A R package for aligning and updating taxonomic names from the Australian Plant Census (APC)
ausflora relies on another R package datastorr, both of which are
currently available from
GitHub only. You can install
both these packages with:
# install.packages("remotes")
remotes::install_github("traitecoevo/datastorr")
remotes::install_github("traitecoevo/ausflora")
library(ausflora)First, we will retrieve the current release of APC
names.
The dataset_access_function saves the download in a local, temporary
folder.
tmp <- dataset_access_function("0.0.0.9000")
tmp
#> $APC
#> # A tibble: 100,835 × 34
#> taxonID nameT…¹ accep…² accep…³ nomen…⁴ taxon…⁵ proPa…⁶ scien…⁷ scien…⁸
#> <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr>
#> 1 https://id.b… scient… https:… Planta… <NA> accept… FALSE Planta… https:…
#> 2 https://id.b… scient… https:… Charop… <NA> orthog… FALSE Charac… https:…
#> 3 https://id.b… scient… https:… Charop… <NA> accept… FALSE Charop… https:…
#> 4 https://id.b… scient… https:… Equise… <NA> orthog… FALSE Equise… https:…
#> 5 https://id.b… scient… https:… Equise… <NA> accept… FALSE Equise… https:…
#> 6 https://id.b… scient… https:… Equise… <NA> taxono… FALSE Spheno… https:…
#> 7 https://id.b… scient… https:… Cycadi… <NA> accept… FALSE Cycadi… https:…
#> 8 https://id.b… scient… https:… Cycada… <NA> taxono… FALSE Zamiin… https:…
#> 9 https://id.b… scient… https:… Cycada… <NA> accept… FALSE Cycada… https:…
#> 10 https://id.b… scient… https:… Cycada… <NA> taxono… FALSE Stange… https:…
#> # … with 100,825 more rows, 25 more variables: canonicalName <chr>,
#> # scientificNameAuthorship <chr>, parentNameUsageID <chr>, taxonRank <chr>,
#> # taxonRankSortOrder <dbl>, kingdom <chr>, class <chr>, subclass <chr>,
#> # family <chr>, created <dttm>, modified <dttm>, datasetName <chr>,
#> # taxonConceptID <chr>, nameAccordingTo <chr>, nameAccordingToID <chr>,
#> # taxonRemarks <chr>, taxonDistribution <chr>, higherClassification <chr>,
#> # firstHybridParentName <chr>, firstHybridParentNameID <chr>, …
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
#>
#> $APNI
#> # A tibble: 154,302 × 42
#> scientificN…¹ scien…² canon…³ canon…⁴ nameE…⁵ scien…⁶ nameT…⁷ taxon…⁸ nomen…⁹
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Aaron's Beard <commo… Aaron'… <commo… Aaron'… https:… common unplac… [n/a]
#> 2 Aaron's Rod <commo… Aaron'… <commo… Aaron'… https:… common unplac… [n/a]
#> 3 Abacopteris … <scien… Abacop… <scien… Abacop… https:… scient… includ… <NA>
#> 4 Abacopteris … <scien… Abacop… <scien… Abacop… https:… scient… unplac… nom. i…
#> 5 Abacopteris … <scien… Abacop… <scien… aspera https:… scient… includ… <NA>
#> 6 Abacopteris … <scien… Abacop… <scien… presli… https:… scient… unplac… <NA>
#> 7 Abacopteris … <scien… Abacop… <scien… triphy… https:… scient… includ… <NA>
#> 8 Abarema Pitt… <scien… Abarema <scien… Abarema https:… scient… unplac… <NA>
#> 9 Abarema clyp… <scien… Abarem… <scien… praini… https:… scient… unplac… nom. i…
#> 10 Abarema gran… <scien… Abarem… <scien… grandi… https:… scient… includ… <NA>
#> # … with 154,292 more rows, 33 more variables: scientificNameAuthorship <chr>,
#> # cultivarEpithet <chr>, autonym <lgl>, hybrid <lgl>, cultivar <lgl>,
#> # formula <lgl>, scientific <lgl>, nomInval <lgl>, nomIlleg <lgl>,
#> # namePublishedIn <chr>, namePublishedInYear <dbl>, nameInstanceType <chr>,
#> # originalNameUsage <chr>, originalNameUsageID <chr>, typeCitation <chr>,
#> # kingdom <chr>, family <chr>, genericName <chr>, specificEpithet <chr>,
#> # infraspecificEpithet <chr>, taxonRank <chr>, taxonRankSortOrder <dbl>, …
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable namesNow we will load a toy plant species list and try align it with the APC species names.
#remotes::install_packages("readr")
species_list <- readr::read_csv(system.file("extdata", "species.csv", package = "ausflora"))
species_list
#> # A tibble: 199 × 1
#> name
#> <chr>
#> 1 Eucalyptus tectifica
#> 2 Eulalia aurea
#> 3 Triodia bitextura
#> 4 Terminalia canescens
#> 5 Annual forb
#> 6 Erythrophleum chlorostachys
#> 7 Schizachyrium fragile
#> 8 Acacia hemsleyi
#> 9 Eriachne obtusa
#> 10 Corchorus sidoides
#> # … with 189 more rows
#> # ℹ Use `print(n = ...)` to see more rowsalign_taxa will try match the species names in our list to those
listed in the APC. You can specify a file path and file name using the
argument output to save this aligned data
aligned_data <- align_taxa(species_list$name, output = "taxonomic_updates.csv")
#> Checking alignments of 199 taxa
#> - reading existing data from taxonomic_updates.csv
#> - all taxa are already checked, yay!
aligned_data
#> # A tibble: 199 × 7
#> original_name cleaned_…¹ align…² source known checked strip…³
#> <chr> <chr> <chr> <chr> <lgl> <lgl> <chr>
#> 1 Annual forb Annual fo… <NA> <NA> FALSE TRUE annual…
#> 2 Tephrosia sp. macarthur river Tephrosia… <NA> <NA> FALSE TRUE tephro…
#> 3 No id No id <NA> <NA> FALSE TRUE no id
#> 4 Sorghum sp. Sorghum s… <NA> <NA> FALSE TRUE sorghu…
#> 5 Tephrosia sp. Tephrosia… <NA> <NA> FALSE TRUE tephro…
#> 6 Corymbia sp. Corymbia … <NA> <NA> FALSE TRUE corymb…
#> 7 Alphitonia sp. Alphitoni… <NA> <NA> FALSE TRUE alphit…
#> 8 Poaceae sp. Poaceae s… <NA> <NA> FALSE TRUE poacea…
#> 9 Pultenaea sp. Pultenaea… <NA> <NA> FALSE TRUE pulten…
#> 10 Eucalyptus tectifica Eucalyptu… Eucaly… APC l… TRUE TRUE eucaly…
#> # … with 189 more rows, and abbreviated variable names ¹cleaned_name,
#> # ²aligned_name, ³stripped_name
#> # ℹ Use `print(n = ...)` to see more rowsOnce alignment is complete we can update our species names, replacing
synonyms to current APC names. Note that this function drops the
observations where names cannot be aligned. Again, this updated species
list can be saved using the output argument.
aligned_species_list <- update_taxonomy(aligned_data$aligned_name)
#> loading object `taxonomic_resources` into global environment