Extracting environmental data with rvdat

We often want to extract the environmental data that some of our receivers log. You can do this by going into Innovasea’s VUE or Fathom Connect programs and clicking a bunch of buttons, or you can do it programmatically and reproducibly by combining matos and rvdat.

library(matos)
library(rvdat)

Download the data

If you’re an ACT member, log into your MATOS account. If you’re not, we’ll skip to the next section and assume you already have your files ready.

matos_login()
#> ! Please log in.
#> v Login successful!

Next, tell rvdat the location of the vdat.exe executable (check out the rvdat documentation for more information on this).

vdat_here()

Using matos, I’m going to find the VDAT files associated with the Mid-Bay Chesapeake Backbone array. Note that things like temperature and tilt are logged within the VRL/VDAT files, and so will be under the “detections” file type.

detection_files <- list_project_files(
  project = "UMCES Chesapeake Backbone, Mid-Bay",
  file_type = "detections"
)

head(detection_files)
#>   project                  file_type upload_date
#> 3     161 Tag Detections - .vfl file  2024-08-06
#> 4     161 Tag Detections - .vfl file  2024-08-06
#> 5     161 Tag Detections - .vfl file  2024-08-06
#> 6     161 Tag Detections - .vfl file  2024-08-06
#> 7     161 Tag Detections - .vfl file  2024-08-06
#> 8     161 Tag Detections - .vfl file  2024-08-06
#>                     file_name
#> 3 VR2AR_551436_20240805_1.vrl
#> 4 VR2AR_551431_20240805_1.vrl
#> 5 VR2AR_550744_20240805_1.vrl
#> 6 VR2AR_550740_20240805_1.vrl
#> 7 VR2AR_550737_20240805_1.vrl
#> 8 VR2AR_545764_20240805_1.vrl
#>                                                       url
#> 3 https://matos.asascience.com/projectfile/download/13872
#> 4 https://matos.asascience.com/projectfile/download/13871
#> 5 https://matos.asascience.com/projectfile/download/13870
#> 6 https://matos.asascience.com/projectfile/download/13869
#> 7 https://matos.asascience.com/projectfile/download/13868
#> 8 https://matos.asascience.com/projectfile/download/13867

I’ll download the first file into a temporary directory.

get_project_file(
  url = detection_files$url[1],
  out_dir = tempdir()
)
#> 
#> -- Downloading files ---------------------------------------------------
#> v File(s) saved to:
#>    C:\Users\darpa2\AppData\Local\Temp\RtmpusjJ5z\VR2AR_551436_20240805_1.vrl
#> [1] "C:\\Users\\darpa2\\AppData\\Local\\Temp\\RtmpusjJ5z\\VR2AR_551436_20240805_1.vrl"

vrl_file <- list.files(tempdir(), pattern = "vrl$", full.names = T)

Convert VRL using `rvdat`

Now, I’ll use rvdat::vdat_to_folder to convert that file into a folder of CSV files, each one representing one data type.

vdat_to_folder(
  vrl_file,
  outdir = tempdir()
)
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#> v File converted:
#>   C:\Users\darpa2\AppData\Local\Temp\RtmpusjJ5z/VR2AR_551436_20240805_1.vrl
#> i Files saved in:
#>   C:\Users\darpa2\AppData\Local\Temp\RtmpusjJ5z/VR2AR_551436_20240805_1.csv-fathom-split

There are quite a few files in there, but for this we’re going to focus on the temperature records stored in TEMP.csv.

list.files(tempdir(), pattern = "fathom-split", full.names = T) |>
  list.files()
#>  [1] "ATTITUDE.csv"         "BATTERY.csv"         
#>  [3] "CFG_CHANNEL.csv"      "CFG_STUDY.csv"       
#>  [5] "CFG_TRANSMITTER.csv"  "CLOCK_REF.csv"       
#>  [7] "DATA_SOURCE_FILE.csv" "DEPTH.csv"           
#>  [9] "DET.csv"              "DIAG.csv"            
#> [11] "EVENT.csv"            "EVENT_INIT.csv"      
#> [13] "EVENT_OFFLOAD.csv"    "HEALTH_VR2AR.csv"    
#> [15] "TEMP.csv"

bwt_file <- list.files(tempdir(), pattern = "TEMP", full.names = T, recursive = T)

Read it into R

Let’s read in the data.

read.csv(bwt_file)
#> Error in read.table(file = file, header = header, sep = sep, quote = quote, : more columns than column names

An error, oh no! Let’s see what’s causing it.

read.csv(bwt_file,
  header = FALSE,
  nrows = 5
)
#>                  V1                  V2
#> 1 \357\273\277VEMCO DATA LOG               2.0.0
#> 2         TEMP_DESC   Device Time (UTC)
#> 3              TEMP 2024-03-21 20:00:00
#> 4              TEMP 2024-03-21 21:00:00
#> 5              TEMP 2024-03-21 22:00:00
#>                                    V3              V4
#> 1 vdat-11.0.1-20240830-d01c0d-release                
#> 2                                Time Time Offset (h)
#> 3                                                    
#> 4                                                    
#> 5                                                    
#>                    V5       V6            V7              V8
#> 1                                                           
#> 2 Time Correction (s)    Model Serial Number Ambient (deg C)
#> 3                     VR2AR-69        551436          20.722
#> 4                     VR2AR-69        551436          13.957
#> 5                     VR2AR-69        551436          10.479
#>                 V9
#> 1                 
#> 2 Internal (deg C)
#> 3                 
#> 4                 
#> 5

Ah, the data doesn’t really start until the second row. Skip the first one and take a look.

bwt <- read.csv(
  bwt_file,
  skip = 1
)

head(bwt)
#>   TEMP_DESC   Device.Time..UTC. Time Time.Offset..h.
#> 1      TEMP 2024-03-21 20:00:00   NA              NA
#> 2      TEMP 2024-03-21 21:00:00   NA              NA
#> 3      TEMP 2024-03-21 22:00:00   NA              NA
#> 4      TEMP 2024-03-21 23:00:00   NA              NA
#> 5      TEMP 2024-03-22 00:00:00   NA              NA
#> 6      TEMP 2024-03-22 01:00:00   NA              NA
#>   Time.Correction..s.    Model Serial.Number Ambient..deg.C.
#> 1                  NA VR2AR-69        551436          20.722
#> 2                  NA VR2AR-69        551436          13.957
#> 3                  NA VR2AR-69        551436          10.479
#> 4                  NA VR2AR-69        551436           8.328
#> 5                  NA VR2AR-69        551436           6.157
#> 6                  NA VR2AR-69        551436           5.136
#>   Internal..deg.C.
#> 1               NA
#> 2               NA
#> 3               NA
#> 4               NA
#> 5               NA
#> 6               NA

Great! The data are in. I’m going to clean up the names a little bit and convert the time column from a character string to POSIX time.

names(bwt) <- gsub("[_\\.]", "", tolower(names(bwt)))

names(bwt)
#> [1] "tempdesc"        "devicetimeutc"   "time"           
#> [4] "timeoffseth"     "timecorrections" "model"          
#> [7] "serialnumber"    "ambientdegc"     "internaldegc"

bwt$devicetimeutc <- as.POSIXct(bwt$devicetimeutc,
  tz = "UTC",
  format = "%F %T"
)

head(bwt)
#>   tempdesc       devicetimeutc time timeoffseth timecorrections
#> 1     TEMP 2024-03-21 20:00:00   NA          NA              NA
#> 2     TEMP 2024-03-21 21:00:00   NA          NA              NA
#> 3     TEMP 2024-03-21 22:00:00   NA          NA              NA
#> 4     TEMP 2024-03-21 23:00:00   NA          NA              NA
#> 5     TEMP 2024-03-22 00:00:00   NA          NA              NA
#> 6     TEMP 2024-03-22 01:00:00   NA          NA              NA
#>      model serialnumber ambientdegc internaldegc
#> 1 VR2AR-69       551436      20.722           NA
#> 2 VR2AR-69       551436      13.957           NA
#> 3 VR2AR-69       551436      10.479           NA
#> 4 VR2AR-69       551436       8.328           NA
#> 5 VR2AR-69       551436       6.157           NA
#> 6 VR2AR-69       551436       5.136           NA

See what we’ve got

Now that we have our time series, let’s see what it looks like!

plot(ambientdegc ~ devicetimeutc,
  data = bwt,
  type = "l"
)

Other variables

We can pull out other variables in a similar manner. Take, for example, receiver tilt, located in ATTITUDE.csv.

env_import <- function(file) {
  hold <- list.files(
    tempdir(),
    pattern = file,
    full.names = T,
    recursive = T
  ) |>
    read.csv(skip = 1)

  names(hold) <- gsub("[_\\.]", "", tolower(names(hold)))

  hold$devicetimeutc <- as.POSIXct(hold$devicetimeutc,
    tz = "UTC",
    format = "%F %T"
  )

  hold
}

tilt <- env_import("ATTITUDE")

plot(tiltdeg ~ devicetimeutc, data = tilt, type = "l")

Depth, in DEPTH.csv:

depth <- env_import("DEPTH")

plot(depthm ~ devicetimeutc, data = depth)

Battery, in BATTERY.csv:

battery <- env_import("BATTERY")

plot(batteryvoltagev ~ devicetimeutc, data = battery)

Noise, which is actually in the diagnostic file DIAG.csv:

diagnostics <- env_import("DIAG")

plot(noisemeanmv ~ devicetimeutc, data = diagnostics)

Hourly summaries of pings and detections are also in the diagnositc file:

plot(ppmdetections ~ devicetimeutc, data = diagnostics)

plot(ppmpings ~ devicetimeutc, data = diagnostics)

But are really located in DET.csv.

dets <- env_import("DET")

head(dets[, ])
#>   detdesc       devicetimeutc time timeoffseth timecorrections    model
#> 1     DET 2024-03-21 19:40:52   NA          NA              NA VR2AR-69
#> 2     DET 2024-03-21 19:45:01   NA          NA              NA VR2AR-69
#> 3     DET 2024-03-21 19:50:11   NA          NA              NA VR2AR-69
#> 4     DET 2024-03-21 19:59:46   NA          NA              NA VR2AR-69
#> 5     DET 2024-03-21 20:06:18   NA          NA              NA VR2AR-69
#> 6     DET 2024-03-21 20:07:05   NA          NA              NA VR2AR-69
#>   serialnumber channel detectiontype         fullid    id rawdata
#> 1       551436       1           PPM A69-1601-60140 60140      NA
#> 2       551436       1           PPM A69-1601-60838 60838      NA
#> 3       551436       1           PPM A69-1601-60140 60140      NA
#> 4       551436       1           PPM A69-1601-60140 60140      NA
#> 5       551436       1           PPM A69-1601-60838 60838      NA
#> 6       551436       1           PPM A69-1601-60228 60228      NA
#>   transmitterserial signalstrengthdb noisedb gaindb qualityscore
#> 1                NA               NA      NA     NA           NA
#> 2                NA               NA      NA     NA           NA
#> 3                NA               NA      NA     NA           NA
#> 4                NA               NA      NA     NA           NA
#> 5                NA               NA      NA     NA           NA
#> 6                NA               NA      NA     NA           NA
#>   stationname latitude longitude gpshdop
#> 1          NA       NA        NA      NA
#> 2          NA       NA        NA      NA
#> 3          NA       NA        NA      NA
#> 4          NA       NA        NA      NA
#> 5          NA       NA        NA      NA
#> 6          NA       NA        NA      NA

These data were gleaned from a particular receiver programmed in a particular way – note that many of the data fields are empty! Depending on the receiver, how you programmed it, and the arguments you passed to rvdat (time correction, anyone?), your split CSV could look rather different.