sfn_data
snf_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.
sfn_data
class has twelve different slots:
sapf_data: Tibble containing the sap flow data, each column representing an individual tree, without any TIMESTAMP variable.
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.
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
.
env_flags: Tibble with the same dimensions as env_data
, containing the flags for each observation in env_data
.
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.
timestamp: POSIXct vector of length equal to nrow(sapf_data)
with the timestamp values.
solar_timestamp: POSIXct vector of length equal to nrow(sapf_data)
with the apparent solar timestamp.
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.
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.
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.
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.
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
designThe schematics of the class are summarised in Fig 1.
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", ]
Some methods are included in the class:
show
: calling a sfn_data object shows info about itlibrary(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.
#> # … with 302 more rows, and 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.
#> # … with 302 more rows, and 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.
#> # … with 302 more rows, and 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 "" "" ""
#> # … with 302 more rows, and 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…
#> # … with 302 more rows, and 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_firs… si_addcontr_inst… si_addcontr_las… si_code
#> <chr> <chr> <chr> <chr> <chr>
#> 1 <NA> Pablo Instituto Nacion… Peri ARG_TRE
#> # … 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>
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,…
#> # … 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 <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, measur… 23.5 ARG_TRE… 23.6 NA 29.7
#> 2 140 No azimuthal corr… 22.5 ARG_TRE… 20.2 NA 13.0
#> 3 140 No azimuthal corr… 22 ARG_TRE… 23.8 NA 29.8
#> 4 140 No azimuthal corr… 23.5 ARG_TRE… 19.5 NA 15.1
#> # … 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>
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
#> # … 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>
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
<- get_site_md(ARG_TRE)
foo_site_md 'si_biome']]
foo_site_md[[#> [1] "Woodland/Shrubland"
'si_biome']] <- 'Temperate forest'
foo_site_md[[# 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
<- get_sapf_data(ARG_TRE)
foo_bad_sapf # pull a row, now it has diferent dimensions then
<- foo_bad_sapf[-1,]
foo_bad_sapf # 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)
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
<- sfn_mutate(ARG_TRE, ws = ws * 3600/1000)
foo_mutated 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.
<- sfn_mutate_at(
foo_mutated_2
ARG_TRE,vars(one_of(names(get_sapf_data(ARG_TRE)[,-1]))),
list(~ case_when(
> 25 ~ NA_real_,
ws 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.
#> # … with 302 more rows, and 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.
#> # … with 302 more rows, and 1 more variable: ARG_TRE_Nan_Jt_4 <dbl>
*_metrics
functionsFamily of functions to aggregate and summarise the site data. See ?metrics
for more details.
<- daily_metrics(ARG_TRE)
foo_daily #> [1] "Crunching data for ARG_TRE. In large datasets this could take a while"
#> [1] "General data for ARG_TRE"
'sapf']][['sapf_gen']]
foo_daily[[#> 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.
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')
<- sfn_data_multi(ARG_TRE, ARG_MAZ, AUS_CAN_ST2_MIX)
multi_sfn
# 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.
#> # … with 302 more rows, and 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.
#> # … with 278 more rows, and 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_Am… AUS_CAN_ST2_MIX_E… AUS_CAN_ST2_MIX_A…
#> <dttm> <dbl> <dbl> <dbl>
#> 1 2006-06-20 11:00:00 NA NA NA
#> 2 2006-06-20 11:30:00 NA NA NA
#> 3 2006-06-20 12:00:00 NA NA NA
#> 4 2006-06-20 12:30:00 NA NA NA
#> 5 2006-06-20 13:00:00 NA NA NA
#> 6 2006-06-20 13:30:00 NA NA NA
#> 7 2006-06-20 14:00:00 NA NA NA
#> 8 2006-06-20 14:30:00 NA NA NA
#> 9 2006-06-20 15:00:00 NA NA NA
#> 10 2006-06-20 15:30:00 NA NA NA
#> # … with 17,798 more rows, and 31 more variables:
#> # 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>,
#> # AUS_CAN_ST2_MIX_Egl_Js_14 <dbl>, AUS_CAN_ST2_MIX_Egl_Js_15 <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, measur… 23.5 ARG_TRE… 23.6 NA 29.7
#> 2 140 No azimuthal corr… 22.5 ARG_TRE… 20.2 NA 13.0
#> 3 140 No azimuthal corr… 22 ARG_TRE… 23.8 NA 29.8
#> 4 140 No azimuthal corr… 23.5 ARG_TRE… 19.5 NA 15.1
#> # … 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
#> # 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, 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>
#>
#> $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 c… NA AUS_CAN_S… 13.1 14.3 NA
#> 2 14.5 No azimuthal c… NA AUS_CAN_S… 10.4 12.9 NA
#> 3 14.5 No azimuthal c… NA AUS_CAN_S… 12.8 14.1 NA
#> 4 14.5 No azimuthal c… NA AUS_CAN_S… 17.1 19.4 NA
#> 5 14.5 No azimuthal c… NA AUS_CAN_S… 11.6 13.6 NA
#> 6 14.5 No azimuthal c… NA AUS_CAN_S… 19.2 20.5 NA
#> 7 14.5 No azimuthal c… NA AUS_CAN_S… 12.8 16.6 NA
#> 8 14.5 No azimuthal c… NA AUS_CAN_S… 12.5 14.1 NA
#> 9 14.5 No azimuthal c… NA AUS_CAN_S… 13.6 17.2 NA
#> 10 14.5 No azimuthal c… NA AUS_CAN_S… 13.8 14.7 NA
#> # … with 24 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 <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>, pl_sap_units_orig <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, measu… 23.5 ARG_TRE… 23.6 NA 29.7
#> 2 140 No azimuthal cor… 22.5 ARG_TRE… 20.2 NA 13.0
#> 3 140 No azimuthal cor… 22 ARG_TRE… 23.8 NA 29.8
#> 4 140 No azimuthal cor… 23.5 ARG_TRE… 19.5 NA 15.1
#> 5 180 Corrected, measu… 14.7 ARG_MAZ… 41.1 NA 109.
#> 6 180 No azimuthal cor… 13 ARG_MAZ… 33.2 NA 58.4
#> 7 180 No azimuthal cor… 6 ARG_MAZ… 23.2 NA 35.0
#> 8 180 No azimuthal cor… 21 ARG_MAZ… 55.6 NA 174.
#> 9 180 No azimuthal cor… 10 ARG_MAZ… 38 NA 88
#> 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>
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
<- sfn_plot(multi_sfn, formula = ~ vpd)
multi_plot 'ARG_TRE']] + theme(legend.position = 'none')
multi_plot[['AUS_CAN_ST2_MIX']] + theme(legend.position = 'none')
multi_plot[[#> Warning: Removed 526066 rows containing missing values (geom_point).
sfn_filter
<- sfn_filter(multi_sfn, month(TIMESTAMP) == 11)
multi_filtered 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
<- sfn_mutate(multi_sfn, ws = ws * 3600/1000)
multi_mutated 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
<- c(
vars_to_not_mutate "TIMESTAMP", "ta", "rh", "vpd", "sw_in", "ws",
"precip", "swc_shallow", "ppfd_in", "ext_rad"
)
<- sfn_mutate_at(
multi_mutated_2
multi_sfn,vars(-one_of(vars_to_not_mutate)),
list(~ case_when(
> 25 ~ NA_real_,
ws TRUE ~ .
))
)#> Warning: Unknown columns: `swc_shallow`
'ARG_TRE']]
multi_mutated_2[[#> 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
<- daily_metrics(multi_sfn)
multi_metrics #> [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"
'ARG_TRE']][['sapf']]
multi_metrics[[#> # A tibble: 14 × 21
#> TIMESTAMP ARG_TRE_Nan_Jt_1_mean ARG_TRE_Nan_Jt_2… ARG_TRE_Nan_Jt_3…
#> <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 17 more variables: 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>, ARG_TRE_Nan_Jt_4_q_95 <dbl>, …