---
title: "Preparing the data for fluxible"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Preparing the data for fluxible}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
bibliography: biblio_phd_zot.bib
csl: emerald-harvard.csl
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
options(tidyverse.quiet = TRUE)
```

Providing functions to read data files directly from loggers or instruments would be an endless tasks, as there are countless varieties of formats.
Additionally, such a function already exists: `readr::read_delim` (@readr2024).

We provide here some guidelines and examples on how to use `read_delim` to prepare your raw data files for `fluxible`.

# Checklists for inputs

The first function to use when processing ecosystem gas fluxes data with `fluxible` is `flux_match`, which require two inputs: `row_conc` and `field_record`.

## Input `raw_conc`
The input `raw_conc` is the file with the gas concentration measured over time, typically the file exported by the logger or instrument.

- Column that will be used in `fluxible` do not contain space or special characters;
- A gas concentration column as numeric;
- A column in datetime format (`yyyy-mm-dd hh:mm:ss`) corresponding to each concentration data points.


## Input `field_record`
The input `field_record` is the file that is telling which sample or plot was measured when, and eventually providing other meta data, such as campaign, site, type of measurement and so on.

- Column that will be used in `fluxible` do not contain space or special characters;
- A column indicating the start of each measurement in datetime format (`yyyy-mm-dd hh:mm:ss`).

Note that the current version of `flux_match` does not support non fixed measurement length, indicating an end column instead of the `measurement_length` argument.
But it is possible to mimic `flux_match` and directly start with `flux_fitting` (see below).

## By-passing `flux_match`
The `flux_match` function only intends to attribute a unique `flux_id` to each measurement.
Depending on your setup, this step might not be necessary.
The `flux_fitting` function is the step after `flux_match` and its input should check the following points:

- Column that will be used in `fluxible` do not contain space or special characters;
- A gas concentration column as numeric;
- A column in datetime format (`yyyy-mm-dd hh:mm:ss`) corresponding to each concentration data points;
- A column with a unique ID for each measurements;
- A column indicating the start of each measurement in datetime format (`yyyy-mm-dd hh:mm:ss`);
- A column indicating the end of each measurement in datetime format (`yyyy-mm-dd hh:mm:ss`).


# Importing a single file
In this example we will import the file `26124054001.#00`, which is a text file extracted from a logger with the ad-hoc licensed 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 should not be imported, and that it is comma separated with a dot as a decimal point.](squirrel_head.png){#fig-header}

\
We will read the file with `read_delim`, and then use `rename` and `mutate` (from the `dplyr` package [^1] [@dplyr2023]) to transform the columns into what we want, and `dmy` and `as_datetime` from the `lubridate` package @lubridate2011 to get our datetime column in the right format:

[^1]: both `dplyr` and `readr` are part of the `tidyverse` [@tidyverse2019].

```{r, message = FALSE}
library(tidyverse)
# readr is part of tidyverse, and since we will also use dplyr
# we might as well load 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
)

# let's see
head(raw_conc)
```

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 lighter for later: `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 what we want), 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 gathered in one and transformed in `yyyy-mm-dd hh:mm:ss` format.

```{r}
library(lubridate) # lubridate is what you want to deal with datetime issues

raw_conc <- raw_conc |>
  rename(
    co2_conc = "CO2_calc (ppm)"
  ) |>
  mutate(
    Date = dmy(Date), # to transform the date as a yyyy-mm-dd format
    datetime = paste(Date, Time), # we paste date and time together
    datetime = as_datetime(datetime) # datetime instead of character
  ) |>
  select(datetime, co2_conc)

head(raw_conc) # Et voila!
```

Note that it is also possible to use the `col_names` and `col_select` arguments directly in `read_delim`, but it has a higher risk of errors.

```{r, message = FALSE}

raw_conc <- read_delim(
  "ex_data/26124054001.#00",
  delim = ",", # our file is comma separated
  skip = 26, # removing the first 25th row and the header
  col_select = c(1, 2, 6),
  col_names = c("date", "time", rep(NA, 3), "co2_conc", NA)
)
head(raw_conc)
```

# Importing multiple files

Quite often a field campaign will result in several files, because the logger was restarted or other events.
In this example we will read all the files in "ex_data/" that contains "CO2" in their names.

```{r, message = FALSE}
library(fs)

raw_conc <- dir_ls( #listing all the files
  "ex_data", # at location "ex_data"
  regexp = "*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)",
    datetime = "Date/Time"
  ) |>
  mutate(
    datetime = dmy_hms(datetime)
  ) |>
  select(datetime, conc)

head(raw_conc)
```



# The one file per flux approach
The Fluxible R package 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 then can by-pass `flux_match`.

```{r}
#| message: FALSE
library(tidyverse)
library(lubridate)
library(fs)

raw_conc <- dir_ls( #listing all the files
  "ex_data/field_campaign" # at location "ex_data/field_campaign"
) |>
  map_dfr( # we map read_tsv on all the files
    read_tsv, # read_tsv is for tab separated value files
    skip = 3,
    id = "filename" # column with the filename, that we can use as flux ID
  ) |>
  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),
    datetime = as.POSIXct(
      datetime, format = "%Y-%m-%d %H:%M:%OS"
    ), # we get rid of the milliseconds
    pressure = pressure / 101.325, # conversion from kPa to atm
    filename = substr(filename, 24, 70) # removing folder names
  ) |>
  select(datetime, co2_conc, h2o_conc, air_temp, pressure, filename)

head(raw_conc)
```


# 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, that is with 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](tricky.png){#fig-tricky}

\
Let's try the usual way first:

```{r, message = FALSE, warning = FALSE}

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
)

head(raw_conc)
```

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:

```{r, message = FALSE, warning = FALSE}
raw_conc <- read_csv(
  "ex_data/011023001.#01",
  skip = 1, # this time we skip the row with the column names
  col_names = FALSE, # and we tell read_csv that we do not provide column names
  na = "#N/A" # we tell read_csv what NA look like in that file
)

head(raw_conc)
```

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 reprocess 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:

```{r}
# we read each row of our file as an element of a list
list <- readLines("ex_data/011023001.#01")
list <- list[-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
listenv <- list[seq(1, length(list), 10)]
env_df <- read.csv(
  textConnection(listenv), # 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
list_other <- list[-seq(1, length(list), 10)]
other_df <- read.csv(
  textConnection(list_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 <- full_join(env_df, other_df, by = c("datetime", "co2_conc")) |>
  arrange(datetime) # I like my dataframes in chronological order
head(conc_df)
```

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 CO~2~ concentration, those between 5 and 20 probably temperature, and soil temperature should be lower than air temperature).
But it worked...
