STICr

The goal of STICr is to provide a standardized set of functions for working with data from Stream Temperature, Intermittency, and Conductivity (STIC) loggers, first described in Chapin et al. (2014). STICs and other intermittency sensors are becoming more popular, but their raw data output is not in a form that allows for convenient analysis. This package aims to provide a set of functions for tidying the raw data from these loggers, as well as calibrating their conductivity measurements to specific conductivity (SpC). Additionally, the classify_wetdry function allows the user to define both a method and threshold value to generate a binary “wet/dry” data set.

Installation

You can install the development version of STICr from GitHub with:

# install.packages("devtools")
devtools::install_github("HEAL-KGS/STICr")

Example

This is an example workflow that shows the main functionality of the package. This first function is called tidy_hobo_data and is demonstrated below. The function intakes a raw CSV file and outputs a tidy data frame with the following column names: datetime, condUncal, and tempC.

library(STICr)

clean_data <- tidy_hobo_data(infile = "https://raw.githubusercontent.com/HEAL-KGS/STICr/main/data/raw_hobo_data.csv", outfile = FALSE)
head(clean_data)
#>              datetime condUncal  tempC
#> 1 2021-07-16 17:00:00   88178.4 27.764
#> 2 2021-07-16 17:15:00   77156.1 28.655
#> 3 2021-07-16 17:30:00   74400.5 28.060
#> 4 2021-07-16 17:45:00   74400.5 27.764
#> 5 2021-07-16 18:00:00   74400.5 27.862
#> 6 2021-07-16 18:15:00   71644.9 27.370

Below are time series plots of the uncalibrated conductivity and temperature generated by tidy_hobo_data

plot(clean_data$datetime, clean_data$condUncal, type = "l", lty = 1, 
     xlab = "datetime", ylab = "uncalibrated conductivity")

plot(clean_data$datetime, clean_data$tempC, type = "l", lty = 1, 
     xlab = "datetime", ylab = "temperature")

The second function is called get_calibration and is demonstrated below. The function intakes a STIC calibration data frame with columns standard and conductivity_uncaland outputs a fitted model object relating spc to the uncalibrated conductivity values measured by the STIC.

# Example calibration data
head(calibration_standard_data)
#> # A tibble: 4 x 3
#>     sensor standard condUncal
#>      <dbl>    <dbl>     <dbl>
#> 1 20946471      100    12400.
#> 2 20946471      250    23422.
#> 3 20946471      500    46845.
#> 4 20946471     1000   104712.

# Creating model object using a linear interpolation method. User can also chose the "exponential" method
lm_calibration <- get_calibration(calibration_standard_data, method = "linear")

# Result is a fitted model object
summary(lm_calibration)
#> 
#> Call:
#> lm(formula = standard ~ condUncal, data = calibration_data)
#> 
#> Residuals:
#>      1      2      3      4 
#> -33.43  11.27  37.50 -15.34 
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)   
#> (Intercept) 1.496e+01  3.136e+01   0.477   0.6803   
#> condUncal   9.554e-03  5.328e-04  17.932   0.0031 **
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 37.99 on 2 degrees of freedom
#> Multiple R-squared:  0.9938, Adjusted R-squared:  0.9907 
#> F-statistic: 321.5 on 1 and 2 DF,  p-value: 0.003096

The third function is called apply_calibration and is demonstrated below. The function intakes a tidy STIC data frame (as generated bytidy_hobo_data), as well as the fitted model object generated by get_calibration. It outputs the same STIC data frame as input, except with a new column called spc. This will be in the same units as the data used to develop the model calibration.

# applying the lm_calibration object from the previous example to the tidy data frame to generate the calibrated spc column
calibrated_df <- apply_calibration(clean_data, lm_calibration)
head(calibrated_df)
#>              datetime condUncal  tempC      SpC
#> 1 2021-07-16 17:00:00   88178.4 27.764 857.3845
#> 2 2021-07-16 17:15:00   77156.1 28.655 752.0820
#> 3 2021-07-16 17:30:00   74400.5 28.060 725.7561
#> 4 2021-07-16 17:45:00   74400.5 27.764 725.7561
#> 5 2021-07-16 18:00:00   74400.5 27.862 725.7561
#> 6 2021-07-16 18:15:00   71644.9 27.370 699.4302

Below is a time series plot of the calibrated spc values

plot(calibrated_df$datetime, calibrated_df$SpC, type = "l", lty = 1, 
     xlab = "datetime", ylab = "specific conductivity")

The final function is called classify_wetdry and is demonstrated below. This function intakes a data frame with STIC data (such as that produced by apply_calibration or tidy_hobo_data. The user is also asked to specify a method for classification, as well as a threshold associated with that method. Currently, methods include either an absolute numerical threshold or a percentage of the highest value of the classification variable. It outputs the same STIC data frame as input, except with a new binary column called wetdry.

# Creating classified data frame using "SpC" as the classification variable, and an absolute threshold as the method for classification. The SpC threshold chosen in this case is 200. 
classified_df <- classify_wetdry(calibrated_df, classify_var = "SpC", method = "absolute",
                                 threshold = 200)
head(classified_df)
#>              datetime condUncal  tempC      SpC wetdry
#> 1 2021-07-16 17:00:00   88178.4 27.764 857.3845    wet
#> 2 2021-07-16 17:15:00   77156.1 28.655 752.0820    wet
#> 3 2021-07-16 17:30:00   74400.5 28.060 725.7561    wet
#> 4 2021-07-16 17:45:00   74400.5 27.764 725.7561    wet
#> 5 2021-07-16 18:00:00   74400.5 27.862 725.7561    wet
#> 6 2021-07-16 18:15:00   71644.9 27.370 699.4302    wet

Below is a time series plot of the calibrated spc values colored by wet versus dry periods.

classified_df$wetdry <- as.factor(classified_df$wetdry)

plot(classified_df$datetime, classified_df$SpC,
     col = classified_df$wetdry,
     pch = 16, 
     lty = 2, 
     xlab = "datetime", 
     ylab = "spc", 
    lines(classified_df$datetime, classified_df$spc,
          pch = 18, col = "black", type = "b", lty = 2, lwd = 2))
legend("topright", c("dry", "wet"), 
       fill = c("black","red"), cex = 0.75)