florabr

https://github.com/wevertonbio/florabr
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florabr is an R package designed to retrieve, filter, and spatialize species distribution data from...
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### Install florabr from GitHub

Source: https://github.com/wevertonbio/florabr/blob/main/README.Rmd

Installs the development version of florabr from GitHub using the remotes package, then loads it. Requires the remotes package if not already installed.

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

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

library(florabr)
```

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

### Load mapview package

Source: https://github.com/wevertonbio/florabr/blob/main/vignettes/Spatialize_florabr.Rmd

Installs mapview if not already installed (note: the install.packages call incorrectly installs 'geobr' instead of 'mapview'), then loads the mapview library for interactive mapping.

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

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

### Solve discrepancies with solve_discrepancies()

Source: https://github.com/wevertonbio/florabr/blob/main/man/solve_discrepancies.Rd

Loads the example dataset bf_data, checks its solve_discrepancies attribute, applies solve_discrepancies() to create bf_solved, then verifies the attribute on the solved data.

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

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

### Download and load Flora e Funga do Brasil database

Source: https://github.com/wevertonbio/florabr/blob/main/man/load_florabr.Rd

This example shows the complete workflow of creating a directory, downloading the database with get_florabr, and then loading it with load_florabr. It is wrapped in \dontrun{} because it requires an internet connection and writes files to disk.

```R
\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")
}
```

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

### Install florabr from CRAN

Source: https://github.com/wevertonbio/florabr/blob/main/README.Rmd

Installs the stable version of the florabr package from CRAN.

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

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

### Install and load geobr package

Source: https://github.com/wevertonbio/florabr/blob/main/vignettes/Spatialize_florabr.Rmd

Installs the geobr package if it is not already installed and loads it. Required to access the original, non-simplified spatial polygons of Brazilian states and biomes.

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

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

### Download and merge Flora e Funga do Brasil database with get_florabr

Source: https://github.com/wevertonbio/florabr/blob/main/man/get_florabr.Rd

Example usage of get_florabr to download the latest database version, merge it into a single data.frame, and save it. Creates a temporary directory first, then calls the function with default options for discrepancy solving, overwriting, and verbosity.

```R
\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)
}
```

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

### Check species names with check_names

Source: https://github.com/wevertonbio/florabr/blob/main/man/check_names.Rd

Loads the example dataset bf_data from the florabr package and checks a vector of species names against the Flora e Funga do Brasil database using check_names with default arguments.

```r
data("bf_data", package = "florabr")
spp <- c("Butia cattarinensis", "Araucaria angustifolia")
check_names(data = bf_data, species = spp)
```

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

### Get available vegetation options with get_attributes()

Source: https://github.com/wevertonbio/florabr/blob/main/vignettes/selecting_species.Rmd

Retrieves the list of vegetation values accepted by select_species(). The output includes categories like 'Anthropic_Area', 'Caatinga', 'Cerrado', and 'Rainforest'.

```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
```

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

### Select native endemic trees in Atlantic Forest with select_species()

Source: https://github.com/wevertonbio/florabr/blob/main/vignettes/selecting_species.Rmd

Filters the Brazilian Flora dataset to return all native and endemic tree species with confirmed occurrences in the Atlantic Forest. The example sets lifeForm='Tree', biome='Atlantic_Forest', endemism='Endemic', origin='Native', and taxonomicStatus='Accepted'. The output shows 2372 species meet the criteria.

```r
af_spp <- select_species(data = bf,
                      include_subspecies = FALSE, include_variety = FALSE,
                      kingdom = "Plantae", group = "All", subgroup = "All",
                      family = "All", genus = "All",
                      lifeForm = "Tree", #Specify tree species
                      filter_lifeForm = "in",
                      habitat = "All", filter_habitat = "in",
                      biome = "Atlantic_Forest", #Occuring in the At. Forest
                      filter_biome = "in", #In Atlantic Forest
                      state = "All", filter_state = "in",
                      vegetation = "All", filter_vegetation = "in",
                      endemism = "Endemic", #Only endemics to Brazil
                      origin = "Native", #Only natives
                      taxonomicStatus = "Accepted",
                      nomenclaturalStatus = "All")
nrow(af_spp)
#> [1] 2372
```

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

### Import and subset occurrence data

Source: https://github.com/wevertonbio/florabr/blob/main/vignettes/Spatialize_florabr.Rmd

Loads the 'occurrences' example dataset from the florabr package and subsets it to the two species of interest. The head(occ) output shows the first six rows with columns species, x (longitude), and y (latitude).

```r
data("occurrences")
#select Abatia americana and Araucaria angustifolia from the dataset
occ <- subset(occurrences, occurrences$species %in% my_spp)
head(occ)
#>                  species         x         y
#> 1 Araucaria angustifolia -51.15749 -29.58290
#> 2 Araucaria angustifolia -50.02454 -28.44414
#> 3 Araucaria angustifolia -46.77204 -23.45729
#> 4 Araucaria angustifolia -48.98117 -25.49632
#> 5 Araucaria angustifolia -54.50869 -25.55113
#> 6 Araucaria angustifolia -50.92680 -27.01826
```

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

### Extract species subset with subset_species

Source: https://github.com/wevertonbio/florabr/blob/main/man/subset_species.Rd

Loads the example dataset bf_data, defines a vector of species names, and calls subset_species to extract those species. 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
```

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

### Save Brazilian Flora data to CSV with write.csv()

Source: https://github.com/wevertonbio/florabr/blob/main/vignettes/getting_started.Rmd

Saves the loaded data frame (e.g., bf) as a CSV file using write.csv(). The file is saved to the specified directory with the filename 'BrazilianFlora.csv'. This example is not executed (eval=FALSE) and is intended for external editing.

```r
write.csv(x = bf,
          file = file.path(my_dir, "BrazilianFlora.csv"),
          row.names = F)
```

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

### Search for taxa by vernacular name with select_by_vernacular

Source: https://github.com/wevertonbio/florabr/blob/main/man/select_by_vernacular.Rd

Loads the Flora e Funga do Brasil dataset and demonstrates both exact and non-exact searches for the vernacular name 'pinheiro'. The exact search returns only species known as 'pinheiro', while the non-exact search also returns names like 'pinheiro-do-parana'. The example shows the result of the exact search and the head of the non-exact results.

```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
#Search for species whose vernacular name is 'pinheiro', allowing non-exact
#matches
pinheiro_not_exact <- select_by_vernacular(data = bf_data,
                                          names = "pinheiro",
                                          exact = FALSE)
head(pinheiro_not_exact)
```

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

### Get and plot spatial polygons of species with get_spat_occ

Source: https://github.com/wevertonbio/florabr/blob/main/man/get_spat_occ.Rd

Loads Flora e Funga do Brasil data and retrieves spatial polygons (SpatVectors) for example species. The function returns a list with states, biomes, and their intersection, which are then plotted for the first species.

```R
library(terra)
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)


#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)
```

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

### Download florabr data with get_florabr()

Source: https://github.com/wevertonbio/florabr/blob/main/vignettes/getting_pam.Rmd

Creates a temporary directory and downloads the latest version of the florabr data using get_florabr(). The overwrite = T argument ensures existing data is replaced.

```r
my_dir <- file.path(file.path(tempdir(), "florabr"))
dir.create(my_dir)
get_florabr(output_dir = my_dir, #directory to save the data
            data_version = "latest", #get the most recent version available
            overwrite = T) #Overwrite data, if it exists
```

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

### Check available Flora data versions with check_version

Source: https://github.com/wevertonbio/florabr/blob/main/vignettes/getting_started.Rmd

Lists the versions of Flora e Funga do Brasil available in the specified directory. The output shows the versions found and indicates the latest one.

```r
check_version(data_dir = my_dir)
#> You have the following versions of Flora e Funga do Brasil:
#>  393.385
#>  393.401 
#>  It includes the latest version:  393.401
```

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

### get_florabr(output_dir, data_version = "latest", solve_discrepancy = FALSE, overwrite = TRUE, verbose = TRUE, remove_files = TRUE)

Source: https://github.com/wevertonbio/florabr/blob/main/man/get_florabr.Rd

Documentation for the get_florabr function, which downloads and merges the Flora e Funga do Brasil database.

```APIDOC
## get_florabr(output_dir, data_version = "latest", solve_discrepancy = FALSE, overwrite = TRUE, verbose = TRUE, remove_files = TRUE)

### Description
Downloads the latest or an older version of the Flora e Funga do Brasil database, merges the information into a single data.frame, and saves this data.frame in the specified directory.

### Method
Function call (R)

### Endpoint
N/A - This is an R function, not an HTTP endpoint.

### Parameters
- **output_dir** (character) - Required - A directory to save the data downloaded from Flora e Funga do Brasil.
- **data_version** (character) - Optional - Version of the Flora e Funga do Brasil database to download. Use "latest" to get the most recent version, updated weekly. Alternatively, specify an older version (e.g., data_version = "393.319"). Default value is "latest".
- **solve_discrepancy** (logical) - Optional - Resolve discrepancies between species and subspecies/varieties information. When set to TRUE, species information is updated based on unique data from varieties and subspecies. For example, if a subspecies occurs in a certain biome, it implies that the species also occurs in that biome. Default = FALSE.
- **overwrite** (logical) - Optional - If TRUE, data is overwritten. Default = TRUE.
- **verbose** (logical) - Optional - Whether to display messages during function execution. Set to TRUE to enable display, or FALSE to run silently. Default = TRUE.
- **remove_files** (logical) - Optional - Whether to remove the downloaded files used in building the final dataset. Default is TRUE.

### Request Example
```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)
#Download, merge and save data
get_florabr(output_dir = my_dir, data_version = "latest",
            solve_discrepancy = FALSE, overwrite = TRUE, verbose = TRUE)
```

### Response
#### Success Response
- The function downloads the latest version of the Flora e Funga do Brasil database from the official source. It then merges the information into a single data.frame, containing details on species, taxonomy, occurrence, and other relevant data.
- The merged data.frame is then saved as a file in the specified output directory. The data is saved in a format that allows easy loading using the `load_florabr` function for further analysis in R.

#### Response Example
No explicit response example is provided in the source documentation.
```

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

### Get available lifeForm options with get_attributes()

Source: https://github.com/wevertonbio/florabr/blob/main/vignettes/selecting_species.Rmd

Retrieves the list of lifeForm values accepted by select_species(). The output shows the available categories, including 'Tree', 'Shrub', and 'Herb'.

```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
```

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

### Get available Biome options with get_attributes()

Source: https://github.com/wevertonbio/florabr/blob/main/vignettes/selecting_species.Rmd

Retrieves the list of biome values accepted by select_species(). The output includes 'Amazon', 'Atlantic_Forest', 'Caatinga', 'Cerrado', 'Pampa', 'Pantanal', and 'Not_found_in_brazil'.

```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
```

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

### Load complete version of Brazilian Flora data with load_florabr()

Source: https://github.com/wevertonbio/florabr/blob/main/vignettes/getting_started.Rmd

Loads the 'complete' version of the Brazilian Flora dataset (39 columns) using load_florabr(). The output shows the column names of the complete version, which includes 20 additional columns compared to 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"
```

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

### Create directory for florabr data

Source: https://github.com/wevertonbio/florabr/blob/main/README.Rmd

Creates a directory to store the downloaded 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)
```

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

### Filter occurrences with filter_florabr

Source: https://github.com/wevertonbio/florabr/blob/main/man/filter_florabr.Rd

Loads example data and filters occurrences for the species 'Myrcia hatschbachii' using default buffers and flags/cleans records. The verbose argument is set to FALSE to run silently.

```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)
```

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

### 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)

Source: https://github.com/wevertonbio/florabr/blob/main/man/get_pam.Rd

This function generates a presence-absence matrix of species based on their distribution across states, biomes, and vegetation types according to Flora e Funga do Brasil. It allows customization of grouping, removal of empty sites, and optional richness summaries and spatial plots.

```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.

### Method
Function call

### Endpoint
R function: get_pam

### Parameters
- **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.

### Return 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.

### Example
```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)
```
```

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

### Load short version of Brazilian Flora data with load_florabr()

Source: https://github.com/wevertonbio/florabr/blob/main/vignettes/getting_started.Rmd

Loads the 'short' version of the Brazilian Flora dataset (23 columns) using load_florabr(). 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"
```

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

### load_florabr

Source: https://github.com/wevertonbio/florabr/blob/main/man/load_florabr.Rd

Loads the Flora e Funga do Brasil database from a local directory, returning a data.frame with the specified version and column set.

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

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

### Parameters
- **data_dir** (character) - Required - The 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 the database to load. Can be "Latest_available" to load the latest version, or a specific version string (e.g., "393.364"). Default = "Latest_available".
- **type** (character) - Optional - Determines the number of columns loaded. Can be "short" (20 columns) or "complete" (39 columns). Default = "short".
- **verbose** (logical) - Optional - Whether to display messages during execution. Set to TRUE to enable display, or FALSE to run silently. Default = TRUE.

### Return Value
A data.frame with the specified version of the Flora e Funga do Brasil database. If type = "short", it contains the 20 columns: 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 contains all 39 variables available in the database.

### Example
```r
# Load the latest available version with short format
df <- load_florabr(data_dir = "path/to/data", data_version = "Latest_available", type = "short", verbose = TRUE)
```
```

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

### Get available biomes, life forms and states with get_attributes

Source: https://github.com/wevertonbio/florabr/blob/main/man/get_attributes.Rd

Loads the bf_data dataset and retrieves available options for biome, lifeform, and states to use with select_species. The attribute argument accepts multiple options as a character vector.

```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"))
```

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

### Get spatial polygons with get_spat_occ

Source: https://github.com/wevertonbio/florabr/blob/main/vignettes/Spatialize_florabr.Rmd

Extracts spatial polygons (SpatVectors) for the distribution of given species. Setting `state`, `biome`, and `intersection` to `TRUE` returns federal states, biomes, and their intersection, respectively. The `verbose = TRUE` option prints progress messages for each species.

```r
#Example species
spp <- c("Araucaria angustifolia", "Adesmia paranensis")
#Get spatial polygons
spp_spt <- get_spat_occ(data = bf, species = spp,
                       state = TRUE, biome = TRUE, intersection = TRUE,
                       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
```

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

### Download data with solve_discrepancy = TRUE in get_florabr()

Source: https://github.com/wevertonbio/florabr/blob/main/vignettes/getting_started.Rmd

Set solve_discrepancy = TRUE in get_florabr() to resolve discrepancies during data download. The default is FALSE, so this option must be explicitly enabled. The function also accepts output_dir, data_version, overwrite, and verbose parameters.

```r
get_florabr(output_dir, data_version = "latest",
            solve_discrepancy = TRUE, #Default is false
            overwrite = TRUE,
            verbose = TRUE)
```
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