Package 'responses.build'

Description

Format BibEntry object according to desired style using RefManageR

Usage

bib_print(
  bib,
  .opts = list(first.inits = TRUE, max.names = 1000, style = "markdown")
)

Arguments

Value

Character string of formatted reference

Description

Add version information to AusTraits

Usage

build_add_version(austraits, version, git_sha)

Arguments

Value

AusTraits database object with version information added

Description

build_combine compiles all the loaded studies into a single AusTraits database object as a large list.

Usage

build_combine(..., d = list(...))

Arguments

Value

AusTraits compilation database as a large list

Description

build_setup_pipeline rewrites the remake.yml file to include new studies.

Usage

build_setup_pipeline(
  dataset_ids = dir("data"),
  method = "base",
  database_name = "database",
  template = select_pipeline_template(method),
  workers = 1
)

Arguments

Value

Updated remake.yml file

Description

Identify duplicates preventing pivoting wider

Usage

check_pivot_duplicates(
  database_object,
  dataset_ids = unique(database_object$traits$dataset_id)
)

Arguments

Value

Tibble with duplicates and pivot columns

Description

Test whether the traits table of a dataset can pivot wider with the minimum required columns.

Usage

check_pivot_wider(dataset)

Arguments

Value

Number of rows with duplicates preventing pivoting wider

Description

create_tree_branch() is used to create a tree structure to show how things are related. In AusTraits, this is used in the vignettes to show the file structure of the repository and also to show the different components of the AusTraits database.

Usage

create_tree_branch(x, title, prefix = "")

Arguments

Value

Vector of character strings for the tree structure

Description

Alias for dataset_update_taxonomy().

Usage

data_update_taxonomy(austraits_raw, taxa)

Arguments

Details

Wenk et al. 2024 (Ecological Informatics 83:102773) names this step data_update_taxonomy in the Fig. 1 pipeline diagram, but no such function has ever existed, so the published workflow could not be run as written. The paper is the public specification of this workflow, so the name it documents resolves here rather than being treated as an erratum.

Value

List of AusTraits compiled data with taxonomic updates applied

Description

Joins the relational tables of a built database into one wide table, by calling austraits::flatten_database().

Usage

database_create_combined_table(database, ...)

Arguments

Details

Wenk et al. 2024 (Ecological Informatics 83:102773) presents database_create_combined_table as the route to the combined table, but it was only ever assigned here and never exported, so the published workflow could not be followed. The paper is the public specification of this workflow, so the name it documents resolves.

This is a thin pass-through rather than a reimplementation on purpose. The joins it relies on are eight functions totalling ~264 lines in austraits, and querying a built compilation is that package's job – this one builds the database. Arguments are passed straight through so the defaults have a single definition.

Note that austraits is currently in Depends. When it moves to Suggests this needs a util_require_package("austraits", ...) guard, as does build_combine() – see #225.

Value

A single wide table combining the database's relational tables

Description

Build specified dataset. This function completes three steps, which can be executed separately if desired: dataset_configure, dataset_process, dataset_update_taxonomy

Usage

dataset_build(
  filename_metadata,
  filename_data_raw,
  definitions,
  unit_conversion_functions,
  schema,
  resource_metadata,
  taxon_list,
  filter_missing_values = TRUE
)

Arguments

Value

List, AusTraits database object

Examples

## Not run: 
dataset_build(
  "data/Falster_2003/data.csv",
  "data/Falster_2003/metadata.yml",
  read_yaml("config/traits.yml"),
   get_unit_conversions("config/unit_conversions.csv"),
   get_schema(),
   get_schema("config/metadata.yml", "metadata"),
   read_csv_char("config/taxon_list.csv")
)

## End(Not run)

Description

Creates the config object which gets passed onto dataset_process. The config list contains the subset of definitions and unit conversions for those traits for a each study. dataset_configure is used in the remake::make process to configure individual studies mapping the individual traits found in that study along with any relevant unit conversions and definitions. dataset_configure and dataset_process are applied to every study in the remake.yml file.

Usage

dataset_configure(filename_metadata, definitions)

Arguments

Value

List with dataset_id, metadata, definitions and unit_conversion_functions

Examples

## Not run: 
dataset_configure("data/Falster_2003/metadata.yml", read_yaml("config/traits.yml"))

## End(Not run)

Description

Find list of unique datasets within compilation containing specified taxa

Usage

dataset_find_taxon(taxa, austraits, original_name = FALSE)

Arguments

Value

List of unique datasets within compilation containing each taxon

Description

dataset_process is used to load individual studies using the config file generated from dataset_configure(). dataset_configure and dataset_process are applied to every study in the remake.yml file.

Usage

dataset_process(
  filename_data_raw,
  config_for_dataset,
  schema,
  resource_metadata,
  unit_conversion_functions,
  filter_missing_values = TRUE
)

Arguments

Value

List, AusTraits database object

Examples

## Not run: 
dataset_process("data/Falster_2003/data.csv", dataset_configure("data/Falster_2003/metadata.yml",
read_yaml("config/traits.yml")),
get_schema(),
get_schema("config/metadata.yml", "metadata"),
get_unit_conversions("config/unit_conversions.csv"))

## End(Not run)

Description

dataset_test() reports characters that the database does not allow, but fixing them has been a manual edit for each one. This applies the known replacements in util_disallowed_char_replacements() and reports whatever it could not resolve.

Usage

dataset_replace_disallowed_chars(
  dataset_id,
  path_data = "data",
  files = "metadata.yml",
  dry_run = TRUE
)

Arguments

Details

Only the offending characters are rewritten; the rest of the file is left as it was. In particular this does not round-trip metadata.yml through write_metadata(), which would reflow the whole file and bury the change in an unreviewable diff.

Characters with no known replacement are reported rather than guessed at. Each is either a genuine character that should be added to the exceptions argument of check_disallowed_chars(), or a mangling that only the curator can interpret.

A file that is not valid UTF-8 is reported and skipped, since recovering one means knowing which encoding it was written in.

Value

A data frame with one row per disallowed character found, with columns dataset_id, file, line, char, code, replacement and status. status is "replaced" when a replacement is known, "no replacement known" otherwise, or "file not valid UTF-8" for a file that was skipped. Returned invisibly when dry_run = FALSE.

Scope of data.csv

data.csv is not in files by default. It can be processed by asking for it, but the two files are very different problems, and the scope for data is still an open question (see #251).

dataset_test() applies its ASCII-only rule for data to parsed_data$traits$value — the values that actually reach the built database. This function reads whole files, so for data.csv it sees every column, including ones that never become a trait value. Measured over all 601 downstream datasets, that is the difference between 95 disallowed characters in metadata.yml and 170,946 in data.csv across 147 files — almost none of which dataset_test() reports. The bulk is invisible junk in provenance columns (110,666 zero-width joiners in one created_by column) plus legitimate typography and accented names that the ASCII-only rule can only ever report, never resolve.

So pass files = c("metadata.yml", "data.csv") deliberately, and read the dry-run report first.

Examples

## Not run: 
# See what would change across every dataset
report <- dataset_replace_disallowed_chars(dir("data"))

# Apply it
dataset_replace_disallowed_chars(dir("data"), dry_run = FALSE)

# Include the data files, which is a much wider sweep -- read the report first
dataset_replace_disallowed_chars(dir("data"), files = c("metadata.yml", "data.csv"))

## End(Not run)

Description

Builds a detailed report for every dataset with a unique dataset_id, based on the template Rmd file provided. The reports are rendered as html files and saved in the specified output folder.

Usage

dataset_report(
  dataset_id,
  austraits,
  overwrite = FALSE,
  output_path = "export/reports",
  input_file = system.file("support", "report_dataset.Rmd", package = "responses.build"),
  quiet = TRUE,
  keep = FALSE
)

Arguments

Value

Invisibly, a logical named by dataset_id, TRUE where the report was written. Reports are rendered as html files in output_path. A dataset whose report fails to render warns and returns FALSE rather than aborting the batch.

Description

Run tests to ensure that specified dataset_id has the correct setup.

Usage

dataset_test(
  dataset_ids,
  path_config = "config",
  path_data = "data",
  reporter = testthat::CompactProgressReporter
)

Arguments

Description

Run tests to ensure that specified dataset_id has the correct setup.

Usage

dataset_test_worker(
  test_dataset_ids,
  path_config = "config",
  path_data = "data",
  schema = get_schema(),
  definitions = get_schema(file.path(path_config, "traits.yml"), I("traits"))
)

Arguments

Description

Applies taxonomic updates to austraits_raw.

Usage

dataset_update_taxonomy(austraits_raw, taxa)

Arguments

Value

List of AusTraits compiled data with taxonomic updates applied

Description

Load schema for an responses.build data compilation (excluding traits)

Usage

get_schema(
  path = system.file("support", "responses.build_schema.yml", package =
    "responses.build"),
  subsection = NULL
)

Arguments

Value

a list

Examples

{

schema <- get_schema()
}

Description

Make unit conversion functions

Usage

get_unit_conversions(filename)

Arguments

Value

List of conversion functions

Examples

## Not run: 
get_unit_conversions("config/unit_conversions.csv")

## End(Not run)

Description

This functions asks users which columns in the dataframe they would like to keep and records this appropriately in the metadata. The input data is assumed to be in wide format. The output may require additional manual editing.

Usage

metadata_add_contexts(dataset_id, overwrite = FALSE, user_responses = NULL)

Arguments

Description

This functions asks users which columns in the dataframe they would like to keep and records this appropriately in the metadata. The input data is assumed to be in wide format. The output may require additional manual editing if the user selects a non-standard identifier type.

Usage

metadata_add_identifiers(dataset_id, overwrite = FALSE, user_responses = NULL)

Arguments

Value

Invisibly, the updated metadata list. Called for the side effect of writing metadata.yml for dataset_id.

Description

This functions asks users which columns in the dataframe they would like to keep and records this appropriately in the metadata. The input data is assumed to be in wide format. The output may require additional manual editing.

Usage

metadata_add_locations(dataset_id, location_data, user_responses = NULL)

Arguments

Examples

## Not run: 
austraits$locations %>% dplyr::filter(dataset_id == "Falster_2005_1") %>%
select(-dataset_id) %>% spread(location_property, value) %>% type_convert() -> location_data
metadata_add_locations("Falster_2005_1", location_data)

## End(Not run)

Description

Adds citation details to a metadata file for given study

Usage

metadata_add_source_bibtex(
  dataset_id,
  file,
  type = "primary",
  drop = c("dateobj", "month")
)

Arguments

Value

metadata.yml file with citation details added

Description

Uses rcrossref package to access publication details from the crossref database. rcrossref is an optional dependency; it is only needed when bib is not supplied, and can be installed with install.packages("rcrossref").

Usage

metadata_add_source_doi(..., doi, bib = NULL)

Arguments

Value

metadata.yml file with citation details added

Description

metadata_add_substitution is used to align the categorical trait values used by a contributor to the categorical values supported by the database. These values are defined in the traits.yml file.

Usage

metadata_add_substitution(dataset_id, trait_name, find, replace)

Arguments

Value

metadata.yml file with a substitution added

Description

Add a dataframe of trait value substitutions into a metadata file for a dataset_id

Usage

metadata_add_substitutions_list(dataset_id, substitutions)

Arguments

Value

metadata.yml file with multiple trait value substitutions added

Description

Function that simultaneously adds many trait value replacements, potentially across many trait_name's and dataset_id's, to the respective metadata.yml files. This function will be used to quickly re-align/re-assign trait values across all studies.

Usage

metadata_add_substitutions_table(
  dataframe_of_substitutions,
  dataset_id,
  trait_name,
  find,
  replace
)

Arguments

Value

Modified metadata files with trait value replacements

Examples

## Not run: 
read_csv("export/dispersal_syndrome_substitutions.csv") %>%
  select(-extra) %>%
  filter(dataset_id == "Angevin_2011") -> dataframe_of_substitutions
metadata_add_substitutions_table(dataframe_of_substitutions, dataset_id, trait_name, find, replace)

## End(Not run)

Description

Add a single taxonomic change into the metadata.yml file for a specific study.

Usage

metadata_add_taxonomic_change(
  dataset_id,
  find,
  replace,
  reason,
  taxonomic_resolution,
  overwrite = TRUE
)

Arguments

Value

metadata.yml file with taxonomic change added

Description

Add multiple taxonomic changes to the metadata.yml file using a dataframe containing the taxonomic changes to be made.

Usage

metadata_add_taxonomic_changes_list(dataset_id, taxonomic_updates)

Arguments

Value

metadata.yml file with multiple taxonomic updates added

Description

This function asks users which traits they would like to keep, and adds a template for those traits in the metadata. This template must then be finished manually.

Usage

metadata_add_traits(dataset_id, user_responses = NULL)

Arguments

Details

Can also be used to add a trait to an existing metadata file.

Description

Function to check the output of running custom_R_code specified in the metadata.yml file for specified dataset_id. For the specified dataset_id, reads in the file data.csv and applies manipulations as described in the file metadata.yml

Usage

metadata_check_custom_R_code(dataset_id, path_data = "data")

Arguments

Description

Includes place-holders for major sections of the metadata.

Usage

metadata_create_template(
  dataset_id,
  path = file.path("data", dataset_id),
  skip_manual = FALSE,
  user_responses = NULL
)

Arguments

Value

A yaml file template for metadata

Description

Exclude observations in a yaml file for a dataset_id

Usage

metadata_exclude_observations(dataset_id, variable, find, reason)

Arguments

Value

metadata.yml file with excluded observations

Description

Find dataset_id's with a given taxonomic change

Usage

metadata_find_taxonomic_change(find, replace = NULL, studies = NULL)

Arguments

Description

Path to the metadata.yml file for specified dataset_id

Usage

metadata_path_dataset_id(dataset_id, path_data = "data")

Arguments

Value

A string

Description

Remove a taxonomic change from a yaml file for a dataset_id

Usage

metadata_remove_taxonomic_change(dataset_id, find)

Arguments

Value

metadata.yml file with a taxonomic change removed

Description

Update a taxonomic change into a yaml file for a dataset_id

Usage

metadata_update_taxonomic_change(
  dataset_id,
  find,
  replace,
  reason,
  taxonomic_resolution
)

Arguments

Value

metadata.yml file with added substitution

Description

metadata_user_select_column is used to select which columns in a dataframe/tibble corresponds to the variable of interest. It is used to compile the metadata yaml file by prompting the user to choose the relevant columns. It is used in metadata_add_locations and metadata_create_template.

Usage

metadata_user_select_column(column, choices)

Arguments

Description

metadata_user_select_names is used to prompt the user to select the variables that are relevant for compiling the metadata yaml file. It is currently used for metadata_add_traits, metadata_add_locations and metadata_add_contexts.

Usage

metadata_user_select_names(title, vars)

Arguments

Description

Creates a string of random letters with 8 characters as the default, useful for defining unique hyperlinks

Usage

notes_random_string(n = 8)

Arguments

Value

character string with 8 letters

Description

Add a note to the note recorder as a new row

Usage

notetaker_add_note(notes, new_note)

Arguments

Value

A tibble with additional notes added

Description

Creates a tibble with two columns with one column consisting of a randomly generated string of letters

Usage

notetaker_as_note(note, link = NA_character_)

Arguments

Value

a tibble with two columns named note and link

Description

Returns a specific row from notes specified by i. Default is nrow(notes) which returns the last note

Usage

notetaker_get_note(notes, i = nrow(notes))

Arguments

Value

a single row from a tibble

Description

Print all notes

Usage

notetaker_print_all(notes, ..., numbered = TRUE)

Arguments

Value

character string containing the notes

Description

Print note (needs review?)

Usage

notetaker_print_note(
  note,
  as_anchor = FALSE,
  anchor_text = "",
  link_text = "link"
)

Arguments

Value

character string containing the notes

Description

Prints a specific row from notes specified by i

Usage

notetaker_print_notes(notes, i = nrow(notes), ...)

Arguments

Value

character string containing the notes

Description

Note recorder used in report_study.Rmd file to initiate note recorder

Usage

notetaker_start()

Value

A tibble where notes are recorded

Description

Add or remove columns of data as needed so that all datasets have the same columns. Also adds in an error column.

Usage

process_add_all_columns(data, vars, add_error_column = TRUE)

Arguments

Value

Tibble with the correct selection of columns including an error column

Description

Convert units to desired type

Usage

process_convert_units(data, definitions, unit_conversion_functions)

Arguments

Value

Tibble with converted units

Description

Creates 3-part entity id codes that combine a segment for species, population, and, when applicable, individual. This depends upon a parsing_id being established when the data.csv file is first read in.

Usage

process_create_observation_id(data, metadata)

Arguments

Value

Character string

Description

Applies custom data manipulations if the metadata field custom_R_code is not empty Otherwise no manipulations will be done by applying the identity function. The code custom_R_code assumes a single input.

Usage

process_custom_code(txt)

Arguments

Value

character text containing custom_R_code if custom_R_code is not empty, otherwise no changes are made

Description

Checks the metadata yaml file for any excluded observations. If there are none, returns the original data. If there are excluded observations returns the mutated data with excluded observations flagged in a new column.

Usage

process_flag_excluded_observations(data, metadata)

Arguments

Value

Dataframe with flagged excluded observations if there are any

Description

Flags any numeric values that are outside the allowable range defined in the traits.yml file.

Usage

process_flag_out_of_range_values(data, definitions)

Arguments

Value

Tibble with flagged values outside of allowable range

Description

Disallowed characters are flagged as errors, including for numeric traits, prior to unit conversions to avoid their conversion to NAs during the unit conversion process.

Usage

process_flag_unsupported_characters(data)

Arguments

Value

Tibble with flagged values containing unsupported characters

Description

Flag any unrecognised traits, as defined in the traits.yml file.

Usage

process_flag_unsupported_traits(data, definitions)

Arguments

Value

Tibble with unrecognised traits flagged in the "error" column

Description

Flags any categorical traits values that are not on the list of allowed values defined in the traits.yml file. NA values are flagged as errors. Numeric values that cannot convert to numeric are also flagged as errors.

Usage

process_flag_unsupported_values(data, definitions)

Arguments

Value

Tibble with flagged values that are unsupported categorical trait values, missing values or numeric trait values that cannot be converted to numeric

Description

Format context data read in from the metadata.yml file. Converts from list to tibble.

Usage

process_format_contexts(my_list, dataset_id, traits)

Arguments

Value

Tibble with context details if available

Examples

## Not run: 
process_format_contexts(read_metadata("data/Apgaua_2017/metadata.yml")$context, dataset_id, traits)

## End(Not run)

Description

Format contributors, read in from the metadata.yml file. Converts from list to tibble.

Usage

process_format_contributors(my_list, dataset_id, schema)

Arguments

Value

Tibble with details of contributors

Examples

## Not run: 
process_format_contributors(read_metadata("data/Falster_2003/metadata.yml")$contributors)

## End(Not run)

Description

Format identifiers, read in from the metadata.yml file. Converts from list to tibble.

Usage

process_format_identifiers(my_list, dataset_id, schema)

Arguments

Value

Tibble with details of identifiers

Examples

## Not run: 
process_format_identifiers(read_metadata("data/Falster_2003/metadata.yml")$identifiers)

## End(Not run)

Description

Format location data read in from the metadata.yml file. Converts from list to tibble.

Usage

process_format_locations(my_list, dataset_id, schema)

Arguments

Value

Tibble with location details if available

Examples

## Not run: 
process_format_locations(read_metadata("data/Falster_2003/metadata.yml")$locations, "Falster_2003")

## End(Not run)

Description

Function to generate sequence of integer ids from vector of names Determines number of 00s needed based on number of records

Usage

process_generate_id(x, prefix, sort = FALSE)

Arguments

Value

Vector of ids

Description

Function to generate sequence of integer ids for methods

Usage

process_generate_method_ids(metadata_traits)

Arguments

Value

Tibble with traits, methods, and method_id

Description

Process a single dataset with dataset_id using the associated data.csv and metadata.yml files. Adds a unique observation id for each row of observation, trait names are formatted using AusTraits accepted names and trait substitutions are added. ⁠parse data⁠ is used in the core workflow pipeline (i.e. in ⁠load study⁠).

Usage

process_parse_data(
  data,
  dataset_id,
  metadata,
  contexts,
  schema,
  identifiers,
  definitions
)

Arguments

Value

Tibble in long format with AusTraits formatted trait names, trait substitutions and unique observation id added

Description

Enforces some standards on species names.

Usage

process_standardise_names(x)

Arguments

Value

Vector with standardised species names

Description

Applies taxonomic updates to the study data from the metadata.yml file.

Usage

process_taxonomic_updates(data, metadata)

Arguments

Value

Tibble with the taxonomic updates applied

Description

Creates the unit conversion name based on the original units and the units to be converted to.

Usage

process_unit_conversion_name(from, to)

Arguments

Value

Character string containing the name what units are being converted to

Description

Reads in a csv file using the read_csv function from readr with columns as characters.

Usage

read_csv_char(...)

Arguments

Value

A tibble

Description

Read in a metadata.yml file for a study

Usage

read_metadata(path)

Arguments

Description

Read the metadata.yml file for specified dataset_id

Usage

read_metadata_dataset(dataset_id, path_data = "data")

Arguments

Value

A list with contents of metadata for specified dataset_id

Description

Read yaml (from package yaml)

Description

Add an item to the end of a list

Usage

util_append_to_list(my_list, to_append)

Arguments

Value

A list merged with an added item at the end

Examples

## Not run: 
util_append_to_list(as.list(dplyr::starwars)[c(1,2)], as.list(dplyr::starwars)[c(3,4)])

## End(Not run)

Description

Convert BibEntry object to a list

Usage

util_bib_to_list(bib)

Arguments

Value

List

Description

util_check_all_values_in checks if values in vector x are in y. Values in x may contain multiple values separated by sep so these are split first using str_split.

Usage

util_check_all_values_in(x, y, sep = " ")

Arguments

Value

Vector of logical values

Description

util_check_disallowed_chars checks if values in a vector do not contain disallowed characters, i.e. values outside of ASCII.

Usage

util_check_disallowed_chars(object)

Arguments

Value

Vector of logical values

Description

Convert all columns in data frame to character

Usage

util_df_convert_character(df)

Arguments

Value

A dataframe

Examples

lapply(responses.build:::util_df_convert_character(dplyr::starwars), class)

Description

Convert a dataframe to a named list, useful when converting to yaml.

Usage

util_df_to_list(df)

Arguments

Value

A (yaml) list

Examples

util_df_to_list(dplyr::starwars)

Description

Extract a trait element from the definitions$traits$elements

Usage

util_extract_list_element(i, my_list, var)

Arguments

Value

The element/properties of a trait

Examples

## Not run: 
util_extract_list_element(1, definitions$traits$elements, "units")

## End(Not run)

Description

Get SHA string for the latest commit on Github for the repository. SHA is the abbreviated SHA-1 40 digit hexadecimal number which Github uses as the Commit ID to track changes made to a repo

Usage

util_get_SHA(path = ".")

Arguments

Value

40-digit SHA character string for the latest commit to the repository

Description

Retrieve version for compilation from definitions

Usage

util_get_version(path = "config/metadata.yml")

Arguments

Value

a string

Description

Format table with kable and default styling for html

Usage

util_kable_styling_html(...)

Arguments

Description

Convert a list of elements into a BibEntry object

Usage

util_list_to_bib(ref)

Arguments

Value

BibEntry object

Description

Usage

util_list_to_df1(my_list)

Arguments

Value

A tibble with two columns

Examples

## Not run: 
util_list_to_df1(as.list(dplyr::starwars)[2])

## End(Not run)

Description

Convert a list of lists to dataframe; requires that every list have same named elements.

Usage

util_list_to_df2(my_list, as_character = TRUE, on_empty = NA)

Arguments

Examples

util_list_to_df2(util_df_to_list(dplyr::starwars))

Description

util_replace_null converts NULL values a different value. Default is converting NULL to NA.

Usage

util_replace_null(x, val = NA)

Arguments

Value

NA or a non-NULL object

Examples

## Not run: 
util_replace_null(NULL)

## End(Not run)

Description

util_separate_and_sort: For a vector x in which individual cell may have multiple values (separated by 'sep'), sort records within each cell alphabetically.

Usage

util_separate_and_sort(x, sep = " ")

Arguments

Value

A vector of alphabetically sorted records

Examples

## Not run: util_separate_and_sort("z y x")

Description

Standardise doi

Usage

util_standardise_doi(doi)

Arguments

Description

Write metadata.yml file with custom R code formatted to allow line breaks.

Usage

write_metadata(data, path, style_code = FALSE)

Arguments

Examples

## Not run: 
f <- "data/Falster_2003/metadata.yml"
data <- read_metadata(f)
write_metadata(data, f)

## End(Not run)

Description

Write the YAML representation of metadata.yml for specified dataset_id to file data/dataset_id/metadata.yml

Usage

write_metadata_dataset(metadata, dataset_id)

Arguments

Value

A yaml file

Description

Export AusTraits version as plain text

Usage

write_plaintext(austraits, path)

Arguments

Value

csv files of tibbles containing traits, locations, contexts, methods, excluded_data, taxonomic updates, taxa, contributors

Description

write yaml (from package yaml)