The goal of STICr (pronounced “sticker”) 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) and
classifying the conductivity data to generate a classified “wet/dry”
data set.
Installation
You can install STICr from CRAN or the development version of STICr from GitHub with:
# install.packages("STICr") # if needed: install package from CRAN # devtools::install_github("HEAL-KGS/STICr") # if needed: install dev version from GitHub library(STICr)
Example
This is an example workflow that shows the main functionality of the package. A more detailed version is available in the package vignette.
Step 1: Load data
# read in raw HOBO data and tidy df_tidy <- tidy_hobo_data(infile = "https://samzipper.com/data/raw_hobo_data.csv", outfile = FALSE) head(df_tidy) #> datetime condUncal tempC #> 1 2021-07-16 22:00:00 88178.4 27.764 #> 2 2021-07-16 22:15:00 77156.1 28.655 #> 3 2021-07-16 22:30:00 74400.5 28.060 #> 4 2021-07-16 22:45:00 74400.5 27.764 #> 5 2021-07-16 23:00:00 74400.5 27.862 #> 6 2021-07-16 23:15:00 71644.9 27.370
Step 2: Get and apply calibration
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.
# load calibration lm_calibration <- get_calibration(calibration_standard_data) # apply calibration df_calibrated <- apply_calibration( stic_data = df_tidy, calibration = lm_calibration, outside_std_range_flag = T ) head(df_calibrated) #> datetime condUncal tempC SpC outside_std_range #> 1 2021-07-16 22:00:00 88178.4 27.764 857.3845 #> 2 2021-07-16 22:15:00 77156.1 28.655 752.0820 #> 3 2021-07-16 22:30:00 74400.5 28.060 725.7561 #> 4 2021-07-16 22:45:00 74400.5 27.764 725.7561 #> 5 2021-07-16 23:00:00 74400.5 27.862 725.7561 #> 6 2021-07-16 23:15:00 71644.9 27.370 699.4302
Step 3: Classify data
# classify data df_classified <- classify_wetdry( stic_data = df_calibrated, classify_var = "SpC", threshold = 100, method = "absolute" ) head(df_classified) #> datetime condUncal tempC SpC outside_std_range wetdry #> 1 2021-07-16 22:00:00 88178.4 27.764 857.3845 wet #> 2 2021-07-16 22:15:00 77156.1 28.655 752.0820 wet #> 3 2021-07-16 22:30:00 74400.5 28.060 725.7561 wet #> 4 2021-07-16 22:45:00 74400.5 27.764 725.7561 wet #> 5 2021-07-16 23:00:00 74400.5 27.862 725.7561 wet #> 6 2021-07-16 23:15:00 71644.9 27.370 699.4302 wet
Step 4: QAQC
# apply qaqc function df_qaqc <- qaqc_stic_data( stic_data = df_classified, spc_neg_correction = T, inspect_deviation = T, deviation_size = 2, window_size = 96 ) head(df_qaqc) #> datetime condUncal tempC SpC wetdry QAQC #> 1 2021-07-16 22:00:00 88178.4 27.764 857.3845 wet #> 2 2021-07-16 22:15:00 77156.1 28.655 752.0820 wet #> 3 2021-07-16 22:30:00 74400.5 28.060 725.7561 wet #> 4 2021-07-16 22:45:00 74400.5 27.764 725.7561 wet #> 5 2021-07-16 23:00:00 74400.5 27.862 725.7561 wet #> 6 2021-07-16 23:15:00 71644.9 27.370 699.4302 wet table(df_qaqc$QAQC) #> #> DO O #> 916 1 83
Step 5: Plot classified data
# plot SpC through time, colored by wetdry plot(df_classified$datetime, df_classified$SpC, col = as.factor(df_classified$wetdry), pch = 16, lty = 2, xlab = "Datetime", ylab = "Specific conductivity" ) legend("topright", c("dry", "wet"), fill = c("black", "red"), cex = 0.75 )
Step 6: Compare to field observations
# create validation data frame stic_validation <- validate_stic_data( stic_data = classified_df, field_observations = field_obs, max_time_diff = 30, join_cols = NULL, get_SpC = TRUE, get_QAQC = FALSE ) # compare the field observations and classified STIC data in table head(stic_validation) #> datetime wetdry_obs SpC_obs condUncal_STIC wetdry_STIC SpC_STIC #> 1 2021-07-16 18:03:00 wet 612.1672 74400.5 wet 725.7561 #> 2 2021-07-19 15:01:00 wet 589.4157 88178.4 wet 857.3845 #> 3 2021-07-21 02:44:00 dry 599.6622 85422.8 wet 831.0587 #> 4 2021-07-23 13:55:00 wet 916.8215 88178.4 wet 857.3845 #> 5 2021-07-25 16:27:00 wet 631.9857 77156.1 wet 752.0820 #> timediff_min #> 1 3 #> 2 1 #> 3 -1 #> 4 -5 #> 5 -3 # calculate percent classification accuracy sum(stic_validation$wetdry_obs == stic_validation$wetdry_STIC) / length(stic_validation$wetdry_STIC) #> [1] 0.8 # compare SpC as a plot plot(stic_validation$SpC_obs, stic_validation$SpC_STIC, xlab = "Observed SpC", ylab = "STIC SpC" )
