sfn_datasfn_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.
sfn_data schematics
This design have some characteristics:
Dimensions: Dimensions must comply:
nrow(sapf_data) == nrow(env_data) == nrow(sapf_flags) == nrow(env_flags) == length(timestamp) == length(solar_timestamp)
ncol(sapf_data) == ncol(sapf_flags)
ncol(env_data) == ncol(env_flags)
In the case of differences in the dimensions between environmental and sap flow data, NAs must be added in the corresponding places when equalising both TIMESTAMPs (i.e. if env data is longer then sapf data, rows are added to the later with NAs to make them equal, and flags (“NA_ADDED”) are raised to indicate the adding).
si_code slot: This slot indicates the site. This is useful for identifying the site in the metrics functions results.
Compartmentalization: timestamp and
solar_timestamp are isolated from data (sapf and env) in
their own slot. This made easier subsetting the object and open the
possibility of working with the timestamps only.
Flags: Two slots are used for storing data flags. As the flag slots have the same dimensions as the corresponding dataset slot, selection of flagged data is straightforward:
sapf_data[flag_data == "flag", ]
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 624get 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_J…¹ ARG_T…²
#> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 2009-11-18 00:00:00 322. 190. 313. 218.
#> 2 2009-11-18 01:00:00 293. 157. 293. 293.
#> 3 2009-11-18 02:00:00 298. 178. 331. 309.
#> 4 2009-11-18 03:00:00 298. 168. 299. 258.
#> 5 2009-11-18 04:00:00 301. 170. 292. 301.
#> 6 2009-11-18 05:00:00 320. 198. 295. 261.
#> 7 2009-11-18 06:00:00 343. 196. 301. 285.
#> 8 2009-11-18 07:00:00 324. 194. 292. 282.
#> 9 2009-11-18 08:00:00 353. 284. 323. 374.
#> 10 2009-11-18 09:00:00 433. 340. 352. 372.
#> # … with 302 more rows, and abbreviated variable names ¹ARG_TRE_Nan_Jt_3,
#> # ²ARG_TRE_Nan_Jt_4
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_J…¹ ARG_T…²
#> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 2009-11-17 22:24:58 322. 190. 313. 218.
#> 2 2009-11-17 23:24:58 293. 157. 293. 293.
#> 3 2009-11-18 00:24:43 298. 178. 331. 309.
#> 4 2009-11-18 01:24:43 298. 168. 299. 258.
#> 5 2009-11-18 02:24:43 301. 170. 292. 301.
#> 6 2009-11-18 03:24:43 320. 198. 295. 261.
#> 7 2009-11-18 04:24:43 343. 196. 301. 285.
#> 8 2009-11-18 05:24:43 324. 194. 292. 282.
#> 9 2009-11-18 06:24:43 353. 284. 323. 374.
#> 10 2009-11-18 07:24:43 433. 340. 352. 372.
#> # … with 302 more rows, and abbreviated variable names ¹ARG_TRE_Nan_Jt_3,
#> # ²ARG_TRE_Nan_Jt_4
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_J…¹ ARG_T…²
#> <dttm> <chr> <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 abbreviated variable names ¹ARG_TRE_Nan_Jt_3,
#> # ²ARG_TRE_Nan_Jt_4
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_…¹ si_ad…² si_ad…³ si_ad…⁴ si_code si_co…⁵ si_co…⁶ si_co…⁷ si_co…⁸
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 <NA> Pablo Instit… Peri ARG_TRE <NA> Seabst… Univer… Pfauts…
#> # … with 15 more variables: 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>, and abbreviated variable names ¹si_addcontr_email,
#> # ²si_addcontr_firstname, ³si_addcontr_institution, ⁴si_addcontr_lastname,
#> # ⁵si_contact_email, ⁶si_contact_firstname, ⁷si_contact_institution, …
get_stand_md(ARG_TRE)
#> # A tibble: 1 × 18
#> st_age st_asp…¹ st_ba…² st_cl…³ st_de…⁴ st_gr…⁵ st_he…⁶ st_lai st_name st_re…⁷
#> <int> <chr> <dbl> <lgl> <int> <chr> <int> <lgl> <lgl> <lgl>
#> 1 140 Flat 19.4 NA 512 Natura… 6 NA NA NA
#> # … with 8 more variables: 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>, and
#> # abbreviated variable names ¹st_aspect, ²st_basal_area, ³st_clay_perc,
#> # ⁴st_density, ⁵st_growth_condition, ⁶st_height, ⁷st_remarks
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_azi…¹ pl_ba…² pl_code pl_dbh pl_he…³ pl_le…⁴ pl_name pl_ra…⁵ pl_re…⁶
#> <int> <chr> <dbl> <chr> <dbl> <lgl> <dbl> <chr> <chr> <lgl>
#> 1 140 Correct… 23.5 ARG_TR… 23.6 NA 29.7 Na-1 Correc… NA
#> 2 140 No azim… 22.5 ARG_TR… 20.2 NA 13.0 Na-2 Correc… NA
#> 3 140 No azim… 22 ARG_TR… 23.8 NA 29.8 Na-3 Correc… NA
#> 4 140 No azim… 23.5 ARG_TR… 19.5 NA 15.1 Na-4 Correc… NA
#> # … with 16 more variables: 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>, and abbreviated variable names ¹pl_azimut_int,
#> # ²pl_bark_thick, ³pl_height, ⁴pl_leaf_area, ⁵pl_radial_int, ⁶pl_remarks
get_env_md(ARG_TRE)
#> # A tibble: 1 × 17
#> env_l…¹ env_n…² env_p…³ env_p…⁴ env_p…⁵ env_r…⁶ env_rh env_s…⁷ env_s…⁸ env_s…⁹
#> <lgl> <chr> <chr> <chr> <chr> <lgl> <chr> <lgl> <int> <chr>
#> 1 NA Not pr… leaf: … Not pr… Cleari… NA Clear… NA 15 Cleari…
#> # … with 7 more variables: env_ta <chr>, env_time_daylight <lgl>,
#> # env_timestep <int>, env_time_zone <chr>, env_vpd <chr>, env_ws <chr>,
#> # si_code <chr>, and abbreviated variable names ¹env_leafarea_seasonal,
#> # ²env_netrad, ³env_plant_watpot, ⁴env_ppfd_in, ⁵env_precip, ⁶env_remarks,
#> # ⁷env_swc_deep_depth, ⁸env_swc_shallow_depth, ⁹env_sw_inassignation: methods for "<-" are
implemented, allowing transformation of the slots. This is used
internally in some functions but is not recommended except for updating
metadata slots. In data slots this changes are not reflected in the
flags, so the reproducibility and traceability will be lost if used
without care. See ?sfn_replacement_methods for more
details.# extraction and modification
foo_site_md <- get_site_md(ARG_TRE)
foo_site_md[['si_biome']]
#> [1] "Woodland/Shrubland"
foo_site_md[['si_biome']] <- 'Temperate forest'
# assignation
get_site_md(ARG_TRE) <- foo_site_md
# check it worked
get_site_md(ARG_TRE)[['si_biome']]
#> [1] "Temperate forest"validation: a validation method is implemented in order
to avoid creation of invalid sfn_data objects (i.e objects with
different data dimensions, objects without metadata…)# get sap flow data
foo_bad_sapf <- get_sapf_data(ARG_TRE)
# pull a row, now it has diferent dimensions then
foo_bad_sapf <- foo_bad_sapf[-1,]
# try to assign the incorrect data fails
get_sapf_data(ARG_TRE) <- foo_bad_sapf[,-1] # ERROR#> Error in validObject(object) :
#> invalid class "sfn_data" object: 1: nrow(sapf_data) != nrow(env_data)
#> invalid class "sfn_data" object: 2: nrow(sapf_data) != length(timestamp) | nrow(env_data) != length(timestamp)
#> invalid class "sfn_data" object: 3: nrow(*_flags) =! nrow(*_flags) | nrow(*_flags) =! nrow(*_data)
#> Error in `get_sapf_data<-`(`*tmp*`, value = structure(list(ARG_TRE_Nan_Jt_1 = c(292.9710029771, : new data is not valid: Error in validObject(object) :
#> invalid class "sfn_data" object: 1: nrow(sapf_data) != nrow(env_data)
#> invalid class "sfn_data" object: 2: nrow(sapf_data) != length(timestamp) | nrow(env_data) != length(timestamp)
#> invalid class "sfn_data" object: 3: nrow(*_flags) =! nrow(*_flags) | nrow(*_flags) =! nrow(*_data)
# try to build a new object also fails
sfn_data(
sapf_data = foo_bad_sapf[,-1], # remember to remove timestamp column
env_data = get_env_data(ARG_TRE)[,-1],
sapf_flags = get_env_flags(ARG_TRE)[,-1],
env_flags = get_env_flags(ARG_TRE)[,-1],
si_code = get_si_code(ARG_TRE),
timestamp = get_timestamp(ARG_TRE),
solar_timestamp = get_solar_timestamp(ARG_TRE),
site_md = get_site_md(ARG_TRE),
stand_md = get_stand_md(ARG_TRE),
species_md = get_species_md(ARG_TRE),
plant_md = get_plant_md(ARG_TRE),
env_md = get_env_md(ARG_TRE)
)#> Error in validObject(.Object): invalid class "sfn_data" object: 1: nrow(sapf_data) != nrow(env_data)
#> invalid class "sfn_data" object: 2: nrow(sapf_data) != length(timestamp) | nrow(env_data) != length(timestamp)
#> invalid class "sfn_data" object: 3: nrow(*_flags) =! nrow(*_flags) | nrow(*_flags) =! nrow(*_data)
sapfluxnetr package offers some utilities to visualize
and work with sfn_data objects:
sfn_plotThis 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_filterThis 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 624sfn_mutateThis function allows mutation of data variables inside the
sfn_data object. Useful when you need to transform a
variable to another units or similar. A flag (‘USER_MODF’) will be added
to all values in the mutated variable. See sfn_mutate for
more details.
At this moment, mutate does not allows creating new variables, only mutate existing variables
# transform ws from m/s to km/h
foo_mutated <- sfn_mutate(ARG_TRE, ws = ws * 3600/1000)
get_env_data(foo_mutated)[['ws']][1:10]
#> [1] 48.888 56.952 81.144 48.888 24.192 73.080 16.128 8.064 8.064 16.128sfn_mutate_atThis function mutates all variables declared with the function
provided. Useful when you need to conditionally transform the data,
i.e. converting to NA sap flow values when an environmental variable
exceeds some threshold. See sfn_mutate_at for more
details.
foo_mutated_2 <- sfn_mutate_at(
ARG_TRE,
vars(one_of(names(get_sapf_data(ARG_TRE)[,-1]))),
list(~ case_when(
ws > 25 ~ NA_real_,
TRUE ~ .
))
)
# see the difference between ARG_TRE and foo_mutated_2
get_sapf_data(ARG_TRE)
#> # A tibble: 312 × 5
#> TIMESTAMP ARG_TRE_Nan_Jt_1 ARG_TRE_Nan_Jt_2 ARG_TRE_Nan_J…¹ ARG_T…²
#> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 2009-11-18 00:00:00 322. 190. 313. 218.
#> 2 2009-11-18 01:00:00 293. 157. 293. 293.
#> 3 2009-11-18 02:00:00 298. 178. 331. 309.
#> 4 2009-11-18 03:00:00 298. 168. 299. 258.
#> 5 2009-11-18 04:00:00 301. 170. 292. 301.
#> 6 2009-11-18 05:00:00 320. 198. 295. 261.
#> 7 2009-11-18 06:00:00 343. 196. 301. 285.
#> 8 2009-11-18 07:00:00 324. 194. 292. 282.
#> 9 2009-11-18 08:00:00 353. 284. 323. 374.
#> 10 2009-11-18 09:00:00 433. 340. 352. 372.
#> # … with 302 more rows, and abbreviated variable names ¹ARG_TRE_Nan_Jt_3,
#> # ²ARG_TRE_Nan_Jt_4
get_sapf_data(foo_mutated_2)
#> # A tibble: 312 × 5
#> TIMESTAMP ARG_TRE_Nan_Jt_1 ARG_TRE_Nan_Jt_2 ARG_TRE_Nan_J…¹ ARG_T…²
#> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 2009-11-18 00:00:00 322. 190. 313. 218.
#> 2 2009-11-18 01:00:00 293. 157. 293. 293.
#> 3 2009-11-18 02:00:00 298. 178. 331. 309.
#> 4 2009-11-18 03:00:00 298. 168. 299. 258.
#> 5 2009-11-18 04:00:00 301. 170. 292. 301.
#> 6 2009-11-18 05:00:00 320. 198. 295. 261.
#> 7 2009-11-18 06:00:00 343. 196. 301. 285.
#> 8 2009-11-18 07:00:00 324. 194. 292. 282.
#> 9 2009-11-18 08:00:00 353. 284. 323. 374.
#> 10 2009-11-18 09:00:00 433. 340. 352. 372.
#> # … with 302 more rows, and abbreviated variable names ¹ARG_TRE_Nan_Jt_3,
#> # ²ARG_TRE_Nan_Jt_4*_metrics functionsFamily of functions to aggregate and summarise the site data. See
?metrics for more details.
foo_daily <- daily_metrics(ARG_TRE)
#> [1] "Crunching data for ARG_TRE. In large datasets this could take a while"
#> [1] "General data for ARG_TRE"
foo_daily[['sapf']][['sapf_gen']]
#> NULLFor full control of metrics and custom aggregations see
vignette('custom-aggregation', package = 'sapfluxnetr')
sfn_data_multisfn_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')
multi_sfn <- sfn_data_multi(ARG_TRE, ARG_MAZ, AUS_CAN_ST2_MIX)
# show method
multi_sfn
#> sfn_data_multi object
#> 3 sites: ARG_TRE ARG_MAZ AUS_CAN_ST2_MIX
#> Approximate time span (UTC) for the combined sites: 2006-06-20 10:54:42 UTC--2009-11-18 22:24:18 UTCget 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_J…¹ ARG_T…²
#> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 2009-11-18 00:00:00 322. 190. 313. 218.
#> 2 2009-11-18 01:00:00 293. 157. 293. 293.
#> 3 2009-11-18 02:00:00 298. 178. 331. 309.
#> 4 2009-11-18 03:00:00 298. 168. 299. 258.
#> 5 2009-11-18 04:00:00 301. 170. 292. 301.
#> 6 2009-11-18 05:00:00 320. 198. 295. 261.
#> 7 2009-11-18 06:00:00 343. 196. 301. 285.
#> 8 2009-11-18 07:00:00 324. 194. 292. 282.
#> 9 2009-11-18 08:00:00 353. 284. 323. 374.
#> 10 2009-11-18 09:00:00 433. 340. 352. 372.
#> # … with 302 more rows, and abbreviated variable names ¹ARG_TRE_Nan_Jt_3,
#> # ²ARG_TRE_Nan_Jt_4
#>
#> $ARG_MAZ
#> # A tibble: 288 × 6
#> TIMESTAMP ARG_MAZ_Npu_Jt_1 ARG_MAZ_Npu_Jt_2 ARG_M…¹ ARG_M…² ARG_M…³
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2009-11-19 00:00:00 1955. 754. 742. 2679. 1494.
#> 2 2009-11-19 01:00:00 1833. 752. 631. 2527. 1504.
#> 3 2009-11-19 02:00:00 1817. 795. 597. 2514. 1446.
#> 4 2009-11-19 03:00:00 1598. 788. 579. 1938. 1265.
#> 5 2009-11-19 04:00:00 1419. 780. 518. 2061. 1181.
#> 6 2009-11-19 05:00:00 1506. 735. 430. 1977. 1231.
#> 7 2009-11-19 06:00:00 1649. 720. 539. 1984. 1310.
#> 8 2009-11-19 07:00:00 1630. 882. 542. 2371. 1506.
#> 9 2009-11-19 08:00:00 1909. 1015. 655. 2926. 1690.
#> 10 2009-11-19 09:00:00 2849. 1459. 930. 3802. 2265.
#> # … with 278 more rows, and abbreviated variable names ¹ARG_MAZ_Npu_Jt_3,
#> # ²ARG_MAZ_Npu_Jt_4, ³ARG_MAZ_Npu_Jt_5
#>
#> $AUS_CAN_ST2_MIX
#> # A tibble: 17,808 × 35
#> TIMESTAMP AUS_CAN…¹ AUS_C…² AUS_C…³ AUS_C…⁴ AUS_C…⁵ AUS_C…⁶ AUS_C…⁷
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2006-06-20 11:00:00 NA NA NA NA NA NA NA
#> 2 2006-06-20 11:30:00 NA NA NA NA NA NA NA
#> 3 2006-06-20 12:00:00 NA NA NA NA NA NA NA
#> 4 2006-06-20 12:30:00 NA NA NA NA NA NA NA
#> 5 2006-06-20 13:00:00 NA NA NA NA NA NA NA
#> 6 2006-06-20 13:30:00 NA NA NA NA NA NA NA
#> 7 2006-06-20 14:00:00 NA NA NA NA NA NA NA
#> 8 2006-06-20 14:30:00 NA NA NA NA NA NA NA
#> 9 2006-06-20 15:00:00 NA NA NA NA NA NA NA
#> 10 2006-06-20 15:30:00 NA NA NA NA NA NA NA
#> # … with 17,798 more rows, 27 more variables: 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>, AUS_CAN_ST2_MIX_Ame_Js_16 <dbl>,
#> # AUS_CAN_ST2_MIX_Egl_Js_17 <dbl>, AUS_CAN_ST2_MIX_Ame_Js_18 <dbl>,
#> # AUS_CAN_ST2_MIX_Ame_Js_19 <dbl>, AUS_CAN_ST2_MIX_Egl_Js_20 <dbl>, …
# get plant metadata
get_plant_md(multi_sfn)
#> $ARG_TRE
#> # A tibble: 4 × 26
#> pl_age pl_azi…¹ pl_ba…² pl_code pl_dbh pl_he…³ pl_le…⁴ pl_name pl_ra…⁵ pl_re…⁶
#> <int> <chr> <dbl> <chr> <dbl> <lgl> <dbl> <chr> <chr> <lgl>
#> 1 140 Correct… 23.5 ARG_TR… 23.6 NA 29.7 Na-1 Correc… NA
#> 2 140 No azim… 22.5 ARG_TR… 20.2 NA 13.0 Na-2 Correc… NA
#> 3 140 No azim… 22 ARG_TR… 23.8 NA 29.8 Na-3 Correc… NA
#> 4 140 No azim… 23.5 ARG_TR… 19.5 NA 15.1 Na-4 Correc… NA
#> # … with 16 more variables: 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>, and abbreviated variable names ¹pl_azimut_int,
#> # ²pl_bark_thick, ³pl_height, ⁴pl_leaf_area, ⁵pl_radial_int, ⁶pl_remarks
#>
#> $ARG_MAZ
#> # A tibble: 5 × 26
#> pl_age pl_azi…¹ pl_ba…² pl_code pl_dbh pl_he…³ pl_le…⁴ pl_name pl_ra…⁵ pl_re…⁶
#> <int> <chr> <dbl> <chr> <dbl> <lgl> <dbl> <chr> <chr> <lgl>
#> 1 180 Correct… 14.7 ARG_MA… 41.1 NA 109. Np-1 Correc… NA
#> 2 180 No azim… 13 ARG_MA… 33.2 NA 58.4 Np-2 Correc… NA
#> 3 180 No azim… 6 ARG_MA… 23.2 NA 35.0 Np-3 Correc… NA
#> 4 180 No azim… 21 ARG_MA… 55.6 NA 174. Np-4 Correc… NA
#> 5 180 No azim… 10 ARG_MA… 38 NA 88 Np-5 Correc… NA
#> # … with 16 more variables: 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>, and abbreviated variable names ¹pl_azimut_int,
#> # ²pl_bark_thick, ³pl_height, ⁴pl_leaf_area, ⁵pl_radial_int, ⁶pl_remarks
#>
#> $AUS_CAN_ST2_MIX
#> # A tibble: 34 × 26
#> pl_age pl_az…¹ pl_ba…² pl_code pl_dbh pl_he…³ pl_le…⁴ pl_name pl_ra…⁵ pl_re…⁶
#> <dbl> <chr> <lgl> <chr> <dbl> <dbl> <lgl> <chr> <chr> <chr>
#> 1 14.5 No azi… NA AUS_CA… 13.1 14.3 NA CRR4AE5 Measur… The tr…
#> 2 14.5 No azi… NA AUS_CA… 10.4 12.9 NA CRR2AE… Measur… <NA>
#> 3 14.5 No azi… NA AUS_CA… 12.8 14.1 NA CRR3AE… Measur… <NA>
#> 4 14.5 No azi… NA AUS_CA… 17.1 19.4 NA CRR3EA… Measur… <NA>
#> 5 14.5 No azi… NA AUS_CA… 11.6 13.6 NA CRR3AE… Measur… <NA>
#> 6 14.5 No azi… NA AUS_CA… 19.2 20.5 NA CRR3EA… Measur… <NA>
#> 7 14.5 No azi… NA AUS_CA… 12.8 16.6 NA CRR3EA… Measur… <NA>
#> 8 14.5 No azi… NA AUS_CA… 12.5 14.1 NA CRR3AE… Measur… <NA>
#> 9 14.5 No azi… NA AUS_CA… 13.6 17.2 NA CRR4EA… Measur… <NA>
#> 10 14.5 No azi… NA AUS_CA… 13.8 14.7 NA CRR4AE… Measur… <NA>
#> # … with 24 more rows, 16 more variables: 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>, and abbreviated
#> # variable names ¹pl_azimut_int, ²pl_bark_thick, ³pl_height, ⁴pl_leaf_area, …
# with metadata, we can collapse
get_plant_md(multi_sfn, collapse = TRUE)
#> # A tibble: 43 × 26
#> pl_age pl_az…¹ pl_ba…² pl_code pl_dbh pl_he…³ pl_le…⁴ pl_name pl_ra…⁵ pl_re…⁶
#> <dbl> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 140 Correc… 23.5 ARG_TR… 23.6 NA 29.7 Na-1 Correc… <NA>
#> 2 140 No azi… 22.5 ARG_TR… 20.2 NA 13.0 Na-2 Correc… <NA>
#> 3 140 No azi… 22 ARG_TR… 23.8 NA 29.8 Na-3 Correc… <NA>
#> 4 140 No azi… 23.5 ARG_TR… 19.5 NA 15.1 Na-4 Correc… <NA>
#> 5 180 Correc… 14.7 ARG_MA… 41.1 NA 109. Np-1 Correc… <NA>
#> 6 180 No azi… 13 ARG_MA… 33.2 NA 58.4 Np-2 Correc… <NA>
#> 7 180 No azi… 6 ARG_MA… 23.2 NA 35.0 Np-3 Correc… <NA>
#> 8 180 No azi… 21 ARG_MA… 55.6 NA 174. Np-4 Correc… <NA>
#> 9 180 No azi… 10 ARG_MA… 38 NA 88 Np-5 Correc… <NA>
#> 10 14.5 No azi… NA AUS_CA… 13.1 14.3 NA CRR4AE5 Measur… The tr…
#> # … with 33 more rows, 16 more variables: 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>, and abbreviated
#> # variable names ¹pl_azimut_int, ²pl_bark_thick, ³pl_height, ⁴pl_leaf_area, …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_plotmulti_plot <- sfn_plot(multi_sfn, formula = ~ vpd)
multi_plot[['ARG_TRE']] + theme(legend.position = 'none')
multi_plot[['AUS_CAN_ST2_MIX']] + theme(legend.position = 'none')
#> Warning: Removed 526066 rows containing missing values (`geom_point()`).sfn_filtermulti_filtered <- sfn_filter(multi_sfn, month(TIMESTAMP) == 11)
get_timestamp(multi_filtered[['AUS_CAN_ST2_MIX']])[1:10]
#> [1] "2006-11-01 00:00:00 +10" "2006-11-01 00:30:00 +10"
#> [3] "2006-11-01 01:00:00 +10" "2006-11-01 01:30:00 +10"
#> [5] "2006-11-01 02:00:00 +10" "2006-11-01 02:30:00 +10"
#> [7] "2006-11-01 03:00:00 +10" "2006-11-01 03:30:00 +10"
#> [9] "2006-11-01 04:00:00 +10" "2006-11-01 04:30:00 +10"sfn_mutatemulti_mutated <- sfn_mutate(multi_sfn, ws = ws * 3600/1000)
get_env_data(multi_mutated[['AUS_CAN_ST2_MIX']])[['ws']][1:10]
#> [1] 0.00 0.00 0.00 0.00 0.36 0.36 0.00 0.00 0.00 0.36sfn_mutate_atvars_to_not_mutate <- c(
"TIMESTAMP", "ta", "rh", "vpd", "sw_in", "ws",
"precip", "swc_shallow", "ppfd_in", "ext_rad"
)
multi_mutated_2 <- sfn_mutate_at(
multi_sfn,
vars(-one_of(vars_to_not_mutate)),
list(~ case_when(
ws > 25 ~ NA_real_,
TRUE ~ .
))
)
#> Warning: Unknown columns: `swc_shallow`
multi_mutated_2[['ARG_TRE']]
#> sfn_data object
#> Data from ARG_TRE site
#>
#> Data kindly provided by Seabstian Pfautsch from University of Sydney
#> and Pablo Peri from Instituto Nacional de Tecnología Agropecuaria
#>
#> Site related literature: DOI 10.1007/s00468-013-0935-4
#>
#> Sapflow data: 312 observations of 4 trees/plants
#> Species present: Nothofagus antarctica
#>
#> Environmental data: 312 observations.
#> Variables present:
#> ta rh vpd sw_in ws precip swc_shallow ppfd_in ext_rad
#>
#> Biome: Temperate forest
#>
#> TIMESTAMP span: 2009-11-18 -03--2009-11-30 23:00:00 -03
#>
#> Solar TIMESTAMP span: 2009-11-17 22:24:58 UTC--2009-11-30 21:20:48 UTC
#>
#> Sapflow data flags:
#> USER_MODF
#> 1248
#>
#> Environmental data flags:
#> RANGE_WARN OUT_WARN CALCULATED
#> 18 24 624*_metricsmulti_metrics <- daily_metrics(multi_sfn)
#> [1] "Crunching data for ARG_TRE. In large datasets this could take a while"
#> [1] "General data for ARG_TRE"
#> [1] "Crunching data for ARG_MAZ. In large datasets this could take a while"
#> [1] "General data for ARG_MAZ"
#> [1] "Crunching data for AUS_CAN_ST2_MIX. In large datasets this could take a while"
#> [1] "General data for AUS_CAN_ST2_MIX"
multi_metrics[['ARG_TRE']][['sapf']]
#> # A tibble: 14 × 21
#> TIMESTAMP ARG_TRE…¹ ARG_T…² ARG_T…³ ARG_T…⁴ ARG_T…⁵ ARG_T…⁶ ARG_T…⁷
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2009-11-17 00:00:00 308. 173. 303. 255. 20.7 23.2 14.0
#> 2 2009-11-18 00:00:00 507. 376. 432. 490. 170. 174. 130.
#> 3 2009-11-19 00:00:00 541. 380. 391. 524. 262. 169. 150.
#> 4 2009-11-20 00:00:00 330. 218. 272. 334. 139. 67.2 74.6
#> 5 2009-11-21 00:00:00 338. 219. 278. 351. 190. 108. 113.
#> 6 2009-11-22 00:00:00 384. 243. 310. 383. 268. 172. 184.
#> 7 2009-11-23 00:00:00 492. 300. 390. 513. 327. 200. 228.
#> 8 2009-11-24 00:00:00 573. 389. 497. 626. 313. 222. 261.
#> 9 2009-11-25 00:00:00 601. 400. 484. 644. 193. 133. 170.
#> 10 2009-11-26 00:00:00 502. 360. 450. 613. 277. 233. 308.
#> 11 2009-11-27 00:00:00 544. 411. 506. 740. 271. 221. 285.
#> 12 2009-11-28 00:00:00 573. 451. 589. 840. 180. 169. 249.
#> 13 2009-11-29 00:00:00 371. 285. 357. 547. 233. 220. 197.
#> 14 2009-11-30 00:00:00 386. 293. 381. 602. 273. 209. 288.
#> # … with 13 more variables: 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>,
#> # ARG_TRE_Nan_Jt_1_centroid <dbl>, ARG_TRE_Nan_Jt_2_centroid <dbl>,
#> # ARG_TRE_Nan_Jt_3_centroid <dbl>, ARG_TRE_Nan_Jt_4_centroid <dbl>, and …