorVar = corrFac)

# drop observations to account for instrument equilibration
write_csv(waterTibble, path = "waterData.csv", na = "NA")
Our vignette on tidy evaluation in driftR includes an example session using magrittr pipe operators (%>%).