Package 'forrescalc'

Description

Datasets for which this package has been developed, typically contain measurements of observations. Absence is often not reported explicitly (e.g. there exists no record of a species that is not observed in a plot), while it can be important to include these zero values in an analysis (e.g. mean coverage per species in a certain forest reserve; mean stem number per diameter class in a forest reserve). This function automatically adds missing combinations with value zero to the dataset for each combination of values of the variables given in comb_vars (within each value of grouping_vars). All variables that are not mentioned in comb_vars or grouping_vars, are considered to be numerical variables and will get value 0 (zero). Note that if a certain value is not present in the dataset (or in one of the subsets defined by grouping_vars), it will not be added automatically; at least one record should be added manually for this value (e.g. a plot or diameterclass that doesn't exist in the given dataset, but has to be included in the output). The data in forresdat already contain one record with zeros per plot (with NA value for species and/or diameterclass), resulting in records to be added automatically if 'plot_id' is added to comb_vars.

Usage

add_zeros(
  dataset,
  comb_vars,
  grouping_vars,
  add_zero_no_na = NA,
  remove_na_records_in_comb_vars = NA,
  defaults_to_na = NA
)

Arguments

Value

dataframe based on dataset to which records are added with value 0 (zero) for each measurement.

Examples

library(forrescalc)
library(dplyr)
dendro_by_plot_species <-
  read_forresdat_table(tablename = "dendro_by_plot_species") %>%
  select(
    -year, -plottype, -starts_with("survey_"), -data_processed,
    -starts_with("game_")
  )
add_zeros(
  dataset = dendro_by_plot_species,
  comb_vars = c("plot_id", "period", "species"),
  grouping_vars = c("forest_reserve")
)
add_zeros(
  dataset = dendro_by_plot_species,
  comb_vars = c("plot_id", "period", "species"),
  grouping_vars = c("forest_reserve"),
  remove_na_records_in_comb_vars = "species"
)
add_zeros(
  dataset = dendro_by_plot_species,
  comb_vars = c("plot_id", "period", "species"),
  grouping_vars = c("forest_reserve"),
  defaults_to_na = "stems_per_tree"
)

Description

This function calculates for each plot and year the volume logs and standing dead wood per hectare and per decay stage.

Usage

calc_deadw_decay_plot(plotinfo, data_deadwood = NA, data_dendro_calc = NA)

Arguments

Value

dataframe with columns plot, year, decaystage, vol_log_m3_ha

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")

data_deadwood <- load_data_deadwood(path_to_fieldmapdb)
data_dendro <- load_data_dendrometry(path_to_fieldmapdb)
data_shoots <- load_data_shoots(path_to_fieldmapdb)
data_stems <- compose_stem_data(data_dendro, data_shoots)
# omit argument 'example_dataset = TRUE' below to use all height models
height_model <- load_height_models(example_dataset = TRUE)
data_stems_calc <- calc_variables_stem_level(data_stems, height_model)
data_dendro_calc <- calc_variables_tree_level(data_dendro, data_stems_calc)
plotinfo <- load_plotinfo(path_to_fieldmapdb)
calc_deadw_decay_plot(plotinfo, data_deadwood, data_dendro_calc)

Description

This function calculates for each plot, tree species and year the volume logs and standing dead wood per hectare and per decay stage.

Usage

calc_deadw_decay_plot_species(
  plotinfo,
  data_deadwood = NA,
  data_dendro_calc = NA
)

Arguments

Value

dataframe with columns plot, year, tree_species, decaystage, vol_log_m3_ha

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")

data_deadwood <- load_data_deadwood(path_to_fieldmapdb)
data_dendro <- load_data_dendrometry(path_to_fieldmapdb)
data_shoots <- load_data_shoots(path_to_fieldmapdb)
data_stems <- compose_stem_data(data_dendro, data_shoots)
# omit argument 'example_dataset = TRUE' below to use all height models
height_model <- load_height_models(example_dataset = TRUE)
data_stems_calc <- calc_variables_stem_level(data_stems, height_model)
data_dendro_calc <- calc_variables_tree_level(data_dendro, data_stems_calc)
plotinfo <- load_plotinfo(path_to_fieldmapdb)
calc_deadw_decay_plot_species(plotinfo, data_deadwood, data_dendro_calc)

Description

This function calculates for each plot and year some values per hectare: number of tree species, number of trees, basal area and volume.

Usage

calc_dendro_plot(data_dendro_calc, data_deadwood, plotinfo)

Arguments

Value

dataframe with columns plot, year, number_of_tree_species, number_of_trees_ha, basal_area_m2_ha, volume_m3_ha

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")

data_dendro <- load_data_dendrometry(path_to_fieldmapdb)
data_shoots <- load_data_shoots(path_to_fieldmapdb)
data_deadwood <- load_data_deadwood(path_to_fieldmapdb)
data_stems <- compose_stem_data(data_dendro, data_shoots)
# omit argument 'example_dataset = TRUE' below to use all height models
height_model <- load_height_models(example_dataset = TRUE)
data_stems_calc <- calc_variables_stem_level(data_stems, height_model)
data_dendro_calc <- calc_variables_tree_level(data_dendro, data_stems_calc)
plotinfo <- load_plotinfo(path_to_fieldmapdb)
calc_dendro_plot(data_dendro_calc, data_deadwood, plotinfo)

Description

This function calculates for each plot, tree species and year some values per hectare: number of trees, basal area and volume.

Usage

calc_dendro_plot_species(data_dendro_calc, data_deadwood, plotinfo)

Arguments

Value

dataframe with columns plot, year, tree_species, number_of_trees_ha, basal_area_m2_ha, volume_m3_ha

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")

data_dendro <- load_data_dendrometry(path_to_fieldmapdb)
data_shoots <- load_data_shoots(path_to_fieldmapdb)
data_deadwood <- load_data_deadwood(path_to_fieldmapdb)
data_stems <- compose_stem_data(data_dendro, data_shoots)
# omit argument 'example_dataset = TRUE' below to use all height models
height_model <- load_height_models(example_dataset = TRUE)
data_stems_calc <- calc_variables_stem_level(data_stems, height_model)
data_dendro_calc <- calc_variables_tree_level(data_dendro, data_stems_calc)
plotinfo <- load_plotinfo(path_to_fieldmapdb)
calc_dendro_plot_species(data_dendro_calc, data_deadwood, plotinfo)

Description

This function calculates for each plot, year and diameter class some values per hectare: number of stems, basal area and volume of standing trees (for coppice based on data on shoot level), and volume of logs (= lying deadwood).

Usage

calc_diam_plot(data_stems_calc, data_deadwood, plotinfo)

Arguments

Value

dataframe with columns plot, year, dbh_class_5cm, number_of_trees_ha, basal_area_m2_ha, volume_m3_ha

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")

data_dendro <- load_data_dendrometry(path_to_fieldmapdb)
data_shoots <- load_data_shoots(path_to_fieldmapdb)
data_stems <- compose_stem_data(data_dendro, data_shoots)
# omit argument 'example_dataset = TRUE' below to use all height models
height_model <- load_height_models(example_dataset = TRUE)
data_stems_calc <- calc_variables_stem_level(data_stems, height_model)
data_deadwood <- load_data_deadwood(path_to_fieldmapdb)
plotinfo <- load_plotinfo(path_to_fieldmapdb)
calc_diam_plot(data_stems_calc, data_deadwood, plotinfo)

Description

This function calculates for each plot, tree species, year and diameter class some values per hectare: number of stems, basal area and volume of standing trees (for coppice based on data on shoot level), and volume of logs (= lying deadwood).

Usage

calc_diam_plot_species(data_stems_calc, data_deadwood, plotinfo)

Arguments

Value

dataframe with columns plot, year, tree_species, dbh_class_5cm, basal_area_m2_ha, volume_m3_ha

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")

data_dendro <- load_data_dendrometry(path_to_fieldmapdb)
data_shoots <- load_data_shoots(path_to_fieldmapdb)
data_stems <- compose_stem_data(data_dendro, data_shoots)
# omit argument 'example_dataset = TRUE' below to use all height models
height_model <- load_height_models(example_dataset = TRUE)
data_stems_calc <- calc_variables_stem_level(data_stems, height_model)
data_deadwood <- load_data_deadwood(path_to_fieldmapdb)
plotinfo <- load_plotinfo(path_to_fieldmapdb)
calc_diam_plot_species(data_stems_calc, data_deadwood, plotinfo)

Description

This function calculates the diameter distribution on the level of forest reserve, species and year

Usage

calc_diam_statistics_species(data_stems)

Arguments

Value

dataframe with columns forest_reserve, species, year and measures on diameter distribution (min, max, mean, median, Q1, Q3)

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
data_dendro <- load_data_dendrometry(path_to_fieldmapdb)
data_shoots <- load_data_shoots(path_to_fieldmapdb)
data_stems <- compose_stem_data(data_dendro, data_shoots)
calc_diam_statistics_species(data_stems)

Description

In Core Areas, some lying deadwood is marked as 'complete tree' by giving variable intact_fragm value 10 (intact) instead of 20 (fragment) to save time (while in general all fragments are measured separately). This function calculates the total volume (sum of bole and crown volume) for this intact deadwood and keeps the initial volume in case of fragments.

Usage

calc_intact_deadwood(data_deadwood)

Arguments

Value

A similar dataframe (data_deadwood) in which the volume of intact deadwood is replaced by a volume calculated based on tariffs. Intermediate results vol_crown_m3 and vol_bole_m3 are added as columns (which are NA in case of deadwood fragments).

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
data_deadwood <- load_data_deadwood(path_to_fieldmapdb)
calc_intact_deadwood(data_deadwood)

Description

This function calculates for each plot, height, species and year the number of regeneration per ha (or interval with mean and confidence interval using a log transformation), the number and percentage of subplots in which the species is regenerating and the approximate rubbing damage percentage per hectare. This calculation is designed for core areas, that consist of different subplots.

Usage

calc_reg_core_area_height_spec(data_regeneration)

Arguments

Value

dataframe with columns plot, species, year, height, nr_of_subplots_with_regeneration, perc_subplots_with_regeneration, approx_rubbing_damage_perc, mean_number_of_regeneration_ha, lci_number_of_regeneration_ha, uci_number_of_regeneration_ha and approx_nr_regeneration_ha.

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
data_regeneration_CA <-
  load_data_regeneration(path_to_fieldmapdb, plottype = "CA")
calc_reg_core_area_height_spec(data_regeneration_CA)

Description

This function calculates for each plot, species and year the number of seedlings and established regeneration per ha (or interval with mean and confidence interval using a log transformation), the number and percentage of subplots in which the species is regenerating and the approximate rubbing damage percentage per hectare. This calculation is designed for core areas,that consist of different subplots.

Usage

calc_reg_core_area_species(data_regeneration)

Arguments

Value

dataframe with columns plot, species, year, nr_of_subplots_with_regeneration, perc_subplots_with_regeneration, mean_number_established_ha, lci_number_established_ha, uci_number_established_ha, mean_number_seedlings_ha, lci_number_seedlings_ha, uci_number_seedlings_ha, mean_rubbing_damage_perc_established, lci_rubbing_damage_perc_established, uci_rubbing_damage_perc_established, mean_rubbing_damage_perc_seedlings, lci_rubbing_damage_perc_seedlings, uci_rubbing_damage_perc_seedlings, approx_nr_established_ha, approx_nr_seedlings_ha, approx_rubbing_damage_perc_established, approx_rubbing_damage_perc_seedlings.

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
data_regeneration_CA <-
  load_data_regeneration(path_to_fieldmapdb, plottype = "CA")
calc_reg_core_area_species(data_regeneration_CA)

Description

This function calculates for each plot and year the number of species, total number of seedlings and established regeneration (or interval with mean and confidence interval using a log transformation) and approximate rubbing damage percentage for seedlings and established regeneration. For core area plots, these variables are calculated for each subplot.

Usage

calc_reg_plot(data_regeneration)

Arguments

Value

dataframe with columns plot, subplot, year, period, number_of_tree_species, nr_of_tree_species_established, mean_number_established_ha, lci_number_established_ha, uci_number_established_ha, mean_number_seedlings_ha, lci_number_seedlings_ha, uci_number_seedlings_ha, mean_rubbing_damage_perc_established, lci_rubbing_damage_perc_established, uci_rubbing_damage_perc_established, mean_rubbing_damage_perc_seedlings, lci_rubbing_damage_perc_seedlings, uci_rubbing_damage_perc_seedlings, approx_nr_established_ha, approx_nr_seedlings_ha, approx_rubbing_damage_perc_established, approx_rubbing_damage_perc_seedlings.

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
data_regeneration <- load_data_regeneration(path_to_fieldmapdb)
calc_reg_plot(data_regeneration)

Description

This function calculates for each plot, tree height class and year the number of species, total number of regeneration (or interval with mean and confidence interval using a log transformation) and approximate rubbing damage percentage for regeneration. For core area plots, these variables are calculated for each subplot.

Usage

calc_reg_plot_height(data_regeneration)

Arguments

Value

dataframe with columns plot, subplot, year, period, height_class, number_of_tree_species, approx_rubbing_damage_perc, mean_number_of_regeneration_ha, lci_number_of_regeneration_ha, uci_number_of_regeneration_ha and approx_nr_regeneration_ha.

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
data_regeneration <- load_data_regeneration(path_to_fieldmapdb)
calc_reg_plot_height(data_regeneration)

Description

This function calculates for each plot, tree height class, species and year the number of regeneration (or interval with mean and confidence interval using a log transformation) and the approximate rubbing damage percentage per hectare for regeneration. For core area plots, these variables are calculated for each subplot.

Usage

calc_reg_plot_height_species(data_regeneration)

Arguments

Value

dataframe with columns plot, subplot, year, height_class, species, approx_rubbing_damage_perc, mean_number_of_regeneration_ha, lci_number_of_regeneration_ha, uci_number_of_regeneration_ha and approx_nr_regeneration_ha.

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
data_regeneration <- load_data_regeneration(path_to_fieldmapdb)
calc_reg_plot_height_species(data_regeneration)

Description

This function calculates for each plot, species and year the number of seedlings and established regeneration per ha (or interval with mean and confidence interval using a log transformation), and the approximate rubbing damage percentage for seedlings and established regeneration. For core area plots, these variables are calculated for each subplot.

Usage

calc_reg_plot_species(data_regeneration)

Arguments

Value

dataframe with columns plot, subplot, species, year, period, mean_number_established_ha, lci_number_established_ha, uci_number_established_ha, mean_number_seedlings_ha, lci_number_seedlings_ha, uci_number_seedlings_ha, mean_rubbing_damage_perc_established, lci_rubbing_damage_perc_established, uci_rubbing_damage_perc_established, mean_rubbing_damage_perc_seedlings, lci_rubbing_damage_perc_seedlings, uci_rubbing_damage_perc_seedlings, approx_nr_established_ha, approx_nr_seedlings_ha, approx_rubbing_damage_perc_established, approx_rubbing_damage_perc_seedlings.

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
data_regeneration <- load_data_regeneration(path_to_fieldmapdb)
calc_reg_plot_species(data_regeneration)

Description

This function calculates additional variables based on measurements, such as

• calc_height_m: calculated height based on dbh_mm and a species specific diameter-height model

• basal_area_m2

• vol_bole_m3: calculated based on dbh_mm, calc_height_m and species specific tariffs

• vol_crown_m3: calculated based on dbh_mm and species specific tariffs

• vol_tot_m3: sum of vol_bole_m3 and vol_crown_m3

• basal_area_alive_m2_ha

• basal_area_dead_m2_ha

• vol_alive_m3_ha

• vol_dead_standing_m3_ha

• vol_bole_alive_m3_ha

• vol_bole_dead_m3_ha

Usage

calc_variables_stem_level(data_stems, height_model)

Arguments

Value

Dataframe with ...

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")

data_dendro <- load_data_dendrometry(path_to_fieldmapdb)
data_shoots <- load_data_shoots(path_to_fieldmapdb)
data_stems <- compose_stem_data(data_dendro, data_shoots)
# omit argument 'example_dataset = TRUE' below to use all height models
height_model <- load_height_models(example_dataset = TRUE)
calc_variables_stem_level(data_stems, height_model)

Description

This function calculates additional variables based on measurements, such as

• nr_of_stems: the number of shoots in the tree (= 1 for an individual tree; >= 1 when coppice)

• individual: true for individual tree or coppice, false if record is a secondary shoot

• calc_height_m: calculated height based on dbh_mm and a species specific diameter-height model

• basal_area_m2

• vol_bole_m3: calculated based on dbh_mm, calc_height_m and species specific tariffs

• vol_crown_m3: calculated based on dbh_mm and species specific tariffs

• vol_tot_m3: sum of vol_bole_m3 and vol_crowwn_m3

• dbh_mm (based on average for coppice trees)

• decaystage (based on average for coppice trees)

• basal_area_alive_m2_ha

• basal_area_dead_m2_ha

• vol_alive_m3_ha

• vol_dead_standing_m3_ha

• vol_bole_alive_m3_ha

• vol_bole_dead_m3_ha

Usage

calc_variables_tree_level(data_dendro, data_stems_calc)

Arguments

Value

Dataframe with ...

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")

data_dendro <- load_data_dendrometry(path_to_fieldmapdb)
data_shoots <- load_data_shoots(path_to_fieldmapdb)
data_stems <- compose_stem_data(data_dendro, data_shoots)
# omit argument 'example_dataset = TRUE' below to use all height models
height_model <- load_height_models(example_dataset = TRUE)
data_stems_calc <- calc_variables_stem_level(data_stems, height_model)
calc_variables_tree_level(data_dendro, data_stems_calc)

Description

This function calculates for each plot, species and year the percentage of subplots in which the species is present and the percentage of subplots where the species is browsed (relative to the plots where it is present). A difference is made between browsed (which contains all damage) and seriously browsed, which is reported if the damage is more than 1/20. This calculation is designed for core areas, that consist of different subplots. Year refers to year of recording of that specific species (source is table data_herblayer), and is possibly different for spring flora than for other species in the same subplot.

Usage

calc_veg_core_area_species(data_herblayer)

Arguments

Value

dataframe with columns plot, species, year (year of recording of specific species, possibly different for spring flora), number_of_subplots (= number of subplots where the species occurs), perc_of_subplots (= percentage of subplots with species), number_of_subplots_browsed, perc_of_subplots_browsed, number_of_subplots_seriously_browsed, perc_of_subplots_seriously_browsed and mean_coverage_class_average_perc

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
data_herblayer_CA <- load_data_herblayer(path_to_fieldmapdb, plottype = "CA")
calc_veg_core_area_species(data_herblayer_CA)

Description

This function calculates for each plot (subplot in case of core area) and year the total coverage and the number of species in the vegetation layer. Year refers to year of the main vegetation survey (source is table "data_vegetation"), and will in some cases differ from the year of the spring flora survey.

Usage

calc_veg_plot(data_vegetation, data_herblayer)

Arguments

Value

dataframe with columns plot, subplot, date, year (year of main vegetation survey, possible deviating year of spring survey not taken into account), number_of_tree_species and min/max/mid cover of the different vegetation layers (moss, herb, shrub, tree), the waterlayer and since 2015 also of the soildisturbance by game.

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
data_vegetation <- load_data_vegetation(path_to_fieldmapdb)
data_herblayer <- load_data_herblayer(path_to_fieldmapdb)
calc_veg_plot(data_vegetation, data_herblayer)

Description

This function calculates additional variables and makes aggregations of individual tree measures on the levels of

• plot and year

• plot, tree species and year

• diameter class, plot and year

• diameter class, plot, tree species and year

and it makes aggregations of volume data on logs on the levels of

• decay stage, plot and year

• decay stage, plot, tree species and year

Usage

calculate_dendrometry(
  data_dendro,
  data_deadwood,
  data_shoots,
  height_model,
  plotinfo
)

Arguments

Value

List of dataframes that are mentioned in the above description

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")

data_dendro <- load_data_dendrometry(path_to_fieldmapdb)
data_deadwood <- load_data_deadwood(path_to_fieldmapdb)
data_shoots <- load_data_shoots(path_to_fieldmapdb)
# omit argument 'example_dataset = TRUE' below to use all height models
height_model <- load_height_models(example_dataset = TRUE)
plotinfo <- load_plotinfo(path_to_fieldmapdb)
calculate_dendrometry(
  data_dendro, data_deadwood, data_shoots, height_model, plotinfo)

Description

This function makes aggregations of tree generation data on the levels of

• plot and year (and subplot for core area)

• plot, height class and year (and subplot for core area)

• plot, tree species and year (and subplot for core area)

• plot, height class, tree species and year (and subplot for core area)

For core area plots it makes additional aggregations on the levels of

• core area, tree species and year

• core area, height class, tree species and year

Usage

calculate_regeneration(data_regeneration)

Arguments

Value

List of dataframes that are mentioned in the above description

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
data_regeneration <- load_data_regeneration(path_to_fieldmapdb)
calculate_regeneration(data_regeneration)

Description

This function makes aggregations of vegetation data on the levels of

• plot and year

• subplot and year (only for plot type 'core area')

• plot, species and year (only for plot type 'core area')

Usage

calculate_vegetation(data_vegetation, data_herblayer)

Arguments

Value

List of dataframes that are mentioned in the above description

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
data_vegetation <- load_data_vegetation(path_to_fieldmapdb)
data_herblayer <- load_data_herblayer(path_to_fieldmapdb)
calculate_vegetation(data_vegetation, data_herblayer)

Description

This function retrieves the important fields of table Deadwood (of all periods) from the given database and checks for missing data or wrong input.

Usage

check_data_deadwood(database, forest_reserve = "all")

Arguments

Value

Dataframe with inconsistent data with ID's and additional columns aberrant_field (which column is wrong) and anomaly (what is wrong with the input)

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
check_data_deadwood(path_to_fieldmapdb)
check_data_deadwood(path_to_fieldmapdb, forest_reserve = "Everzwijnbad")

Description

This function retrieves the important fields of tables Trees, Shoots, Deadwood, Regeneration, Regspecies, Vegetation, Herblayer, Plots and Plotdetails (of all periods) from the given database and checks for missing data or wrong input.

Usage

check_data_fmdb(database, forest_reserve = "all")

Arguments

Value

Dataframe with inconsistent data with layer, ID's and additional columns aberrant_field (which column is wrong) and anomaly (what is wrong with the input)

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
check_data_fmdb(path_to_fieldmapdb)
check_data_fmdb(path_to_fieldmapdb, forest_reserve = "Everzwijnbad")

Description

This function retrieves the important fields of table Herblayer (of all periods) from the given database and checks for missing data or wrong input.

Usage

check_data_herblayer(database, forest_reserve = "all")

Arguments

Value

Dataframe with inconsistent data with ID's and additional columns aberrant_field (which column is wrong) and anomaly (what is wrong with the input)

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
check_data_herblayer(path_to_fieldmapdb)
check_data_herblayer(path_to_fieldmapdb, forest_reserve = "Everzwijnbad")

Description

This function retrieves the important fields of table Plotdetails (of all periods) from the given database and checks for missing data or wrong input.

Usage

check_data_plotdetails(database, forest_reserve = "all")

Arguments

Value

Dataframe with inconsistent data with ID's and additional columns aberrant_field (which column is wrong) and anomaly (what is wrong with the input)

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
check_data_plotdetails(path_to_fieldmapdb)
check_data_plotdetails(path_to_fieldmapdb, forest_reserve = "Everzwijnbad")

Description

This function retrieves the important fields of table Plots from the given database and checks for missing data or wrong input.

Usage

check_data_plots(database, forest_reserve = "all")

Arguments

Value

Dataframe with inconsistent data with ID's and additional columns aberrant_field (which column is wrong) and anomaly (what is wrong with the input)

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
check_data_plots(path_to_fieldmapdb)
check_data_plots(path_to_fieldmapdb, forest_reserve = "Everzwijnbad")

Description

This function retrieves the important fields of table Regeneration (of all periods) from the given database and checks for missing data or wrong input.

Usage

check_data_regeneration(database, forest_reserve = "all")

Arguments

Value

Dataframe with inconsistent data with ID's and additional columns aberrant_field (which column is wrong) and anomaly (what is wrong with the input)

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
check_data_regeneration(path_to_fieldmapdb)
check_data_regeneration(path_to_fieldmapdb, forest_reserve = "Everzwijnbad")

Description

This function retrieves the important fields of tables HeightClass and RegSpecies (of all periods) from the given database and checks for missing data or wrong input.

Usage

check_data_regspecies(database, forest_reserve = "all")

Arguments

Value

Dataframe with inconsistent data with ID's and additional columns aberrant_field (which column is wrong) and anomaly (what is wrong with the input)

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
check_data_regspecies(path_to_fieldmapdb)
check_data_regspecies(path_to_fieldmapdb, forest_reserve = "Everzwijnbad")

Description

This function retrieves the important fields of table Shoots (of all periods) from the given database and checks for missing data or wrong input.

Usage

check_data_shoots(database, forest_reserve = "all")

Arguments

Value

Dataframe with inconsistent data with ID's and additional columns aberrant_field (which column is wrong) and anomaly (what is wrong with the input)

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
check_data_shoots(path_to_fieldmapdb)
check_data_shoots(path_to_fieldmapdb, forest_reserve = "Everzwijnbad")

Description

This function retrieves the important fields of table Trees (of all periods) from the given database and checks for missing data or wrong input.

Usage

check_data_trees(database, forest_reserve = "all")

Arguments

Value

Dataframe with inconsistent data with ID's and additional columns aberrant_field (which column is wrong) and anomaly (what is wrong with the input)

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
check_data_trees(path_to_fieldmapdb)
check_data_trees(path_to_fieldmapdb, forest_reserve = "Everzwijnbad")

Description

This function retrieves the important fields of table Vegetation (of all periods) from the given database and checks for missing data or wrong input.

Usage

check_data_vegetation(database, forest_reserve = "all")

Arguments

Value

Dataframe with inconsistent data with ID's and additional columns aberrant_field (which column is wrong) and anomaly (what is wrong with the input)

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
check_data_vegetation(path_to_fieldmapdb)
check_data_vegetation(path_to_fieldmapdb, forest_reserve = "Everzwijnbad")

Description

This function retrieves the important fields of table Trees (of all periods) from the given database and checks for anomalies between periods, such as zombies, shifters, outlier_height, outlier_diameter or walkers.

Usage

check_trees_evolution(database, forest_reserve = "all")

Arguments

Value

Dataframe with inconsistent data with ID's and additional columns aberrant_field (which column is wrong) and anomaly (what is wrong with the input)

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
check_trees_evolution(path_to_fieldmapdb)
check_trees_evolution(path_to_fieldmapdb, forest_reserve = "Everzwijnbad")

Description

This function compares for each plot (and other provided variables) the differences between periods/years for the column names given in parameter measure_vars. It gives results for differences between subsequent measures (based on period) and between the last and the first measure. All column names of the dataset that are not added to parameter measure_vars, are considered as grouping variables, except for period. If the result is not as expected, please verify that the dataset only consists of grouping variables, variables added to measure_vars and period.

Usage

compare_periods_per_plot(dataset, measure_vars, replace_na_in_vars = NA)

Arguments

Value

dataframe with columns plot, year_diff, n_years, grouping variables and differences between periods for each column of measure_vars

Examples

library(forrescalc)
library(dplyr)
treenr_by_plot <-
  read_forresdat_table(tablename = "dendro_by_plot") %>%
  select(
    period, year, plot_id, number_of_tree_species, number_of_trees_ha
  ) %>%
  distinct()
compare_periods_per_plot(
  treenr_by_plot, c("year", "number_of_tree_species", "number_of_trees_ha")
)

Description

This function replaces in the given dendrometric data (result from function load_data_dendrometry()) the diameters, height, decay stage and info on intact/snag from coppice trees by their separate stems given in the shoot data (result from function load_data_shoots()).

Usage

compose_stem_data(data_dendro, data_shoots, extra_variables = FALSE)

Arguments

Value

Dataframe with shoot data

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
data_dendro <- load_data_dendrometry(path_to_fieldmapdb)
data_shoots <- load_data_shoots(path_to_fieldmapdb)
compose_stem_data(data_dendro, data_shoots)

#to include iufro-classes and other additional variables:
data_dendro <-
  load_data_dendrometry(path_to_fieldmapdb, extra_variables = TRUE)
data_shoots <-
  load_data_shoots(path_to_fieldmapdb, extra_variables = TRUE)
compose_stem_data(data_dendro, data_shoots, extra_variables = TRUE)

Description

This function calculates statistics for the given data (e.g. from the git-repository forresdat) on the specified level (e.g. forest_reserve, period and species) and for the specified variables (e.g. basal_area and volume). Calculated statistics include number of observations, mean, variance and confidence interval with lower and upper limit (lci and uci).

These summary statistics are calculated on the given data, not taking into account absence of observations unless explicitly added as a record with value zero. E.g. if a certain species only occurs in 3 plots out of 10 and no records are added for the 7 remaining plots, the summary statistics (e.g. mean coverage) are calculated on 3 plots. Records with value zero for certain variables (e.g. coverage of a certain species or number of trees for a certain diameter class) can automatically be added using the function add_zeros().

In case of intervals, the variance and confidence interval are calculated based on the minimum and maximum values of the intervals of the individual records (which is considered a CI, so lci and uci can serve as min and max). For this, dataset must contain columns with minimum and maximum values, variables must contain a name for the output of this variable, and interval_information must contain the variable names for minimum, maximum and output that should be used. In interval_information it can be specified if a logarithmic transformation is needed to compensate of unequal interval widths. In this case, mean and the confidence interval are transformed back, but variance is not, as this result would be confusing rather than useful. For typical forresdat variables, the default value of interval_information can be used and in this case, the variable mentioned in variables should be named after the values in forresdat, omitting min_, ⁠_min⁠, max_ or ⁠_max⁠ (see example on interval data).

Usage

create_statistics(
  dataset,
  level = c("period", "forest_reserve"),
  variables,
  include_year_range = FALSE,
  na_rm = FALSE,
  interval_information = suppressMessages(read_csv2(system.file("extdata/class_data.csv",
    package = "forrescalc")))
)

Arguments

Value

dataframe with the columns chosen for level, a column variable with the chosen variables, and the columns n_obs, mean, variance, lci (lower limit of confidence interval) and uci (upper limit of confidence interval)

Examples

library(forrescalc)
dendro_by_plot <- read_forresdat_table(tablename = "dendro_by_plot")
create_statistics(
  dataset = dendro_by_plot,
  level = c("forest_reserve", "period"),
  variables = "vol_alive_m3_ha"
)
dendro_by_diam_plot_species <-
  read_forresdat_table(tablename = "dendro_by_diam_plot_species")
create_statistics(
  dataset = dendro_by_diam_plot_species,
  level = c("forest_reserve", "year", "species", "dbh_class_5cm"),
  variables = c("basal_area_alive_m2_ha", "basal_area_dead_m2_ha")
)
#example on interval data (shrub_cover and tree_cover)
veg_by_plot <- read_forresdat_table(tablename = "veg_by_plot")
create_statistics(dataset = veg_by_plot,
  level = c("forest_reserve", "period", "plottype"),
  variables = c("number_of_species", "shrub_cover", "tree_cover")
)
# example on data with confidence interval (number_established_ha and
# number_seedlings_ha)
reg_by_plot <-
  read_forresdat_table(tablename = "reg_by_plot")
create_statistics(dataset = reg_by_plot,
  level = c("forest_reserve", "period", "plot_id"),
  variables = c("number_established_ha", "number_seedlings_ha")
)

Description

This function creates a unique ID for each tree, that allows to group (f.e. by use of make_table_wide()) all given information on the life stages of an individual tree during different measures.

Usage

create_unique_tree_id(data_dendro)

Arguments

Value

a dataset with 1 record per tree measurement, containing the given data of each tree in different years (= data_dendro) and a link to a unique tree_id.

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
data_dendro <-
  load_data_dendrometry(path_to_fieldmapdb, extra_variables = TRUE)
create_unique_tree_id(data_dendro)

Description

This function loads one or more tables from the access database (or an SQLite database) and saves them in the git repository forresdat. Table names in camel case in the database are renamed to snake case before saving in forresdat.

Usage

from_access_to_forresdat(
  database,
  tables,
  repo_path,
  metadata_path,
  push = FALSE,
  strict = TRUE,
  branch = "develop"
)

Arguments

Value

No value is returned, the tables are saved in the git repository.

Examples

## Not run: 
#make a local clone of forresdat and change path before running
library(forrescalc)
# add path to your local clone of forresdat
path_to_forresdat <- "xxx/forresdat"
# if you don't have a local clone yet, make it:
git2r::clone("https://github.com/inbo/forresdat.git", path_to_forresdat)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
# add path to metadata here
temp <- tempfile(fileext = ".xlsx")
dl <- googledrive::drive_download(
         googledrive::as_id("12x2H9lp86R-AFPdN2JXB9nqwJ2_A6PF6"),
         path = temp, overwrite = TRUE
       )

from_access_to_forresdat(
  database = path_to_fieldmapdb,
  tables = c("qCoverHerbs", "qtotalCover"),
  repo_path = path_to_forresdat,
  metadata_path = temp
)

## End(Not run)

Description

This function loads one or more tables from git repository forresdat and saves them in an Access (or SQLite) database.

Usage

from_forresdat_to_access(
  tables,
  database,
  remove_tables = FALSE,
  plottype = NA,
  join_plotinfo = TRUE
)

Arguments

Value

No value is returned, the tables are saved in the access database.

Examples

library(forrescalc)
# (add path to your own database here)
path_to_database <- "my-db.sqlite"
from_forresdat_to_access(
  tables = "dendro_by_plot",
  database = path_to_database
)
# if tables don't contain column plot_id, or it is not relevant to add
# information on the plots, add argument join_plotinfo = FALSE
from_forresdat_to_access(
  tables = c("qalive_dead", "qdecaystage"),
  database = path_to_database,
  join_plotinfo = FALSE
)

file.remove("my-db.sqlite")

Description

This function returns a factor with diameter classes of 5 cm for a given vector with diameter data in mm.

Usage

give_diamclass_5cm(diameterdata)

Arguments

Value

vector with factors in diameter classes of 5 cm

Examples

library(forrescalc)
give_diamclass_5cm(c(80, 1512, 2222))

Description

This function queries the given database to retrieve data on deadwood (logs) (ready for use in calculate_dendrometry() function).

Usage

load_data_deadwood(
  database,
  plottype = NA,
  forest_reserve = NA,
  extra_variables = FALSE,
  processed = TRUE
)

Arguments

Value

Dataframe with data on logs

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
load_data_deadwood(path_to_fieldmapdb)

Description

This function queries the given database to retrieve data on dendrometry (ready for use in calculate_dendrometry() function).

Usage

load_data_dendrometry(
  database,
  plottype = NA,
  forest_reserve = NA,
  extra_variables = FALSE,
  processed = TRUE
)

Arguments

Value

Dataframe with dendrometry data

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
load_data_dendrometry(path_to_fieldmapdb)

Description

This function queries the given database to retrieve data on vegetation (ready for use in calculate_vegetation() function). year_main_survey refers to year of the main vegetation survey (source is table vegetation), while year refers to year of recording of that specific species (possibly different for spring flora; source is table herblayer)

Usage

load_data_herblayer(
  database,
  plottype = NA,
  forest_reserve = NA,
  processed = TRUE
)

Arguments

Value

Dataframe with vegetation data on the species level ('herb layer'), containing columns as species, coverage_id, browse_index_id, date_vegetation (= date of survey of specific species, different for spring flora and other flora in the same plot), year (= year of survey of specific species, possibly different for spring flora and other flora), ....

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
load_data_herblayer(path_to_fieldmapdb)

Description

This function queries the given database to retrieve data on regeneration (ready for use in calculate_regeneration() function).

Usage

load_data_regeneration(
  database,
  plottype = NA,
  forest_reserve = NA,
  processed = TRUE
)

Arguments

Value

Dataframe with regeneration data

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
load_data_regeneration(path_to_fieldmapdb)
load_data_regeneration(path_to_fieldmapdb, plottype = "CP")

Description

This function queries the given database to retrieve additional data on shoots to use with dendrometry data.

Usage

load_data_shoots(database, extra_variables = FALSE)

Arguments

Value

Dataframe with shoot data

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
load_data_shoots(path_to_fieldmapdb)

Description

This function queries the given database to retrieve data on vegetation (ready for use in calculate_vegetation function).

Usage

load_data_vegetation(
  database,
  plottype = NA,
  forest_reserve = NA,
  processed = TRUE
)

Arguments

Value

Dataframe with vegetation data, containing columns as total_herb_cover, total_shrub_cover, total_tree_cover, total_soildisturbance_game, date_vegetation (= date of vegetation survey), year_main_survey (= year of vegetation survey), ....

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
load_data_vegetation(path_to_fieldmapdb)

Description

This function groups the information on height models from the .csv files in the git repository forresheights together in one dataframe.

Usage

load_height_models(example_dataset = FALSE)

Arguments

Value

Dataframe with height model data

Examples

## Not run: 
# example ignored during checks due to high elapsed time
library(forrescalc)
load_height_models()

## End(Not run)

Description

This function queries the given database to retrieve additional data on plots to save in forresdat and link with the datasets that are saved there.

Usage

load_plotinfo(database, plottype = NA, forest_reserve = NA, processed = TRUE)

Arguments

Value

Dataframe with columns plot_id, plottype, forest_reserve, period, year of dendrometric survey and information on (1) whether there has been a dendro, deadwood, regeneration and/or vegetation survey and (2) whether the data have been processed or not.

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
load_plotinfo(path_to_fieldmapdb)

Description

This function changes a dataframe from long to wide format, e.g. to show data from 2 different periods in different columns.

Usage

make_table_wide(table_long, column_to_repeat, columns_for_comparison)

Arguments

Value

the dataframe in long format

Examples

library(forrescalc)
library(dplyr)
table_long <- read_forresdat_table(tablename = "dendro_by_plot_species") %>%
  filter(plot_id < 110) %>%
  select(plot_id, species, period, number_of_trees_ha, vol_alive_m3_ha)
table_wide <-
  make_table_wide(
    table_long, column_to_repeat = "period",
    columns_for_comparison = c("number_of_trees_ha", "vol_alive_m3_ha"))
#if number_of_trees_ha is not mentioned in columns_for_comparison, it is
#considered as a grouping variable while it has different values for each
#period.
#This gives an unwanted result with still many rows and a lot of NA values:
table_wide <-
  make_table_wide(table_long, column_to_repeat = "period",
                  columns_for_comparison = c("vol_alive_m3_ha"))

Description

This function reads the data package from git repository forresdat (and saves the forresdat data to a local temp directory to avoid unneeded downloading in the future). This data package contains both data and metadata and can be explored using functions of the frictionless package.

Data available in forresdat only contain observations, so no records with zero values are added for for instance species that were not observed and hence absent. These zero value records can easily be added by using the function add_zeros().

The different tables of this dataset contain data that are collected using 2 different methods (plot types): circular plots (CP) and core areas (CA). It is advised to only use one of them for analyses, as the data are likely to differ due to method related differences.

General information on the plot level is available in table plotinfo, which can easily be joined to other tables on plot_id and period (or only plot_id if period is absent).

Usage

read_forresdat()

Value

A frictionless data package with all tables and metadata from GitHub repository forresdat, which can be explored using package frictionless. To be able to recall the version of the data, this data package contains an attribute with the version number of the release of forresdat from which the data are taken.

Examples

library(forrescalc)
datapackage <- read_forresdat()
frictionless::resources(datapackage)
attr(datapackage, "forresdat")

Description

This function reads a table in .csv format from git repository forresdat (and saves the forresdat data to a local temp directory to avoid unneeded downloading in the future). Data available in forresdat only contains observations, so no records with zero values are added for for instance species that were not observed and hence absent. These zero value records can easily be added by using the function add_zeros(). To load table plotinfo, set argument join_plotinfo = FALSE.

Usage

read_forresdat_table(
  tablename,
  join_plotinfo = TRUE,
  plottype = c("CP", "CA", "all")
)

Arguments

Value

A dataframe with the specified table, default columns plottype, forest_reserve, survey_dendro/deadw/reg/veg (TRUE or FALSE) and data_processed (TRUE or FALSE). To be able to recall the version of the data, this dataframe contains an attribute with the version number of the release of forresdat from which the data are taken.

Examples

library(forrescalc)
data_dendro <- read_forresdat_table(tablename = "dendro_by_plot")
data_dendro
attr(data_dendro, "forresdat")

Description

This function removes the last commit from the active branch of the specified git repository. ONLY USE THIS FUNCTION IF YOUR COMMIT IS NOT YET PUSHED TO THE REMOTE!!! This function is meant for users that are not familiar with Git to easily remove an automatically generated commit in forresdat after they discovered mistakes in it.

Usage

remove_last_commit_forresdat(repo_path)

Arguments

Value

A dataframe with the specified table

Examples

## Not run: 
#change paths before running
library(forrescalc)
# add path to your local clone of forresdat
path_to_forresdat <- "xxx/forresdat"

# only run this after writing a commit with `save_results_forresdat()` or
# `from_access_to_forresdat()` that has not yet been pushed to Github!
remove_last_commit_forresdat(repo_path = path_to_forresdat)

## End(Not run)

Description

This function removes one or more tables from the data package forresdat by making a commit on a local clone of the git repository. While removing the table(s), it also updates the metadata (.json file)

Usage

remove_table_forresdat(tables, repo_path, push = FALSE, branch = "develop")

Arguments

Value

No value is returned, the tables are removed from the git repository.

Examples

## Not run: 
#make a local clone of forresdat and change path before running
library(forrescalc)
# add path to your local clone of forresdat
path_to_forresdat <- "xxx/forresdat"
# if you don't have a local clone yet, make it:
git2r::clone("https://github.com/inbo/forresdat.git", path_to_forresdat)

remove_table_forresdat(
  tables = c("qCoverHerbs", "qtotalCover"),
  repo_path = path_to_forresdat
)

## End(Not run)

Description

This function saves the results from calculations in the forrescalc package (or any other named list with dataframes) in an Access database. List item names will be used to name each of the tables, which contain as a content the different dataframes.

Usage

save_results_access(results, database, remove_tables = FALSE)

Arguments

Value

No value is returned, data are saved in the specified database

Examples

library(forrescalc)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")

# do calculations
data_dendro <- load_data_dendrometry(path_to_fieldmapdb)
data_deadwood <- load_data_deadwood(path_to_fieldmapdb)
data_shoots <- load_data_shoots(path_to_fieldmapdb)
# omit argument 'example_dataset = TRUE' below to use all height models
height_model <- load_height_models(example_dataset = TRUE)
plotinfo <- load_plotinfo(path_to_fieldmapdb)
result_dendro <-
  calculate_dendrometry(
    data_dendro, data_deadwood, data_shoots, height_model, plotinfo)

# save the results
save_results_access(result = result_dendro, database = path_to_fieldmapdb)
# Repeating the previous line of code will give an error, because you try to
# overwrite a table that was already saved in the database on the first run.
# To overwrite previously saved tables, use this command:
save_results_access(
  result = result_dendro, database = path_to_fieldmapdb, remove_tables = TRUE
)
# To remove the tables again in the example database (undo the changes),
# use this code:
con <- forrescalc:::connect_to_database(path_to_fieldmapdb)
for (tablename in names(result_dendro)) {
  DBI::dbRemoveTable(con, tablename)
}
DBI::dbDisconnect(con)

Description

This function saves the results from calculations in the forrescalc package (or any other named list with dataframes) in a predefined folder. List item names will be used to name each of the tables, which contain as a content the different dataframes.

Usage

save_results_csv(results, output_dir)

Arguments

Value

No value is returned, data are saved in the specified folder

Examples

library(forrescalc)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
path_to_plotlevel_csv <- getwd()
data_regeneration <- load_data_regeneration(database = path_to_fieldmapdb)
regeneration <- calculate_regeneration(data_regeneration)
save_results_csv(results = regeneration, output_dir = path_to_plotlevel_csv)

files <- list.files()
files <- files[grepl("^reg_by.*\\.csv$", files)]
file.remove(files)

Description

This function saves the results from calculations by the forrescalc package (or any other named list with dataframes) in git repository forresdat. List item names will be used to name each of the tables, which contain as a content the different dataframes.

Usage

save_results_forresdat(
  results,
  repo_path,
  metadata_path,
  push = FALSE,
  strict = TRUE,
  branch = "develop"
)

Arguments

Value

No value is returned, data are saved in the specified git repository

Examples

## Not run: 
#make a local clone of forresdat and change path before running
library(forrescalc)
# add path to your local clone of forresdat
path_to_forresdat <- "xxx/forresdat"
# if you don't have a local clone yet, make it:
git2r::clone("https://github.com/inbo/forresdat.git", path_to_forresdat)
# (add path to your own fieldmap database here)
path_to_fieldmapdb <-
  system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
# add path to metadata here
temp <- tempfile(fileext = ".xlsx")
dl <- googledrive::drive_download(
         googledrive::as_id("17M_TfOyjpqLzsFqQ_w1DXitzI7tnULR6"),
         path = temp, overwrite = TRUE
       )

data_regeneration <- load_data_regeneration(path_to_fieldmapdb)
result_regeneration <- calculate_regeneration(data_regeneration)
save_results_forresdat(
  results = result_regeneration,
  repo_path = path_to_forresdat,
  metadata_path = temp
)

## End(Not run)