You can install traitdata from Github with:
# Install devtools if not available if(!"remotes" %in% installed.packages()[,"Package"]) install.packages("remotes") # Install traitdata package from Github remotes::install_github("RS-eco/traitdata", build_vignettes = T, force=T)
After installation, simply load the traitdata
package:
library(traitdata)
If you encounter a bug or if you have any problems, please file an issue on Github.
There are 32 different data sets, which are included in this package:
datasetName | basisOfRecord | rightsHolder | DOI ----------- | ------------- | ------------------ | ----------- amniota | traitDatabase | Myrhvold et al. 2016 | 10.1890/15-0846R.1 amphi_lifehist | traitDatabase | Trochet et al. 2014 | 10.3897/BDJ.2.e4123 amphibio | traitDatabase | Oliveira et al. 2017 | 10.1038/sdata.2017.123 an_age | traitDatabase | Tacutu et al. 2018 | 10.1093/nar/gkx1042 anuran_morpho | traitDatabase | Mendoza-Henao et al. 2019 | 10.1002/ecy.2685 arthropods | traitDatabase | Gossner et al. 2015 | 10.1038/sdata.2015.13 atlantic_birds | traitDatabase | Rodrigues et al. 2019 | 10.1002/ecy.2647 australian_birds | traitDatabase | Garnett et al. 2015 | 10.1038/sdata.2015.61 AvianBodySize | traitDatabase | Lislevand et al. 2007 | 10.1890/06-2054 AVONET | traitDatabase | Tobias et al. 2021 | 10.6084/m9.figshare.16586228.v2 bird_behav | traitDatabase | Tobias & Pigot 2019 | 10.1098/rstb.2019.0012 carabids | traitDatabase | van der Plas et al. 2017 | 10.5061/dryad.53ds2 climber | traitDatabase | Schweiger et al. 2014 | 10.3897/zookeys.367.6185 disperse | traitDatabase | Sarremejane et al. 2020 | 10.1038/s41597-020-00732-7 elton_birds | traitDatabase | Wilman et al. 2014 | 10.1890/13-1917.1 elton_mammals | traitDatabase | Wilman et al. 2014 | 10.1890/13-1917.1 epiphytes | traitDatabase | Hietz et al. 2021 | 10.1111/1365-2745.13802 eubirds | traitDatabase | Storchová & Hořák 2017 | 10.1111/geb.12709 fishmorph | traitDatabase | Brosse et al. 2021 | 10.1111/geb.13395 globalHWI | traitDatabase | Sheard et al. 2020 | 10.1038/s41467-020-16313-6 globTherm | traitDatabase | Bennett et al. 2018 | 10.1038/sdata.2018.22 heteroptera | traitDatabase | Gossner et al. 2016 | 10.6084/m9.figshare.c.3307611.v1 heteropteraRaw | traitDatabase | Gossner et al. 2016 | 10.6084/m9.figshare.c.3307611.v1 lizard_traits | traitDatabase | Meiri 2018 | 10.1111/geb.12773 mammal_diet | traitDatabase | Kissling et al. 2014 | 10.1002/ece3.1136 mammal_diet2 | traitDatabase | Gainsbury et al. 2018 | 10.1111/mam.12119 marsupials | traitDatabase | Fisher et al. 2001 | 10.1890/0012-9658(2001)082[3531:TEBOLH]2.0.CO;2 migbehav_birds | literatureData | Eyres & Fritz | 10.12761/SGN.2017.10058 pantheria | traitDatabase | Jones et al. 2009 | 10.1890/08-1494.1 passerines | traitDatabase | Ricklefs 2017 | 10.1002/ecy.1783 primates | traitDatabase | Galán-Acedo et al. 2020 | 10.1038/s41597-019-0059-9 reptile_lifehist | traitDatabase | Grimm et al. 2014 | 10.3897/natureconservation.9.8908 tetra_density | traitDatabase | Santini et al. 2018 | 10.1111/geb.12756
Note: The code for how these datatsets were downloaded and processed can be found in the data-raw folder.
See also https://opentraits.org/datasets.html for an extensive list of Trait datasets.
An overview of the different datasets can also be found here: vignette("data_info")
.
Note: Not all species names could be standardised, therefore some data entries might not contain a scientificNameStd value, please then refer to the Genus and Species column. In case you are interested in which species could not be standardised have a look at the names_nonStd
file. Please also check out the synonyms
data file, for species where an alternative name has been used for standardising the scientific name.
An overview of all variables with a description of each variable can be found in the trait_glossary
data file:
data(trait_glossary)
or in the glossary
vignette:
vignette("trait_glossary")
To connect to one or more datasets, we simply use the data()
function.
# Load Elton Traits data("elton_birds")
Now we can use standard R calls to have a look at the data.
First, we look at the column names of our dataset.
# Look at the variable names colnames(elton_birds)
Then, we check the class and first 6 rows of the first 10 columns of the elton_traits
dataset:
class(elton_birds) head(elton_birds[,1:10])
For more information on how to use the data within the package, check out the access-data
and all the other vignettes.
vignette("access-data")
Additional examples of how to use the different trait datasets can be found in the following vignettes:
vignette("island-birds") vignette("migbehav_birds") vignette("morpho-indices") vignette("pantheria") vignette("passerines")
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