The austraits package is designed to aid users in
accessing data from AusTraits, a curated plant
trait database for the Australian flora. This package contains several
core functions to wrangle and visualise data. Below, we include a
tutorial to illustrate how to use these functions in greater detail.
Note that we are using a reduced subset of v.3.0.2, so the number of records and taxa will not be match your values
Getting started
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)Loading AusTraits database
By default, load_austraits will download AusTraits to a
specified path e.g. data/austraits and will reload it from
this location in the future. You can set update = TRUE so
the austrait versions are downloaded fresh from Zenodo. Note that
load_austraits will happily accept a DOI of a particular
version.
austraits <- load_austraits(version = "3.0.2", path = "data/austraits")You can check out different versions and their associated DOI of AusTraits by using:
get_versions(path = "data/austraits")The AusTraits object is a very long list with various of
elements. 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 version 0.0.0.900 of austraits!
#>
#> This object contains a total of 5859 records for 1245 species and 33 traits.
#>
#> This object is a 'list' with the following components:
#>
#> - `traits`: A table containing measurements of plant traits.
#> - `sites`: A table containing observations of site characteristics associated with information in `traits`. Cross referencing between the two dataframes is possible using combinations of the variables `dataset_id`, `site_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`, `context_name`.
#> - `methods`: A table containing details on methods with which data were collected, including time frame and source.
#> - `excluded_data`: A table of data that did not pass quality test and so were excluded from the master dataset.
#> - `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.
#> - `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.
#> - `sources`: Bibtex entries for all primary and secondary sources in the compilation.
#> - `contributors`: A table of people contributing to each study.
#> - `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).
#> - `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.
#>
#> To access a component, try using the $ e.g. austraits$traitsDescriptive summaries of traits and taxa
AusTraits contains 33 plant traits. Check out definitions of the traits to learn more about how each trait is defined.
Have a look at what trait or taxa we have with:
summarise_austraits(austraits, "trait_name") %>% head()#> trait_name n_records n_dataset n_taxa percent_total
#> branch_mass_fraction 45 1 45 0.007680
#> fire_cued_seeding 5 1 5 0.000853
#> fire_response 73 2 72 0.012500
#> flowering_time 31 1 31 0.005290
#> huber_value 193 2 57 0.032900
#> leaf_angle 38 1 38 0.006490
summarise_austraits(austraits, var = "family") %>% head()#> family n_records n_dataset n_taxa percent_total
#> Acanthaceae 1 1 1 0.000171
#> Akaniaceae 3 1 1 0.000512
#> Anacardiaceae 20 2 7 0.003410
#> Annonaceae 15 2 5 0.002560
#> Apiaceae 17 1 2 0.002900
#> Apocynaceae 32 3 9 0.005460
summarise_austraits(austraits, "genus") %>% head()#> genus n_records n_dataset n_taxa percent_total
#> Acacia 184 4 77 0.031400
#> Aceratium 5 1 3 0.000854
#> Ackama 10 1 2 0.001710
#> Acradenia 3 1 2 0.000512
#> Acronychia 21 3 7 0.003590
#> Actinotus 10 1 1 0.001710Interested in a specific trait? Try lookup_trait
lookup_trait(austraits, "leaf") %>% head()#> [1] "leaf_angle" "leaf_area" "leaf_compoundness"
#> [4] "leaf_N_per_dry_mass" "specific_leaf_area" "leaf_cell_wall_fraction"Extracting data
In most cases, users would like to extract a subset of
austraits for their own research
purposes.extract_dataset subsets a particular study,
whereas extract_traitsubsets by certain traits. Note that
the other tables and elements of the AusTraits data are extracted too,
not just the main trait table. See ?extract_dataset and
?extract_trait for more details
Extracting by study
Filtering one particular study and assigning it to an object
subset_data <- extract_dataset(austraits, "Falster_2005_2")
subset_data$traits %>% head()#> # A tibble: 6 × 12
#> dataset_id taxon…¹ site_…² conte…³ obser…⁴ trait…⁵ value unit date value…⁶ repli…⁷ origi…⁸
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <fct> <chr> <chr>
#> 1 Falster_2005… Acacia… Myall_… <NA> Falste… fire_r… fire… <NA> 2002… expert… <NA> Acacia…
#> 2 Falster_2005… Acacia… Myall_… <NA> Falste… huber_… 0.00… mm2_… 2002… site_m… unknown Acacia…
#> 3 Falster_2005… Acacia… Myall_… <NA> Falste… huber_… 0.00… mm2_… 2002… site_m… unknown Acacia…
#> 4 Falster_2005… Acacia… Myall_… <NA> Falste… huber_… 0.00… mm2_… 2002… site_m… unknown Acacia…
#> 5 Falster_2005… Acacia… Myall_… <NA> Falste… huber_… 0.00… mm2_… 2002… site_m… unknown Acacia…
#> 6 Falster_2005… Acacia… Myall_… <NA> Falste… leaf_a… 1761 mm2 2002… site_m… 4 Acacia…
#> # … with abbreviated variable names ¹taxon_name, ²site_name, ³context_name, ⁴observation_id,
#> # ⁵trait_name, ⁶value_type, ⁷replicates, ⁸original_nameFiltering multiple studies by two different lead authors and assigning it to an object
subset_multi_studies <- extract_dataset(austraits,
dataset_id = c("Thompson_2001","Ilic_2000"))
subset_multi_studies$traits %>% head()#> # A tibble: 6 × 12
#> dataset_id taxon_name site_…¹ conte…² obser…³ trait…⁴ value unit date value…⁵ repli…⁶ origi…⁷
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <fct> <chr> <chr>
#> 1 Ilic_2000 Acacia ac… <NA> <NA> Ilic_2… wood_d… 0.904 mg/m… <NA> unknown unknown Acacia…
#> 2 Ilic_2000 Acacia ac… <NA> <NA> Ilic_2… wood_d… 0.895 mg/m… <NA> unknown unknown Acacia…
#> 3 Ilic_2000 Acacia ac… <NA> <NA> Ilic_2… wood_d… 1.008 mg/m… <NA> unknown unknown Acacia…
#> 4 Ilic_2000 Acacia ad… <NA> <NA> Ilic_2… wood_d… 0.887 mg/m… <NA> unknown unknown Acacia…
#> 5 Ilic_2000 Acacia am… <NA> <NA> Ilic_2… wood_d… 0.568 mg/m… <NA> unknown unknown Acacia…
#> 6 Ilic_2000 Acacia an… <NA> <NA> Ilic_2… wood_d… 1.035 mg/m… <NA> unknown unknown Acacia…
#> # … with abbreviated variable names ¹site_name, ²context_name, ³observation_id, ⁴trait_name,
#> # ⁵value_type, ⁶replicates, ⁷original_nameFiltering multiple studies by same lead author (e.g. Falster) and assigning it to an object.
# First, we need to identify all studies with an id that includes "Falster"
( dataset_ids <- austraits$methods$dataset_id %>% unique() %>% subset(., grepl("Falster",.))) #> [1] "Falster_2003" "Falster_2005_1" "Falster_2005_2"
# Then we extract
data_falster_studies <- extract_dataset(austraits, dataset_ids)
data_falster_studies$traits %>% head()#> # A tibble: 6 × 12
#> dataset_id taxon_…¹ site_…² conte…³ obser…⁴ trait…⁵ value unit date value…⁶ repli…⁷ origi…⁸
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <fct> <chr> <chr>
#> 1 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 66.1 degr… <NA> site_m… 3 Acacia…
#> 2 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 319 mm2 <NA> site_m… 3 Acacia…
#> 3 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_c… simp… <NA> <NA> expert… <NA> Acacia…
#> 4 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 71.7 degr… <NA> site_m… 3 Acacia…
#> 5 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 562 mm2 <NA> site_m… 3 Acacia…
#> 6 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_c… simp… <NA> <NA> expert… <NA> Acacia…
#> # … with abbreviated variable names ¹taxon_name, ²site_name, ³context_name, ⁴observation_id,
#> # ⁵trait_name, ⁶value_type, ⁷replicates, ⁸original_nameExtracting by taxonomic level
# 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 myrtifolia" "Acacia suaveolens" "Acacia floribunda" "Acacia celsa"
#> [5] "Acacia longifolia" "Acacia terminalis"Extracting by trait
Filtering one particular trait and assigning it to an object
data_wood_dens <- extract_trait(austraits, "wood_density")
head(data_wood_dens$traits)#> # A tibble: 6 × 12
#> dataset_id taxon…¹ site_…² conte…³ obser…⁴ trait…⁵ value unit date value…⁶ repli…⁷ origi…⁸
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <fct> <chr> <chr>
#> 1 Falster_2005… Acacia… Athert… <NA> Falste… wood_d… 0.498 mg/m… 2002… site_m… 3 Acacia…
#> 2 Falster_2005… Acrony… Athert… <NA> Falste… wood_d… 0.525 mg/m… 2002… site_m… 3 Acrony…
#> 3 Falster_2005… Alphit… Athert… <NA> Falste… wood_d… 0.413 mg/m… 2002… site_m… 3 Alphit…
#> 4 Falster_2005… Glochi… Athert… <NA> Falste… wood_d… 0.566 mg/m… 2002… site_m… 3 Glochi…
#> 5 Falster_2005… Homala… Athert… <NA> Falste… wood_d… 0.319 mg/m… 2002… site_m… 3 Homala…
#> 6 Falster_2005… Melico… Athert… <NA> Falste… wood_d… 0.346 mg/m… 2002… site_m… 3 Melico…
#> # … with abbreviated variable names ¹taxon_name, ²site_name, ³context_name, ⁴observation_id,
#> # ⁵trait_name, ⁶value_type, ⁷replicates, ⁸original_nameCombining lookup_trait with extract_trait
to obtain all traits with ‘leaf’ in the trait name and
assigning it to an object. Note we use the . notation to
pass on the lookup_trait results to
extract_trait
data_leaf <- lookup_trait(austraits, "leaf") %>% extract_trait(austraits, .)
head(data_leaf$traits)#> # A tibble: 6 × 12
#> dataset_id taxon_…¹ site_…² conte…³ obser…⁴ trait…⁵ value unit date value…⁶ repli…⁷ origi…⁸
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <fct> <chr> <chr>
#> 1 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 66.1 degr… <NA> site_m… 3 Acacia…
#> 2 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 319 mm2 <NA> site_m… 3 Acacia…
#> 3 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_c… NA <NA> <NA> expert… <NA> Acacia…
#> 4 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 71.7 degr… <NA> site_m… 3 Acacia…
#> 5 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 562 mm2 <NA> site_m… 3 Acacia…
#> 6 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_c… NA <NA> <NA> expert… <NA> Acacia…
#> # … with abbreviated variable names ¹taxon_name, ²site_name, ³context_name, ⁴observation_id,
#> # ⁵trait_name, ⁶value_type, ⁷replicates, ⁸original_nameJoin data from other tables and elements
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_taxonomy appends
taxonomic information to the traits dataframe. See
?join_all for more details.
#> # A tibble: 6 × 16
#> dataset_id taxon_…¹ site_…² conte…³ obser…⁴ trait…⁵ value unit date value…⁶ repli…⁷ origi…⁸
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <fct> <chr> <chr>
#> 1 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 66.1 degr… <NA> site_m… 3 Acacia…
#> 2 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 319 mm2 <NA> site_m… 3 Acacia…
#> 3 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_c… NA <NA> <NA> expert… <NA> Acacia…
#> 4 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 71.7 degr… <NA> site_m… 3 Acacia…
#> 5 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 562 mm2 <NA> site_m… 3 Acacia…
#> 6 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_c… NA <NA> <NA> expert… <NA> Acacia…
#> # … with 4 more variables: family <chr>, genus <chr>, taxonRank <chr>,
#> # acceptedNameUsageID <chr>, and abbreviated variable names ¹taxon_name, ²site_name,
#> # ³context_name, ⁴observation_id, ⁵trait_name, ⁶value_type, ⁷replicates, ⁸original_name#> # A tibble: 6 × 16
#> dataset_id taxon_…¹ site_…² conte…³ obser…⁴ trait…⁵ value unit date value…⁶ repli…⁷ origi…⁸
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <fct> <chr> <chr>
#> 1 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 66.1 degr… <NA> site_m… 3 Acacia…
#> 2 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 319 mm2 <NA> site_m… 3 Acacia…
#> 3 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_c… NA <NA> <NA> expert… <NA> Acacia…
#> 4 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 71.7 degr… <NA> site_m… 3 Acacia…
#> 5 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 562 mm2 <NA> site_m… 3 Acacia…
#> 6 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_c… NA <NA> <NA> expert… <NA> Acacia…
#> # … with 4 more variables: methods <chr>, year_collected_start <chr>, year_collected_end <chr>,
#> # collection_type <chr>, and abbreviated variable names ¹taxon_name, ²site_name,
#> # ³context_name, ⁴observation_id, ⁵trait_name, ⁶value_type, ⁷replicates, ⁸original_name#> # A tibble: 6 × 14
#> dataset_id taxon_…¹ site_…² conte…³ obser…⁴ trait…⁵ value unit date value…⁶ repli…⁷ origi…⁸
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <fct> <chr> <chr>
#> 1 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 66.1 degr… <NA> site_m… 3 Acacia…
#> 2 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 319 mm2 <NA> site_m… 3 Acacia…
#> 3 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_c… NA <NA> <NA> expert… <NA> Acacia…
#> 4 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 71.7 degr… <NA> site_m… 3 Acacia…
#> 5 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 562 mm2 <NA> site_m… 3 Acacia…
#> 6 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_c… NA <NA> <NA> expert… <NA> Acacia…
#> # … with 2 more variables: `latitude (deg)` <chr>, `longitude (deg)` <chr>, and abbreviated
#> # variable names ¹taxon_name, ²site_name, ³context_name, ⁴observation_id, ⁵trait_name,
#> # ⁶value_type, ⁷replicates, ⁸original_name#> # A tibble: 6 × 16
#> datas…¹ taxon…² site_…³ conte…⁴ obser…⁵ trait…⁶ value unit date value…⁷ repli…⁸ origi…⁹ type
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <fct> <chr> <chr> <chr>
#> 1 Falste… Acacia… Ku-rin… <NA> Falste… leaf_a… 66.1 degr… <NA> site_m… 3 Acacia… <NA>
#> 2 Falste… Acacia… Ku-rin… <NA> Falste… leaf_a… 319 mm2 <NA> site_m… 3 Acacia… <NA>
#> 3 Falste… Acacia… Ku-rin… <NA> Falste… leaf_c… NA <NA> <NA> expert… <NA> Acacia… <NA>
#> 4 Falste… Acacia… Ku-rin… <NA> Falste… leaf_a… 71.7 degr… <NA> site_m… 3 Acacia… <NA>
#> 5 Falste… Acacia… Ku-rin… <NA> Falste… leaf_a… 562 mm2 <NA> site_m… 3 Acacia… <NA>
#> 6 Falste… Acacia… Ku-rin… <NA> Falste… leaf_c… NA <NA> <NA> expert… <NA> Acacia… <NA>
#> # … with 3 more variables: description <chr>, leaf_parts <chr>, plant_age <chr>, and abbreviated
#> # variable names ¹dataset_id, ²taxon_name, ³site_name, ⁴context_name, ⁵observation_id,
#> # ⁶trait_name, ⁷value_type, ⁸replicates, ⁹original_name#> # A tibble: 6 × 22
#> dataset_id taxon_…¹ site_…² conte…³ obser…⁴ trait…⁵ value unit date value…⁶ repli…⁷ origi…⁸
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <fct> <chr> <chr>
#> 1 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 66.1 degr… <NA> site_m… 3 Acacia…
#> 2 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 319 mm2 <NA> site_m… 3 Acacia…
#> 3 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_c… NA <NA> <NA> expert… <NA> Acacia…
#> 4 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 71.7 degr… <NA> site_m… 3 Acacia…
#> 5 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_a… 562 mm2 <NA> site_m… 3 Acacia…
#> 6 Falster_2003 Acacia … Ku-rin… <NA> Falste… leaf_c… NA <NA> <NA> expert… <NA> Acacia…
#> # … with 10 more variables: `latitude (deg)` <chr>, `longitude (deg)` <chr>, family <chr>,
#> # genus <chr>, taxonRank <chr>, acceptedNameUsageID <chr>, methods <chr>,
#> # year_collected_start <chr>, year_collected_end <chr>, collection_type <chr>, and abbreviated
#> # variable names ¹taxon_name, ²site_name, ³context_name, ⁴observation_id, ⁵trait_name,
#> # ⁶value_type, ⁷replicates, ⁸original_nameVisualising data by site
plot_site_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_sites to
append latitude and longitude information into the trait dataframe
before plotting
data_wood_dens <- data_wood_dens %>% join_sites()
plot_site_locations(data_wood_dens$traits)
Visualising data distribution and variance
plot_trait_distribution creates histograms and beeswarm plots for
specific traits to help users visualise the variance of the data. Users
can specify whether to create separate beeswarm plots at the level of
taxonomic family or for each dataset_id
austraits %>% plot_trait_distribution_beeswarm("wood_density", "family")
austraits %>% plot_trait_distribution_beeswarm("wood_density", "dataset_id")
Pivotting from long to wide format
The table of traits in AusTraits comes in long
format, where data for all trait information are denoted by two columns
called trait_name and value. You can convert
this to wide format, where each trait is in a separate column, using the
function trait_pivot_wider.
The function operates differently depending on the version of AusTraits
Pivot wider <=3.0.2
In AusTraits <=3.0.2, some studies will have multiple
observations for some traits, e.g. huber values. This can prevent
pivoting from long format to wide. There are two ways to collapse
multiple observations. The first option bind_trait_values
concatenates the observations into a single string, thereby retaining
all information.
Alternatively, if you don’t want to bind the trait values, you can
give summarise_trait_means a go, which computes means with
multiple observations. Note that this method condenses the traits table
and you won’t be able to revert it back unless you
load_austraits again
bind_trait_values
data_wide_bound <- data_falster_studies$traits %>%
bind_trait_values() %>% # Joining multiple obs with `--`
trait_pivot_wider() #Pivot wide
data_wide_bound$value # The trait values table#> # A tibble: 103 × 17
#> dataset_id taxon_n…¹ site_…² conte…³ obser…⁴ leaf_…⁵ leaf_…⁶ leaf_…⁷ branc…⁸ huber…⁹ leaf_…˟
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Falster_2003 Acacia m… Ku-rin… <NA> Falste… 66.1 319 simple <NA> <NA> <NA>
#> 2 Falster_2003 Acacia s… Ku-rin… <NA> Falste… 71.7 562 simple <NA> <NA> <NA>
#> 3 Falster_2003 Angophor… Ku-rin… <NA> Falste… 50.8 1590 simple <NA> <NA> <NA>
#> 4 Falster_2003 Banksia … Ku-rin… <NA> Falste… 53.1 198 simple <NA> <NA> <NA>
#> 5 Falster_2003 Banksia … Ku-rin… <NA> Falste… 45 1061 simple <NA> <NA> <NA>
#> 6 Falster_2003 Boronia … Ku-rin… <NA> Falste… 43.9 151 simple <NA> <NA> <NA>
#> 7 Falster_2003 Conosper… Ku-rin… <NA> Falste… 72.3 1363 simple <NA> <NA> <NA>
#> 8 Falster_2003 Epacris … Ku-rin… <NA> Falste… 42.9 5 simple <NA> <NA> <NA>
#> 9 Falster_2003 Eriostem… Ku-rin… <NA> Falste… 62.1 102 simple <NA> <NA> <NA>
#> 10 Falster_2003 Corymbia… Ku-rin… <NA> Falste… 59.3 1111 simple <NA> <NA> <NA>
#> # … with 93 more rows, 6 more variables: seed_mass <chr>, specific_leaf_area <chr>,
#> # wood_density <chr>, fire_response <chr>, plant_height <chr>, original_name <chr>, and
#> # abbreviated variable names ¹taxon_name, ²site_name, ³context_name, ⁴observation_id,
#> # ⁵leaf_angle, ⁶leaf_area, ⁷leaf_compoundness, ⁸branch_mass_fraction, ⁹huber_value,
#> # ˟leaf_N_per_dry_mass
# Check out the 'bounded' trait values
data_wide_bound$value %>%
select(-c(1:5)) %>% #Excluding values that are not traits so we can see which columns contains bounded values
filter_all(.vars_predicate = any_vars(str_detect(., "--"))) #> # A tibble: 55 × 12
#> leaf_angle leaf_area leaf_co…¹ branc…² huber…³ leaf_…⁴ seed_…⁵ speci…⁶ wood_…⁷ fire_…⁸ plant…⁹
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 <NA> 2786 <NA> 0.67 0.0003… 21.4 10.3 6.8965… 0.498 <NA> <NA>
#> 2 <NA> 14302 <NA> 0.48 0.0001… 24.3 <NA> 11.764… 0.525 <NA> <NA>
#> 3 <NA> 6820 <NA> 0.42 0.0002… 16.3 27.2 6.7114… 0.413 <NA> <NA>
#> 4 <NA> 3209 <NA> 0.41 0.0005… 13.7 10.41 9.4339… 0.566 <NA> <NA>
#> 5 <NA> 10682 <NA> 0.47 0.0004… 22 7 12.195… 0.319 <NA> <NA>
#> 6 <NA> 6955 <NA> 0.58 0.0002… 26.8 1.61 13.333… 0.346 <NA> <NA>
#> 7 <NA> 5228 <NA> 0.58 0.0005… 16.4 176.1 10.752… 0.352 <NA> <NA>
#> 8 <NA> 6806 <NA> 0.42 0.0001… 15.4 8.35 12.658… 0.397 <NA> <NA>
#> 9 <NA> 11157 <NA> 0 0.0002… 17.9 23.09 9.2592… 0.582 <NA> <NA>
#> 10 <NA> 3401 <NA> 0.34 0.0002… 11.1 <NA> 7.7519… 0.763 <NA> <NA>
#> # … with 45 more rows, 1 more variable: original_name <chr>, and abbreviated variable names
#> # ¹leaf_compoundness, ²branch_mass_fraction, ³huber_value, ⁴leaf_N_per_dry_mass, ⁵seed_mass,
#> # ⁶specific_leaf_area, ⁷wood_density, ⁸fire_response, ⁹plant_heightIf you would like to revert the bounded trait values, you have to use
trait_pivot_longer first, then call
separate_trait_values:
data_wide_bound %>%
trait_pivot_longer() %>%
separate_trait_values(., austraits$definitions)#> # A tibble: 691 × 12
#> dataset_id taxon…¹ site_…² conte…³ obser…⁴ trait…⁵ value unit date value…⁶ repli…⁷ origi…⁸
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <fct> <chr> <chr>
#> 1 Falster_2003 Acacia… Ku-rin… <NA> Falste… leaf_a… 66.1 degr… <NA> <NA> 3 Acacia…
#> 2 Falster_2003 Acacia… Ku-rin… <NA> Falste… leaf_a… 319 mm2 <NA> <NA> 3 Acacia…
#> 3 Falster_2003 Acacia… Ku-rin… <NA> Falste… leaf_c… simp… <NA> <NA> <NA> <NA> Acacia…
#> 4 Falster_2003 Acacia… Ku-rin… <NA> Falste… leaf_a… 71.7 degr… <NA> <NA> 3 Acacia…
#> 5 Falster_2003 Acacia… Ku-rin… <NA> Falste… leaf_a… 562 mm2 <NA> <NA> 3 Acacia…
#> 6 Falster_2003 Acacia… Ku-rin… <NA> Falste… leaf_c… simp… <NA> <NA> <NA> <NA> Acacia…
#> 7 Falster_2003 Angoph… Ku-rin… <NA> Falste… leaf_a… 50.8 degr… <NA> <NA> 3 Angoph…
#> 8 Falster_2003 Angoph… Ku-rin… <NA> Falste… leaf_a… 1590 mm2 <NA> <NA> 3 Angoph…
#> 9 Falster_2003 Angoph… Ku-rin… <NA> Falste… leaf_c… simp… <NA> <NA> <NA> <NA> Angoph…
#> 10 Falster_2003 Banksi… Ku-rin… <NA> Falste… leaf_a… 53.1 degr… <NA> <NA> 3 Banksi…
#> # … with 681 more rows, and abbreviated variable names ¹taxon_name, ²site_name, ³context_name,
#> # ⁴observation_id, ⁵trait_name, ⁶value_type, ⁷replicates, ⁸original_name
summarise_trait_means
data_wide_summarised <- data_falster_studies$traits %>%
summarise_trait_means() %>%
trait_pivot_wider()
data_wide_summarised$value %>% head()#> # A tibble: 6 × 17
#> datas…¹ taxon…² site_…³ conte…⁴ obser…⁵ leaf_…⁶ leaf_…⁷ leaf_…⁸ branc…⁹ leaf_…˟ seed_…˟ speci…˟
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Falste… Acacia… Ku-rin… <NA> Falste… 66.1 319 NA NA NA NA NA
#> 2 Falste… Acacia… Ku-rin… <NA> Falste… 71.7 562 NA NA NA NA NA
#> 3 Falste… Angoph… Ku-rin… <NA> Falste… 50.8 1590 NA NA NA NA NA
#> 4 Falste… Banksi… Ku-rin… <NA> Falste… 53.1 198 NA NA NA NA NA
#> 5 Falste… Banksi… Ku-rin… <NA> Falste… 45 1061 NA NA NA NA NA
#> 6 Falste… Boroni… Ku-rin… <NA> Falste… 43.9 151 NA NA NA NA NA
#> # … with 5 more variables: wood_density <dbl>, huber_value <dbl>, fire_response <dbl>,
#> # plant_height <dbl>, original_name <chr>, and abbreviated variable names ¹dataset_id,
#> # ²taxon_name, ³site_name, ⁴context_name, ⁵observation_id, ⁶leaf_angle, ⁷leaf_area,
#> # ⁸leaf_compoundness, ⁹branch_mass_fraction, ˟leaf_N_per_dry_mass, ˟seed_mass,
#> # ˟specific_leaf_area