florabr

https://wevertonbio.github.io/florabr/
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florabr is an R package designed to retrieve, filter, and spatialize species distribution and...
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### Get available attributes example

Source: https://wevertonbio.github.io/florabr/reference/get_attributes.html

Example showing how to retrieve available biomes, life forms, and states from the loaded data.

```R
data("bf_data") #Load Flora e Funga do Brasil data
# Get available biomes, life forms and states to filter species
d <- get_attributes(data = bf_data,
                    attribute = c("biome", "lifeform", "states"))
```

--------------------------------

### Install and Load mapview

Source: https://wevertonbio.github.io/florabr/articles/Spatialize_florabr.html

Install and load the mapview package for interactive mapping.

```R
#Install mapview if necessary and load package
if(!require(mapview)){
    install.packages("geobr")
}
#Load mapview
library(mapview)
```

--------------------------------

### get_florabr usage example

Source: https://wevertonbio.github.io/florabr/reference/get_florabr.html

Example usage of get_florabr. Creates a temporary directory and downloads the latest database version. The example is wrapped in \dontrun and should be run manually.

```r
if (FALSE) { # \dontrun{
#Creating a folder in a temporary directory
#Replace 'file.path(tempdir(), "florabr")' by a path folder to be create in
#your computer
my_dir <- file.path(file.path(tempdir(), "florabr"))
dir.create(my_dir)
#Download, merge and save data
get_florabr(output_dir = my_dir, data_version = "latest",
            solve_discrepancy = FALSE, overwrite = TRUE, verbose = TRUE)
} # }
```

--------------------------------

### Example: Get states, biomes, and intersections for species

Source: https://wevertonbio.github.io/florabr/reference/get_spat_occ.html

Loads Flora e Funga do Brasil data, defines two example species, and calls get_spat_occ to retrieve SpatVectors for states, biomes, and their intersections. The output messages show the progress for each species. Then plots the results for Araucaria angustifolia.

```r
library(terra)
#> terra 1.8.70
data("bf_data") #Load Flora e Funga do Brasil data
spp <- c("Araucaria angustifolia", "Adesmia paranensis") #Example species
#Get states, biomes and intersection states-biomes of species
spp_spt <- get_spat_occ(data = bf_data, species = spp, state = TRUE,
                       biome = TRUE, intersection = TRUE, state_vect = NULL,
                       biome_vect = NULL, verbose = TRUE)
#> Getting states of Araucaria angustifolia
#> Getting biomes of Araucaria angustifolia
#> Getting biomes of Araucaria angustifolia
#> Getting states of Adesmia paranensis
#> Getting biomes of Adesmia paranensis
#> Getting biomes of Adesmia paranensis


#Plot states of occurrence of Araucaria angustifolia
plot(spp_spt[[1]]$states, main = names(spp_spt)[[1]])

#Plot biomes of occurrence of Araucaria angustifolia
plot(spp_spt[[2]]$biomes, main = names(spp_spt)[[2]])

#Plot intersection between states and biomes of occurrence of
#Araucaria angustifolia
plot(spp_spt[[1]]$states_biomes)
```

--------------------------------

### Install and load geobr

Source: https://wevertonbio.github.io/florabr/articles/Spatialize_florabr.html

Install the geobr package if it is not already present in your environment.

```R
#Install geobr if necessary and load package
if(!require(geobr)){
    install.packages("geobr")
}
#Load geobr
library(geobr)
```

--------------------------------

### Example solving discrepancies with bf_data

Source: https://wevertonbio.github.io/florabr/reference/solve_discrepancies.html

Loads example data, checks and solves discrepancies, and verifies the solution attribute is set to TRUE.

```r
data("bf_data") #Load Flora e Funga do Brasil data
#Check if discrepancies were solved in the dataset
attr(bf_data, "solve_discrepancies")
#> [1] FALSE
#Solve discrepancies
bf_solved <- solve_discrepancies(bf_data)
#Check if discrepancies were solved in the dataset
attr(bf_solved, "solve_discrepancies")
#> [1] TRUE
```

--------------------------------

### Install florabr from GitHub

Source: https://wevertonbio.github.io/florabr/index.html

Installs the development version of florabr from GitHub using the remotes package, then loads the library.

```R
if(!require(remotes)){
    install.packages("remotes")
}

if(!require(florabr)){
remotes::install_github("wevertonbio/florabr")}

library(florabr)
```

--------------------------------

### Example usage of load_florabr

Source: https://wevertonbio.github.io/florabr/reference/load_florabr.html

Demonstrates creating a directory, downloading data with get_florabr, and loading it using load_florabr.

```R
if (FALSE) { # \dontrun{
#Creating a folder in a temporary directory
#Replace 'file.path(tempdir(), "florabr")' by a path folder to be create in
#your computer
my_dir <- file.path(file.path(tempdir(), "florabr"))
dir.create(my_dir)
#Download, merge and save data
get_florabr(output_dir = my_dir, data_version = "latest", overwrite = TRUE,
            verbose = TRUE)
#Load data
df <- load_florabr(data_dir = my_dir, data_version = "Latest_available",
type = "short")
} # }
```

--------------------------------

### Example: Check Version in Current Directory

Source: https://wevertonbio.github.io/florabr/reference/check_version.html

Checks for the presence of the dataset in the current working directory and provides a message if the version is outdated or missing.

```R
#Check if there is a version of Flora e Funga do Brasil data available in the
#current directory
check_version(data_dir = getwd())
#> You do not have any version of Flora e Funga do Brasil in this directory.
#>     The latest version is 393.419. Please, change the directory or
#>     run the function get_florabr() to download the latest version of Flora e
#>     Funga do Brasil.
```

--------------------------------

### Install florabr from CRAN

Source: https://wevertonbio.github.io/florabr/index.html

Installs the stable version of the florabr package from CRAN.

```R
install.packages("florabr")
```

--------------------------------

### Subset species including subspecies and varieties

Source: https://wevertonbio.github.io/florabr/articles/selecting_species.html

This example shows how to include both subspecies and varieties when subsetting species data.

```R
my_sp2 <- subset_species(data = bf, species = accepted_names,
                      include_subspecies = TRUE,
                      include_variety = TRUE,
                      kingdom = "Plantae")
my_sp2[1:5,]
#>                      species                            scientificName
#> 11785      Solanum restingae                 Solanum restingae S.Knapp
#> 26790 Araucaria angustifolia   Araucaria angustifolia (Bertol.) Kuntze
#> 35204 Araucaria angustifolia    Araucaria angustifolia var. alba Reitz
#> 35205 Araucaria angustifolia  Araucaria angustifolia var. caiova Reitz
#> 35206 Araucaria angustifolia Araucaria angustifolia var. caiuva Mattos
#>                 acceptedName kingdom       Group Subgroup        family
#> 11785                   <NA> Plantae Angiosperms     <NA>    Solanaceae
#> 26790                   <NA> Plantae Gymnosperms     <NA> Araucariaceae
#> 35204 Araucaria angustifolia Plantae Gymnosperms     <NA> Araucariaceae
#> 35205 Araucaria angustifolia Plantae Gymnosperms     <NA> Araucariaceae
#> 35206 Araucaria angustifolia Plantae Gymnosperms     <NA> Araucariaceae
#>           genus lifeForm     habitat                 Biome            States
#> 11785   Solanum    Shrub Terrestrial       Atlantic_Forest                BA
#> 26790 Araucaria     Tree Terrestrial Atlantic_Forest;Pampa MG;PR;RJ;RS;SC;SP
#> 35204 Araucaria                                                             
#> 35205 Araucaria                                                             
#> 35206 Araucaria                                                             
#>       vegetationType
#> 11785 Restinga
#> 26790 High_Altitude_Grassland;Mixed_Ombrophyllous_Forest;
#>       Seasonally_Semideciduous_Forest
#> 35204                                                                         
#> 35205                                                                          
#> 35206                                                                        
#>       origin    endemism taxonomicStatus nomenclaturalStatus
#> 11785 Native     Endemic        Accepted             Correct
#> 26790 Native Non-endemic        Accepted             Correct
#> 35204   <NA>        <NA>         Synonym                <NA>
#> 35205   <NA>        <NA>         Synonym                <NA>
#> 35206   <NA>        <NA>         Synonym                <NA>
#>       vernacularName
#> 11785 <NA>
#> 26790 araucaria, pinheiro-do-parana, curi, pinheiro-brasileiro,
#> pinho-do-parana
#> 35204 <NA>
#> 35205 <NA>
#> 35206 <NA>
#>       taxonRank
#> 11785   Species
#> 26790   Species
#> 35204   Variety
#> 35205   Variety
#> 35206   Variety
```

--------------------------------

### Extracting species synonyms example

Source: https://wevertonbio.github.io/florabr/reference/get_synonym.html

Example usage of get_synonym to retrieve synonyms for specific species, including the resulting data frame output.

```R
data("bf_data") #Load Flora e Funga do Brasil data
#Species to extract synonyms
spp <- c("Araucaria angustifolia", "Adesmia paranensis")
spp_synonyms <- get_synonym(data = bf_data, species = spp,
                            include_subspecies = TRUE,
                            include_variety = TRUE)
spp_synonyms
#>                  acceptedName                                    synonym
#> 35312  Araucaria angustifolia           Araucaria angustifolia var. alba
#> 35313  Araucaria angustifolia         Araucaria angustifolia var. caiova
#> 35314  Araucaria angustifolia         Araucaria angustifolia var. caiuva
#> 35315  Araucaria angustifolia      Araucaria angustifolia var. dependens
#> 35316  Araucaria angustifolia    Araucaria angustifolia var. indehiscens
#> 35317  Araucaria angustifolia        Araucaria angustifolia var. monoica
#> 35318  Araucaria angustifolia          Araucaria angustifolia var. nigra
#> 35319  Araucaria angustifolia Araucaria angustifolia var. sancti-josephi
#> 35320  Araucaria angustifolia       Araucaria angustifolia var. semialba
#> 35321  Araucaria angustifolia        Araucaria angustifolia var. stricta
#> 35322  Araucaria angustifolia        Araucaria angustifolia var. vinacea
#> 35323  Araucaria angustifolia                       Araucaria brasiliana
#> 35324  Araucaria angustifolia          Araucaria brasiliana var. elegans
#> 35325  Araucaria angustifolia                     Araucaria brasiliensis
#> 35326  Araucaria angustifolia        Araucaria brasiliensis var. saviana
#> 35327  Araucaria angustifolia                           Araucaria dioica
#> 35328  Araucaria angustifolia                          Araucaria elegans
#> 35329  Araucaria angustifolia                       Araucaria ridolfiana
#> 35330  Araucaria angustifolia                          Araucaria saviana
#> 35332  Araucaria angustifolia                      Columbea angustifolia
#> 35333  Araucaria angustifolia                        Columbea brasiliana
#> 60644  Araucaria angustifolia                               Pinus dioica
#> 141010 Araucaria angustifolia       Araucaria brasiliana var. ridolfiana
#> 141020 Araucaria angustifolia                         Araucaria bibbiani
#> 141021 Araucaria angustifolia                       Araucaria lindleyana
#> 141041 Araucaria angustifolia                       Araucaria brasiliana
#> 85308      Adesmia paranensis         Adesmia psoraleoides var. glabrata
#>        taxonomicStatus      nomenclaturalStatus
#> 35312          Synonym                     <NA>
#> 35313          Synonym                     <NA>
#> 35314          Synonym                     <NA>
#> 35315          Synonym                     <NA>
#> 35316          Synonym                     <NA>
#> 35317          Synonym                     <NA>
#> 35318          Synonym                     <NA>
#> 35319          Synonym                     <NA>
#> 35320          Synonym                     <NA>
#> 35321          Synonym                     <NA>
#> 35322          Synonym                     <NA>
#> 35323          Synonym                     <NA>
#> 35324          Synonym                     <NA>
#> 35325          Synonym                     <NA>
#> 35326          Synonym                     <NA>
#> 35327          Synonym                     <NA>
#> 35328          Synonym                     <NA>
#> 35329          Synonym                     <NA>
#> 35330          Synonym                     <NA>
#> 35332          Synonym Legitimate_but_incorrect
#> 35333          Synonym Legitimate_but_incorrect
#> 60644          Synonym                     <NA>
#> 141010         Synonym                     <NA>
#> 141020         Synonym                     <NA>
#> 141021         Synonym                     <NA>
#> 141041         Synonym             Illegitimate
#> 85308          Synonym Legitimate_but_incorrect
```

--------------------------------

### Example: search for 'pinheiro' with exact = TRUE

Source: https://wevertonbio.github.io/florabr/reference/select_by_vernacular.html

Loads the bf_data dataset and searches for species whose vernacular name exactly matches 'pinheiro'. The output shows the resulting data.frame with species like Araucaria columnaris and various Pinus species.

```r
data("bf_data") #Load Flora e Funga do Brasil data
#Search for species whose vernacular name is 'pinheiro'
pinheiro_exact <- select_by_vernacular(data = bf_data,
                                       names = "pinheiro",
                                       exact = TRUE)
pinheiro_exact
#>                     species
#> 134218 Araucaria columnaris
#> 140840    Pinus canariensis
#> 140842      Pinus banksiana
#> 140856      Pinus palustris
#> 140864          Pinus nigra
#> 140890       Pinus echinata
#> 140902     Pinus densiflora
#> 140986    Pinus tecunumanii
#> 140991     Pinus thunbergii
#>                                               scientificName acceptedName
#> 134218               Araucaria columnaris (J.R.Forst.) Hook.         <NA>
#> 140840                               Pinus canariensis C.Sm.         <NA>
#> 140842                                 Pinus banksiana Lamb.         <NA>
#> 140856                                 Pinus palustris Mill.         <NA>
#> 140864                                Pinus nigra J.F.Arnold         <NA>
#> 140890                                  Pinus echinata Mill.         <NA>
#> 140902                      Pinus densiflora Siebold & Zucc.         <NA>
#> 140986 Pinus tecunumanii F.Schwerdtf. ex Eguiluz & J.P.Perry         <NA>
#> 140991                                Pinus thunbergii Parl.         <NA>
#>        kingdom       group subgroup       phylum     class   order
#> 134218 Plantae Gymnosperms     <NA> Tracheophyta Pinopsida Pinales
#> 140840 Plantae Gymnosperms     <NA> Tracheophyta Pinopsida Pinales
#> 140842 Plantae Gymnosperms     <NA> Tracheophyta Pinopsida Pinales
#> 140856 Plantae Gymnosperms     <NA> Tracheophyta Pinopsida Pinales
#> 140864 Plantae Gymnosperms     <NA> Tracheophyta Pinopsida Pinales
#> 140890 Plantae Gymnosperms     <NA> Tracheophyta Pinopsida Pinales
#> 140902 Plantae Gymnosperms     <NA> Tracheophyta Pinopsida Pinales
#> 140986 Plantae Gymnosperms     <NA> Tracheophyta Pinopsida Pinales
#> 140991 Plantae Gymnosperms     <NA> Tracheophyta Pinopsida Pinales
#>               family     genus lifeForm     habitat   biome
#> 134218 Araucariaceae Araucaria     Tree Terrestrial Unknown
#> 140840      Pinaceae     Pinus     Tree Terrestrial Unknown
#> 140842      Pinaceae     Pinus     Tree Terrestrial Unknown
#> 140856      Pinaceae     Pinus     Tree Terrestrial Unknown
#> 140864      Pinaceae     Pinus     Tree Terrestrial Unknown
#> 140890      Pinaceae     Pinus     Tree Terrestrial Unknown
#> 140902      Pinaceae     Pinus     Tree Terrestrial Unknown
#> 140986      Pinaceae     Pinus     Tree Terrestrial Unknown
#> 140991      Pinaceae     Pinus     Tree Terrestrial Unknown
#>                         states     vegetation      origin    endemism
#> 134218 MG;PB;PE;PR;RS;SC;SE;SP Anthropic_Area  Cultivated Non-endemic
#> 140840             MG;RJ;RS;SP Anthropic_Area  Cultivated Non-endemic
#> 140842                      RS Anthropic_Area  Cultivated Non-endemic
#> 140856             PR;RS;SC;SP Anthropic_Area  Cultivated Non-endemic
#> 140864                   RS;SP Anthropic_Area  Cultivated Non-endemic
#> 140890                PR;RS;SP Anthropic_Area  Cultivated Non-endemic
#> 140902                      MG Anthropic_Area  Cultivated Non-endemic
#> 140986                      SP Anthropic_Area Naturalized Non-endemic
#> 140991                   PR;SP Anthropic_Area  Cultivated Non-endemic
#>        taxonomicStatus nomenclaturalStatus
#> 134218        Accepted             Correct
#> 140840        Accepted                <NA>
#> 140842        Accepted                <NA>
#> 140856        Accepted                <NA>
#> 140864        Accepted                <NA>
#> 140890        Accepted                <NA>
#> 140902        Accepted                <NA>
#> 140986        Accepted                <NA>
```

--------------------------------

### Generate a PAM with get_pam

Source: https://wevertonbio.github.io/florabr/articles/getting_pam.html

Filter species data using select_species and generate a presence-absence matrix with get_pam. This example demonstrates filtering for endemic tree species in the Amazon biome.

```R
#Load Flora e Funga do Brasil data
data("bf_data")
#Select endemic and native species of trees with occurrence only in Amazon
am_trees <- select_species(data = bf_data,
                          include_subspecies = FALSE,
                          include_variety = FALSE,
                          kingdom = "Plantae",
                          group = "All", subgroup = "All",
                          family = "All", genus = "All",
                          lifeForm = "Tree", filter_lifeForm = "only",
                          habitat = "All", filter_habitat = "in",
                          biome = "Amazon",
                          filter_biome = "only",
                          state = "All", filter_state = "and",
                          vegetation = "All",
                          filter_vegetation = "in",
                          endemism = "Endemic", origin = "Native",
                          taxonomicStatus = "Accepted",
                          nomenclaturalStatus = "All")
#Get presence-absence matrix
pam_am <- get_pam(data = am_trees, by_biome = TRUE, by_state = TRUE,
                 by_vegetation = FALSE, remove_empty_sites = TRUE,
                 return_richness_summary = TRUE,
                 return_spatial_richness = TRUE,
                 return_plot = TRUE)
#Visualize (as tibble) the PAM for the first 5 species and 7 sites
tibble::tibble(pam_am$PAM[1:7, 1:5])
# # A tibble: 7 × 5
#   Biome  States `Euxylophora paraensis` `Galipea congestiflora` `Hortia longifolia`
#   <fct>  <fct>                    <dbl>                   <dbl>               <dbl>
# 1 Amazon AM                           1                       0                   1
# 2 Amazon MA                           1                       1                   0
# 3 Amazon PA                           1                       1                   1
# 4 Amazon TO                           1                       1                   0
# 5 Amazon MT                           0                       0                   1
# 6 Amazon RR                           0                       0                   1
# 7 Amazon AC                           0                       0                   0
```

--------------------------------

### Get available filter options with get_attributes

Source: https://wevertonbio.github.io/florabr/articles/selecting_species.html

Shows how to use get_attributes() to list the available filter options for lifeForm, Biome, and vegetation attributes. The output includes the exact options that can be used in the select_species() function. Note the attribute names are case-sensitive and the output includes a 'Not_found_in_brazil' option for Biome.

```r
#Get available options to filter by lifeForm
get_attributes(data = bf,
               attribute = "lifeForm")[[1]]
#>               lifeForm
#> 1      Aquatic-Benthos
#> 2      Aquatic-Neuston
#> 3     Aquatic-Plankton
#> 4                Shrub
#> 5                 Tree
#> 6               Bamboo
#> 7              Cushion
#> 8             Dendroid
#> 9              Unknown
#> 10          Dracaenoid
#> 13                Herb
#> 14          Flabellate
#> 15             Foliose
#> 16 Liana/scandent/vine
#> 19           Palm_tree
#> 21             Pendent
#> 23            Subshrub
#> 24           Succulent
#> 25            Thallose
#> 26                 Mat
#> 27                Weft
#> 28                Tuft
```

```r
#Get available options to filter by  Biome
get_attributes(data = bf,
               attribute = "Biome")[[1]]
#>                 biome
#> 1              Amazon
#> 2     Atlantic_Forest
#> 3            Caatinga
#> 4             Cerrado
#> 5               Pampa
#> 6            Pantanal
#> 7 Not_found_in_brazil
```

```r
#Get available options to filter by vegetation
get_attributes(data = bf,
               attribute = "vegetation")[[1]]
#>                         vegetation
#> 1                   Anthropic_Area
#> 2                         Caatinga
#> 3            Amazonian_Campinarana
#> 4          High_Altitude_Grassland
#> 5                    Flooded_Field
#> 6                        Grassland
#> 7             Highland_Rocky_Field
#> 8                         Carrasco
#> 9                          Cerrado
#> 10                  Gallery_Forest
#> 11          Inundated_Forest_Igapo
#> 12              Terra_Firme_Forest
#> 13                Inundated_Forest
#> 14    Seasonallly_Deciduous_Forest
#> 15       Seasonal_Evergreen_Forest
#> 16 Seasonally_Semideciduous_Forest
#> 17                      Rainforest
#> 18      Mixed_Ombrophyllous_Forest
#> 19                        Mangrove
#> 20                      Palm_Grove
#> 21                        Restinga
#> 22               Amazonian_Savanna
#> 23              Aquatic_vegetation
#> 24         Rock_outcrop_vegetation
#> 25             Not_found_in_Brazil
```

--------------------------------

### filter_florabr function definition and example usage

Source: https://wevertonbio.github.io/florabr/reference/filter_florabr.html

Two code blocks: the function signature showing all arguments and defaults, and an example usage that loads data, subsets occurrences for a species, and calls filter_florabr with verbose = FALSE. The example returns a list with flagged and cleaned data.frames by default.

```R
filter_florabr(data, occ, species = "species", long = "x", lat = "y",
                      by_state = TRUE, buffer_state = 20, by_biome = TRUE,
                      buffer_biome = 20, by_endemism = TRUE,
                      buffer_brazil = 20, state_vect = NULL,
                      state_column = NULL, biome_vect = NULL,
                      biome_column = NULL, br_vect = NULL,
                      value = "flag&clean", keep_columns = TRUE,
                      verbose = TRUE)
```

```R
data("bf_data") #Load Flora e Funga do Brasil data
data("occurrences") #Load occurrences
pts <- subset(occurrences, species == "Myrcia hatschbachii")
fd <- filter_florabr(data = bf_data, occ = pts,
                    by_state = TRUE, buffer_state = 20,
                    by_biome = TRUE, buffer_biome = 20,
                    by_endemism = TRUE, buffer_brazil = 20,
                    state_vect = NULL,
                    biome_vect = NULL, br_vect = NULL,
                    value = "flag&clean", keep_columns = TRUE,
                    verbose = FALSE)
```

--------------------------------

### load_florabr(data_dir, data_version, type, verbose)

Source: https://wevertonbio.github.io/florabr/reference/load_florabr.html

Loads the Flora e Funga do Brasil database from a local directory. The function returns a data.frame with the specified version and column set, which is required for most package functions.

```APIDOC
## load_florabr(data_dir, data_version, type, verbose)

### Description
Loads the Flora e Funga do Brasil database from a local directory. The function returns a data.frame with the specified version and column set, which is required for most package functions.

### Method
R function

### Endpoint
load_florabr(data_dir, data_version = "Latest_available", type = "short", verbose = TRUE)

### Parameters
#### Arguments
- **data_dir** (character) - Required - The same directory used to save the data downloaded from Flora e Funga do Brasil using the get_florabr function.
- **data_version** (character) - Optional - The version of Flora e Funga do Brasil database to be loaded. Can be "Latest_available" to load the latest version available, or another specified version (e.g., "393.364"). Default = "Latest_available".
- **type** (character) - Optional - Determines the number of columns to load. Can be "short" or "complete". Default = "short". See details.
- **verbose** (logical) - Optional - Whether to display messages during function execution. Set to TRUE to enable display, or FALSE to run silently. Default = TRUE.

### Request Example
```r
load_florabr(data_dir = my_dir, data_version = "Latest_available", type = "short")
```

### Response
#### Value
A data.frame with the specified version (default is the latest available) of the Flora e Funga do Brasil database. This data.frame is necessary to run most of the functions of the package.

#### Details
If type = "short", it loads a data.frame with the 20 columns needed to run other functions: species, scientificName, acceptedName, kingdom, Group, Subgroup, family, genus, lifeForm, habitat, Biome, States, vegetationType, Origin, Endemism, taxonomicStatus, nomenclaturalStatus, vernacularName, taxonRank, and id. If type = "complete", it loads a data.frame with all 39 variables available in the Flora e Funga do Brasil database.
```

--------------------------------

### Example: Extract binomial names from a vector of scientific names

Source: https://wevertonbio.github.io/florabr/reference/get_binomial.html

Example usage of get_binomial with a vector of scientific names. The function is called with default arguments, and the output shows the extracted binomial names, including infraspecific epithets when present (e.g., 'var. alba' and 'subsp. punctata').

```R
spp <- c("Araucaria angustifolia (Bertol.) Kuntze",
         "Araucaria angustifolia var. alba Reitz",
         "Butia catarinensis Noblick & Lorenzi",
         "Butia eriospatha subsp. punctata",
         "Adesmia paranensis Burkart")
spp_new <- get_binomial(species_names = spp,
                       include_subspecies = TRUE,
                       include_variety = TRUE)
spp_new
#> [1] "Araucaria angustifolia"           "Araucaria angustifolia var. alba"
#> [3] "Butia catarinensis"               "Butia eriospatha subsp. punctata"
#> [5] "Adesmia paranensis"
```

--------------------------------

### Get Brazil Polygon

Source: https://wevertonbio.github.io/florabr/articles/Spatialize_florabr.html

Retrieve the Brazil country polygon using geobr.

```R
#Get Brazl Polygon
br <- geobr::read_country()
```

--------------------------------

### get_pam(data, by_biome, by_state, by_vegetation, remove_empty_sites, return_richness_summary, return_spatial_richness, return_plot)

Source: https://wevertonbio.github.io/florabr/reference/get_pam.html

Generates a presence-absence matrix of species based on their distribution (states, biomes, and vegetation types) according to Flora e Funga do Brasil. The function accepts a data.frame imported with load_florabr or generated by select_species or subset_species, and returns a list with the PAM, richness summary, and spatial richness if requested.

```APIDOC
## get_pam(data, by_biome = TRUE, by_state = TRUE, by_vegetation = FALSE, remove_empty_sites = TRUE, return_richness_summary = TRUE, return_spatial_richness = TRUE, return_plot = TRUE)

### Description
Get a presence-absence matrix of species based on its distribution (states, biomes and vegetation types) according to Flora e Funga do Brasil.

### Usage
```r
get_pam(data, by_biome = TRUE, by_state = TRUE,
        by_vegetation = FALSE, remove_empty_sites = TRUE,
        return_richness_summary = TRUE,
        return_spatial_richness = TRUE,
        return_plot = TRUE)
```

### Arguments
- **data** (data.frame) - Required - a data.frame imported with the `load_florabr` function or generated by either `select_species` or `subset_species` functions
- **by_biome** (logical) - Optional - get occurrences by biome. Default = TRUE
- **by_state** (logical) - Optional - get occurrences by State. Default = TRUE
- **by_vegetation** (logical) - Optional - get occurrences by vegetation type. Default = FALSE
- **remove_empty_sites** (logical) - Optional - remove empty sites (sites without any species) from final presence-absence matrix. Default = TRUE
- **return_richness_summary** (logical) - Optional - return a data.frame with the number of species in each site. Default = TRUE
- **return_spatial_richness** (logical) - Optional - return a SpatVector with the number of species in each site. Default = TRUE
- **return_plot** (logical) - Optional - plot map with the number of species in each site. Only works if return_spatial_richness = TRUE. Default = TRUE

### Value
If return_richness_summary and/or return_spatial_richness is set to TRUE, return a list with:
- **PAM**: the presence-absence matrix (PAM)
- **Richness_summary**: a data.frame with the number of species in each site
- **Spatial_richness**: a SpatVector with the number of species in each site (only by State and biome)

If return_richness_summary and return_spatial_richness is set to FALSE, return a presence-absence matrix.

### Examples
```r
data("bf_data") #Load Flora e Funga do Brasil data
#Select endemic and native species of trees with occurrence only in Amazon
am_trees <- select_species(data = bf_data,
                          include_subspecies = FALSE,
                          include_variety = FALSE,
                          kingdom = "Plantae",
                          group = "All", subgroup = "All",
                          family = "All", genus = "All",
                          lifeForm = "Tree", filter_lifeForm = "only",
                          habitat = "All", filter_habitat = "in",
                          biome = "Amazon",
                          filter_biome = "only",
                          state = "All", filter_state = "and",
                          vegetation = "All",
                          filter_vegetation = "in",
                          endemism = "Endemic", origin = "Native",
                          taxonomicStatus = "Accepted",
                          nomenclaturalStatus = "All")
#Get presence-absence matrix
pam_am <- get_pam(data = am_trees, by_biome = TRUE, by_state = TRUE,
                 by_vegetation = FALSE, remove_empty_sites = TRUE,
                 return_richness_summary = TRUE,
                 return_spatial_richness = TRUE,
                 return_plot = TRUE)
```
```

--------------------------------

### Extract species with subset_species

Source: https://wevertonbio.github.io/florabr/reference/subset_species.html

Example usage of subset_species. Loads the built-in bf_data dataset, defines a vector of species names, and calls subset_species with default arguments. The output shows the resulting data.frame with columns such as species, scientificName, kingdom, family, and states. The example demonstrates that the function returns a data.frame with the selected species.

```r
data("bf_data") #Load Flora e Funga do Brasil data
#Species to extract from database
spp <- c("Araucaria angustifolia", "Adesmia paranensis")
spp_bf <- subset_species(data = bf_data, species = spp,
                      include_subspecies = FALSE,
                      include_variety = FALSE)
spp_bf
#>                      species                          scientificName
#> 26797 Araucaria angustifolia Araucaria angustifolia (Bertol.) Kuntze
#> 82146     Adesmia paranensis              Adesmia paranensis Burkart
#>       acceptedName kingdom       group subgroup       phylum         class
#> 26797         <NA> Plantae Gymnosperms     <NA> Tracheophyta     Pinopsida
#> 82146         <NA> Plantae Angiosperms     <NA> Tracheophyta Magnoliopsida
#>         order        family     genus lifeForm     habitat
#> 26797 Pinales Araucariaceae Araucaria     Tree Terrestrial
#> 82146 Fabales      Fabaceae   Adesmia Subshrub Terrestrial
#>                       biome            states
#> 26797 Atlantic_Forest;Pampa MG;PR;RJ;RS;SC;SP
#> 82146       Atlantic_Forest             PR;SC
#>                                                                               vegetation
#> 26797 High_Altitude_Grassland;Mixed_Ombrophyllous_Forest;Seasonally_Semideciduous_Forest
#> 82146                                                            High_Altitude_Grassland
#>       origin    endemism taxonomicStatus nomenclaturalStatus
#> 26797 Native Non-endemic        Accepted             Correct
#> 82146 Native     Endemic        Accepted             Correct
#>                                                                  vernacularName
#> 26797 araucaria, pinheiro-do-parana, curi, pinheiro-brasileiro, pinho-do-parana
#> 82146                                                                      <NA>
#>       taxonRank     id
#> 26797   Species  33971
#> 82146   Species 101033
```

--------------------------------

### Compare simplified and original geometries

Source: https://wevertonbio.github.io/florabr/articles/Spatialize_florabr.html

Visualize the difference in complexity between simplified and non-simplified state geometries.

```R
#Plot only State of Parana as example
par(mfrow = c(1, 2), mar = c(2, 2, 2, 2)) 
plot(br_states[br_states$abbrev_state == "PR"],
     main = "Not simplified geometries", mar = NA)
plot(states[states$abbrev_state == "PR"],
     main = "Simplified geometries",
     mar = NA)
```

--------------------------------

### get_pam function signature

Source: https://wevertonbio.github.io/florabr/reference/get_pam.html

Shows the usage of the get_pam function with its default arguments.

```r
get_pam(data, by_biome = TRUE, by_state = TRUE,
               by_vegetation = FALSE, remove_empty_sites = TRUE,
               return_richness_summary = TRUE,
               return_spatial_richness = TRUE,
               return_plot = TRUE)
```

--------------------------------

### Create directory for florabr data

Source: https://wevertonbio.github.io/florabr/index.html

Creates a directory to save the Flora e Funga do Brasil data. Replace the path with a folder on your computer.

```R
#Creating a folder in a temporary directory
#Replace 'file.path(tempdir(), "florabr")' by a path folder to be create in your computer
my_dir <- file.path(file.path(tempdir(), "florabr"))
dir.create(my_dir)
```

--------------------------------

### Retrieve synonyms with get_synonym

Source: https://wevertonbio.github.io/florabr/articles/selecting_species.html

This example demonstrates searching for synonyms of 'Araucaria angustifolia' and 'Adesmia paranensis' using the get_synonym function.

```R
spp <- c("Araucaria angustifolia", "Adesmia paranensis")
spp_syn <- get_synonym(data = bf, species = spp)
spp_syn
#>                       synonym           acceptedName taxonomicStatus      nomenclaturalStatus
#> 35323    Araucaria brasiliana Araucaria angustifolia         Synonym                     <NA>
#> 35325  Araucaria brasiliensis Araucaria angustifolia         Synonym                     <NA>
#> 35327        Araucaria dioica Araucaria angustifolia         Synonym                     <NA>
#> 35328       Araucaria elegans Araucaria angustifolia         Synonym                     <NA>
#> 35329    Araucaria ridolfiana Araucaria angustifolia         Synonym                     <NA>
#> 35330       Araucaria saviana Araucaria angustifolia         Synonym                     <NA>
#> 35332   Columbea angustifolia Araucaria angustifolia         Synonym Legitimate_but_incorrect
#> 35333     Columbea brasiliana Araucaria angustifolia         Synonym Legitimate_but_incorrect
#> 60644            Pinus dioica Araucaria angustifolia         Synonym                     <NA>
#> 141020     Araucaria bibbiani Araucaria angustifolia         Synonym                     <NA>
#> 141021   Araucaria lindleyana Araucaria angustifolia         Synonym                     <NA>
#> 141041   Araucaria brasiliana Araucaria angustifolia         Synonym             Illegitimate
#> 85308    Adesmia psoraleoides     Adesmia paranensis         Synonym Legitimate_but_incorrect
```

--------------------------------

### Load complete version of florabr data with load_florabr()

Source: https://wevertonbio.github.io/florabr/articles/getting_started.html

Loads the complete version (39 columns) of the florabr dataset. The output shows the column names of the complete version, which has 20 more columns than the short version.

```R
#Complete version
bf_complete <- load_florabr(data_dir = my_dir,
                   data_version = "Latest_available",
                   type = "complete") #complete

colnames(bf_complete) #See variables from complete version
#>  [1] "id"                       "taxonID"                 
#>  [3] "acceptedNameUsageID"      "parentNameUsageID"       
#>  [5] "originalNameUsageID"      "group"                   
#>  [7] "subgroup"                 "species"                 
#>  [9] "acceptedName"             "scientificName"          
#> [11] "acceptedNameUsage"        "parentNameUsage"         
#> [13] "namePublishedIn"          "namePublishedInYear"     
#> [15] "higherClassification"     "kingdom"                 
#> [17] "phylum"                   "class"                   
#> [19] "order"                    "family"                  
#> [21] "genus"                    "specificEpithet"         
#> [23] "infraspecificEpithet"     "taxonRank"               
#> [25] "scientificNameAuthorship" "taxonomicStatus"         
#> [27] "nomenclaturalStatus"      "vernacularName"          
#> [29] "lifeForm"                 "habitat"                 
#> [31] "vegetation"               "origin"                  
#> [33] "endemism"                 "biome"                   
#> [35] "states"                   "countryCode"             
#> [37] "modified"                 "bibliographicCitation"   
#> [39] "references"
```

--------------------------------

### Load short version of florabr data with load_florabr()

Source: https://wevertonbio.github.io/florabr/articles/getting_started.html

Loads the short version (23 columns) of the florabr dataset. By default, it searches for the latest available version in the directory. The output shows the column names of the short version.

```R
#Short version
bf <- load_florabr(data_dir = my_dir,
                   data_version = "Latest_available",
                   type = "short") #short
#> Loading version 393.401
colnames(bf) #See variables from short version
#>  [1] "species"             "scientificName"      "acceptedName"       
#>  [4] "kingdom"             "group"               "subgroup"
#>  [7] "phylum"              "class"               "order"             
#> [10] "family"              "genus"               "lifeForm"           
#> [13] "habitat"             "biome"               "states"             
#> [16] "vegetation"          "origin"              "endemism"           
#> [19] "taxonomicStatus"     "nomenclaturalStatus" "vernacularName"     
#> [22] "taxonRank"           "id"
```
Tokens:
Raw