The package forrescalc is developed to analyse tree
measure data from Vandekerkhove et al. (2021) that are saved in a
Fieldmap database. The package contains functions to
aggregate the data to different levels. As the dataset is rather large
and these calculations take a lot of time, the package also contains
functions to save results in a git repository and retrieve them
afterwards for further analysis or visualisation of results. Primary
results of the aggregations are saved in the git repository
forresdat, which is meant to be data source for
aggregated data on forests. Calculations are done by using ID codes, and
lookup tables for these codes are saved as separate tables in
forresdat.
To install forrescalc from the INBO universe, start a new
R session and run this code (before loading any packages):
# Enable the INBO universe
# (not needed for INBO employees, as this is the default setting)
options(
repos = c(
inbo = "https://inbo.r-universe.dev", CRAN = "https://cloud.r-project.org"
)
)
# Install the package
install.packages("forrescalc")To install forrescalc from GitHub, start a new R session
and run this code (before loading any packages):
Some functions require local access to the GitHub repository forresdat.
The package forrescalc can be used for different
reasons:
forresdat up-to-dateFieldmap database or
forresdat to do visualisations or analysesFieldmap databaseThe next part shortly describes some common tasks and mentions the
functions that are useful for these tasks. For detailed information on
the use of specific functions (including an example), we refer to the help
of the function (accessible in R by typing the function name
preceded by a ?, for instance
?load_data_dendrometry). Updating forresdat
should be done following a strict routine, for other tasks it is up to
the user to cherry-pick the desired functions.
Fieldmap databaseWith the calculations for forresdat in mind, we
developed some functions to load specific data from the
Fieldmap database: load_plotinfo(),
load_data_dendrometry(), load_data_shoots(),
load_data_deadwood(),
load_data_regeneration(),
load_data_vegetation() and
load_data_herblayer(). After retrieving data from the
database, they might calculate some additional variables that are needed
for further analysis (e.g. they calculate year starting
from the observation date). The output of these functions is ready to be
used in the calculation functions.
To be able to calculate tree volumes starting from tree and shoot
data, height models are available from git repository forresheights
using function load_height_models().
To validate the Fieldmap database, package
forrescalc also contains ‘check’-functions for different
tables of the database, and an umbrella function
check_data_fmdb() that runs all these functions and lists
all missing data and wrong input.
The calculations, which are in fact aggregations of very detailed data on tree level to plot or plot-species level, are grouped in 3 umbrella functions:
calculate_dendrometry()calculate_regeneration()calculate_vegetation()Each of these functions bundles different subfunctions for the same
dataset(s), but with a result on a different level.
calculate_dendrometry() performs for instance the
subfunctions calc_dendro_plot(),
calc_dendro_plot_species(),
calc_deadw_decay_plot(),
calc_deadw_decay_plot_species(),
calc_diam_plot() and calc_diam_plot_species()
(after calculating tree volumes using functions
compose_stem_data(),
calc_variables_stem_level(),
calc_variables_tree_level() and
calc_intact_deadwood()). The result of
calculate_dendrometry() is a list of 6 dataframes, each
being the result of one of the subfunctions. To update
forresdat, all subfunctions have to be executed, so here it
is easier to use these bundled functions. Users that only need one
subfunction, can also use these subfunctions.
Subfunctions that aggregate data, have a name starting with the
abbreviation of the above mentioned high level functions
(calc_dendro_, calc_regeneration_ or
calc_vegetation_) and ending with their grouping
variable(s). For instance a function that aggregates dendrometry data on
the level of plot and year is named calc_dendro_plot().
Each of these functions makes this aggregation for different variables,
for instance number of species, basal area and volume. These details are
described in the help of the specific functions
(e.g. ?calc_dendro_plot).
Subfunctions that not typically aggregate data, can have other names:
create_unique_tree_id() brings data from individual
trees over different years togetheradd_zeros(): as absence is not reported explicitly,
this function allows to add records with zero values for missing
species, missing height classes,…compare_periods_per_plot() compares parameters between
periods (years that parameters are measured)create_statistics() gives summary statistics for the
specified variables on the specified levelcalc_diam_statistics_species() gives the diameter
distribution on the level of forest reserve, species and yearAnd there are also some functions that we thought to be useful (or
user request functions) (these are not used to manipulate data for
forresdat):
make_table_wide() changes a dataframe from long to
wide, what you might want to do with (a part of) the results from
create_unique_tree_id()NOTE: to be able to save data to forresdat or
another git repository, one should first clone the repository to a local
RStudio project. This can be done following the 4 steps described here
(see also the 2 figures below the text). The https link to
forresdat can be copied from its GitHub page.
With the workflow for the update of forresdat in mind,
we developed 3 functions to save the results from the umbrella
functions: save_results_forresdat() (to save the tables in
forresdat or another git repository),
save_results_access() (equivalent function to save the
tables in an Access database) and save_results_csv()
(equivalent function to save the tables as .csv). All three
functions save the dataframes from the list as separate tables. They use
the functionality of the git2rdata
package.
Tables from git can be loaded again in R with the function
read_forresdat_table(), and the whole ‘data package’
forresdat can be loaded using
read_forresdat(). The result of the latter is in the
frictionless data package format and it can be treated
using the frictionless
package.
Two other functions allow to copy tables from an Access database to a
git repository and visa versa (from_access_to_forresdat()
and from_forresdat_to_access()). The first one allows to
add lookup tables to forresdat and the second one might be
useful to copy tables from forresdat to a personal analysis
database in Access.
Functions save_results_forresdat() and
from_access_to_forresdat() save tables in your local copy
of the git repository (grouped as 1 commit for each function, and with
adapting the .json file that allows it to be used as a
frictionless data package). Changes can be checked in the
local repository (project forresdat, tab Git,
button History) and pushed to the remote
repository on GitHub. If you are not satisfied with the changes, you can
remove the last commit with the function
remove_last_commit_forresdat(), but only BEFORE YOU PUSHED
THIS COMMIT TO THE REMOTE.
forresdatTo guarantee consistency, the data repository forresdat
should be updated using a strict routine. This routine is written in a
script called main.R that is in the root of the installed
package (and in the inst folder of the git repository of
the package forrescalc). In this script, paths to the
Fieldmap database and the git repository should be adapted
to the local situation. Evidently changes in the commits should be
checked before pushing them to the remote, as is described above.
When removing a table from forresdat, this should only
be done using function remove_table_forresdat() to make
sure the .json file (that makes it a
frictionless data package) is updated correctly.
Vandekerkhove K., Van de Kerckhove P., Leyman A., De Keersmaeker L., Lommelen E., Esprit M. and Goessens S., 2021. Monitoring programme on strict forest reserves in Flanders (Belgium): Methods and operational protocols: With an overview of the intensive monitoring sites. Reports of the Research Institute for Nature and Forest 2021(28). Research Institute for Nature and Forest, Brussels. https://doi.org/10.21436/inbor.38677490