# Custom aggregation

#### Victor Granda (Sapfluxnet Team)

#### 2020-08-27

`sapfluxnetr` package offers a very flexible but powerful API based on the `tidyverse` packages to aggregate and summarise the site/s data in the form of the `sfn_metrics` function. All the metrics family of functions (`?metrics`) make use of the `sfn_metrics` function under the hood. If you want full control to the statistics returned and aggregation periods, we recommend you to use this API. This vignette will show you how.

## Pre-fixed summarising functions

1. `daily_metrics`
2. `monthly_metrics`
3. `predawn_metrics`
4. `midday_metrics`
5. `nightly_metrics`
6. `daylight_metrics`

See each function help for a detailed description and examples of use.

## Custom summarising functions

`daily_metrics` and related functions return a complete set of metrics ready for use, but if you want different metrics you can supply your own summarising functions using the `.funs` argument.
The correct way of specifying the functions to use is described in the `summarise_all` help (`?dplyr::summarise_all`). The recommended way is a list of formulas with the function call:

    # libraries
    library(sapfluxnetr)
    library(dplyr)
    #>
    #> Attaching package: 'dplyr'
    #> The following objects are masked from 'package:stats':
    #>
    #>     filter, lag
    #> The following objects are masked from 'package:base':
    #>
    #>     intersect, setdiff, setequal, union

    ### only mean and sd at a daily scale
    # data
    data('ARG_TRE', package = 'sapfluxnetr')

    # summarising funs (as a list of formulas)
    custom_funs <- list(mean = ~ mean(., na.rm = TRUE), std_dev = ~ sd(., na.rm = TRUE))

    # metrics
    foo_simpler_metrics <- sfn_metrics(
      ARG_TRE,
      period = '1 day',
      .funs = custom_funs,
      solar = TRUE,
      interval = 'general'
    )
    #> [1] "Crunching data for ARG_TRE. In large datasets this could take a while"
    #> [1] "General data for ARG_TRE"

    foo_simpler_metrics[['sapf']]
    #> # A tibble: 14 x 9
    #>    TIMESTAMP           ARG_TRE_Nan_Jt_… ARG_TRE_Nan_Jt_… ARG_TRE_Nan_Jt_…
    #>    <dttm>                         <dbl>            <dbl>            <dbl>
    #>  1 2009-11-17 00:00:00             308.             173.             303.
    #>  2 2009-11-18 00:00:00             507.             376.             432.
    #>  3 2009-11-19 00:00:00             541.             380.             391.
    #>  4 2009-11-20 00:00:00             330.             218.             272.
    #>  5 2009-11-21 00:00:00             338.             219.             278.
    #>  6 2009-11-22 00:00:00             384.             243.             310.
    #>  7 2009-11-23 00:00:00             492.             300.             390.
    #>  8 2009-11-24 00:00:00             573.             389.             497.
    #>  9 2009-11-25 00:00:00             601.             400.             484.
    #> 10 2009-11-26 00:00:00             502.             360.             450.
    #> 11 2009-11-27 00:00:00             544.             411.             506.
    #> 12 2009-11-28 00:00:00             573.             451.             589.
    #> 13 2009-11-29 00:00:00             371.             285.             357.
    #> 14 2009-11-30 00:00:00             386.             293.             381.
    #> # … with 5 more variables: ARG_TRE_Nan_Jt_4_mean <dbl>,
    #> #   ARG_TRE_Nan_Jt_1_std_dev <dbl>, ARG_TRE_Nan_Jt_2_std_dev <dbl>,
    #> #   ARG_TRE_Nan_Jt_3_std_dev <dbl>, ARG_TRE_Nan_Jt_4_std_dev <dbl>

> When supplying only one function to .funs, names of variables are not changed to contain the metric name at the end, as the summary function returns the same columns as the original data

## Special interest intervals

You can also choose if the “special interest” intervals (predawn, midday, nighttime or daylight) are calculated or not. For example, if you are only interested in the midday interval you can use:

    foo_simpler_metrics_midday <- sfn_metrics(
      ARG_TRE,
      period = '1 day',
      .funs = custom_funs,
      solar = TRUE,
      interval = 'midday', int_start = 11, int_end = 13
    )
    #> [1] "Crunching data for ARG_TRE. In large datasets this could take a while"
    #> [1] "midday data for ARG_TRE"

    foo_simpler_metrics_midday[['sapf']]
    #> # A tibble: 13 x 9
    #>    TIMESTAMP_md        ARG_TRE_Nan_Jt_… ARG_TRE_Nan_Jt_… ARG_TRE_Nan_Jt_…
    #>    <dttm>                         <dbl>            <dbl>            <dbl>
    #>  1 2009-11-18 00:00:00             685.             665.             614.
    #>  2 2009-11-19 00:00:00             879.             594.             626.
    #>  3 2009-11-20 00:00:00             438.             272.             258.
    #>  4 2009-11-21 00:00:00             631.             379.             533.
    #>  5 2009-11-22 00:00:00             783.             535.             680.
    #>  6 2009-11-23 00:00:00             841.             478.             618.
    #>  7 2009-11-24 00:00:00             951.             636.             789.
    #>  8 2009-11-25 00:00:00             907.             602.             789.
    #>  9 2009-11-26 00:00:00             861.             697.             925.
    #> 10 2009-11-27 00:00:00             806.             594.             706.
    #> 11 2009-11-28 00:00:00             837.             730.             925.
    #> 12 2009-11-29 00:00:00             638.             605.             666.
    #> 13 2009-11-30 00:00:00             548.             371.             444.
    #> # … with 5 more variables: ARG_TRE_Nan_Jt_4_mean_md <dbl>,
    #> #   ARG_TRE_Nan_Jt_1_std_dev_md <dbl>, ARG_TRE_Nan_Jt_2_std_dev_md <dbl>,
    #> #   ARG_TRE_Nan_Jt_3_std_dev_md <dbl>, ARG_TRE_Nan_Jt_4_std_dev_md <dbl>

## Custom aggregation periods

`period` argument in `sfn_metrics` is passed to `.collapse_timestamp` function, and so, it can use the same input:

- “frequency period” format, where frequency is a number and period is an interval as character (i.e. “1 year”, “7 days”)

    # weekly
    foo_weekly <- sfn_metrics(
      ARG_TRE,
      period = '7 days',
      .funs = custom_funs,
      solar = TRUE,
      interval = 'general'
    )
    #> [1] "Crunching data for ARG_TRE. In large datasets this could take a while"
    #> [1] "General data for ARG_TRE"

    foo_weekly[['env']]
    #> # A tibble: 3 x 19
    #>   TIMESTAMP           ta_mean rh_mean vpd_mean sw_in_mean ws_mean precip_mean
    #>   <dttm>                <dbl>   <dbl>    <dbl>      <dbl>   <dbl>       <dbl>
    #> 1 2009-11-15 00:00:00    4.81    35.3    0.598       280.    15.5     0.00612
    #> 2 2009-11-22 00:00:00    6.15    35.3    0.656       327.    24.5     0.192
    #> 3 2009-11-29 00:00:00    2.55    40.9    0.453       261.    23.1     0.122
    #> # … with 12 more variables: swc_shallow_mean <dbl>, ppfd_in_mean <dbl>,
    #> #   ext_rad_mean <dbl>, ta_std_dev <dbl>, rh_std_dev <dbl>, vpd_std_dev <dbl>,
    #> #   sw_in_std_dev <dbl>, ws_std_dev <dbl>, precip_std_dev <dbl>,
    #> #   swc_shallow_std_dev <dbl>, ppfd_in_std_dev <dbl>, ext_rad_std_dev <dbl>

- A custom function name (without quotes). This way you can build irregular or custom periods. This function first argument must always be the timestamp to be collapsed, and can have other arguments that will be supplied in the dots (`...`) argument of `sfn_metrics`. Also, this function always must return a vector of timestamps of the same length as the original timestamp.
  For example, if you want to summarise by quarters we can use the `quarter` function from the lubridate package:

    foo_custom <- sfn_metrics(
      AUS_CAN_ST2_MIX,
      period = lubridate::quarter,
      .funs = custom_funs,
      solar = TRUE,
      interval = 'general',
      with_year = TRUE # argument for lubridate::quarter
    )
    #> [1] "Crunching data for AUS_CAN_ST2_MIX. In large datasets this could take a while"
    #> Warning in .period_to_minutes(period, .data$TIMESTAMP, unique(.data$timestep)): when using a custom function as period, coverage calculation
    #>             can be less accurate

    #> Warning in .period_to_minutes(period, .data$TIMESTAMP, unique(.data$timestep)): when using a custom function as period, coverage calculation
    #>             can be less accurate
    #> [1] "General data for AUS_CAN_ST2_MIX"
    foo_custom['env']
    #> $env
    #> # A tibble: 5 x 17
    #>   TIMESTAMP ta_mean vpd_mean sw_in_mean ws_mean precip_mean ppfd_in_mean rh_mean
    #>       <dbl>   <dbl>    <dbl>      <dbl>   <dbl>       <dbl>        <dbl>   <dbl>
    #> 1     2006.    8.48    0.106       25.4   0.161      0.0158         53.7    93.1
    #> 2     2006.   10.5     0.278       82.0   0.318      0.0399        173.     86.9
    #> 3     2006.   16.6     0.826      219.    0.581      0.0200        463.     72.8
    #> 4     2007.   20.9     0.985      197.    0.439      0.0333        416.     75.7
    #> 5     2007.   15.8     0.386      110.    0.200      0.0181        231.     86.7
    #> # … with 9 more variables: ext_rad_mean <dbl>, ta_std_dev <dbl>,
    #> #   vpd_std_dev <dbl>, sw_in_std_dev <dbl>, ws_std_dev <dbl>,
    #> #   precip_std_dev <dbl>, ppfd_in_std_dev <dbl>, rh_std_dev <dbl>,
    #> #   ext_rad_std_dev <dbl>

## Extra parameters

`sfn_metrics` has a `...` parameter intended to supply additional parameters to the internal functions used:

1. `.collapse_timestamp` accepts the following extra arguments:

   - `side`
     “start” by default in the sfn_metrics implementation

2. `dplyr::summarise_all` accepts extra arguments intended to be applied to the summarising functions provided (to all, so they all must have the argument provided or an error will be raised). That’s the reason because we recommend to use the list way, as the arguments are specified for the individual functions.

For example, if we want the TIMESTAMPs after aggregation to show the end of the period instead the begining (default) we can do the following:

    foo_simpler_metrics_end <- sfn_metrics(
      ARG_TRE,
      period = '1 day',
      .funs = custom_funs,
      solar = TRUE,
      interval = 'general',
      side = "end"
    )
    #> [1] "Crunching data for ARG_TRE. In large datasets this could take a while"
    #> [1] "General data for ARG_TRE"

    foo_simpler_metrics_end[['sapf']]
    #> # A tibble: 14 x 9
    #>    TIMESTAMP           ARG_TRE_Nan_Jt_… ARG_TRE_Nan_Jt_… ARG_TRE_Nan_Jt_…
    #>    <dttm>                         <dbl>            <dbl>            <dbl>
    #>  1 2009-11-18 00:00:00             308.             173.             303.
    #>  2 2009-11-19 00:00:00             507.             376.             432.
    #>  3 2009-11-20 00:00:00             541.             380.             391.
    #>  4 2009-11-21 00:00:00             330.             218.             272.
    #>  5 2009-11-22 00:00:00             338.             219.             278.
    #>  6 2009-11-23 00:00:00             384.             243.             310.
    #>  7 2009-11-24 00:00:00             492.             300.             390.
    #>  8 2009-11-25 00:00:00             573.             389.             497.
    #>  9 2009-11-26 00:00:00             601.             400.             484.
    #> 10 2009-11-27 00:00:00             502.             360.             450.
    #> 11 2009-11-28 00:00:00             544.             411.             506.
    #> 12 2009-11-29 00:00:00             573.             451.             589.
    #> 13 2009-11-30 00:00:00             371.             285.             357.
    #> 14 2009-12-01 00:00:00             386.             293.             381.
    #> # … with 5 more variables: ARG_TRE_Nan_Jt_4_mean <dbl>,
    #> #   ARG_TRE_Nan_Jt_1_std_dev <dbl>, ARG_TRE_Nan_Jt_2_std_dev <dbl>,
    #> #   ARG_TRE_Nan_Jt_3_std_dev <dbl>, ARG_TRE_Nan_Jt_4_std_dev <dbl>

If it is compared with the `foo_simpler_metrics` calculated before, now the period is identified in the TIMESTAMP by the ending of the period (daily in this case).

> When supplying custom functions as “period” argument, the default coverage statistic is not reliable as there is no way of knowing beforehand the period/s in minutes.

## Temporary columns helpers

The internal aggregation process in `sfn_metrics` generates some transitory columns which can be used in the summarising functions:

### `TIMESTAMP_coll`

When aggregating by the declared period (i.e. `"daily"`), the TIMESTAMP column collapses to the period start/end value (meaning that all the TIMESTAMP values for the same day becomes identical).
This makes impossible to use any summarise functions that obtain the time of the day at which one event happens (i.e. time of the day at which the maximum sap flow occurs) because all TIMESTAMP values are identical. For that kind of summarising functions, a transitory column called `TIMESTAMP_coll` is created. So in this case we can create a function that takes de variable values for the day, the TIMESTAMP_coll values for the day and return the TIMESTAMP at which the max sap flow occurs and use it with `sfn_metrics`:

    max_time <- function(x, time) {

      # x: vector of values for a day
      # time: TIMESTAMP for the day

      # if all the values in x are NAs (a daily summmarise of no measures day for
      # example) this will return a length 0 POSIXct vector, which will crash
      # dplyr summarise step. So, check if all NA and if true return NA as POSIXct
      if(all(is.na(x))) {
        return(as.POSIXct(NA, tz = attr(time, 'tz'), origin = lubridate::origin))
      } else {
        time[which.max(x)]
      }
    }

    custom_funs <- list(max = ~ max(., na.rm = TRUE), ~ max_time(., TIMESTAMP_coll))

    max_time_metrics <- sfn_metrics(
      ARG_TRE,
      period = '1 day',
      .funs = custom_funs,
      solar = TRUE,
      interval = 'general'
    )
    #> [1] "Crunching data for ARG_TRE. In large datasets this could take a while"
    #> [1] "General data for ARG_TRE"

    max_time_metrics[['sapf']]
    #> # A tibble: 14 x 9
    #>    TIMESTAMP           ARG_TRE_Nan_Jt_… ARG_TRE_Nan_Jt_… ARG_TRE_Nan_Jt_…
    #>    <dttm>                         <dbl>            <dbl>            <dbl>
    #>  1 2009-11-17 00:00:00             322.             190.             313.
    #>  2 2009-11-18 00:00:00             778.             715.             679.
    #>  3 2009-11-19 00:00:00            1015.             694.             633.
    #>  4 2009-11-20 00:00:00             648.             401.             442.
    #>  5 2009-11-21 00:00:00             664.             406.             539.
    #>  6 2009-11-22 00:00:00             812.             564.             816.
    #>  7 2009-11-23 00:00:00            1085.             676.             935.
    #>  8 2009-11-24 00:00:00             992.             736.            1115.
    #>  9 2009-11-25 00:00:00             976.             646.             951.
    #> 10 2009-11-26 00:00:00             932.             766.            1087.
    #> 11 2009-11-27 00:00:00             862.             704.             921.
    #> 12 2009-11-28 00:00:00             845.             763.            1165.
    #> 13 2009-11-29 00:00:00             714.             747.             701.
    #> 14 2009-11-30 00:00:00             875.             646.             919.
    #> # … with 5 more variables: ARG_TRE_Nan_Jt_4_max <dbl>,
    #> #   ARG_TRE_Nan_Jt_1_max_time <dttm>, ARG_TRE_Nan_Jt_2_max_time <dttm>,
    #> #   ARG_TRE_Nan_Jt_3_max_time <dttm>, ARG_TRE_Nan_Jt_4_max_time <dttm>