sapfluxnetr (Not So) Quick Guide

Víctor Granda (SAPFLUXNET Team)

2021-10-04

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 that 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 estructure

SAPFLUXNET database estrucuture 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 that SAPFLUXNET provides, and for that 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 than 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
#> sfn_data object
#> Data from ARG_MAZ site
#> 
#> Data kindly provided by  Sebastian 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:  288  observations of  5  trees/plants
#> Species present:  Nothofagus pumilio 
#> 
#> Environmental data:  288  observations.
#> Variables present:
#>   ta rh vpd sw_in ws precip swc_shallow ppfd_in ext_rad 
#> 
#> Biome:  Woodland/Shrubland 
#> 
#> TIMESTAMP span:  2009-11-19 -03--2009-11-30 23:00:00 -03 
#> 
#> Solar TIMESTAMP span:  2009-11-18 22:24:18 UTC--2009-11-30 21:20:24 UTC 
#> 
#> Sapflow data flags:
#> No flags present
#> 
#> Environmental data flags:
#> RANGE_WARN   OUT_WARN CALCULATED 
#>          9         17        576

At first glance, we know by this summary that 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 that the environmental data has some flags (more on that 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
#> # A tibble: 288 x 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.
#> # … with 278 more rows, and 2 more variables: ARG_MAZ_Npu_Jt_4 <dbl>,
#> #   ARG_MAZ_Npu_Jt_5 <dbl>

# env_data with calculated aparent solar time
arg_maz_env <- get_env_data(arg_maz, solar = TRUE)
arg_maz_env
#> # A tibble: 288 x 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 22:24:18  4.99  55.6 0.387     0  12.9    0         0.359    0   
#>  2 2009-11-18 23:24:18  4.57  60   0.339     0   9.7    0         0.359    0   
#>  3 2009-11-19 00:24:02  4.99  60.4 0.345     0  11.3    0         0.358    0   
#>  4 2009-11-19 01:24:02  3.31  66   0.263     0   9.7    0.2       0.357    0   
#>  5 2009-11-19 02:24:02  2.03  68.7 0.221     0  12.9    0         0.357    0   
#>  6 2009-11-19 03:24:02  2.46  58.8 0.300     0  17.7    0         0.356    0   
#>  7 2009-11-19 04:24:02  2.46  55.2 0.327     0  17.7    0         0.356    0   
#>  8 2009-11-19 05:24:02  2.89  53.7 0.348     3  14.5    0         0.356    6.34
#>  9 2009-11-19 06:24:02  4.15  52.8 0.388    78   9.7    0         0.356  165.  
#> 10 2009-11-19 07:24:02  5.4   43.2 0.509   275  11.3    0         0.355  581.  
#> # … with 278 more rows, and 1 more variable: ext_rad <dbl>

You can see that 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
#> # A tibble: 1 x 24
#>   si_addcontr_email si_addcontr_fir… si_addcontr_insti… si_addcontr_las… si_code
#>   <chr>             <chr>            <chr>              <chr>            <chr>  
#> 1 <NA>              Pablo            Instituto Naciona… Peri             ARG_MAZ
#> # … with 19 more variables: 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>
arg_maz_stand_md <- get_stand_md(arg_maz)
arg_maz_stand_md
#> # A tibble: 1 x 18
#>   st_age st_aspect st_basal_area st_clay_perc st_density st_growth_condition    
#>    <int> <chr>             <dbl> <lgl>             <int> <chr>                  
#> 1    180 E                  59.1 NA                  518 Naturally regenerated,…
#> # … with 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 <lgl>, st_soil_texture <chr>, st_terrain <chr>,
#> #   st_treatment <lgl>, si_code <chr>, st_USDA_soil_texture <chr>
arg_maz_species_md <- get_species_md(arg_maz)
arg_maz_species_md
#> # A tibble: 1 x 5
#>   sp_basal_area_perc sp_leaf_habit  sp_name            sp_ntrees si_code
#>                <int> <chr>          <chr>                  <int> <chr>  
#> 1                100 cold deciduous Nothofagus pumilio         5 ARG_MAZ
arg_maz_plant_md <- get_plant_md(arg_maz)
arg_maz_plant_md
#> # A tibble: 5 x 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, measur…          14.7 ARG_MAZ…   41.1 NA               109. 
#> 2    180 No azimuthal corr…          13   ARG_MAZ…   33.2 NA                58.4
#> 3    180 No azimuthal corr…           6   ARG_MAZ…   23.2 NA                35.0
#> 4    180 No azimuthal corr…          21   ARG_MAZ…   55.6 NA               174. 
#> 5    180 No azimuthal corr…          10   ARG_MAZ…   38   NA                88  
#> # … with 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_env_md <- get_env_md(arg_maz)
arg_maz_env_md
#> # A tibble: 1 x 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  
#> # … with 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>

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')
#> Description:
#> Location of air temperature sensor
#> 
#> Values:
#> Above canopy | Within canopy | Clearing | Off-site | Not provided | 
#> 
#> Units:
#> Fixed values
#> 
#> Type:
#> Character

# or pl_species?
describe_md_variable('pl_species')
#> Description:
#> Species identity of the measured plant
#> 
#> Values:
#> Contributor defined | 
#> 
#> Units:
#> Scientific name without author abbreviation, as accepted by The Plant List
#> 
#> Type:
#> Character

There is also some getters that 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
#> # A tibble: 288 x 6
#>    TIMESTAMP           ARG_MAZ_Npu_Jt_1 ARG_MAZ_Npu_Jt_2 ARG_MAZ_Npu_Jt_3
#>    <dttm>              <chr>            <chr>            <chr>           
#>  1 2009-11-18 22:24:18 ""               ""               ""              
#>  2 2009-11-18 23:24:18 ""               ""               ""              
#>  3 2009-11-19 00:24:02 ""               ""               ""              
#>  4 2009-11-19 01:24:02 ""               ""               ""              
#>  5 2009-11-19 02:24:02 ""               ""               ""              
#>  6 2009-11-19 03:24:02 ""               ""               ""              
#>  7 2009-11-19 04:24:02 ""               ""               ""              
#>  8 2009-11-19 05:24:02 ""               ""               ""              
#>  9 2009-11-19 06:24:02 ""               ""               ""              
#> 10 2009-11-19 07:24:02 ""               ""               ""              
#> # … with 278 more rows, and 2 more variables: ARG_MAZ_Npu_Jt_4 <chr>,
#> #   ARG_MAZ_Npu_Jt_5 <chr>

arg_maz_env_flags <- get_env_flags(arg_maz, solar = TRUE)
arg_maz_env_flags
#> # A tibble: 288 x 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 22:24:18 ""    ""    ""    ""    ""    ""      ""          CALCUL…
#>  2 2009-11-18 23:24:18 ""    ""    ""    ""    ""    ""      ""          CALCUL…
#>  3 2009-11-19 00:24:02 ""    ""    ""    ""    ""    ""      ""          CALCUL…
#>  4 2009-11-19 01:24:02 ""    ""    ""    ""    ""    "OUT_W… ""          CALCUL…
#>  5 2009-11-19 02:24:02 ""    ""    ""    ""    ""    ""      ""          CALCUL…
#>  6 2009-11-19 03:24:02 ""    ""    ""    ""    ""    ""      ""          CALCUL…
#>  7 2009-11-19 04:24:02 ""    ""    ""    ""    ""    ""      ""          CALCUL…
#>  8 2009-11-19 05:24:02 ""    ""    ""    ""    ""    ""      ""          CALCUL…
#>  9 2009-11-19 06:24:02 ""    ""    ""    ""    ""    ""      ""          CALCUL…
#> 10 2009-11-19 07:24:02 ""    ""    ""    ""    ""    ""      ""          CALCUL…
#> # … with 278 more rows, and 1 more variable: ext_rad <chr>

This datasets store any flag that 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 that the value may be out of normal variable range):

arg_maz_env_flags %>%
  filter_all(any_vars(stringr::str_detect(., 'RANGE_WARN')))
#> # A tibble: 9 x 10
#>   TIMESTAMP           ta    rh    vpd   sw_in ws      precip swc_shallow ppfd_in
#>   <dttm>              <chr> <chr> <chr> <chr> <chr>   <chr>  <chr>       <chr>  
#> 1 2009-11-25 13:22:41 ""    ""    ""    ""    RANGE_… ""     ""          CALCUL…
#> 2 2009-11-26 08:22:30 ""    ""    ""    ""    RANGE_… ""     ""          CALCUL…
#> 3 2009-11-26 09:22:30 ""    ""    ""    ""    RANGE_… ""     ""          CALCUL…
#> 4 2009-11-26 10:22:31 ""    ""    ""    ""    RANGE_… ""     ""          CALCUL…
#> 5 2009-11-26 11:22:31 ""    ""    ""    ""    RANGE_… ""     ""          CALCUL…
#> 6 2009-11-26 12:22:32 ""    ""    ""    ""    RANGE_… ""     ""          CALCUL…
#> 7 2009-11-26 13:22:32 ""    ""    ""    ""    RANGE_… ""     ""          CALCUL…
#> 8 2009-11-28 10:21:09 ""    ""    ""    ""    RANGE_… ""     ""          CALCUL…
#> 9 2009-11-28 12:21:09 ""    ""    ""    ""    RANGE_… ""     ""          CALCUL…
#> # … with 1 more variable: ext_rad <chr>

We see that 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)
#> # A tibble: 9 x 2
#>   TIMESTAMP              ws
#>   <dttm>              <dbl>
#> 1 2009-11-25 13:22:41  45.1
#> 2 2009-11-26 08:22:30  48.3
#> 3 2009-11-26 09:22:30  51.5
#> 4 2009-11-26 10:22:31  48.3
#> 5 2009-11-26 11:22:31  62.8
#> 6 2009-11-26 12:22:32  57.9
#> 7 2009-11-26 13:22:32  48.3
#> 8 2009-11-28 10:21:09  46.7
#> 9 2009-11-28 12:21:09  48.3

and confirm that 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 that 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)
#> [1] "Crunching data for ARG_MAZ. In large datasets this could take a while"
#> [1] "General data for ARG_MAZ"

names(arg_maz_daily)
#> [1] "sapf" "env"
names(arg_maz_daily[['sapf']])
#>  [1] "TIMESTAMP"                 "ARG_MAZ_Npu_Jt_1_mean"    
#>  [3] "ARG_MAZ_Npu_Jt_2_mean"     "ARG_MAZ_Npu_Jt_3_mean"    
#>  [5] "ARG_MAZ_Npu_Jt_4_mean"     "ARG_MAZ_Npu_Jt_5_mean"    
#>  [7] "ARG_MAZ_Npu_Jt_1_sd"       "ARG_MAZ_Npu_Jt_2_sd"      
#>  [9] "ARG_MAZ_Npu_Jt_3_sd"       "ARG_MAZ_Npu_Jt_4_sd"      
#> [11] "ARG_MAZ_Npu_Jt_5_sd"       "ARG_MAZ_Npu_Jt_1_coverage"
#> [13] "ARG_MAZ_Npu_Jt_2_coverage" "ARG_MAZ_Npu_Jt_3_coverage"
#> [15] "ARG_MAZ_Npu_Jt_4_coverage" "ARG_MAZ_Npu_Jt_5_coverage"
#> [17] "ARG_MAZ_Npu_Jt_1_q_95"     "ARG_MAZ_Npu_Jt_2_q_95"    
#> [19] "ARG_MAZ_Npu_Jt_3_q_95"     "ARG_MAZ_Npu_Jt_4_q_95"    
#> [21] "ARG_MAZ_Npu_Jt_5_q_95"     "ARG_MAZ_Npu_Jt_1_centroid"
#> [23] "ARG_MAZ_Npu_Jt_2_centroid" "ARG_MAZ_Npu_Jt_3_centroid"
#> [25] "ARG_MAZ_Npu_Jt_4_centroid" "ARG_MAZ_Npu_Jt_5_centroid"
names(arg_maz_daily[['env']])
#>  [1] "TIMESTAMP"            "ta_mean"              "rh_mean"             
#>  [4] "vpd_mean"             "sw_in_mean"           "ws_mean"             
#>  [7] "precip_mean"          "swc_shallow_mean"     "ppfd_in_mean"        
#> [10] "ext_rad_mean"         "ta_sd"                "rh_sd"               
#> [13] "vpd_sd"               "sw_in_sd"             "ws_sd"               
#> [16] "precip_sd"            "swc_shallow_sd"       "ppfd_in_sd"          
#> [19] "ext_rad_sd"           "ta_coverage"          "rh_coverage"         
#> [22] "vpd_coverage"         "sw_in_coverage"       "ws_coverage"         
#> [25] "precip_coverage"      "swc_shallow_coverage" "ppfd_in_coverage"    
#> [28] "ext_rad_coverage"     "ta_q_95"              "rh_q_95"             
#> [31] "vpd_q_95"             "sw_in_q_95"           "ws_q_95"             
#> [34] "precip_q_95"          "swc_shallow_q_95"     "ppfd_in_q_95"        
#> [37] "ext_rad_q_95"         "precip_accumulated"

We can see that 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'))
#> # A tibble: 13 x 6
#>    TIMESTAMP           ARG_MAZ_Npu_Jt_1_q… ARG_MAZ_Npu_Jt_2_… ARG_MAZ_Npu_Jt_3_…
#>    <dttm>                            <dbl>              <dbl>              <dbl>
#>  1 2009-11-18 00:00:00               1949.               754.               736.
#>  2 2009-11-19 00:00:00               4603.              2835.              1692.
#>  3 2009-11-20 00:00:00               2561.              1289.               801.
#>  4 2009-11-21 00:00:00               4001.              2459.              1508.
#>  5 2009-11-22 00:00:00               4396.              2761.              1466.
#>  6 2009-11-23 00:00:00               5192.              3160.              1842.
#>  7 2009-11-24 00:00:00               5135.              3160.              1879.
#>  8 2009-11-25 00:00:00               4724.              3077.              1522.
#>  9 2009-11-26 00:00:00               5642.              3449.              1845.
#> 10 2009-11-27 00:00:00               6560.              4174.              2251.
#> 11 2009-11-28 00:00:00               7142.              4288.              2362.
#> 12 2009-11-29 00:00:00               5319.              3494.              2000.
#> 13 2009-11-30 00:00:00               2408.              1804.               711.
#> # … with 2 more variables: ARG_MAZ_Npu_Jt_4_q_95 <dbl>,
#> #   ARG_MAZ_Npu_Jt_5_q_95 <dbl>

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

arg_maz_daily[['env']] %>%
  select(TIMESTAMP, ends_with('mean'))
#> # A tibble: 13 x 10
#>    TIMESTAMP           ta_mean rh_mean vpd_mean sw_in_mean ws_mean precip_mean
#>    <dttm>                <dbl>   <dbl>    <dbl>      <dbl>   <dbl>       <dbl>
#>  1 2009-11-18 00:00:00   4.78     57.8    0.363         0     11.3     0      
#>  2 2009-11-19 00:00:00   5.56     45.7    0.521       300.    12.0     0.00833
#>  3 2009-11-20 00:00:00   1.39     73.5    0.181       134.    12.6     0.1    
#>  4 2009-11-21 00:00:00   2.44     69.6    0.236       207.    14.2     0.00833
#>  5 2009-11-22 00:00:00   2.21     71.9    0.216       282.    15.8     0.00833
#>  6 2009-11-23 00:00:00   3.19     56.7    0.364       335.    11.4     0      
#>  7 2009-11-24 00:00:00   6.16     52.1    0.511       312.    13.7     0      
#>  8 2009-11-25 00:00:00   6.11     58.5    0.409       250.    21.7     0      
#>  9 2009-11-26 00:00:00   4.99     72.4    0.269       218.    34.2     0.258  
#> 10 2009-11-27 00:00:00   4.98     61.2    0.363       336.    25.8     0      
#> 11 2009-11-28 00:00:00   5.87     61.6    0.411       212.    25.2     0.0167 
#> 12 2009-11-29 00:00:00   1.45     63.6    0.266       246.    17.7     0.45   
#> 13 2009-11-30 00:00:00   0.299    78.0    0.140       241.    18.0     0.00909
#> # … with 3 more variables: swc_shallow_mean <dbl>, ppfd_in_mean <dbl>,
#> #   ext_rad_mean <dbl>

If interested in custom metrics or custom aggregations, there is a generic function, sfn_metrics that 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 that aggregated TIMESTAMPS are fixed to the beginning of the period aggregated, meaning that 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 that 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 that 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

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)
#> [1] "processing site ARG_MAZ (1 of 3)"
#> [1] "processing site ARG_TRE (2 of 3)"
#> [1] "processing site AUS_CAN_ST2_MIX (3 of 3)"

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']]
#> # A tibble: 43 x 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  180   Corrected, measu…          14.7 ARG_MAZ…   41.1      NA          109. 
#>  2  180   No azimuthal cor…          13   ARG_MAZ…   33.2      NA           58.4
#>  3  180   No azimuthal cor…           6   ARG_MAZ…   23.2      NA           35.0
#>  4  180   No azimuthal cor…          21   ARG_MAZ…   55.6      NA          174. 
#>  5  180   No azimuthal cor…          10   ARG_MAZ…   38        NA           88  
#>  6  140   Corrected, measu…          23.5 ARG_TRE…   23.6      NA           29.7
#>  7  140   No azimuthal cor…          22.5 ARG_TRE…   20.2      NA           13.0
#>  8  140   No azimuthal cor…          22   ARG_TRE…   23.8      NA           29.8
#>  9  140   No azimuthal cor…          23.5 ARG_TRE…   19.5      NA           15.1
#> 10   14.5 No azimuthal cor…          NA   AUS_CAN…   13.1      14.3         NA  
#> # … with 33 more rows, and 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>, pl_sap_units_orig <chr>

Listing the sites in a folder and filtering by metadata

Now that 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
#> [1] "ARG_MAZ"         "ARG_TRE"         "AUS_CAN_ST2_MIX"

We can filter these sites by any metadata variable, to select those that 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
#> [1] "ARG_MAZ"         "ARG_TRE"         "AUS_CAN_ST2_MIX"

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
#> [1] "ARG_MAZ" "ARG_TRE"

Remember that 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()
#> $site_md
#>  [1] "si_name"                 "si_country"             
#>  [3] "si_contact_firstname"    "si_contact_lastname"    
#>  [5] "si_contact_email"        "si_contact_institution" 
#>  [7] "si_addcontr_firstname"   "si_addcontr_lastname"   
#>  [9] "si_addcontr_email"       "si_addcontr_institution"
#> [11] "si_lat"                  "si_long"                
#> [13] "si_elev"                 "si_paper"               
#> [15] "si_dist_mgmt"            "si_igbp"                
#> [17] "si_flux_network"         "si_dendro_network"      
#> [19] "si_remarks"              "si_code"                
#> [21] "si_mat"                  "si_map"                 
#> [23] "si_biome"               
#> 
#> $stand_md
#>  [1] "st_name"              "st_growth_condition"  "st_treatment"        
#>  [4] "st_age"               "st_height"            "st_density"          
#>  [7] "st_basal_area"        "st_lai"               "st_aspect"           
#> [10] "st_terrain"           "st_soil_depth"        "st_soil_texture"     
#> [13] "st_sand_perc"         "st_silt_perc"         "st_clay_perc"        
#> [16] "st_remarks"           "st_USDA_soil_texture"
#> 
#> $species_md
#> [1] "sp_name"            "sp_ntrees"          "sp_leaf_habit"     
#> [4] "sp_basal_area_perc"
#> 
#> $plant_md
#>  [1] "pl_name"           "pl_species"        "pl_treatment"     
#>  [4] "pl_dbh"            "pl_height"         "pl_age"           
#>  [7] "pl_social"         "pl_sapw_area"      "pl_sapw_depth"    
#> [10] "pl_bark_thick"     "pl_leaf_area"      "pl_sens_meth"     
#> [13] "pl_sens_man"       "pl_sens_cor_grad"  "pl_sens_cor_zero" 
#> [16] "pl_sens_calib"     "pl_sap_units"      "pl_sap_units_orig"
#> [19] "pl_sens_length"    "pl_sens_hgt"       "pl_sens_timestep" 
#> [22] "pl_radial_int"     "pl_azimut_int"     "pl_remarks"       
#> [25] "pl_code"          
#> 
#> $env_md
#>  [1] "env_time_zone"         "env_time_daylight"     "env_timestep"         
#>  [4] "env_ta"                "env_rh"                "env_vpd"              
#>  [7] "env_sw_in"             "env_ppfd_in"           "env_netrad"           
#> [10] "env_ws"                "env_precip"            "env_swc_shallow_depth"
#> [13] "env_swc_deep_depth"    "env_plant_watpot"      "env_leafarea_seasonal"
#> [16] "env_remarks"

# and see what values we must use for filtering by pl_sens_meth
describe_md_variable('pl_sens_meth')
#> Description:
#> Sap flow measures method
#> 
#> Values:
#> CAG | HD | CHP | CHD | HFD | HPTM | HR | SFPLUS | SHB | TSHB | Other/unknown | 
#> 
#> 
#> Units:
#> Fixed values
#> 
#> Type:
#> Character

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
#> sfn_data_multi object
#> 3 sites: ARG_MAZ ARG_TRE 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

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']]
#> sfn_data object
#> Data from AUS_CAN_ST2_MIX site
#> 
#> Data kindly provided by  David Forrester  from  
#> Swiss Federal Institute for Forest, Snow and Landscape Research WSL
#> 
#> Site related literature:  10.1016/j.foreco.2009.07.036 
#> 
#> Sapflow data:  17808  observations of  34  trees/plants
#> Species present:  Eucalyptus globulus, Acacia mearnsii 
#> 
#> Environmental data:  17808  observations.
#> Variables present:
#>   ta vpd sw_in ws precip ppfd_in rh ext_rad 
#> 
#> Biome:  Woodland/Shrubland 
#> 
#> TIMESTAMP span:  2006-06-20 11:00:00 +10--2007-06-26 10:30:00 +10 
#> 
#> Solar TIMESTAMP span:  2006-06-20 10:54:42 UTC--2007-06-26 10:23:32 UTC 
#> 
#> Sapflow data flags:
#> OUT_REMOVED    NA_ADDED    OUT_WARN  NA_PRESENT 
#>          40        3400        5460      504952 
#> 
#> Environmental data flags:
#>   OUT_WARN   NA_ADDED CALCULATED 
#>      10826      15320      53424

Multi aggregation

Now we can aggregate all sites at once

temperate_aggregated <- temperate_sites %>%
  daily_metrics()
#> 
#> Attaching package: 'purrr'
#> The following object is masked from 'package:magrittr':
#> 
#>     set_names
#> [1] "Crunching data for ARG_MAZ. In large datasets this could take a while"
#> [1] "General data for ARG_MAZ"
#> [1] "Crunching data for ARG_TRE. In large datasets this could take a while"
#> [1] "General data for ARG_TRE"
#> [1] "Crunching data for AUS_CAN_ST2_MIX. In large datasets this could take a while"
#> [1] "General data for AUS_CAN_ST2_MIX"

names(temperate_aggregated)
#> [1] "ARG_MAZ"         "ARG_TRE"         "AUS_CAN_ST2_MIX"

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

Tidy metrics

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)
#> [1] "Crunching data for ARG_MAZ. In large datasets this could take a while"
#> [1] "General data for ARG_MAZ"
#> [1] "Crunching data for ARG_TRE. In large datasets this could take a while"
#> [1] "General data for ARG_TRE"
#> [1] "Crunching data for AUS_CAN_ST2_MIX. In large datasets this could take a while"
#> [1] "General data for AUS_CAN_ST2_MIX"

temperate_tidy
#> # A tibble: 12,769 x 129
#>    TIMESTAMP           si_code  pl_code sapflow_mean sapflow_sd sapflow_coverage
#>    <dttm>              <chr>    <chr>          <dbl>      <dbl>            <dbl>
#>  1 2006-06-20 00:00:00 AUS_CAN… AUS_CA…          NaN         NA                0
#>  2 2006-06-20 00:00:00 AUS_CAN… AUS_CA…          NaN         NA                0
#>  3 2006-06-20 00:00:00 AUS_CAN… AUS_CA…          NaN         NA                0
#>  4 2006-06-20 00:00:00 AUS_CAN… AUS_CA…          NaN         NA                0
#>  5 2006-06-20 00:00:00 AUS_CAN… AUS_CA…          NaN         NA                0
#>  6 2006-06-20 00:00:00 AUS_CAN… AUS_CA…          NaN         NA                0
#>  7 2006-06-20 00:00:00 AUS_CAN… AUS_CA…          NaN         NA                0
#>  8 2006-06-20 00:00:00 AUS_CAN… AUS_CA…          NaN         NA                0
#>  9 2006-06-20 00:00:00 AUS_CAN… AUS_CA…          NaN         NA                0
#> 10 2006-06-20 00:00:00 AUS_CAN… AUS_CA…          NaN         NA                0
#> # … with 12,759 more rows, and 123 more variables: sapflow_q_95 <dbl>,
#> #   sapflow_centroid <dbl>, ta_mean <dbl>, rh_mean <dbl>, vpd_mean <dbl>,
#> #   sw_in_mean <dbl>, ws_mean <dbl>, precip_mean <dbl>, swc_shallow_mean <dbl>,
#> #   ppfd_in_mean <dbl>, ext_rad_mean <dbl>, ta_sd <dbl>, rh_sd <dbl>,
#> #   vpd_sd <dbl>, sw_in_sd <dbl>, ws_sd <dbl>, precip_sd <dbl>,
#> #   swc_shallow_sd <dbl>, ppfd_in_sd <dbl>, ext_rad_sd <dbl>,
#> #   ta_coverage <dbl>, rh_coverage <dbl>, vpd_coverage <dbl>,
#> #   sw_in_coverage <dbl>, ws_coverage <dbl>, precip_coverage <dbl>,
#> #   swc_shallow_coverage <dbl>, ppfd_in_coverage <dbl>, ext_rad_coverage <dbl>,
#> #   ta_q_95 <dbl>, rh_q_95 <dbl>, vpd_q_95 <dbl>, sw_in_q_95 <dbl>,
#> #   ws_q_95 <dbl>, precip_q_95 <dbl>, swc_shallow_q_95 <dbl>,
#> #   ppfd_in_q_95 <dbl>, ext_rad_q_95 <dbl>, precip_accumulated <dbl>,
#> #   pl_age <dbl>, pl_azimut_int <chr>, pl_bark_thick <dbl>, pl_dbh <dbl>,
#> #   pl_height <dbl>, pl_leaf_area <dbl>, 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>,
#> #   pl_sap_units_orig <chr>, si_addcontr_email <chr>,
#> #   si_addcontr_firstname <chr>, si_addcontr_institution <chr>,
#> #   si_addcontr_lastname <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 <chr>, is_inside_country <lgl>, si_mat <dbl>, si_map <dbl>,
#> #   si_biome <chr>, st_age <dbl>, st_aspect <chr>, st_basal_area <dbl>,
#> #   st_clay_perc <lgl>, st_density <int>, st_growth_condition <chr>,
#> #   st_height <dbl>, st_lai <dbl>, st_name <chr>, st_remarks <lgl>,
#> #   st_sand_perc <lgl>, st_silt_perc <lgl>, st_soil_depth <int>,
#> #   st_soil_texture <chr>, …

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)
#> Warning: Removed 10966 rows containing missing values (geom_point).