rosalieb / rhobo

An R package to compute DO correction factors for HOBO sensors calibrations, and append new files to old files. Specifically written for PLANAQUA mesocosm lakes, at the CEREEP-Ecotron (France).

rhobo

The goal of rhobo is to read in the raw HOBO files downloaded through the HOBO software and compute the dissolved oxygen correction factors. Functions are also included to help append new data to previous data files.

The steps match the routine for data from the artificial lakes at the CEREEP-Ecotron, and may need to be adapted to other datasets format to be widely usable.

Authors: Rosalie Bruel (iEES-Paris, CNRS, France) and Sophie Guillon (Mines ParisTech, France)

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("rosalieb/rhobo")
library(rhobo)

Example

None of the following code will run unless you have data in your home folder. You can access the desired structure of the home repository by running struct.dir().

#>  Structure of the directory: 
#>  ('...' below refers to the path2data you gave in the 'path2data' argument.)
#> 
#>  .../PLANAQUA
#>         |
#>         |___ HOBO_Process
#>         |       |
#>         |       |___ old
#>         |       |     |
#>         |       |     |___ [all the previous file (.txt)]
#>         |       |
#>         |       |___ [most-recent file for each lake]
#>         |
#>         |___ HOBO_Raw
#>                 |
#>                 |___ 2021_07_27 (example name)
#>                 |     |
#>                 |     |___ [one file per lake (.txt)]
#>                 |
#>                 |___ [one folder per field mission]

1 - Set and check your input

First, you need to specify a certain number of inputs associated with sensors deployment:

# Start editing here
foldername = "2021_07_27" # Name of the folder with data unloaded. Typically, folder named YYYY_MM_DD using the date of the field mission.
TM_end = "2021/07/27 07:50:00" # Time measurement end # when probes are taken out of the lakes
TC_post = "2021/07/27 09:15:00" # Time calibration end of measurements (post deployment)
TC_pre = "2021/05/07 13:10:00" # Time calibration pre-deployment, after the calibration bucket # before measurements
TM_init = "2021/05/07 13:40:00" # Time measurement init # when probes are back in the lakes
Pbaro_mbar_init = 756.2 *1013/760 # P in millibar
Pbaro_mbar_end = 752.6 *1013/760 # P in millibar
# Stop editing here

The timeline below shows the succession of events: every 3 months or so, the HOBO sensors are taken out of the water (TM_end: time end measurement), put in an oxygenated bucket for one hour (TC_post: time at the end of the hour, with theoretical measurements at max DO saturation), cleaned, put back in the bucket for at least one hour (TC_pre: time at the end of the second “bath”), then re-deployed to the lakes (TC_init). This routine takes about half-a-day, and is indicated in grey in the drawing below. On top of the 4 times that must be written down (in UTC), the atmospheric pressure is also written down at the moment of the two “baths”. Atmospheric pressure and water temperature (measured by the HOBO sensors) are needed to calculate the theoretical DO saturation.

We included a function, rhobo.check.inputs(), to help verify the inputs look ok:

rhobo.check.inputs(path2data = path2data, foldername = foldername, 
                   TC_pre = TC_pre, TC_post = TC_post, 
                   TM_init = TM_init, TM_end = TM_end, 
                   Pbaro_mbar_init = Pbaro_mbar_init, 
                   Pbaro_mbar_end = Pbaro_mbar_end)

2 - Compute correction factor

2.1 - Automated part

The dissolved oxygen correction factor are computed within the function for each lake with available data. Check the function description ?rhobo.CF to see the details of each argument.

metadata_QAQC <- rhobo.CF(path2data = path2data, 
                          foldername = foldername,
                          TC_pre = TC_pre, TC_post = TC_post,
                          Pbaro_mbar_init = Pbaro_mbar_init, 
                          Pbaro_mbar_end = Pbaro_mbar_end, 
                          n = 4)

2.2 - Manual part

You can then check the outputs using the function pCF(), and edit any correction factor using the function specify.DO(). This step is necessary as the function will not automatically adjust an outlier correction factor. Typically, the correction factor shouldn’t be above 1.15 for example.

Note that the pCF.all() function goes in all the folders within the folder Hobo_Raw and find previous correction factor files. This function won’t be useful the first time you use rhobo, but later on, it will allow you to visualize the previous correction factors.

# Look at the plot and make sure there are no outliers for the correction factor
pCF(metadata_QAQC)

# If some values seems off, add a manual DO correction factor:
# Example, if we think the DO factor for "lac11" and "lacO1L" are too high at the end of the measurement:
metadata_QAQC <- specify.DO.CF(metadata_QAQC, "lac01L", "End", 1.05)
metadata_QAQC <- specify.DO.CF(metadata_QAQC, "lac11", "End", 1.05)
# Replace by NA if after all we were happy with the values for "lac01L":
metadata_QAQC <- specify.DO.CF(metadata_QAQC, "lac01L", "End", NA)

# Look at the plot again - old values are shown with higher transparency
pCF(metadata_QAQC)

Figure 1. Correction factor for dissolved oxygen measurements after manual edit.

If happy with the results, you will save the outputs by:

• writing the metadata (note that you can edit the output filename, but please leave the pattern “DO_Correction_factor” at the beginning, as well as Sys.Date() at the end, as this is what a later script recognize to compute a plot.)
• save the plot (optional)

# Write metadata
write.csv(metadata_QAQC, paste0(path2data, "/Hobo_Raw/", foldername, "/DO_Correction_factor_", Sys.Date(), ".csv"), 
          row.names = FALSE)

# Save plot
ggsave(paste0(path2data, "/Hobo_Raw/", foldername, "/DO_Correction_factor_plot_", Sys.Date(), ".png"),
       width = 8, height = 6)

3 - Append new files to previous files

Finally, this step allows you to go into the Hobo_Process folder, find the most-recent file for each lake, and append the new data. Old data will be moved to the folder Hobo_Process/old, and only the recent most datasets will be kept in the Hobo_Process folder.

all_data <- rhobo.append(metadata_QAQC, path2data, 
                         foldername, TM_init, TM_end)

4 - Supplementary tools

If you assign rhobo.append() output, you can then access to all the most updated data.

The function rhobo.treatments() can then be used to assign the treatments (read ?rhobo.treatments for all the options) and do some more detailed plots.

# Create a unique dataframe
hobo_all <- bind_rows(all_data)
# Assign treatments
hobo_all <- rhobo.treatments(hobo_all, lakename = "lac") 

The function rhobo.treatments() can be used for any dataframe.

metadata_QAQC <- rhobo.treatments(metadata_QAQC, lakename = 1)
#> Warning: le package 'tidyverse' a été compilé avec la version R 4.1.1
#> -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
#> v tibble  3.1.4     v purrr   0.3.4
#> v readr   2.0.1     v forcats 0.5.1
#> Warning: le package 'tibble' a été compilé avec la version R 4.1.1
#> Warning: le package 'readr' a été compilé avec la version R 4.1.1
#> -- Conflicts ------------------------------------------ tidyverse_conflicts() --
#> x lubridate::as.difftime() masks base::as.difftime()
#> x lubridate::date()        masks base::date()
#> x dplyr::filter()          masks stats::filter()
#> x lubridate::intersect()   masks base::intersect()
#> x dplyr::lag()             masks stats::lag()
#> x lubridate::setdiff()     masks base::setdiff()
#> x lubridate::union()       masks base::union()
ggplot(metadata_QAQC, aes(TC_1, DO_correction_factor)) + 
  geom_hline(yintercept = 1, lty = 2) +
  geom_segment(metadata_QAQC %>% 
                 select(Lake, What, TC_1, DO_correction_factor, treatment) %>%
                   pivot_wider(id_cols = c(treatment, Lake), 
                               names_from  = What, 
                               values_from = c(TC_1, DO_correction_factor)),
                 mapping = aes(x = TC_1_Init, xend = TC_1_End, y = DO_correction_factor_Init, yend = DO_correction_factor_End),
                 lty =2, col = grey(.5)
    ) +
  geom_point() + facet_wrap(~treatment) +
  labs(x = "Date", y = "DO correction factor") 

Figure 2. Correction factor per treatment.