Preparing the data for fluxible

Checklists for inputs

The first function to use when processing ecosystem gas fluxes data with Fluxible is flux_match, which requires two inputs: row_conc and field_record. The columns in both files do not require specific names, and those will be kept during the entire workflow. We however advise that those names do not contain spaces or special characters.

Importing a single file

In this example we will import the file 26124054001.#00, which is a text file extracted from a Squirrel Data Logger 2010 Series (Grant Instruments) through SquirrelView software. The first thing to do when importing a file with read_delim is to open the file in a text editor to look at its structure.

Screenshot of the file 26124054001.#00 in a text editor. We can see that the 25th first rows do not need to be imported, and that it is comma separated with a dot as a decimal point.

We will read the file with read_delim, and then use rename and mutate (from the dplyr package ; Wickham et al., 2023) to transform the columns into what we want, and dmy_hms from the lubridate package (Grolemund and Wickham, 2011) to get our datetime column in the right format:

library(tidyverse)
# readr, dplyr and lubridate are part of tidyverse

raw_conc <- read_delim(
  "ex_data/26124054001.#00",
  delim = ",", # our file is comma separated
  skip = 25 # the first 25 rows are logger infos that we do not want to keep
)

raw_conc structure:

#> spc_tbl_ [17 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
#>  $ Date          : chr [1:17] "26.06.2020" "26.06.2020" "26.06.2020"..
#>  $ Time          : 'hms' num [1:17] 12:40:56 12:40:57 12:40:58 12:40..
#>  $ Type          : chr [1:17] "Interval" "Interval" "Interval" "Int"..
#>  $ CO2 (V)       : num [1:17] 2.08 2.09 2.08 2.08 2.06 ...
#>  $ H2O (V)       : num [1:17] 4e-04 4e-04 4e-04 4e-04 4e-04 4e-04 4e..
#>  $ CO2_calc (ppm): num [1:17] 416 418 416 416 412 ...
#>  $ H2O_calc (ppt): num [1:17] 0.007 0.008 0.007 0.008 0.008 0.007 0...

Not too bad… but we are not quite there yet:

• Some column names contain space
• Some columns are not needed, removing them will make things easier later on: Type (nothing to do with the type of measurement, something from the logger), CO2 (V), H2O (V) (those two are the voltage input to the logger, not needed), and H2O_calc (ppt) (that one was not calibrated for this campaign so better remove it to avoid confusion)
• The Date and Time columns should be united in one and transformed in yyyy-mm-dd hh:mm:ss format

raw_conc <- raw_conc |>
  rename(
    co2_conc = "CO2_calc (ppm)"
  ) |>
  mutate(
    datetime = paste0(Date, Time), # we paste date and time together
    datetime = dmy_hms(datetime) # datetime instead of character
  ) |>
  select(datetime, co2_conc)

Et voilà:

#> tibble [17 × 2] (S3: tbl_df/tbl/data.frame)
#>  $ datetime: POSIXct[1:17], format: "2020-06-26 12:40:56" ...
#>  $ co2_conc: num [1:17] 416 418 416 416 412 ...

Importing multiple files

Quite often a field season will result in several files. In this example we will read all the files in “ex_data/” that contain “CO2” in their names.

library(tidyverse)

raw_conc <- list.files( # list the files
  "ex_data", # at location "ex_data"
  full.names = TRUE,
  pattern = "*CO2*" # that contains "CO2" in their name
) |>
  map_dfr(
    read_csv, # we map read_csv on all the files
    na = c("#N/A", "Over") # "#N/A" and Over should be treated as NA
  ) |>
  rename(
    conc = "CO2 (ppm)"
  ) |>
  mutate(
    datetime = dmy_hms(`Date/Time`)
  ) |>
  select(datetime, conc)

The one file per flux approach

Fluxible is designed to process data that were measured continuously (in a single or several files) and a field_record that records what was measured when. Another strategy while measuring gas fluxes on the field is to create a new file for each measurement, with the file name as the flux ID. The approach is similar to reading multiple files, except we add a column with the file name, and can then by-pass flux_match.

library(tidyverse)

raw_conc <- list.files( #listing all the files
  "ex_data/field_campaign", # at location "ex_data/field_campaign"
  full.names = TRUE
) |>
  map_dfr( # we map read_tsv on all the files
    # read_tsv is the version of read_delim for tab separated value files
    read_tsv,
    skip = 3,
    # creates a column with the filename, that we can use as flux ID
    id = "filename"
  ) |>
  rename( # a bit of renaming to make the columns more practical
    co2_conc = "CO2 (umol/mol)",
    h2o_conc = "H2O (mmol/mol)",
    air_temp = "Temperature (C)",
    pressure = "Pressure (kPa)"
  ) |>
  mutate(
    datetime = paste(Date, Time),
    # we get rid of the milliseconds
    datetime = as.POSIXct(datetime, format="%Y-%m-%d %H:%M:%OS"),
    pressure = pressure / 101.325, # conversion from kPa to atm
    filename = basename(filename) # removing folder names
  ) |>
  select(datetime, co2_conc, h2o_conc, air_temp, pressure, filename)

raw_conc structure:

#> tibble [18 × 6] (S3: tbl_df/tbl/data.frame)
#>  $ datetime: POSIXct[1:18], format: "2023-12-14 10:57:01" ...
#>  $ co2_conc: num [1:18] 416 407 404 421 411 ...
#>  $ h2o_conc: num [1:18] 22.7 22.5 23 22.6 22.8 ...
#>  $ air_temp: num [1:18] 22.4 22.4 22.4 22.3 22.3 ...
#>  $ pressure: num [1:18] 0.79 0.791 0.79 0.79 0.791 ...
#>  $ filename: chr [1:18] "1_2000_east_1_day_a-2023-12-14T105700.txt""..

The tricky one

What happens when you extract a logger file in csv using a computer with settings using comma as a decimal point (which is quite standard in Europe)? Well, you get a comma separated values (csv) file, with decimals separated by… comma.

Ideally the file should have been extracted in European csv, which means comma for decimals and semi-colon as column separator. But here we are.

Screenshot of the file 011023001.#01 opened in a text editor. We can see that it is comma separated, but that the decimal point is also a comma. Additionally, we see that some variables were measured only every 10 seconds, meaning that each row has a different number of commas… Gnnnnnnn

Let’s try the usual way first:

library(tidyverse)

raw_conc <- read_csv( # read_csv is the same as read_delim(delim = ",")
  "ex_data/011023001.#01",
  col_types = "Tcdddddd",
  na = "#N/A" # we tell read_csv what NA look like in that file
)

#> spc_tbl_ [101 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
#>  $ Date/Time     : POSIXct[1:101], format: NA ...
#>  $ Type          : chr [1:101] "Interval" "Interval" "Interval" "In"..
#>  $ CO2_input (V) : num [1:101] 2 2 2 2 2 2 2 2 2 2 ...
#>  $ PAR_input (mV): num [1:101] 28 28 28 35 31 23 58 35 7 3 ...
#>  $ Temp_air ('C) : num [1:101] 0 NA NA NA NA NA NA NA NA NA ...
#>  $ Temp_soil ('C): num [1:101] 80663 NA NA NA NA ...
#>  $ CO2 (ppm)     : num [1:101] 12 NA NA NA NA NA NA NA NA NA ...
#>  $ PAR (umolsm2) : num [1:101] NA NA NA NA NA NA NA NA NA NA ...

It took the column names right, but then of course interpreted all comma as separators, and made a mess. Let’s see if we can skipped the header and then assemble the columns with left and right side of the decimal point:

raw_conc <- read_csv(
  "ex_data/011023001.#01",
  skip = 1, # this time we skip the row with the column names
  col_names = FALSE, # we tell read_csv that column names are not provided
  na = "#N/A" # we tell read_csv what NA looks like in that file
)

#> spc_tbl_ [101 × 14] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
#>  $ X1 : chr [1:101] "01.10.2023 11:23:40" "01.10.2023 11:23:41" "01"..
#>  $ X2 : chr [1:101] "Interval" "Interval" "Interval" "Interval" ...
#>  $ X3 : num [1:101] 2 2 2 2 2 2 2 2 2 2 ...
#>  $ X4 : chr [1:101] "0028" "0028" "0028" "0035" ...
#>  $ X5 : num [1:101] 0 NA NA NA NA NA NA NA NA NA ...
#>  $ X6 : chr [1:101] "080663" NA NA NA ...
#>  $ X7 : num [1:101] 12 NA NA NA NA NA NA NA NA NA ...
#>  $ X8 : chr [1:101] "00" "400" "400" "400" ...
#>  $ X9 : chr [1:101] "10" "56" "55" "70" ...
#>  $ X10: num [1:101] 81 NA NA NA NA NA NA NA NA NA ...
#>  $ X11: num [1:101] 400 NA NA NA NA NA NA NA NA NA ...
#>  $ X12: chr [1:101] "55" "" "" "" ...
#>  $ X13: num [1:101] 20 NA NA NA NA NA NA NA NA NA ...
#>  $ X14: chr [1:101] "60" "" "" "" ...

The problem now is that CO₂ concentration was measured every second (with a comma!), while other variable were measured every 10 seconds. That means every 10th row has 14 comma separated elements, while the others have only 10. Uhhhhhhhhh

At this point, you might want to get the field computer out again and re extract your raw file with a European csv output, or anything that is not comma separated, or set the decimal point as a… point. But for the sake of it, let’s pretend that it is not an option and solve that issue in R:

# we read each row of our file as an element of a list
lines <- readLines("ex_data/011023001.#01")
lines <- lines[-1] # removing the first element with the column names

# we first deal with the elements where we have those environmental data
# that were measured every 10 seconds
linesenv <- lines[seq(1, length(lines), 10)]
env_df <- read.csv(
  textConnection(linesenv), # we read the list into a csv
  header = FALSE, # there is no header
  colClasses = rep("character", 14)
  # specifying that those columns are character is important
  # if read as integer, 06 becomes 6, and when putting columns together,
  # 400.06 will be read as 400.6, which is wrong
)

env_df <- env_df |>
  mutate(
    datetime = dmy_hms(V1),
    temp_air = paste(
      V7, # V7 contains the left side of the decimal point
      V8, # V8 the right side
      sep = "." # this time we put it in american format
    ),
    temp_air = as.double(temp_air), # now we can make it a double
    temp_soil = as.double(paste(V9, V10, sep = ".")),
    co2_conc = as.double(paste(V11, V12, sep = ".")),
    PAR = as.double(paste(V13, V14, sep = "."))
  ) |>
  select(datetime, temp_air, temp_soil, co2_conc, PAR)

# now we do the same with the other elements of the list
lines_other <- lines[-seq(1, length(lines), 10)]
other_df <- read.csv(
  textConnection(lines_other),
  header = FALSE,
  colClasses = rep("character", 10)
)

other_df <- other_df  |>
  mutate(
    datetime = dmy_hms(V1),
    co2_conc = as.double(paste(V8, V9, sep = "."))
  ) |>
  select(datetime, co2_conc)

# and finally we do a full join with both
conc_df <- bind_rows(env_df, other_df) |>
  arrange(datetime) # I like my dataframes in chronological order

Et voilà:

#> 'data.frame':    101 obs. of  5 variables:
#>  $ datetime : POSIXct, format: "2023-10-01 11:23:40" ...
#>  $ temp_air : num  12 NA NA NA NA NA NA NA NA NA ...
#>  $ temp_soil: num  10.8 NA NA NA NA ...
#>  $ co2_conc : num  401 401 401 401 401 ...
#>  $ PAR      : num  20.6 NA NA NA NA NA NA NA NA NA ...

That was a strange mix of tidyverse and base R, and I would definitely try to do some plots to check if the data are making sense (number around 420 are most likely CO₂ concentration, those between 5 and 20 probably temperature, and soil temperature should be lower than air temperature). But it worked…