R client for various sources of species trait data.
Included in traits with the associated function prefix or function name:
betydb_ncbi_traitbank_coral_birdlife_leda_tr_usdatr_zannetr_ernestTalk to us on the issues page if you know of a source of traits data with an API, and we’ll see about including it.
For an introduction to the package, see the vignette.
Stable CRAN version
install.packages("traits")Or development version from GitHub
devtools::install_github("ropensci/traits")library("traits")
library("dplyr")Get trait data for Willow (Salix spp.)
(salix <- betydb_search("Salix Vcmax"))
#> # A tibble: 14 x 36
#> access_level author checked citation_id citation_year city
#> * <int> <chr> <int> <int> <int> <chr>
#> 1 4 Wullschleger 1 51 1993 <NA>
#> 2 4 Wang 1 381 2010 <NA>
#> 3 4 Merilo 1 430 2005 Saare
#> 4 4 Merilo 1 430 2005 Saare
#> 5 4 Merilo 1 430 2005 Saare
#> 6 4 Merilo 1 430 2005 Saare
#> 7 4 Merilo 1 430 2005 Saare
#> 8 4 Merilo 1 430 2005 Saare
#> 9 4 Merilo 1 430 2005 Saare
#> 10 4 Merilo 1 430 2005 Saare
#> 11 4 Merilo 1 430 2005 Saare
#> 12 4 Merilo 1 430 2005 Saare
#> 13 4 Merilo 1 430 2005 Saare
#> 14 4 Merilo 1 430 2005 Saare
#> # ... with 30 more variables: commonname <chr>, cultivar <chr>,
#> # cultivar_id <int>, date <chr>, dateloc <chr>, entity <lgl>,
#> # genus <chr>, id <int>, lat <dbl>, lon <dbl>, mean <dbl>,
#> # method_name <lgl>, month <int>, n <int>, notes <chr>, raw_date <chr>,
#> # result_type <chr>, scientificname <chr>, site_id <int>,
#> # sitename <chr>, species_id <int>, stat <dbl>, statname <chr>,
#> # time <chr>, trait <chr>, trait_description <chr>, treatment <chr>,
#> # treatment_id <int>, units <chr>, year <int>
# equivalent:
# (out <- betydb_search("willow"))Summarise data from the output data.frame
library("dplyr")
salix %>%
group_by(scientificname, trait) %>%
mutate(.mean = as.numeric(mean)) %>%
summarise(mean = round(mean(.mean, na.rm = TRUE), 2),
min = round(min(.mean, na.rm = TRUE), 2),
max = round(max(.mean, na.rm = TRUE), 2),
n = length(n))
#> # A tibble: 4 x 6
#> # Groups: scientificname [?]
#> scientificname trait mean min max n
#> <chr> <chr> <dbl> <dbl> <dbl> <int>
#> 1 Salix Vcmax 65.00 65.00 65.00 1
#> 2 Salix dasyclados Vcmax 46.08 34.30 56.68 4
#> 3 Salix sachalinensis × miyabeana Vcmax 79.28 79.28 79.28 1
#> 4 Salix viminalis Vcmax 43.04 19.99 61.29 8Searching for Balaenoptera musculus (blue whale), page id 328574
res <- traitbank(328574)
res$graph %>%
select(`dwc:measurementtype`) %>%
filter(!is.na(`dwc:measurementtype`))
#> # A tibble: 181 x 1
#> `dwc:measurementtype`
#> <chr>
#> 1 http://eol.org/schema/terms/MineralCompositionOfMilk
#> 2 http://eol.org/schema/terms/MineralCompositionOfMilk
#> 3 http://eol.org/schema/terms/MineralCompositionOfMilk
#> 4 http://eol.org/schema/terms/MineralCompositionOfMilk
#> 5 http://eol.org/schema/terms/MineralCompositionOfMilk
#> 6 http://eol.org/schema/terms/MineralCompositionOfMilk
#> 7 http://eol.org/schema/terms/MineralCompositionOfMilk
#> 8 http://eol.org/schema/terms/MineralCompositionOfMilk
#> 9 http://eol.org/schema/terms/MineralCompositionOfMilk
#> 10 http://eol.org/schema/terms/MineralCompositionOfMilk
#> # ... with 171 more rowsGet the species list and their ids
coral_species()
#> # A tibble: 1,548 x 2
#> name id
#> <chr> <chr>
#> 1 Acanthastrea brevis 3
#> 2 Acanthastrea echinata 4
#> 3 Acanthastrea hemprichi 6
#> 4 Acanthastrea ishigakiensis 8
#> 5 Acanthastrea regularis 12
#> 6 Acanthastrea rotundoflora 13
#> 7 Acanthastrea subechinata 14
#> 8 Acropora abrolhosensis 16
#> 9 Acropora abrotanoides 17
#> 10 Acropora aculeus 18
#> # ... with 1,538 more rowsGet data by taxon
coral_taxa(80)
#> # A tibble: 3,540 x 25
#> observation_id access user_id specie_id specie_name location_id
#> <int> <int> <int> <int> <chr> <int>
#> 1 157133 1 10 80 Acropora hyacinthus 1
#> 2 156961 1 14 80 Acropora hyacinthus 409
#> 3 5781 1 1 80 Acropora hyacinthus 1
#> 4 156610 1 2 80 Acropora hyacinthus 500
#> 5 158118 1 10 80 Acropora hyacinthus 409
#> 6 119211 1 49 80 Acropora hyacinthus 1
#> 7 158211 1 10 80 Acropora hyacinthus 413
#> 8 90294 1 15 80 Acropora hyacinthus 341
#> 9 90294 1 15 80 Acropora hyacinthus 341
#> 10 90294 1 15 80 Acropora hyacinthus 341
#> # ... with 3,530 more rows, and 19 more variables: location_name <chr>,
#> # latitude <dbl>, longitude <dbl>, resource_id <int>,
#> # resource_secondary_id <int>, measurement_id <int>, trait_id <int>,
#> # trait_name <chr>, standard_id <int>, standard_unit <chr>,
#> # methodology_id <int>, methodology_name <chr>, value <chr>,
#> # value_type <chr>, precision <dbl>, precision_type <chr>,
#> # precision_upper <dbl>, replicates <int>, notes <chr>Habitat data
birdlife_habitat(22721692)
#> id Habitat (level 1) Habitat (level 2) Importance
#> 1 22721692 Forest Subtropical/Tropical Dry suitable
#> 2 22721692 Forest Subtropical/Tropical Moist Montane major
#> 3 22721692 Forest Temperate suitable
#> 4 22721692 Shrubland Subtropical/Tropical High Altitude suitable
#> Occurrence
#> 1 breeding
#> 2 non-breeding
#> 3 breeding
#> 4 breedingtraits in R doing citation(package = 'traits')