knitr::opts_chunk$set(collapse = T, comment = "#>") knitr::opts_chunk$set(fig.width=7, fig.height=5) options(tibble.print_min = 6L, tibble.print_max = 6L) library(forestmangr)
For this example we'll use a database of forestry inventories done in the amazon forest, and make a phytosociological analysis of the area.
library(forestmangr) data(exfm20) data_ex <- exfm20 data_ex
First we'll calculate the diversity indexes of the area, with the species_diversity
function. It just needs the data and column name for species:
species_diversity(data_ex, "scientific.name")
We can evaluate similarity between plots by the Jaccard index, using the similarity_matrix
function:
similarity_matrix(data_ex, "scientific.name", "transect", index = "Jaccard")
We can also generate a dendrogram for this analysis:
similarity_matrix(exfm20, "scientific.name", "transect", index = "Jaccard", dendrogram = TRUE, n_groups = 3)
To evaluate the level of aggregation among species in the area, we can use the species_aggreg
function:
species_aggreg(data_ex, "scientific.name", "transect")
We can also evaluate the horizontal structure of the forest. To do this, we can use the forest_structure
function:
forest_structure(data_ex, "scientific.name", "dbh", "transect", 10000)
It's also possible to calculate the vertical and internal structures:
forest_structure(data_ex, "scientific.name", "dbh", "transect", 10000, "canopy.pos", "light")
To check if the forest is regulated, we can use the BDq method, with the bdq_meyer
function:
bdq_meyer(data_ex, "transect", "dbh", 1000,licourt_index = 2)
With the diameter_class
function it's possible to divide the data in diameter classes, and get the number of individuals per species in each class:
classified <- diameter_class(data_ex,"dbh", "transect", 10000, 10, 10, "scientific.name") head(classified)
Another way of visualizing this table is to spread the center of class to columns. We can do this with the cc_to_column
argument:
classified <- diameter_class(data_ex,"dbh", "transect", 10000, 10, 10, "scientific.name", cc_to_column=TRUE) head(classified)
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