sapfluxnetr (Not So) Quick Guide

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

folder <- 'resources'

sapfluxnetr R package provides tools for a tidy data analysis for the first sap flow measurements global database (SAPFLUXNET Project). In this vignette you will learn how to install the package, download the data and get started with some data metrics.

Installing the package

sapfluxnetr is in the CRAN, so the installation is straightforward:

install.packages('sapfluxnetr')

Development versions of the package reside in github. If you want the latest updates, and also the latest bugs ( be advised ;) ), please install the package from the devel branch of the github repository with the remotes package:

# if (!require(remotes)) {install.packages('remotes')}
remotes::install_github(
  'sapfluxnet/sapfluxnetr', ref = 'devel',
  build_opts = c("--no-resave-data", "--no-manual", "--build-vignettes")
)

Now you can load the package, along with the tidyverse-related packages, as they will be needed later:

library(sapfluxnetr)
# tidyverse
library(dplyr)
library(ggplot2)

Download the data

.zip file is about 3GB. Unzipped folders use about 24GB of disk space. Please check thet you have enough space available before downloading and unzipping the files.

Zenodo

Data is publicly available on Zenodo to download. You can manually download the data or use a programmatic approach:

# download the data
download.file(
  url = "https://zenodo.org/record/3971689/files/0.1.5.zip?download=1",
  destfile = '0.1.5.zip'
)
# unzip the data
# BE SURE YOU HAVE AT LEAST 24GB OF DISK SPACE
unzip("0.1.5.zip")
# check if files are present
list.files(file.path('0.1.5', 'RData', 'plant'))
list.files(file.path('0.1.5', 'csv', 'plant'))

SAPFLUXNET Data structure

SAPFLUXNET database structure is as follows:

0.1.5 (database version number)
  |
  |-- RData
  |    |
  |    |-- plant
  |    |-- sapwood
  |    |-- leaf
  |
  |-- csv
  |    |
  |    |-- plant
  |    |-- sapwood
  |    |-- leaf

RData folder contains the RData files for each site divided by sap flow units level:

csv folder contains the csv files (9 files, 5 of metadata, 2 of data and 2 more for the data flags) for each units level available and site. We do not provide scripts or functions to work with the csv files, only the RData objects.

To start working with the data, you have two options:

DISCLAIMER: In order to be able to build the vignette in the CRAN tests the following examples will be based on a small subset of SAPFLUXNET Data, composed by ARG_TRE, ARG_MAZ and AUS_CAN_ST2_MIX sites. Outputs will vary if you follow the vignette examples with the complete database.

Inspecting a site

First, let's get used to the data structure thet SAPFLUXNET provides, and for thet we will choose a site and start playing with it.

Loading a site

In this example we will use the ARG_MAZ site, as it is small and it will be fast seeing the package capabilities. There are sites like FRA_PUE 100 times bigger then this, but as one can imagine, the time is also increasing when analyzing those bigger datasets.
So, let's read the data in the environment:

# read the data
arg_maz <- read_sfn_data('ARG_MAZ', folder = 'RData/plant')

# see a brief summary of the site:
arg_maz
# read the data
arg_maz <- read_sfn_data('ARG_MAZ', folder = folder)

# see a brief summary of the site:
arg_maz

At first glance, we know by this summary thet is an Argentinian forest (first three letters of the site code are always the country code), contributed by Sebastian Pfautsch and Pablo Peri with 5 Nothofagus pumilio trees measured along 15 days in 2009. Also we can see the environmental variables measured (ta, rh, vpd, sw_in, ws, precip, swc_shallow, ppfd_in and ext_rad) and the biome classification. Finally, we can see thet the environmental data has some flags (more on thet later).

Accessing the data and the metadata

sfn_data objects have different slots containing the different data, each of one has a getter function (see ?sfn_get_methods and vignette('sfn-data-classes', package = 'sapfluxnetr' for detailed info):

# sapf data with original site timestamp
arg_maz_sapf <- get_sapf_data(arg_maz, solar = FALSE)
arg_maz_sapf

# env_data with calculated aparent solar time
arg_maz_env <- get_env_data(arg_maz, solar = TRUE)
arg_maz_env

You can see thet the TIMESTAMP variable changes between both kinds of data. That is because the TIMESTAMP returned is controlled by the solar parameter (see ?sfn_get_methods).

Metadata can be accessed in the same way:

arg_maz_site_md <- get_site_md(arg_maz)
arg_maz_site_md
arg_maz_stand_md <- get_stand_md(arg_maz)
arg_maz_stand_md
arg_maz_species_md <- get_species_md(arg_maz)
arg_maz_species_md
arg_maz_plant_md <- get_plant_md(arg_maz)
arg_maz_plant_md
arg_maz_env_md <- get_env_md(arg_maz)
arg_maz_env_md

If in doubt about some of the metadata variables (what it means, units...) a description can be obtained from describe_md_variable function:

# what is env_ta?
describe_md_variable('env_ta')

# or pl_species?
describe_md_variable('pl_species')

There is also some getters thet can come in handy sometimes. get_timestamp and get_solar_timestamp access to the original timestamp and the apparent solar time timestamp. get_si_code access to the site code. See vignette('sfn-data-classes', package = 'sapfluxnetr' for more info.

Flags

sfn_data objects also have two more slots, accessed with get_sapf_flags and get_env_flags.

arg_maz_sapf_flags <- get_sapf_flags(arg_maz, solar = TRUE)
arg_maz_sapf_flags

arg_maz_env_flags <- get_env_flags(arg_maz, solar = TRUE)
arg_maz_env_flags

This datasets store any flag thet each data point may have (possible outlier, data removed in the Quality Check of the data...). For a complete list of flags possible values see vignette('data-flags', package = 'sapfluxnetr'). As an example, let's see which values are marked as "RANGE_WARN" (a warning indicating thet the value may be out of normal variable range):

arg_maz_env_flags %>%
  filter_all(any_vars(stringr::str_detect(., 'RANGE_WARN')))

We see thet the out of range warnings refer to wind variable. We can cross the data to see which values of wind speed are giving the warnings,

arg_maz_env_flags %>%
  filter_all(any_vars(stringr::str_detect(., 'RANGE_WARN'))) %>%
  semi_join(arg_maz_env, ., by = 'TIMESTAMP') %>%
  select(TIMESTAMP, ws)

and confirm thet the warnings refer to values above the "usual" wind speed maximum.

Plotting an object

We can also plot the different data with the help of sfn_plot function. It will return ggplot objects thet can be modified afterwards:

sfn_plot(arg_maz, type = 'sapf', solar = TRUE) +
  facet_wrap(~ Tree) +
  theme(legend.position = 'none')
sfn_plot(arg_maz, type = 'env', solar = TRUE) +
  facet_wrap(~ Variable, scales = 'free_y') +
  theme(legend.position = 'none')

We can also plot environmental variables individually (with the type argument), or an environmental variable versus the sap flow measurements (with the formula_env argument). See ?sfn_plot for a complete description of the available plots.

# vpd individually
sfn_plot(arg_maz, type = 'vpd', solar = TRUE)
# vpd vs sapf
sfn_plot(arg_maz, formula_env = ~vpd, solar = TRUE) +
  theme(legend.position = 'none')

Aggregation

SAPFLUXET data is stored as sub-daily measures with different time step between sites (ranging from 10 minutes to 2 hours).
sapfluxnetr offers some simple, yet powerful aggregation functions returning pre-defined statistics: daily_metrics, monthly_metrics, predawn_metrics, midday_metrics, daylight_metrics and nightly_metrics.
daily_metrics and monthly_metrics perform a daily and monthly aggregation, respectively. predawn_metrics, midday_metrics, daylight_metrics and nightly_metrics perform daily or monthly aggregations (controlled by the period argument) only by hour-defined intervals. All the aggregations are performed both for sap flow and environmental data.

Predefined calculated statistics are:

  1. mean
  2. standard deviation
  3. accumulated for the precipitation data
  4. data coverage (percentage of period covered by the raw data)
  5. quantile 95
  6. diurnal centroid (Only calculated for sap flow measurements when using daily_metrics, see ?diurnal_centroid for limitations in the calculation of this metric)

Let's see some examples:

arg_maz_daily <- daily_metrics(arg_maz, solar = TRUE)

names(arg_maz_daily)
names(arg_maz_daily[['sapf']])
names(arg_maz_daily[['env']])

We can see thet results are divided in sapf and env and inside each of them the metrics are indicated by the end of the variable names.
This way we can select specific variables, for example the 0.95 quantile of sap flow measures:

arg_maz_daily[['sapf']] %>%
  select(TIMESTAMP, ends_with('q_95'))

The same is applicable to the environmental data, in this case the mean values:

arg_maz_daily[['env']] %>%
  select(TIMESTAMP, ends_with('mean'))

If interested in custom metrics or custom aggregations, there is a generic function, sfn_metrics thet allows for customization of the statistics to calculate and the periods to aggregate. See ?sfn_metrics and vignette('custom-aggregation'. package = 'sapfluxnetr') for more details about it.

TIMESTAMP format in aggregations

It's worth to mention thet aggregated TIMESTAMPS are fixed to the beginning of the period aggregated, meaning thet data from 2018-01-01 00:00:00 to 2018-01-01 23:59:59 are aggregated as 2018-01-01 00:00:00.
You can change this using the side parameter (see ?sfn_metrics)

Tidy metrics

The default returned object for the aggregation functions is a list with the sap flow and the environmental data, but given thet usually is more comfortable to have all data (sap flow and environmental) and ancillary data (metadata) altogether in a tidy data frame (each row an observation), all aggregation functions have an argument, tidy thet can be set to TRUE to obtain this kind of data frame. We will cover this in the "Tidy metrics" section.

Working with multiple sites

Getting the insights about one site is interesting, but getting the insights of a common group of sites could be even more interesting. sapfluxnetr allows filtering sites by metadata values (biome, country, species...) and work with them as a unique set.

Building the metadata database {#metadatadatabase}

First thing we have to do is creating a metadata database. It is not mandatory, but filtering sites by metadata can be a time/resources consuming step if we have to temporary build the database each time we want filter sites. So, let's create a cached metadata database. This will take some minutes, so maybe it is a good moment to prepare a hot beverage ;)

sfn_metadata <- read_sfn_metadata(folder = 'RData/plant', .write_cache = TRUE)
# sfn_metadata <- read_sfn_metadata(folder = folder, .write_cache = TRUE)
sfn_metadata <- sapfluxnetr:::.write_metadata_cache(folder = folder, .dry = TRUE)

The important bit here is .write_cache = TRUE. This will write a file called .metadata_cache.RData containing all the metadata for all sites present in folder. This file will be used any time we will filter the metadata, so there is no need of accessing all the data again.
If we take a look at sfn_metadata we can see a list with 5 data frames, one for each metadata class (site, stand, species, plant and environmental metadata).

# access plant metadata
sfn_metadata[['plant_md']]

Listing the sites in a folder and filtering by metadata

Now thet we have our metadata database built, we can inspect the site codes in a folder with sfn_sites_in_folder:

folder <- 'RData/plant/'
sites <- sfn_sites_in_folder(folder)
sites
sites <- sfn_sites_in_folder(folder)
sites

We can filter these sites by any metadata variable, to select those thet met some criteria. This is done with filter_sites_by_md. As a first try, let's list all sites belonging to temperate forests (woodland/shrubland included):

temperate <- sfn_sites_in_folder(folder) %>%
  filter_sites_by_md(
    si_biome %in% c('Woodland/Shrubland', 'Temperate forest'),
    metadata = sfn_metadata
  )

temperate
temperate <- sfn_sites_in_folder(folder) %>%
  filter_sites_by_md(
    si_biome %in% c('Woodland/Shrubland', 'Temperate forest'),
    metadata = sfn_metadata
  )
temperate

You can combine all filters you want:

temperate_hr <- sfn_sites_in_folder(folder) %>%
  filter_sites_by_md(
    si_biome %in% c('Woodland/Shrubland', 'Temperate forest'),
    pl_sens_meth == 'HR',
    metadata = sfn_metadata
  )

temperate_hr
temperate_hr <- sfn_sites_in_folder(folder) %>%
  filter_sites_by_md(
    si_biome %in% c('Woodland/Shrubland', 'Temperate forest'),
    pl_sens_meth == 'HR',
    metadata = sfn_metadata
  )
temperate_hr

Remember thet you can get all the info from a metadata variable with describe_md_variable, and also you can get a complete list of metadata variables for filtering with sfn_vars_to_filter:

sfn_vars_to_filter()

# and see what values we must use for filtering by pl_sens_meth
describe_md_variable('pl_sens_meth')

sfn_data_multi objects

We can load all temperate sites with a simple pipe

temperate_sites <- temperate %>%
  read_sfn_data(folder = 'RData/plant')
temperate_sites
temperate_sites <- temperate %>%
  read_sfn_data(folder = folder)
temperate_sites

This creates an sfn_data_multi object, which is just a list, but adapted to contain sfn_data objects. Main functions of sapfluxnetr work with this type of objects. As in any list, we can access the sites:

# the following is the same as temperate_sites[[3]] or
# temperate_sites$AUS_CAN_ST2_MIX
temperate_sites[['AUS_CAN_ST2_MIX']]

Multi aggregation

Now we can aggregate all sites at once

temperate_aggregated <- temperate_sites %>%
  daily_metrics()

names(temperate_aggregated)

and voilĂ , all sites aggregated and stored in a list, so we can start working with the data.

Tidy metrics {#tidymetrics}

Most of the times it comes in handy to have a tidy dataset with all the sap flow and environmental measurements, along with the metadata for the sites aggregated, in order to work with the data in an easier way. This can be done with the metrics_tidyfier function (see ?metrics_tidyfier). But daily_metrics and related functions also implement the tidy argument. For the tidy argument to work, we must have available the metadata, see the "metadata section" for more details about this.

This allows for returning directly a metrics data frame combining all sites aggregated along with their metadata, saving one step in the workflow:

temperate_tidy <- temperate_sites %>%
  daily_metrics(tidy = TRUE, metadata = sfn_metadata)

temperate_tidy

Now we can start analyzing, modeling, etc.
For example, to look for site effects in the relationship between sap flow and vpd, using the 0.95 quantile as maximum values of sap flow and the plant sapwood area as a third variable:

ggplot(
  temperate_tidy,
  aes(x = vpd_mean, y = sapflow_q_95, colour = si_code, size = pl_sapw_area)
) +
  geom_point(alpha = 0.2)


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sapfluxnetr documentation built on Feb. 16, 2023, 7:52 p.m.