austraits
Fonti Kar, Elizabeth Wenk, Daniel Falster
2024-11-15
Source:vignettes/austraits.Rmd
austraits.Rmdaustraits allow users to access, explore and
wrangle data from traits.build
relational databases. It is also an R interface to AusTraits, the Australian plant trait
database. This package contains functions for joining data from various
tables, filtering to specific records, combining multiple databases and
visualising the distribution of the data. Below, we’ve include a
tutorial using the AusTraits database to illustrate how some these
functions work together to generate useful outputs.
Install and load austraits
austraits is still under development. To install the
current version from GitHub:
#install.packages("remotes")
remotes::install_github("traitecoevo/austraits", dependencies = TRUE, upgrade = "ask")
# Load the austraits package
library(austraits)Retrieve AusTraits database
We will use the latest AusTraits database as an example database.
We can download the AusTraits database by calling
load_austraits(). This function will download AusTraits to
a specified path. By default it is data/austraits. The
function will reload the database from this location in the future. You
can set update = TRUE so the database is downloaded fresh
from Zenodo. Note that
load_austraits() will happily accept a DOI of a particular
version.
austraits <- load_austraits(version = "6.0.0", path = "data/austraits")You can check out different versions of AusTraits and their associated DOI by using:
get_versions(path = "data/austraits")#> # A tibble: 6 × 4
#> publication_date doi version id
#> <date> <chr> <chr> <chr>
#> 1 2024-05-14 10.5281/zenodo.11188867 6.0.0 11188867
#> 2 2023-11-19 10.5281/zenodo.10156222 5.0.0 10156222
#> 3 2023-09-18 10.5281/zenodo.8353840 4.2.0 8353840
#> 4 2023-01-30 10.5281/zenodo.7583087 4.1.0 7583087
#> 5 2022-11-27 10.5281/zenodo.7368074 4.0.0 7368074
#> 6 2021-07-14 10.5281/zenodo.5112001 3.0.2 5112001AusTraits, like all traits.build databases, is a relational database.
In R, it is a very large list with multiple tables. If you are
not familiar with working with lists in R, we recommend having a quick
look at this tutorial. To
learn more about the structure of austraits, check out the
structure
of the database.
austraits
#> ── This is 6.0.0 of AusTraits: a curated plant trait database for the Australian flora! #> ──────────────────────────────────
#> ℹ This database is built using traits.build version 1.1.0.9000
#> ℹ This database contains a total of 1822 records, for 454 taxa and 26 traits.
#>
#> ── This object is a 'list' with the following components: ──
#>
#> • traits: A table containing measurements of traits.
#> • locations: A table containing observations of location/site characteristics associated with information in `traits`.
#> Cross referencing between the two dataframes is possible using combinations of the variables `dataset_id`,
#> `location_name`.
#> • contexts: A table containing observations of contextual characteristics associated with information in `traits`. Cross
#> referencing between the two dataframes is possible using combinations of the variables `dataset_id`, `link_id`, and
#> `link_vals`.
#> • methods: A table containing details on methods with which data were collected, including time frame and source. Cross
#> referencing with the `traits` table is possible using combinations of the variables `dataset_id`, `trait_name`.
#> • excluded_data: A table of data that did not pass quality test and so were excluded from the master dataset.
#> • taxonomic_updates: A table of all taxonomic changes implemented in the construction of AusTraits. Changes are
#> determined by comapring against the APC (Australian Plant Census) and APNI (Australian Plant Names Index).
#> • taxa: A table containing details on taxa associated with information in `traits`. This information has been sourced
#> from the APC (Australian Plant Census) and APNI (Australian Plant Names Index) and is released under a CC-BY3 license.
#> • contributors: A table of people contributing to each study.
#> • sources: Bibtex entries for all primary and secondary sources in the compilation.
#> • definitions: A copy of the definitions for all tables and terms. Information included here was used to process data #> and
#> generate any documentation for the study.
#> • schema: A copy of the schema for all tables and terms. Information included here was used to process data and generate
#> any documentation for the study.
#> • metadata: Metadata associated with the dataset, including title, creators, license, subject, funding sources.
#> • build_info: A description of the computing environment used to create this version of the dataset, including version
#> number, git commit and R session_info.
#> ℹ To access a component, try using the $ e.g. austraits$traitsDescriptive summaries of traits and taxa
AusTraits contains 497 plant traits. Check out definitions of the traits to learn more about how each trait is defined.
Have a look at data coverage by trait or taxa with:
summarise_database(austraits, "trait_name") #> # A tibble: 497 × 5
#> trait_name n_records n_dataset n_taxa percent_total
#> <chr> <int> <int> <int> <dbl>
#> 1 accessory_cost_fraction 47 1 47 0.0000272
#> 2 accessory_cost_mass 47 1 47 0.0000272
#> 3 atmospheric_CO2_concentration 840 4 121 0.000487
#> 4 bark_Al_per_dry_mass 70 1 10 0.0000406
#> 5 bark_B_per_dry_mass 70 1 10 0.0000406
#> 6 bark_C_per_dry_mass 229 2 27 0.000133
#> 7 bark_Ca_per_dry_mass 104 3 21 0.0000603
#> 8 bark_Cu_per_dry_mass 70 1 10 0.0000406
#> 9 bark_Fe_per_dry_mass 70 1 10 0.0000406
#> 10 bark_K_per_dry_mass 104 3 21 0.0000603
#> # ℹ 487 more rows
summarise_database(austraits, "family") #> # A tibble: 310 × 5
#> family n_records n_dataset n_taxa percent_total
#> <chr> <int> <int> <int> <dbl>
#> 1 Acanthaceae 3719 57 149 0.00216
#> 2 Achariaceae 162 14 3 0.0000939
#> 3 Actinidiaceae 186 16 3 0.000108
#> 4 Agapanthaceae 107 13 3 0.000062
#> 5 Aizoaceae 5004 63 102 0.0029
#> 6 Akaniaceae 123 16 1 0.0000713
#> 7 Alismataceae 892 30 20 0.000517
#> 8 Alliaceae 561 19 18 0.000325
#> 9 Alseuosmiaceae 318 13 3 0.000184
#> 10 Alstroemeriaceae 175 15 2 0.000101
#> # ℹ 300 more rows
summarise_database(austraits, "genus") #> # A tibble: 3,177 × 5
#> genus n_records n_dataset n_taxa percent_total
#> <chr> <int> <int> <int> <dbl>
#> 1 (Dockrillia 3 2 1 0.00000174
#> 2 Abelia 16 4 1 0.00000928
#> 3 Abelmoschus 271 19 8 0.000157
#> 4 Abildgaardia 74 7 2 0.0000429
#> 5 Abrodictyum 123 14 3 0.0000713
#> 6 Abroma 39 7 2 0.0000226
#> 7 Abrophyllum 181 19 3 0.000105
#> 8 Abrotanella 183 18 4 0.000106
#> 9 Abrus 202 26 3 0.000117
#> 10 Abutilon 1975 52 54 0.00115
#> # ℹ 3,167 more rowsQuickly look up data
Interested in a specific trait or context property, but unsure what
terms we use? Try our lookup_ functions.
lookup_trait(austraits, "leaf") %>% head()#> [1] "leaf_compoundness" "leaf_phenology" "leaf_length" "leaf_width" "leaf_margin"
#> [6] "leaf_shape"
lookup_context_property(austraits, "fire") %>% head() #> [1] "fire intensity" "fire history" "fire response type" "fire severity" "fire season"
lookup_location_property(austraits, "temperature") %>% head()#> [1] "temperature, max (C)" "temperature, MAT (C)" "temperature, mean summer max (C)"
#> [4] "temperature, mean winter max (C)" "temperature, max MAT (C)" "temperature, min MAT (C)"Extracting data
In most cases, users would like to extract a subset of a database for their research purposes.
-
extract_dataset()filters for a particular study -
extract_trait()filters for a certain trait -
extract_taxa()filters for a specific taxon
Note you can supply a vector to each of these functions to filter for
more than one study/trait/taxa. All our extract_ function
supports partial matching e.g. extract_trait("leaf") would
return all traits containing leaf.
If you would like to extract from other tables or columns, use extract_data
All extract_ functions simultaneously filter across all
tables in the database.
Extracting by dataset
Filtering one particular dataset and assigning it to an object
one_study <- extract_dataset(austraits, "Falster_2005_2")
one_study$traits #> # A tibble: 165 × 26
#> dataset_id taxon_name observation_id trait_name value unit entity_type value_type basis_of_value replicates
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Falster_2005_2 Acacia longifolia 01 huber_val… 0.00… mm2{… population mean measurement unknown
#> 2 Falster_2005_2 Acacia longifolia 01 huber_val… 0.00… mm2{… population mean measurement unknown
#> 3 Falster_2005_2 Acacia longifolia 01 huber_val… 0.00… mm2{… population mean measurement unknown
#> 4 Falster_2005_2 Acacia longifolia 01 huber_val… 0.00… mm2{… population mean measurement unknown
#> 5 Falster_2005_2 Acacia longifolia 01 leaf_N_pe… 23.2 mg/g population mean measurement 4
#> 6 Falster_2005_2 Acacia longifolia 01 leaf_area 1761 mm2 population mean measurement 4
#> 7 Falster_2005_2 Acacia longifolia 01 leaf_mass… 128 g/m2 population mean measurement 4
#> 8 Falster_2005_2 Acacia longifolia 01 plant_hei… 4 m population maximum measurement unknown
#> 9 Falster_2005_2 Acacia longifolia 01 resprouti… fire… <NA> population mode expert_score <NA>
#> 10 Falster_2005_2 Acacia longifolia 01 seed_dry_… 14 mg population mean measurement unknown
#> # ℹ 155 more rows
#> # ℹ 16 more variables: basis_of_record <chr>, life_stage <chr>, population_id <chr>, individual_id <chr>,
#> # repeat_measurements_id <chr>, temporal_context_id <chr>, source_id <chr>, location_id <chr>, entity_context_id <chr>,
#> # plot_context_id <chr>, treatment_context_id <chr>, collection_date <chr>, measurement_remarks <chr>, method_id <chr>,
#> # method_context_id <chr>, original_name <chr>Filtering multiple datasets and assigning it to an object
multi_studies <- extract_dataset(austraits,
dataset_id = c("Thompson_2001","Ilic_2000"))
multi_studies$traits #> # A tibble: 2,209 × 26
#> dataset_id taxon_name observation_id trait_name value unit entity_type value_type basis_of_value replicates
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Ilic_2000 Acacia acradenia 0001 wood_density 0.904 mg/mm3 individual raw measurement unknown
#> 2 Ilic_2000 Acacia acuminata 0002 wood_density 0.895 mg/mm3 individual raw measurement unknown
#> 3 Ilic_2000 Acacia acuminata 0003 wood_density 1.008 mg/mm3 individual raw measurement unknown
#> 4 Ilic_2000 Acacia adsurgens 0004 wood_density 0.887 mg/mm3 individual raw measurement unknown
#> 5 Ilic_2000 Acacia alleniana 0005 wood_density 0.56 mg/mm3 individual raw measurement unknown
#> 6 Ilic_2000 Acacia ampliceps 0006 wood_density 0.568 mg/mm3 individual raw measurement unknown
#> 7 Ilic_2000 Acacia aneura 0007 wood_density 1.035 mg/mm3 individual raw measurement unknown
#> 8 Ilic_2000 Acacia aneura 0008 wood_density 1.019 mg/mm3 individual raw measurement unknown
#> 9 Ilic_2000 Acacia aneura 0009 wood_density 0.861 mg/mm3 individual raw measurement unknown
#> 10 Ilic_2000 Acacia aneura 0010 wood_density 0.996 mg/mm3 individual raw measurement unknown
#> # ℹ 2,199 more rows
#> # ℹ 16 more variables: basis_of_record <chr>, life_stage <chr>, population_id <chr>, individual_id <chr>,
#> # repeat_measurements_id <chr>, temporal_context_id <chr>, source_id <chr>, location_id <chr>, entity_context_id <chr>,
#> # plot_context_id <chr>, treatment_context_id <chr>, collection_date <chr>, measurement_remarks <chr>, method_id <chr>,
#> # method_context_id <chr>, original_name <chr>Filtering multiple datasets by same lead author (e.g. Falster) and assigning it to an object.
falster_studies <- extract_dataset(austraits, "Falster")
falster_studies$traits #> # A tibble: 685 × 26
#> dataset_id taxon_name observation_id trait_name value unit entity_type value_type basis_of_value replicates
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Falster_2003 Acacia floribunda 01 leaf_area 142 mm2 population mean measurement 3
#> 2 Falster_2003 Acacia floribunda 01 leaf_inclin… 57 deg population mean measurement 3
#> 3 Falster_2003 Acacia floribunda 02 leaf_compou… simp… <NA> species mode expert_score <NA>
#> 4 Falster_2003 Acacia myrtifolia 03 leaf_area 319 mm2 population mean measurement 3
#> 5 Falster_2003 Acacia myrtifolia 03 leaf_inclin… 66.1 deg population mean measurement 3
#> 6 Falster_2003 Acacia myrtifolia 04 leaf_compou… simp… <NA> species mode expert_score <NA>
#> 7 Falster_2003 Acacia suaveolens 05 leaf_area 562 mm2 population mean measurement 3
#> 8 Falster_2003 Acacia suaveolens 05 leaf_inclin… 71.7 deg population mean measurement 3
#> 9 Falster_2003 Acacia suaveolens 06 leaf_compou… simp… <NA> species mode expert_score <NA>
#> 10 Falster_2003 Angophora hispida 07 leaf_area 1590 mm2 population mean measurement 3
#> # ℹ 675 more rows
#> # ℹ 16 more variables: basis_of_record <chr>, life_stage <chr>, population_id <chr>, individual_id <chr>,
#> # repeat_measurements_id <chr>, temporal_context_id <chr>, source_id <chr>, location_id <chr>, entity_context_id <chr>,
#> # plot_context_id <chr>, treatment_context_id <chr>, collection_date <chr>, measurement_remarks <chr>, method_id <chr>,
#> # method_context_id <chr>, original_name <chr>Extracting by taxonomy
# By family
proteaceae <- extract_taxa(austraits, family = "Proteaceae")
# Checking that only taxa in Proteaceae have been extracted
proteaceae$taxa$family %>% unique()#> [1] "Proteaceae"
# By genus
acacia <- extract_taxa(austraits, genus = "Acacia")
# Checking that only taxa in Acacia have been extracted
acacia$traits$taxon_name %>% unique() %>% head()#> [1] "Acacia abbatiana" "Acacia abbreviata"
#> [3] "Acacia abrupta" "Acacia acanthaster"
#> [5] "Acacia acanthoclada subsp. acanthoclada" "Acacia acanthoclada subsp. glaucescens"#> [1] "Acacia"Extracting by trait
data_fruit <- extract_trait(austraits, "fruit")
data_fruit$traits #> # A tibble: 216,465 × 26
#> dataset_id taxon_name observation_id trait_name value unit entity_type value_type basis_of_value replicates
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ABRS_1981 Ceratophyllum demers… 0566 fruit_len… 4 mm species minimum measurement <NA>
#> 2 ABRS_1981 Ceratophyllum demers… 0566 fruit_len… 6 mm species maximum measurement <NA>
#> 3 ABRS_1981 Ceratophyllum demers… 0566 fruit_wid… 3 mm species minimum measurement <NA>
#> 4 ABRS_1981 Ceratophyllum demers… 0566 fruit_wid… 3.5 mm species maximum measurement <NA>
#> 5 ABRS_1981 Conospermum petiolare 0680 fruit_len… 2.5 mm species minimum measurement <NA>
#> 6 ABRS_1981 Conospermum petiolare 0680 fruit_wid… 3 mm species minimum measurement <NA>
#> 7 ABRS_1981 Proiphys amboinensis 3182 fruit_len… 15 mm species minimum measurement <NA>
#> 8 ABRS_1981 Proiphys amboinensis 3182 fruit_len… 30 mm species maximum measurement <NA>
#> 9 ABRS_1981 Proiphys amboinensis 3182 fruit_wid… 15 mm species minimum measurement <NA>
#> 10 ABRS_1981 Proiphys amboinensis 3182 fruit_wid… 30 mm species maximum measurement <NA>
#> # ℹ 216,455 more rows
#> # ℹ 16 more variables: basis_of_record <chr>, life_stage <chr>, population_id <chr>, individual_id <chr>,
#> # repeat_measurements_id <chr>, temporal_context_id <chr>, source_id <chr>, location_id <chr>, entity_context_id <chr>,
#> # plot_context_id <chr>, treatment_context_id <chr>, collection_date <chr>, measurement_remarks <chr>, method_id <chr>,
#> # method_context_id <chr>, original_name <chr>Combining lookup_trait with extract_trait
to obtain all traits with ‘leaf’ in the trait name and
assigning it to an object. Note we use the . notation to
pass on the lookup_trait results to
extract_trait
leaf <- lookup_trait(austraits, "leaf") %>% extract_trait(austraits, .)
leaf$traits#> # A tibble: 511,952 × 26
#> dataset_id taxon_name observation_id trait_name value unit entity_type value_type basis_of_value replicates
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ABRS_1981 Acanthocarpus canali… 0001 leaf_comp… simp… <NA> species mode expert_score <NA>
#> 2 ABRS_1981 Acanthocarpus humilis 0002 leaf_comp… simp… <NA> species mode expert_score <NA>
#> 3 ABRS_1981 Acanthocarpus parvif… 0003 leaf_comp… simp… <NA> species mode expert_score <NA>
#> 4 ABRS_1981 Acanthocarpus preiss… 0004 leaf_comp… simp… <NA> species mode expert_score <NA>
#> 5 ABRS_1981 Acanthocarpus robust… 0005 leaf_comp… simp… <NA> species mode expert_score <NA>
#> 6 ABRS_1981 Acanthocarpus rupest… 0006 leaf_comp… simp… <NA> species mode expert_score <NA>
#> 7 ABRS_1981 Acanthocarpus vertic… 0007 leaf_comp… simp… <NA> species mode expert_score <NA>
#> 8 ABRS_1981 Acer pseudoplatanus 0008 leaf_phen… deci… <NA> species mode expert_score <NA>
#> 9 ABRS_1981 Acidonia microcarpa 0009 leaf_comp… comp… <NA> species mode expert_score <NA>
#> 10 ABRS_1981 Callitris acuminata 0010 leaf_comp… simp… <NA> species mode expert_score <NA>
#> # ℹ 511,942 more rows
#> # ℹ 16 more variables: basis_of_record <chr>, life_stage <chr>, population_id <chr>, individual_id <chr>,
#> # repeat_measurements_id <chr>, temporal_context_id <chr>, source_id <chr>, location_id <chr>, entity_context_id <chr>,
#> # plot_context_id <chr>, treatment_context_id <chr>, collection_date <chr>, measurement_remarks <chr>, method_id <chr>,
#> # method_context_id <chr>, original_name <chr>Extracting from other tables
You may want to extract data from tables that have specific column
values. For example calling the code below will return data where “fire”
is mentioned in the context_property column
data_fire <- extract_data(austraits,
table = "contexts",
col = "context_property",
col_value = "fire")
data_fireExtracting from a single table
If you have already manipulated the original database and are working with just the traits table, the extract functions will also work on a single table.
seedling_data <- extract_data(austraits$traits,
col = "life_stage",
col_value = "seedling")
Falster_data <- extract_data(austraits$traits,
col = "dataset_id",
col_value = "Falster")
leaf_data <- extract_trait(austraits$traits,
c("leaf_area", "leaf_N_per_dry_mass"))Join data from other tables
Once users have extracted the data they want, they may want to merge
other study details into the main traits dataframe for
their analyses. For example, users may require taxonomic information for
a phylogenetic analysis. This is where the join_ functions
come in.
There are five join_ functions in total, each designed
to append specific information from other tables and elements in the
austraits object. Their suffixes refer to the type of
information that is joined, e.g. join_taxa appends
taxonomic information to the traits dataframe.
join_taxa()join_methods()join_location_coordinates()join_location_properties()join_context_properties()
We recommend pulling up the help file for each one for more details
e.g ?join_location_coordinates()
Each of the functions has specific default parameters and formatting, but offers versatile joining options.
# Join taxonomic information
(data_fire %>% join_taxa)$traits #> # A tibble: 1,822 × 30
#> dataset_id taxon_name observation_id trait_name value unit entity_type value_type basis_of_value replicates
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Campbell_2006 Acacia falciformis 001 bud_bank_… basa… <NA> population mode expert_score <NA>
#> 2 Campbell_2006 Acacia falciformis 001 resprouti… resp… <NA> population mode expert_score <NA>
#> 3 Campbell_2006 Acacia falciformis 001 seedbank_… soil… <NA> population mode expert_score <NA>
#> 4 Campbell_2006 Acacia falciformis 002 post_fire… post… <NA> population mode expert_score <NA>
#> 5 Campbell_2006 Acacia falciformis 003 dispersers ants <NA> species mode expert_score <NA>
#> 6 Campbell_2006 Acacia falciformis 003 plant_gro… tree <NA> species mode expert_score <NA>
#> 7 Campbell_2006 Acacia irrorata 004 bud_bank_… none <NA> population mode expert_score <NA>
#> 8 Campbell_2006 Acacia irrorata 004 resprouti… fire… <NA> population mode expert_score <NA>
#> 9 Campbell_2006 Acacia irrorata 004 seedbank_… soil… <NA> population mode expert_score <NA>
#> 10 Campbell_2006 Acacia irrorata 005 post_fire… post… <NA> population mode expert_score <NA>
#> # ℹ 1,812 more rows
#> # ℹ 20 more variables: basis_of_record <chr>, life_stage <chr>, population_id <chr>, individual_id <chr>,
#> # repeat_measurements_id <chr>, temporal_context_id <chr>, source_id <chr>, location_id <chr>, entity_context_id <chr>,
#> # plot_context_id <chr>, treatment_context_id <chr>, collection_date <chr>, measurement_remarks <chr>, method_id <chr>,
#> # method_context_id <chr>, original_name <chr>, family <chr>, genus <chr>, taxon_rank <chr>, establishment_means <chr>
# Join methodological information
(data_fire %>% join_methods)$traits#> # A tibble: 1,822 × 27
#> dataset_id taxon_name observation_id trait_name value unit entity_type value_type basis_of_value replicates
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Campbell_2006 Acacia falciformis 001 bud_bank_… basa… <NA> population mode expert_score <NA>
#> 2 Campbell_2006 Acacia falciformis 001 resprouti… resp… <NA> population mode expert_score <NA>
#> 3 Campbell_2006 Acacia falciformis 001 seedbank_… soil… <NA> population mode expert_score <NA>
#> 4 Campbell_2006 Acacia falciformis 002 post_fire… post… <NA> population mode expert_score <NA>
#> 5 Campbell_2006 Acacia falciformis 003 dispersers ants <NA> species mode expert_score <NA>
#> 6 Campbell_2006 Acacia falciformis 003 plant_gro… tree <NA> species mode expert_score <NA>
#> 7 Campbell_2006 Acacia irrorata 004 bud_bank_… none <NA> population mode expert_score <NA>
#> 8 Campbell_2006 Acacia irrorata 004 resprouti… fire… <NA> population mode expert_score <NA>
#> 9 Campbell_2006 Acacia irrorata 004 seedbank_… soil… <NA> population mode expert_score <NA>
#> 10 Campbell_2006 Acacia irrorata 005 post_fire… post… <NA> population mode expert_score <NA>
#> # ℹ 1,812 more rows
#> # ℹ 17 more variables: basis_of_record <chr>, life_stage <chr>, population_id <chr>, individual_id <chr>,
#> # repeat_measurements_id <chr>, temporal_context_id <chr>, source_id <chr>, location_id <chr>, entity_context_id <chr>,
#> # plot_context_id <chr>, treatment_context_id <chr>, collection_date <chr>, measurement_remarks <chr>, method_id <chr>,
#> # method_context_id <chr>, original_name <chr>, methods <chr>
# Join location coordinates
(data_fire %>% join_location_coordinates)$traits #> # A tibble: 1,822 × 29
#> dataset_id taxon_name observation_id trait_name value unit entity_type value_type basis_of_value replicates
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Campbell_2006 Acacia falciformis 001 bud_bank_… basa… <NA> population mode expert_score <NA>
#> 2 Campbell_2006 Acacia falciformis 001 resprouti… resp… <NA> population mode expert_score <NA>
#> 3 Campbell_2006 Acacia falciformis 001 seedbank_… soil… <NA> population mode expert_score <NA>
#> 4 Campbell_2006 Acacia falciformis 002 post_fire… post… <NA> population mode expert_score <NA>
#> 5 Campbell_2006 Acacia falciformis 003 dispersers ants <NA> species mode expert_score <NA>
#> 6 Campbell_2006 Acacia falciformis 003 plant_gro… tree <NA> species mode expert_score <NA>
#> 7 Campbell_2006 Acacia irrorata 004 bud_bank_… none <NA> population mode expert_score <NA>
#> 8 Campbell_2006 Acacia irrorata 004 resprouti… fire… <NA> population mode expert_score <NA>
#> 9 Campbell_2006 Acacia irrorata 004 seedbank_… soil… <NA> population mode expert_score <NA>
#> 10 Campbell_2006 Acacia irrorata 005 post_fire… post… <NA> population mode expert_score <NA>
#> # ℹ 1,812 more rows
#> # ℹ 19 more variables: basis_of_record <chr>, life_stage <chr>, population_id <chr>, individual_id <chr>,
#> # repeat_measurements_id <chr>, temporal_context_id <chr>, source_id <chr>, location_id <chr>, entity_context_id <chr>,
#> # plot_context_id <chr>, treatment_context_id <chr>, collection_date <chr>, measurement_remarks <chr>, method_id <chr>,
#> # method_context_id <chr>, original_name <chr>, location_name <chr>, `latitude (deg)` <chr>, `longitude (deg)` <chr>
# Join information pertaining to location properties
(data_fire %>% join_location_properties)$traits #> # A tibble: 1,822 × 28
#> dataset_id taxon_name observation_id trait_name value unit entity_type value_type basis_of_value replicates
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Campbell_2006 Acacia falciformis 001 bud_bank_… basa… <NA> population mode expert_score <NA>
#> 2 Campbell_2006 Acacia falciformis 001 resprouti… resp… <NA> population mode expert_score <NA>
#> 3 Campbell_2006 Acacia falciformis 001 seedbank_… soil… <NA> population mode expert_score <NA>
#> 4 Campbell_2006 Acacia falciformis 002 post_fire… post… <NA> population mode expert_score <NA>
#> 5 Campbell_2006 Acacia falciformis 003 dispersers ants <NA> species mode expert_score <NA>
#> 6 Campbell_2006 Acacia falciformis 003 plant_gro… tree <NA> species mode expert_score <NA>
#> 7 Campbell_2006 Acacia irrorata 004 bud_bank_… none <NA> population mode expert_score <NA>
#> 8 Campbell_2006 Acacia irrorata 004 resprouti… fire… <NA> population mode expert_score <NA>
#> 9 Campbell_2006 Acacia irrorata 004 seedbank_… soil… <NA> population mode expert_score <NA>
#> 10 Campbell_2006 Acacia irrorata 005 post_fire… post… <NA> population mode expert_score <NA>
#> # ℹ 1,812 more rows
#> # ℹ 18 more variables: basis_of_record <chr>, life_stage <chr>, population_id <chr>, individual_id <chr>,
#> # repeat_measurements_id <chr>, temporal_context_id <chr>, source_id <chr>, location_id <chr>, entity_context_id <chr>,
#> # plot_context_id <chr>, treatment_context_id <chr>, collection_date <chr>, measurement_remarks <chr>, method_id <chr>,
#> # method_context_id <chr>, original_name <chr>, location_name <chr>, location_properties <chr>
# Join information pertaining to location properties
(data_fire %>% join_location_properties(format = "many_columns", vars = "temperature, min MAT (C)"))$traits #> # A tibble: 1,822 × 28
#> dataset_id taxon_name observation_id trait_name value unit entity_type value_type basis_of_value replicates
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Campbell_2006 Acacia falciformis 001 bud_bank_… basa… <NA> population mode expert_score <NA>
#> 2 Campbell_2006 Acacia falciformis 001 resprouti… resp… <NA> population mode expert_score <NA>
#> 3 Campbell_2006 Acacia falciformis 001 seedbank_… soil… <NA> population mode expert_score <NA>
#> 4 Campbell_2006 Acacia falciformis 002 post_fire… post… <NA> population mode expert_score <NA>
#> 5 Campbell_2006 Acacia falciformis 003 dispersers ants <NA> species mode expert_score <NA>
#> 6 Campbell_2006 Acacia falciformis 003 plant_gro… tree <NA> species mode expert_score <NA>
#> 7 Campbell_2006 Acacia irrorata 004 bud_bank_… none <NA> population mode expert_score <NA>
#> 8 Campbell_2006 Acacia irrorata 004 resprouti… fire… <NA> population mode expert_score <NA>
#> 9 Campbell_2006 Acacia irrorata 004 seedbank_… soil… <NA> population mode expert_score <NA>
#> 10 Campbell_2006 Acacia irrorata 005 post_fire… post… <NA> population mode expert_score <NA>
#> # ℹ 1,812 more rows
#> # ℹ 18 more variables: basis_of_record <chr>, life_stage <chr>, population_id <chr>, individual_id <chr>,
#> # repeat_measurements_id <chr>, temporal_context_id <chr>, source_id <chr>, location_id <chr>, entity_context_id <chr>,
#> # plot_context_id <chr>, treatment_context_id <chr>, collection_date <chr>, measurement_remarks <chr>, method_id <chr>,
#> # method_context_id <chr>, original_name <chr>, location_name <chr>,
#> # `location_property: temperature, min MAT (C)` <chr>
# Join context information
(data_fire %>% join_context_properties)$traits#> # A tibble: 1,822 × 31
#> dataset_id taxon_name observation_id trait_name value unit entity_type value_type basis_of_value replicates
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Campbell_2006 Acacia falciformis 001 bud_bank_… basa… <NA> population mode expert_score <NA>
#> 2 Campbell_2006 Acacia falciformis 001 resprouti… resp… <NA> population mode expert_score <NA>
#> 3 Campbell_2006 Acacia falciformis 001 seedbank_… soil… <NA> population mode expert_score <NA>
#> 4 Campbell_2006 Acacia falciformis 002 post_fire… post… <NA> population mode expert_score <NA>
#> 5 Campbell_2006 Acacia falciformis 003 dispersers ants <NA> species mode expert_score <NA>
#> 6 Campbell_2006 Acacia falciformis 003 plant_gro… tree <NA> species mode expert_score <NA>
#> 7 Campbell_2006 Acacia irrorata 004 bud_bank_… none <NA> population mode expert_score <NA>
#> 8 Campbell_2006 Acacia irrorata 004 resprouti… fire… <NA> population mode expert_score <NA>
#> 9 Campbell_2006 Acacia irrorata 004 seedbank_… soil… <NA> population mode expert_score <NA>
#> 10 Campbell_2006 Acacia irrorata 005 post_fire… post… <NA> population mode expert_score <NA>
#> # ℹ 1,812 more rows
#> # ℹ 21 more variables: basis_of_record <chr>, life_stage <chr>, population_id <chr>, individual_id <chr>,
#> # repeat_measurements_id <chr>, temporal_context_id <chr>, source_id <chr>, location_id <chr>, entity_context_id <chr>,
#> # plot_context_id <chr>, treatment_context_id <chr>, collection_date <chr>, measurement_remarks <chr>, method_id <chr>,
#> # method_context_id <chr>, original_name <chr>, treatment_context_properties <chr>, plot_context_properties <chr>,
#> # entity_context_properties <chr>, temporal_context_properties <chr>, method_context_properties <chr>
# Join information from multiple tables
(data_fire %>% join_context_properties %>% join_location_coordinates)$traits #> # A tibble: 1,822 × 34
#> dataset_id taxon_name observation_id trait_name value unit entity_type value_type basis_of_value replicates
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Campbell_2006 Acacia falciformis 001 bud_bank_… basa… <NA> population mode expert_score <NA>
#> 2 Campbell_2006 Acacia falciformis 001 resprouti… resp… <NA> population mode expert_score <NA>
#> 3 Campbell_2006 Acacia falciformis 001 seedbank_… soil… <NA> population mode expert_score <NA>
#> 4 Campbell_2006 Acacia falciformis 002 post_fire… post… <NA> population mode expert_score <NA>
#> 5 Campbell_2006 Acacia falciformis 003 dispersers ants <NA> species mode expert_score <NA>
#> 6 Campbell_2006 Acacia falciformis 003 plant_gro… tree <NA> species mode expert_score <NA>
#> 7 Campbell_2006 Acacia irrorata 004 bud_bank_… none <NA> population mode expert_score <NA>
#> 8 Campbell_2006 Acacia irrorata 004 resprouti… fire… <NA> population mode expert_score <NA>
#> 9 Campbell_2006 Acacia irrorata 004 seedbank_… soil… <NA> population mode expert_score <NA>
#> 10 Campbell_2006 Acacia irrorata 005 post_fire… post… <NA> population mode expert_score <NA>
#> # ℹ 1,812 more rows
#> # ℹ 24 more variables: basis_of_record <chr>, life_stage <chr>, population_id <chr>, individual_id <chr>,
#> # repeat_measurements_id <chr>, temporal_context_id <chr>, source_id <chr>, location_id <chr>, entity_context_id <chr>,
#> # plot_context_id <chr>, treatment_context_id <chr>, collection_date <chr>, measurement_remarks <chr>, method_id <chr>,
#> # method_context_id <chr>, original_name <chr>, treatment_context_properties <chr>, plot_context_properties <chr>,
#> # entity_context_properties <chr>, temporal_context_properties <chr>, method_context_properties <chr>,
#> # location_name <chr>, `latitude (deg)` <chr>, `longitude (deg)` <chr>Alternatively,users can join all information using
flatten_database():
data_fire %>% flatten_database() #> # A tibble: 1,822 × 66
#> dataset_id taxon_name observation_id trait_name value unit entity_type value_type basis_of_value replicates
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Campbell_2006 Acacia falciformis 001 bud_bank_… basa… <NA> population mode expert_score <NA>
#> 2 Campbell_2006 Acacia falciformis 001 resprouti… resp… <NA> population mode expert_score <NA>
#> 3 Campbell_2006 Acacia falciformis 001 seedbank_… soil… <NA> population mode expert_score <NA>
#> 4 Campbell_2006 Acacia falciformis 002 post_fire… post… <NA> population mode expert_score <NA>
#> 5 Campbell_2006 Acacia falciformis 003 dispersers ants <NA> species mode expert_score <NA>
#> 6 Campbell_2006 Acacia falciformis 003 plant_gro… tree <NA> species mode expert_score <NA>
#> 7 Campbell_2006 Acacia irrorata 004 bud_bank_… none <NA> population mode expert_score <NA>
#> 8 Campbell_2006 Acacia irrorata 004 resprouti… fire… <NA> population mode expert_score <NA>
#> 9 Campbell_2006 Acacia irrorata 004 seedbank_… soil… <NA> population mode expert_score <NA>
#> 10 Campbell_2006 Acacia irrorata 005 post_fire… post… <NA> population mode expert_score <NA>
#> # ℹ 1,812 more rows
#> # ℹ 56 more variables: basis_of_record <chr>, life_stage <chr>, population_id <chr>, individual_id <chr>,
#> # repeat_measurements_id <chr>, temporal_context_id <chr>, source_id <chr>, location_id <chr>, entity_context_id <chr>,
#> # plot_context_id <chr>, treatment_context_id <chr>, collection_date <chr>, measurement_remarks <chr>, method_id <chr>,
#> # method_context_id <chr>, original_name <chr>, location_name <chr>, `latitude (deg)` <chr>, `longitude (deg)` <chr>,
#> # location_properties <chr>, treatment_context_properties <chr>, plot_context_properties <chr>,
#> # entity_context_properties <chr>, temporal_context_properties <chr>, method_context_properties <chr>, methods <chr>, …Visualising data by site
plot_locations() graphically summarises where trait data
was collected from and how much data is available. The legend refers to
the number of neighbouring points: the warmer the colour, the more data
that is available. This function only works for studies that are
geo-referenced. Users must first use
join_location_coordinates() to append latitude and
longitude information from the locations dataframe into the traits
dataframe before plotting.
plot_locations() defaults to dividing the data by
trait_name (feature = “trait_name”), but you can select any of the
columns within the traits table - including columns you add with
join_ functions. However, selecting taxon_name
will likely crash R if you are working with a dataframe that still
contains a large number of species.
data_fire <- data_fire %>% join_location_coordinates()
plot_locations(data_fire$traits)
plot of chunk site_plot
Visualising data distribution and variance
plot_trait_distribution() creates histograms and beeswarm plots for
specific traits to help users visualise the variance of the data. Users
can specify whether to create separate beeswarm plots at the level of
taxonomic family, genus or by a column in the traits table, such as
dataset_id
austraits %>% plot_trait_distribution_beeswarm(trait_name = "wood_density", y_axis_category = "family")
plot of chunk beeswarm
austraits %>% plot_trait_distribution_beeswarm(trait_name = "wood_density", y_axis_category = "dataset_id")
plot of chunk beeswarm
Reshaping the traits table
The traits table in AusTraits is in long format,
where data for all trait information are denoted by two columns called
trait_name and value. You can convert this to
wide format, where each trait is in a separate column, using the
function trait_pivot_wider().
Note that the following columns are lost when pivoting: unit, replicates, measurement_remarks, and basis_of_value to provide a useful output.
Pivot wider
Note that the latest version of
trait_pivot_wider() is no longer supporting AusTraits
database versions <=4.0.2. Please refer to our README to install an
older version of the austraits R package to work old
versions of the AusTraits database.
data_fire %>% trait_pivot_wider()#> # A tibble: 1,366 × 49
#> dataset_id taxon_name observation_id entity_type value_type basis_of_record life_stage population_id individual_id
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Campbell_2006 Acacia falc… 001 population mode field adult 01 <NA>
#> 2 Campbell_2006 Acacia falc… 002 population mode field seedling 01 <NA>
#> 3 Campbell_2006 Acacia falc… 003 species mode field adult <NA> <NA>
#> 4 Campbell_2006 Acacia irro… 004 population mode field adult 01 <NA>
#> 5 Campbell_2006 Acacia irro… 005 population mode field seedling 01 <NA>
#> 6 Campbell_2006 Acacia irro… 006 species mode field adult <NA> <NA>
#> 7 Campbell_2006 Acacia maid… 007 population mode field adult 02 <NA>
#> 8 Campbell_2006 Acacia maid… 008 population mode field seedling 02 <NA>
#> 9 Campbell_2006 Acacia maid… 009 species mode field adult <NA> <NA>
#> 10 Campbell_2006 Acacia mela… 010 population mode field adult 02 <NA>
#> # ℹ 1,356 more rows
#> # ℹ 40 more variables: repeat_measurements_id <chr>, temporal_context_id <chr>, source_id <chr>, location_id <chr>,
#> # entity_context_id <chr>, plot_context_id <chr>, treatment_context_id <chr>, collection_date <chr>, method_id <chr>,
#> # method_context_id <chr>, original_name <chr>, location_name <chr>, `latitude (deg)` <chr>, `longitude (deg)` <chr>,
#> # bud_bank_location <chr>, resprouting_capacity <chr>, seedbank_location <chr>, post_fire_recruitment <chr>,
#> # dispersers <chr>, plant_growth_form <chr>, stem_dark_respiration_per_area <chr>, bark_thickness <chr>,
#> # huber_value <chr>, leaf_dry_matter_content <chr>, leaf_dark_respiration_per_area <chr>, …Binding trait values
Some datasets will have multiple observations for some
traits, for instance datasets from floras often report a minimum and
maximum fruit length for a species. You can use
bind_trait_values to merge these into a single cell.
data_fruit <- austraits %>%
extract_trait("fruit_length") %>%
extract_taxa(family = "Rutaceae") %>%
extract_data(table = "traits", col = "value_type", col_value = c("minimum", "maximum"))
data_trait_bound <- data_fruit$traits %>%
bind_trait_values() # Joining multiple obs with `--`
data_trait_bound %>%
dplyr::filter(stringr::str_detect(value, "--"))#> # A tibble: 288 × 26
#> dataset_id taxon_name observation_id trait_name value unit entity_type value_type basis_of_value replicates
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ABRS_2023 Acronychia aberrans 01324 fruit_len… 13--… mm species minimum--… measurement--… NA--NA
#> 2 ABRS_2023 Acronychia acidula 01325 fruit_len… 13--… mm species minimum--… measurement--… NA--NA
#> 3 ABRS_2023 Acronychia acronychi… 01326 fruit_len… 8--13 mm species minimum--… measurement--… NA--NA
#> 4 ABRS_2023 Acronychia acuminata 01327 fruit_len… 12--… mm species minimum--… measurement--… NA--NA
#> 5 ABRS_2023 Acronychia baeuerlen… 01328 fruit_len… 10--… mm species minimum--… measurement--… NA--NA
#> 6 ABRS_2023 Acronychia chooreech… 01329 fruit_len… 10--… mm species minimum--… measurement--… NA--NA
#> 7 ABRS_2023 Acronychia crassipet… 01330 fruit_len… 10--… mm species minimum--… measurement--… NA--NA
#> 8 ABRS_2023 Acronychia imperfora… 01332 fruit_len… 9--16 mm species minimum--… measurement--… NA--NA
#> 9 ABRS_2023 Acronychia laevis 01333 fruit_len… 7--10 mm species minimum--… measurement--… NA--NA
#> 10 ABRS_2023 Acronychia littoralis 01334 fruit_len… 8--14 mm species minimum--… measurement--… NA--NA
#> # ℹ 278 more rows
#> # ℹ 16 more variables: basis_of_record <chr>, life_stage <chr>, population_id <chr>, individual_id <chr>,
#> # repeat_measurements_id <chr>, temporal_context_id <chr>, source_id <chr>, location_id <chr>, entity_context_id <chr>,
#> # plot_context_id <chr>, treatment_context_id <chr>, collection_date <chr>, measurement_remarks <chr>, method_id <chr>,
#> # method_context_id <chr>, original_name <chr>If you would like to revert the bounded trait values, call
separate_trait_values():
data_trait_bound %>%
separate_trait_values(., austraits$definitions)#> # A tibble: 119 × 26
#> dataset_id taxon_name observation_id trait_name value unit entity_type value_type basis_of_value replicates
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <fct> <chr> <chr>
#> 1 Cooper_2013 Acronychia baeuerle… 0071 fruit_len… 15 mm species <NA> measurement <NA>
#> 2 ABRS_2023 Acronychia aberrans 01324 fruit_len… 13 mm species <NA> measurement <NA>
#> 3 ABRS_2023 Acronychia aberrans 01324 fruit_len… 16 mm species <NA> measurement <NA>
#> 4 ABRS_2023 Acronychia eungelle… 01331 fruit_len… 12 mm species <NA> measurement <NA>
#> 5 ABRS_2023 Asterolasia elegans 02248 fruit_len… 10 mm species <NA> measurement <NA>
#> 6 ABRS_2023 Boronia angustisepa… 02910 fruit_len… 6 mm species <NA> measurement <NA>
#> 7 ABRS_2023 Boronia quadrilata 03056 fruit_len… 6 mm species <NA> measurement <NA>
#> 8 ABRS_2023 Bosistoa floydii 03120 fruit_len… 10 mm species <NA> measurement <NA>
#> 9 ABRS_2023 Citrus australasica 04176 fruit_len… 50 mm species <NA> measurement <NA>
#> 10 ABRS_2023 Citrus garrawayi 04178 fruit_len… 100 mm species <NA> measurement <NA>
#> # ℹ 109 more rows
#> # ℹ 16 more variables: basis_of_record <chr>, life_stage <chr>, population_id <chr>, individual_id <chr>,
#> # repeat_measurements_id <chr>, temporal_context_id <chr>, source_id <chr>, location_id <chr>, entity_context_id <chr>,
#> # plot_context_id <chr>, treatment_context_id <chr>, collection_date <chr>, measurement_remarks <chr>, method_id <chr>,
#> # method_context_id <chr>, original_name <chr>