# sfn_data classes

#### Victor Granda (Sapfluxnet Team)

#### 2025-07-24

# `sfn_data`

`sfn_data` is an S4 class designed for store and interact with sap flow data at individual plant level (not raw data), primarily from Sapfluxnet project sites data and metadata.

## S4 Slots

`sfn_data` class has twelve different slots:

1. sapf_data: Tibble containing the sap flow data, each column representing an individual tree, without any TIMESTAMP variable.

2. env_data: Tibble containing the environmental data, each column an environmental variable, without any TIMESTAMP variable. It must have the same `nrow` thet `sapf_data`, in order to be able to combine both in further analyses or aggregations.

3. sapf_flags: Tibble with the same dimensions as `sapf_data`, containing the flags (special remarks indicating possible outliers or any annotation of interest) for each observation in `sapf_data`.

4. env_flags: Tibble with the same dimensions as `env_data`, containing the flags for each observation in `env_data`.

5. si_code: Character vector of length 1 with the site code. Useful for further analyses or aggregations in order to identify the site when working with more then one.

6. timestamp: POSIXct vector of length equal to `nrow(sapf_data)` with the timestamp values.

7. solar_timestamp: POSIXct vector of length equal to `nrow(sapf_data)` with the apparent solar timestamp.

8. site_md: Tibble with the site metadata. See `sfn_vars_to_filter()` for a list of possible metadata variables. This variables are not mandatory, and new ones can be added.

9. stand_md: Tibble with the stand metadata. See `sfn_vars_to_filter()` for a list of possible metadata variables. This variables are not mandatory, and new ones can be added.

10. species_md: Tibble with the species metadata. See `sfn_vars_to_filter()` for a list of possible metadata variables. This variables are not mandatory, and new ones can be added.

11. plant_md: Tibble with the plant metadata. See `sfn_vars_to_filter()` for a list of possible metadata variables. This variables are not mandatory, and new ones can be added.

12. env_md: Tibble with the environmental metadata. See `sfn_vars_to_filter()` for a list of possible metadata variables. This variables are not mandatory, and new ones can be added.

### `sfn_data` design

The schematics of the class are summarised in Fig 1.

sfn_data schematics

This design have some characteristics:

- Dimensions: Dimensions must comply:

  `nrow(sapf_data) == nrow(env_data) == nrow(sapf_flags) == nrow(env_flags) == length(timestamp) == length(solar_timestamp)`

  `ncol(sapf_data) == ncol(sapf_flags)`

  `ncol(env_data) == ncol(env_flags)`

  In the case of differences in the dimensions between environmental and sap flow data, NAs must be added in the corresponding places when equalising both TIMESTAMPs (i.e. if env data is longer then sapf data, rows are added to the later with NAs to make them equal, and flags (“NA_ADDED”) are raised to indicate the adding).

- si_code slot: This slot indicates the site. This is useful for identifying the site in the metrics functions results.

- Compartmentalization: `timestamp` and `solar_timestamp` are isolated from data (sapf and env) in their own slot. This made easier subsetting the object and open the possibility of working with the timestamps only.

- Flags: Two slots are used for storing data flags. As the flag slots have the same dimensions as the corresponding dataset slot, selection of flagged data is straightforward:

  `sapf_data[flag_data == "flag", ]`

## Methods

Some methods are included in the class:

- `show`: calling a sfn_data object shows info about it

    library(sapfluxnetr)
    data('ARG_TRE', package = 'sapfluxnetr')
    ARG_TRE
    #> sfn_data object
    #> Data from ARG_TRE site
    #>
    #> Data kindly provided by  Seabstian Pfautsch  from  University of Sydney
    #> and  Pablo Peri  from  Instituto Nacional de Tecnología Agropecuaria
    #>
    #> Site related literature:  DOI 10.1007/s00468-013-0935-4
    #>
    #> Sapflow data:  312  observations of  4  trees/plants
    #> Species present:  Nothofagus antarctica
    #>
    #> Environmental data:  312  observations.
    #> Variables present:
    #>   ta rh vpd sw_in ws precip swc_shallow ppfd_in ext_rad
    #>
    #> Biome:  Woodland/Shrubland
    #>
    #> TIMESTAMP span:  2009-11-18 -03--2009-11-30 23:00:00 -03
    #>
    #> Solar TIMESTAMP span:  2009-11-17 22:24:58 UTC--2009-11-30 21:20:48 UTC
    #>
    #> Sapflow data flags:
    #> No flags present
    #>
    #> Environmental data flags:
    #> RANGE_WARN   OUT_WARN CALCULATED
    #>         18         24        624

- `get methods`: methods to get any slot outside the object. In the case of sapf and env data, TIMESTAMP is added (based on the `solar` argument) in order to obtain fully functional datasets. See `?sfn_get_methods` for more details.

    get_sapf_data(ARG_TRE, solar = FALSE)
    #> # A tibble: 312 × 5
    #>    TIMESTAMP           ARG_TRE_Nan_Jt_1 ARG_TRE_Nan_Jt_2 ARG_TRE_Nan_Jt_3
    #>    <dttm>                         <dbl>            <dbl>            <dbl>
    #>  1 2009-11-18 00:00:00             322.             190.             313.
    #>  2 2009-11-18 01:00:00             293.             157.             293.
    #>  3 2009-11-18 02:00:00             298.             178.             331.
    #>  4 2009-11-18 03:00:00             298.             168.             299.
    #>  5 2009-11-18 04:00:00             301.             170.             292.
    #>  6 2009-11-18 05:00:00             320.             198.             295.
    #>  7 2009-11-18 06:00:00             343.             196.             301.
    #>  8 2009-11-18 07:00:00             324.             194.             292.
    #>  9 2009-11-18 08:00:00             353.             284.             323.
    #> 10 2009-11-18 09:00:00             433.             340.             352.
    #> # ℹ 302 more rows
    #> # ℹ 1 more variable: ARG_TRE_Nan_Jt_4 <dbl>
    get_sapf_data(ARG_TRE, solar = TRUE)
    #> # A tibble: 312 × 5
    #>    TIMESTAMP           ARG_TRE_Nan_Jt_1 ARG_TRE_Nan_Jt_2 ARG_TRE_Nan_Jt_3
    #>    <dttm>                         <dbl>            <dbl>            <dbl>
    #>  1 2009-11-17 22:24:58             322.             190.             313.
    #>  2 2009-11-17 23:24:58             293.             157.             293.
    #>  3 2009-11-18 00:24:43             298.             178.             331.
    #>  4 2009-11-18 01:24:43             298.             168.             299.
    #>  5 2009-11-18 02:24:43             301.             170.             292.
    #>  6 2009-11-18 03:24:43             320.             198.             295.
    #>  7 2009-11-18 04:24:43             343.             196.             301.
    #>  8 2009-11-18 05:24:43             324.             194.             292.
    #>  9 2009-11-18 06:24:43             353.             284.             323.
    #> 10 2009-11-18 07:24:43             433.             340.             352.
    #> # ℹ 302 more rows
    #> # ℹ 1 more variable: ARG_TRE_Nan_Jt_4 <dbl>
    get_env_data(ARG_TRE) # solar default is FALSE
    #> # A tibble: 312 × 10
    #>    TIMESTAMP              ta    rh   vpd sw_in    ws precip swc_shallow ppfd_in
    #>    <dttm>              <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>       <dbl>   <dbl>
    #>  1 2009-11-18 00:00:00  1     41.7 0.383   0   13.6       0       0.396      0
    #>  2 2009-11-18 01:00:00  1.2   41.5 0.389   0   15.8       0       0.396      0
    #>  3 2009-11-18 02:00:00  1.4   41.4 0.396   0   22.5       0       0.395      0
    #>  4 2009-11-18 03:00:00  1     38.9 0.401   0   13.6       0       0.394      0
    #>  5 2009-11-18 04:00:00  0.6   36.5 0.405   0    6.72      0       0.394      0
    #>  6 2009-11-18 05:00:00  1.65  37.5 0.430   0   20.3       0       0.394      0
    #>  7 2009-11-18 06:00:00  2.7   38.5 0.456   0    4.48      0       0.393      0
    #>  8 2009-11-18 07:00:00  4     38.2 0.503   0    2.24      0       0.392      0
    #>  9 2009-11-18 08:00:00  5.3   37.8 0.554  99.4  2.24      0       0.391    210.
    #> 10 2009-11-18 09:00:00  6.75  33.0 0.660 245    4.48      0       0.391    518.
    #> # ℹ 302 more rows
    #> # ℹ 1 more variable: ext_rad <dbl>
    get_sapf_flags(ARG_TRE) # solar default is FALSE
    #> # A tibble: 312 × 5
    #>    TIMESTAMP           ARG_TRE_Nan_Jt_1 ARG_TRE_Nan_Jt_2 ARG_TRE_Nan_Jt_3
    #>    <dttm>              <chr>            <chr>            <chr>
    #>  1 2009-11-18 00:00:00 ""               ""               ""
    #>  2 2009-11-18 01:00:00 ""               ""               ""
    #>  3 2009-11-18 02:00:00 ""               ""               ""
    #>  4 2009-11-18 03:00:00 ""               ""               ""
    #>  5 2009-11-18 04:00:00 ""               ""               ""
    #>  6 2009-11-18 05:00:00 ""               ""               ""
    #>  7 2009-11-18 06:00:00 ""               ""               ""
    #>  8 2009-11-18 07:00:00 ""               ""               ""
    #>  9 2009-11-18 08:00:00 ""               ""               ""
    #> 10 2009-11-18 09:00:00 ""               ""               ""
    #> # ℹ 302 more rows
    #> # ℹ 1 more variable: ARG_TRE_Nan_Jt_4 <chr>
    get_env_flags(ARG_TRE) # solar default is FALSE
    #> # A tibble: 312 × 10
    #>    TIMESTAMP           ta    rh    vpd   sw_in ws    precip swc_shallow ppfd_in
    #>    <dttm>              <chr> <chr> <chr> <chr> <chr> <chr>  <chr>       <chr>
    #>  1 2009-11-18 00:00:00 ""    ""    ""    ""    ""    ""     ""          CALCULA…
    #>  2 2009-11-18 01:00:00 ""    ""    ""    ""    ""    ""     ""          CALCULA…
    #>  3 2009-11-18 02:00:00 ""    ""    ""    ""    ""    ""     ""          CALCULA…
    #>  4 2009-11-18 03:00:00 ""    ""    ""    ""    ""    ""     ""          CALCULA…
    #>  5 2009-11-18 04:00:00 ""    ""    ""    ""    ""    ""     ""          CALCULA…
    #>  6 2009-11-18 05:00:00 ""    ""    ""    ""    ""    ""     ""          CALCULA…
    #>  7 2009-11-18 06:00:00 ""    ""    ""    ""    ""    ""     ""          CALCULA…
    #>  8 2009-11-18 07:00:00 ""    ""    ""    ""    ""    ""     ""          CALCULA…
    #>  9 2009-11-18 08:00:00 ""    ""    ""    ""    ""    ""     ""          CALCULA…
    #> 10 2009-11-18 09:00:00 ""    ""    ""    ""    ""    ""     ""          CALCULA…
    #> # ℹ 302 more rows
    #> # ℹ 1 more variable: ext_rad <chr>
    get_si_code(ARG_TRE)
    #> [1] "ARG_TRE"
    get_timestamp(ARG_TRE)[1:10]
    #>  [1] "2009-11-18 00:00:00 -03" "2009-11-18 01:00:00 -03"
    #>  [3] "2009-11-18 02:00:00 -03" "2009-11-18 03:00:00 -03"
    #>  [5] "2009-11-18 04:00:00 -03" "2009-11-18 05:00:00 -03"
    #>  [7] "2009-11-18 06:00:00 -03" "2009-11-18 07:00:00 -03"
    #>  [9] "2009-11-18 08:00:00 -03" "2009-11-18 09:00:00 -03"
    get_solar_timestamp(ARG_TRE)[1:10]
    #>  [1] "2009-11-17 22:24:58 UTC" "2009-11-17 23:24:58 UTC"
    #>  [3] "2009-11-18 00:24:43 UTC" "2009-11-18 01:24:43 UTC"
    #>  [5] "2009-11-18 02:24:43 UTC" "2009-11-18 03:24:43 UTC"
    #>  [7] "2009-11-18 04:24:43 UTC" "2009-11-18 05:24:43 UTC"
    #>  [9] "2009-11-18 06:24:43 UTC" "2009-11-18 07:24:43 UTC"
    get_site_md(ARG_TRE)
    #> # A tibble: 1 × 24
    #>   si_addcontr_email si_addcontr_firstname si_addcontr_institution
    #>   <chr>             <chr>                 <chr>
    #> 1 <NA>              Pablo                 Instituto Nacional de Tecnología Agro…
    #> # ℹ 21 more variables: si_addcontr_lastname <chr>, si_code <chr>,
    #> #   si_contact_email <chr>, si_contact_firstname <chr>,
    #> #   si_contact_institution <chr>, si_contact_lastname <chr>, si_country <chr>,
    #> #   si_dendro_network <lgl>, si_dist_mgmt <chr>, si_elev <int>,
    #> #   si_flux_network <lgl>, si_igbp <chr>, si_lat <dbl>, si_long <dbl>,
    #> #   si_name <chr>, si_paper <chr>, si_remarks <lgl>, is_inside_country <lgl>,
    #> #   si_mat <dbl>, si_map <dbl>, si_biome <chr>
    get_stand_md(ARG_TRE)
    #> # A tibble: 1 × 18
    #>   st_age st_aspect st_basal_area st_clay_perc st_density st_growth_condition
    #>    <int> <chr>             <dbl> <lgl>             <int> <chr>
    #> 1    140 Flat               19.4 NA                  512 Naturally regenerated,…
    #> # ℹ 12 more variables: st_height <int>, st_lai <lgl>, st_name <lgl>,
    #> #   st_remarks <lgl>, st_sand_perc <lgl>, st_silt_perc <lgl>,
    #> #   st_soil_depth <int>, st_soil_texture <chr>, st_terrain <chr>,
    #> #   st_treatment <lgl>, si_code <chr>, st_USDA_soil_texture <chr>
    get_species_md(ARG_TRE)
    #> # A tibble: 1 × 5
    #>   sp_basal_area_perc sp_leaf_habit  sp_name               sp_ntrees si_code
    #>                <int> <chr>          <chr>                     <int> <chr>
    #> 1                100 cold deciduous Nothofagus antarctica         4 ARG_TRE
    get_plant_md(ARG_TRE)
    #> # A tibble: 4 × 26
    #>   pl_age pl_azimut_int       pl_bark_thick pl_code pl_dbh pl_height pl_leaf_area
    #>    <int> <chr>                       <dbl> <chr>    <dbl> <lgl>            <dbl>
    #> 1    140 Corrected, measure…          23.5 ARG_TR…   23.6 NA                29.7
    #> 2    140 No azimuthal corre…          22.5 ARG_TR…   20.2 NA                13.0
    #> 3    140 No azimuthal corre…          22   ARG_TR…   23.8 NA                29.8
    #> 4    140 No azimuthal corre…          23.5 ARG_TR…   19.5 NA                15.1
    #> # ℹ 19 more variables: pl_name <chr>, pl_radial_int <chr>, pl_remarks <lgl>,
    #> #   pl_sap_units <chr>, pl_sapw_area <dbl>, pl_sapw_depth <dbl>,
    #> #   pl_sens_calib <lgl>, pl_sens_cor_grad <chr>, pl_sens_cor_zero <chr>,
    #> #   pl_sens_hgt <dbl>, pl_sens_length <int>, pl_sens_man <chr>,
    #> #   pl_sens_meth <chr>, pl_sens_timestep <int>, pl_social <chr>,
    #> #   pl_species <chr>, pl_treatment <lgl>, si_code <chr>,
    #> #   pl_sap_units_orig <chr>
    get_env_md(ARG_TRE)
    #> # A tibble: 1 × 17
    #>   env_leafarea_seasonal env_netrad   env_plant_watpot env_ppfd_in  env_precip
    #>   <lgl>                 <chr>        <chr>            <chr>        <chr>
    #> 1 NA                    Not provided leaf: midday     Not provided Clearing
    #> # ℹ 12 more variables: env_remarks <lgl>, env_rh <chr>,
    #> #   env_swc_deep_depth <lgl>, env_swc_shallow_depth <int>, env_sw_in <chr>,
    #> #   env_ta <chr>, env_time_daylight <lgl>, env_timestep <int>,
    #> #   env_time_zone <chr>, env_vpd <chr>, env_ws <chr>, si_code <chr>

- `assignation`: methods for `"<-"` are implemented, allowing transformation of the slots. This is used internally in some functions but is not recommended except for updating metadata slots. In data slots this changes are not reflected in the flags, so the reproducibility and traceability will be lost if used without care. See `?sfn_replacement_methods` for more details.

    # extraction and modification
    foo_site_md <- get_site_md(ARG_TRE)
    foo_site_md[['si_biome']]
    #> [1] "Woodland/Shrubland"
    foo_site_md[['si_biome']] <- 'Temperate forest'
    # assignation
    get_site_md(ARG_TRE) <- foo_site_md
    # check it worked
    get_site_md(ARG_TRE)[['si_biome']]
    #> [1] "Temperate forest"

- `validation`: a validation method is implemented in order to avoid creation of invalid sfn_data objects (i.e objects with different data dimensions, objects without metadata…)

    # get sap flow data
    foo_bad_sapf <- get_sapf_data(ARG_TRE)
    # pull a row, now it has diferent dimensions then
    foo_bad_sapf <- foo_bad_sapf[-1,]
    # try to assign the incorrect data fails
    get_sapf_data(ARG_TRE) <- foo_bad_sapf[,-1] # ERROR

    #> Error in validObject(object) :
    #>   invalid class "sfn_data" object: 1: nrow(sapf_data) != nrow(env_data)
    #> invalid class "sfn_data" object: 2: nrow(sapf_data) != length(timestamp) | nrow(env_data) != length(timestamp)
    #> invalid class "sfn_data" object: 3: nrow(*_flags) =! nrow(*_flags) | nrow(*_flags) =! nrow(*_data)

    #> Error in `get_sapf_data<-`(`*tmp*`, value = structure(list(ARG_TRE_Nan_Jt_1 = c(292.9710029771, : new data is not valid: Error in validObject(object) :
    #>   invalid class "sfn_data" object: 1: nrow(sapf_data) != nrow(env_data)
    #> invalid class "sfn_data" object: 2: nrow(sapf_data) != length(timestamp) | nrow(env_data) != length(timestamp)
    #> invalid class "sfn_data" object: 3: nrow(*_flags) =! nrow(*_flags) | nrow(*_flags) =! nrow(*_data)

    # try to build a new object also fails
    sfn_data(
      sapf_data = foo_bad_sapf[,-1], # remember to remove timestamp column
      env_data = get_env_data(ARG_TRE)[,-1],
      sapf_flags = get_env_flags(ARG_TRE)[,-1],
      env_flags = get_env_flags(ARG_TRE)[,-1],
      si_code = get_si_code(ARG_TRE),
      timestamp = get_timestamp(ARG_TRE),
      solar_timestamp = get_solar_timestamp(ARG_TRE),
      site_md = get_site_md(ARG_TRE),
      stand_md = get_stand_md(ARG_TRE),
      species_md = get_species_md(ARG_TRE),
      plant_md = get_plant_md(ARG_TRE),
      env_md = get_env_md(ARG_TRE)
    )

    #> Error in validObject(.Object): invalid class "sfn_data" object: 1: nrow(sapf_data) != nrow(env_data)
    #> invalid class "sfn_data" object: 2: nrow(sapf_data) != length(timestamp) | nrow(env_data) != length(timestamp)
    #> invalid class "sfn_data" object: 3: nrow(*_flags) =! nrow(*_flags) | nrow(*_flags) =! nrow(*_data)

## Utilities

`sapfluxnetr` package offers some utilities to visualize and work with sfn_data objects:

### `sfn_plot`

This function allows to plot `sfn_data` objects. See `?sfn_plot` for more details.

    library(ggplot2)

    sfn_plot(ARG_TRE, type = 'env') +
      facet_wrap(~ Variable, ncol = 3, scales = 'free_y') +
      theme(legend.position = 'none')

    sfn_plot(ARG_TRE, formula_env = ~ vpd) +
      theme(legend.position = 'none')

### `sfn_filter`

This function emulates `filter` function from `dplyr` package for `sfn_data` objects. Useful to filter by some specific timestamp. Be advised, using this function to filter by sap flow or environmental variables can create TIMESTAMP gaps. See `sfn_filter` for more details.

    library(lubridate)
    library(dplyr)

    # get only the values for november
    sfn_filter(ARG_TRE, month(TIMESTAMP) == 11)
    #> sfn_data object
    #> Data from ARG_TRE site
    #>
    #> Data kindly provided by  Seabstian Pfautsch  from  University of Sydney
    #> and  Pablo Peri  from  Instituto Nacional de Tecnología Agropecuaria
    #>
    #> Site related literature:  DOI 10.1007/s00468-013-0935-4
    #>
    #> Sapflow data:  312  observations of  4  trees/plants
    #> Species present:  Nothofagus antarctica
    #>
    #> Environmental data:  312  observations.
    #> Variables present:
    #>   ta rh vpd sw_in ws precip swc_shallow ppfd_in ext_rad
    #>
    #> Biome:  Temperate forest
    #>
    #> TIMESTAMP span:  2009-11-18 -03--2009-11-30 23:00:00 -03
    #>
    #> Solar TIMESTAMP span:  2009-11-17 22:24:58 UTC--2009-11-30 21:20:48 UTC
    #>
    #> Sapflow data flags:
    #> No flags present
    #>
    #> Environmental data flags:
    #> RANGE_WARN   OUT_WARN CALCULATED
    #>         18         24        624

### `sfn_mutate`

This function allows mutation of data variables inside the `sfn_data` object. Useful when you need to transform a variable to another units or similar. A flag (‘USER_MODF’) will be added to all values in the mutated variable. See `sfn_mutate` for more details.

> At this moment, mutate does not allows creating new variables, only mutate existing variables

    # transform ws from m/s to km/h
    foo_mutated <- sfn_mutate(ARG_TRE, ws = ws * 3600/1000)
    get_env_data(foo_mutated)[['ws']][1:10]
    #>  [1] 48.888 56.952 81.144 48.888 24.192 73.080 16.128  8.064  8.064 16.128

### `sfn_mutate_at`

This function mutates all variables declared with the function provided. Useful when you need to conditionally transform the data, i.e. converting to NA sap flow values when an environmental variable exceeds some threshold. See `sfn_mutate_at` for more details.

    foo_mutated_2 <- sfn_mutate_at(
      ARG_TRE,
      vars(one_of(names(get_sapf_data(ARG_TRE)[,-1]))),
      list(~ case_when(
        ws > 25 ~ NA_real_,
        TRUE ~ .
      ))
    )

    # see the difference between ARG_TRE and foo_mutated_2
    get_sapf_data(ARG_TRE)
    #> # A tibble: 312 × 5
    #>    TIMESTAMP           ARG_TRE_Nan_Jt_1 ARG_TRE_Nan_Jt_2 ARG_TRE_Nan_Jt_3
    #>    <dttm>                         <dbl>            <dbl>            <dbl>
    #>  1 2009-11-18 00:00:00             322.             190.             313.
    #>  2 2009-11-18 01:00:00             293.             157.             293.
    #>  3 2009-11-18 02:00:00             298.             178.             331.
    #>  4 2009-11-18 03:00:00             298.             168.             299.
    #>  5 2009-11-18 04:00:00             301.             170.             292.
    #>  6 2009-11-18 05:00:00             320.             198.             295.
    #>  7 2009-11-18 06:00:00             343.             196.             301.
    #>  8 2009-11-18 07:00:00             324.             194.             292.
    #>  9 2009-11-18 08:00:00             353.             284.             323.
    #> 10 2009-11-18 09:00:00             433.             340.             352.
    #> # ℹ 302 more rows
    #> # ℹ 1 more variable: ARG_TRE_Nan_Jt_4 <dbl>
    get_sapf_data(foo_mutated_2)
    #> # A tibble: 312 × 5
    #>    TIMESTAMP           ARG_TRE_Nan_Jt_1 ARG_TRE_Nan_Jt_2 ARG_TRE_Nan_Jt_3
    #>    <dttm>                         <dbl>            <dbl>            <dbl>
    #>  1 2009-11-18 00:00:00             322.             190.             313.
    #>  2 2009-11-18 01:00:00             293.             157.             293.
    #>  3 2009-11-18 02:00:00             298.             178.             331.
    #>  4 2009-11-18 03:00:00             298.             168.             299.
    #>  5 2009-11-18 04:00:00             301.             170.             292.
    #>  6 2009-11-18 05:00:00             320.             198.             295.
    #>  7 2009-11-18 06:00:00             343.             196.             301.
    #>  8 2009-11-18 07:00:00             324.             194.             292.
    #>  9 2009-11-18 08:00:00             353.             284.             323.
    #> 10 2009-11-18 09:00:00             433.             340.             352.
    #> # ℹ 302 more rows
    #> # ℹ 1 more variable: ARG_TRE_Nan_Jt_4 <dbl>

### `*_metrics` functions

Family of functions to aggregate and summarise the site data. See `?metrics` for more details.

    foo_daily <- daily_metrics(ARG_TRE)
    #> [1] "Crunching data for ARG_TRE. In large datasets this could take a while"
    #> [1] "General data for ARG_TRE"
    foo_daily[['sapf']][['sapf_gen']]
    #> NULL

For full control of metrics and custom aggregations see `vignette('custom-aggregation', package = 'sapfluxnetr')`

# `sfn_data_multi`

`sfn_data_multi` is an S4 class designed to store multiple `sfn_data` objects. It inherits from list so, in a nutshell, `sfn_data_multi` is a list of `sfn_data` objects.

## Methods

`sfn_data_multi` has the following methods declared.

- `show`: it shows the number, codes and combined timestamp span of sites in the multi object.

    # creating a sfn_data_multi object
    data(ARG_MAZ, package = 'sapfluxnetr')
    data(AUS_CAN_ST2_MIX, package = 'sapfluxnetr')
    multi_sfn <- sfn_data_multi(ARG_TRE, ARG_MAZ, AUS_CAN_ST2_MIX)

    # show method
    multi_sfn
    #> sfn_data_multi object
    #> 3 sites: ARG_TRE ARG_MAZ AUS_CAN_ST2_MIX
    #> Approximate time span (UTC) for the combined sites: 2006-06-20 10:54:42 UTC--2009-11-18 22:24:18 UTC

- `get` methods: they work the same as in the `sfn_data` objects, except they return a list, with the corresponding data for the sites in the `sfn_data_multi` object as elements.

    # get sap flow data
    get_sapf_data(multi_sfn)
    #> $ARG_TRE
    #> # A tibble: 312 × 5
    #>    TIMESTAMP           ARG_TRE_Nan_Jt_1 ARG_TRE_Nan_Jt_2 ARG_TRE_Nan_Jt_3
    #>    <dttm>                         <dbl>            <dbl>            <dbl>
    #>  1 2009-11-18 00:00:00             322.             190.             313.
    #>  2 2009-11-18 01:00:00             293.             157.             293.
    #>  3 2009-11-18 02:00:00             298.             178.             331.
    #>  4 2009-11-18 03:00:00             298.             168.             299.
    #>  5 2009-11-18 04:00:00             301.             170.             292.
    #>  6 2009-11-18 05:00:00             320.             198.             295.
    #>  7 2009-11-18 06:00:00             343.             196.             301.
    #>  8 2009-11-18 07:00:00             324.             194.             292.
    #>  9 2009-11-18 08:00:00             353.             284.             323.
    #> 10 2009-11-18 09:00:00             433.             340.             352.
    #> # ℹ 302 more rows
    #> # ℹ 1 more variable: ARG_TRE_Nan_Jt_4 <dbl>
    #>
    #> $ARG_MAZ
    #> # A tibble: 288 × 6
    #>    TIMESTAMP           ARG_MAZ_Npu_Jt_1 ARG_MAZ_Npu_Jt_2 ARG_MAZ_Npu_Jt_3
    #>    <dttm>                         <dbl>            <dbl>            <dbl>
    #>  1 2009-11-19 00:00:00            1955.             754.             742.
    #>  2 2009-11-19 01:00:00            1833.             752.             631.
    #>  3 2009-11-19 02:00:00            1817.             795.             597.
    #>  4 2009-11-19 03:00:00            1598.             788.             579.
    #>  5 2009-11-19 04:00:00            1419.             780.             518.
    #>  6 2009-11-19 05:00:00            1506.             735.             430.
    #>  7 2009-11-19 06:00:00            1649.             720.             539.
    #>  8 2009-11-19 07:00:00            1630.             882.             542.
    #>  9 2009-11-19 08:00:00            1909.            1015.             655.
    #> 10 2009-11-19 09:00:00            2849.            1459.             930.
    #> # ℹ 278 more rows
    #> # ℹ 2 more variables: ARG_MAZ_Npu_Jt_4 <dbl>, ARG_MAZ_Npu_Jt_5 <dbl>
    #>
    #> $AUS_CAN_ST2_MIX
    #> # A tibble: 17,808 × 35
    #>    TIMESTAMP           AUS_CAN_ST2_MIX_Ame_Js_1 AUS_CAN_ST2_MIX_Egl_Js_2
    #>    <dttm>                                 <dbl>                    <dbl>
    #>  1 2006-06-20 11:00:00                       NA                       NA
    #>  2 2006-06-20 11:30:00                       NA                       NA
    #>  3 2006-06-20 12:00:00                       NA                       NA
    #>  4 2006-06-20 12:30:00                       NA                       NA
    #>  5 2006-06-20 13:00:00                       NA                       NA
    #>  6 2006-06-20 13:30:00                       NA                       NA
    #>  7 2006-06-20 14:00:00                       NA                       NA
    #>  8 2006-06-20 14:30:00                       NA                       NA
    #>  9 2006-06-20 15:00:00                       NA                       NA
    #> 10 2006-06-20 15:30:00                       NA                       NA
    #> # ℹ 17,798 more rows
    #> # ℹ 32 more variables: AUS_CAN_ST2_MIX_Ame_Js_3 <dbl>,
    #> #   AUS_CAN_ST2_MIX_Egl_Js_4 <dbl>, AUS_CAN_ST2_MIX_Egl_Js_5 <dbl>,
    #> #   AUS_CAN_ST2_MIX_Ame_Js_6 <dbl>, AUS_CAN_ST2_MIX_Ame_Js_7 <dbl>,
    #> #   AUS_CAN_ST2_MIX_Egl_Js_8 <dbl>, AUS_CAN_ST2_MIX_Egl_Js_9 <dbl>,
    #> #   AUS_CAN_ST2_MIX_Ame_Js_10 <dbl>, AUS_CAN_ST2_MIX_Ame_Js_11 <dbl>,
    #> #   AUS_CAN_ST2_MIX_Egl_Js_12 <dbl>, AUS_CAN_ST2_MIX_Ame_Js_13 <dbl>, …
    # get plant metadata
    get_plant_md(multi_sfn)
    #> $ARG_TRE
    #> # A tibble: 4 × 26
    #>   pl_age pl_azimut_int       pl_bark_thick pl_code pl_dbh pl_height pl_leaf_area
    #>    <int> <chr>                       <dbl> <chr>    <dbl> <lgl>            <dbl>
    #> 1    140 Corrected, measure…          23.5 ARG_TR…   23.6 NA                29.7
    #> 2    140 No azimuthal corre…          22.5 ARG_TR…   20.2 NA                13.0
    #> 3    140 No azimuthal corre…          22   ARG_TR…   23.8 NA                29.8
    #> 4    140 No azimuthal corre…          23.5 ARG_TR…   19.5 NA                15.1
    #> # ℹ 19 more variables: pl_name <chr>, pl_radial_int <chr>, pl_remarks <lgl>,
    #> #   pl_sap_units <chr>, pl_sapw_area <dbl>, pl_sapw_depth <dbl>,
    #> #   pl_sens_calib <lgl>, pl_sens_cor_grad <chr>, pl_sens_cor_zero <chr>,
    #> #   pl_sens_hgt <dbl>, pl_sens_length <int>, pl_sens_man <chr>,
    #> #   pl_sens_meth <chr>, pl_sens_timestep <int>, pl_social <chr>,
    #> #   pl_species <chr>, pl_treatment <lgl>, si_code <chr>,
    #> #   pl_sap_units_orig <chr>
    #>
    #> $ARG_MAZ
    #> # A tibble: 5 × 26
    #>   pl_age pl_azimut_int       pl_bark_thick pl_code pl_dbh pl_height pl_leaf_area
    #>    <int> <chr>                       <dbl> <chr>    <dbl> <lgl>            <dbl>
    #> 1    180 Corrected, measure…          14.7 ARG_MA…   41.1 NA               109.
    #> 2    180 No azimuthal corre…          13   ARG_MA…   33.2 NA                58.4
    #> 3    180 No azimuthal corre…           6   ARG_MA…   23.2 NA                35.0
    #> 4    180 No azimuthal corre…          21   ARG_MA…   55.6 NA               174.
    #> 5    180 No azimuthal corre…          10   ARG_MA…   38   NA                88
    #> # ℹ 19 more variables: pl_name <chr>, pl_radial_int <chr>, pl_remarks <lgl>,
    #> #   pl_sap_units <chr>, pl_sapw_area <dbl>, pl_sapw_depth <dbl>,
    #> #   pl_sens_calib <lgl>, pl_sens_cor_grad <chr>, pl_sens_cor_zero <chr>,
    #> #   pl_sens_hgt <dbl>, pl_sens_length <int>, pl_sens_man <chr>,
    #> #   pl_sens_meth <chr>, pl_sens_timestep <int>, pl_social <chr>,
    #> #   pl_species <chr>, pl_treatment <lgl>, si_code <chr>,
    #> #   pl_sap_units_orig <chr>
    #>
    #> $AUS_CAN_ST2_MIX
    #> # A tibble: 34 × 26
    #>    pl_age pl_azimut_int      pl_bark_thick pl_code pl_dbh pl_height pl_leaf_area
    #>     <dbl> <chr>              <lgl>         <chr>    <dbl>     <dbl> <lgl>
    #>  1   14.5 No azimuthal corr… NA            AUS_CA…   13.1      14.3 NA
    #>  2   14.5 No azimuthal corr… NA            AUS_CA…   10.4      12.9 NA
    #>  3   14.5 No azimuthal corr… NA            AUS_CA…   12.8      14.1 NA
    #>  4   14.5 No azimuthal corr… NA            AUS_CA…   17.1      19.4 NA
    #>  5   14.5 No azimuthal corr… NA            AUS_CA…   11.6      13.6 NA
    #>  6   14.5 No azimuthal corr… NA            AUS_CA…   19.2      20.5 NA
    #>  7   14.5 No azimuthal corr… NA            AUS_CA…   12.8      16.6 NA
    #>  8   14.5 No azimuthal corr… NA            AUS_CA…   12.5      14.1 NA
    #>  9   14.5 No azimuthal corr… NA            AUS_CA…   13.6      17.2 NA
    #> 10   14.5 No azimuthal corr… NA            AUS_CA…   13.8      14.7 NA
    #> # ℹ 24 more rows
    #> # ℹ 19 more variables: pl_name <chr>, pl_radial_int <chr>, pl_remarks <chr>,
    #> #   pl_sap_units <chr>, pl_sapw_area <dbl>, pl_sapw_depth <dbl>,
    #> #   pl_sens_calib <lgl>, pl_sens_cor_grad <lgl>, pl_sens_cor_zero <chr>,
    #> #   pl_sens_hgt <dbl>, pl_sens_length <lgl>, pl_sens_man <lgl>,
    #> #   pl_sens_meth <chr>, pl_sens_timestep <int>, pl_social <lgl>,
    #> #   pl_species <chr>, pl_treatment <chr>, si_code <chr>, …
    # with metadata, we can collapse
    get_plant_md(multi_sfn, collapse = TRUE)
    #> # A tibble: 43 × 26
    #>    pl_age pl_azimut_int      pl_bark_thick pl_code pl_dbh pl_height pl_leaf_area
    #>     <dbl> <chr>                      <dbl> <chr>    <dbl>     <dbl>        <dbl>
    #>  1  140   Corrected, measur…          23.5 ARG_TR…   23.6      NA           29.7
    #>  2  140   No azimuthal corr…          22.5 ARG_TR…   20.2      NA           13.0
    #>  3  140   No azimuthal corr…          22   ARG_TR…   23.8      NA           29.8
    #>  4  140   No azimuthal corr…          23.5 ARG_TR…   19.5      NA           15.1
    #>  5  180   Corrected, measur…          14.7 ARG_MA…   41.1      NA          109.
    #>  6  180   No azimuthal corr…          13   ARG_MA…   33.2      NA           58.4
    #>  7  180   No azimuthal corr…           6   ARG_MA…   23.2      NA           35.0
    #>  8  180   No azimuthal corr…          21   ARG_MA…   55.6      NA          174.
    #>  9  180   No azimuthal corr…          10   ARG_MA…   38        NA           88
    #> 10   14.5 No azimuthal corr…          NA   AUS_CA…   13.1      14.3         NA
    #> # ℹ 33 more rows
    #> # ℹ 19 more variables: pl_name <chr>, pl_radial_int <chr>, pl_remarks <chr>,
    #> #   pl_sap_units <chr>, pl_sapw_area <dbl>, pl_sapw_depth <dbl>,
    #> #   pl_sens_calib <lgl>, pl_sens_cor_grad <chr>, pl_sens_cor_zero <chr>,
    #> #   pl_sens_hgt <dbl>, pl_sens_length <int>, pl_sens_man <chr>,
    #> #   pl_sens_meth <chr>, pl_sens_timestep <int>, pl_social <chr>,
    #> #   pl_species <chr>, pl_treatment <chr>, si_code <chr>, …

## Utilities

All the utilities thet exists for `sfn_data` work for `sfn_data_multi` objects, executing the function for all the sites contained in the `sfn_data_multi` object:

### `sfn_plot`

    multi_plot <- sfn_plot(multi_sfn, formula = ~ vpd)
    multi_plot[['ARG_TRE']] + theme(legend.position = 'none')
    multi_plot[['AUS_CAN_ST2_MIX']] + theme(legend.position = 'none')
    #> Warning: Removed 526066 rows containing missing values or values outside the scale range
    #> (`geom_point()`).

### `sfn_filter`

    multi_filtered <- sfn_filter(multi_sfn, month(TIMESTAMP) == 11)
    get_timestamp(multi_filtered[['AUS_CAN_ST2_MIX']])[1:10]
    #>  [1] "2006-11-01 00:00:00 +10" "2006-11-01 00:30:00 +10"
    #>  [3] "2006-11-01 01:00:00 +10" "2006-11-01 01:30:00 +10"
    #>  [5] "2006-11-01 02:00:00 +10" "2006-11-01 02:30:00 +10"
    #>  [7] "2006-11-01 03:00:00 +10" "2006-11-01 03:30:00 +10"
    #>  [9] "2006-11-01 04:00:00 +10" "2006-11-01 04:30:00 +10"

### `sfn_mutate`

    multi_mutated <- sfn_mutate(multi_sfn, ws = ws * 3600/1000)
    get_env_data(multi_mutated[['AUS_CAN_ST2_MIX']])[['ws']][1:10]
    #>  [1] 0.00 0.00 0.00 0.00 0.36 0.36 0.00 0.00 0.00 0.36

### `sfn_mutate_at`

    vars_to_not_mutate <- c(
      "TIMESTAMP", "ta", "rh", "vpd", "sw_in", "ws",
      "precip", "swc_shallow", "ppfd_in", "ext_rad"
    )

    multi_mutated_2 <- sfn_mutate_at(
      multi_sfn,
      vars(-one_of(vars_to_not_mutate)),
      list(~ case_when(
        ws > 25 ~ NA_real_,
        TRUE ~ .
      ))
    )
    #> Warning: Unknown columns: `swc_shallow`

    multi_mutated_2[['ARG_TRE']]
    #> sfn_data object
    #> Data from ARG_TRE site
    #>
    #> Data kindly provided by  Seabstian Pfautsch  from  University of Sydney
    #> and  Pablo Peri  from  Instituto Nacional de Tecnología Agropecuaria
    #>
    #> Site related literature:  DOI 10.1007/s00468-013-0935-4
    #>
    #> Sapflow data:  312  observations of  4  trees/plants
    #> Species present:  Nothofagus antarctica
    #>
    #> Environmental data:  312  observations.
    #> Variables present:
    #>   ta rh vpd sw_in ws precip swc_shallow ppfd_in ext_rad
    #>
    #> Biome:  Temperate forest
    #>
    #> TIMESTAMP span:  2009-11-18 -03--2009-11-30 23:00:00 -03
    #>
    #> Solar TIMESTAMP span:  2009-11-17 22:24:58 UTC--2009-11-30 21:20:48 UTC
    #>
    #> Sapflow data flags:
    #> USER_MODF
    #>      1248
    #>
    #> Environmental data flags:
    #> RANGE_WARN   OUT_WARN CALCULATED
    #>         18         24        624

### `*_metrics`

    multi_metrics <- daily_metrics(multi_sfn)
    #> [1] "Crunching data for ARG_TRE. In large datasets this could take a while"
    #> [1] "General data for ARG_TRE"
    #> [1] "Crunching data for ARG_MAZ. In large datasets this could take a while"
    #> [1] "General data for ARG_MAZ"
    #> [1] "Crunching data for AUS_CAN_ST2_MIX. In large datasets this could take a while"
    #> [1] "General data for AUS_CAN_ST2_MIX"
    multi_metrics[['ARG_TRE']][['sapf']]
    #> # A tibble: 14 × 21
    #>    TIMESTAMP           ARG_TRE_Nan_Jt_1_mean ARG_TRE_Nan_Jt_2_mean
    #>    <dttm>                              <dbl>                 <dbl>
    #>  1 2009-11-17 00:00:00                  308.                  173.
    #>  2 2009-11-18 00:00:00                  507.                  376.
    #>  3 2009-11-19 00:00:00                  541.                  380.
    #>  4 2009-11-20 00:00:00                  330.                  218.
    #>  5 2009-11-21 00:00:00                  338.                  219.
    #>  6 2009-11-22 00:00:00                  384.                  243.
    #>  7 2009-11-23 00:00:00                  492.                  300.
    #>  8 2009-11-24 00:00:00                  573.                  389.
    #>  9 2009-11-25 00:00:00                  601.                  400.
    #> 10 2009-11-26 00:00:00                  502.                  360.
    #> 11 2009-11-27 00:00:00                  544.                  411.
    #> 12 2009-11-28 00:00:00                  573.                  451.
    #> 13 2009-11-29 00:00:00                  371.                  285.
    #> 14 2009-11-30 00:00:00                  386.                  293.
    #> # ℹ 18 more variables: ARG_TRE_Nan_Jt_3_mean <dbl>,
    #> #   ARG_TRE_Nan_Jt_4_mean <dbl>, ARG_TRE_Nan_Jt_1_sd <dbl>,
    #> #   ARG_TRE_Nan_Jt_2_sd <dbl>, ARG_TRE_Nan_Jt_3_sd <dbl>,
    #> #   ARG_TRE_Nan_Jt_4_sd <dbl>, ARG_TRE_Nan_Jt_1_coverage <dbl>,
    #> #   ARG_TRE_Nan_Jt_2_coverage <dbl>, ARG_TRE_Nan_Jt_3_coverage <dbl>,
    #> #   ARG_TRE_Nan_Jt_4_coverage <dbl>, ARG_TRE_Nan_Jt_1_q_95 <dbl>,
    #> #   ARG_TRE_Nan_Jt_2_q_95 <dbl>, ARG_TRE_Nan_Jt_3_q_95 <dbl>, …