The goal of ete is to provide an interface to the Evolution of Terrestrial Ecosystems (ETE) Program database.
You can install the released version of ete from GitHub with:
devtools::install_github("smithsonian/ETERnity")The first step to using ETERnity is to load the library, and then use
the load_ete_data function to donwload the latest verion of ETE data
from Figshare, and load it into 6 tables.
library(ETERnity)
data_tables <- load_ete_data(download_if_missing = TRUE)
#> Downloading version 1 of the data...
#> trying URL 'https://ndownloader.figshare.com/articles/[...]'
#> Content type 'application/zip' length 53537971 bytes (51.1 MB)
#> ==================================================
#> downloaded 51.1 MB
#> Unzipping file to /Users/[username]/.ete...
names(data_tables)
#> [1] "dataset_table" "occurrence_table" "sites_table"
#> [4] "sitetrait_table" "species_table" "speciestrait_table"We have created a suite of user functions that allow you to pull data out of the ETE tables by provider. You can pull out yours or anyone else’s.
geteteoccur(provider): Get your occurrence table in long format.
amatangelo_occur <- geteteoccur(data_tables, 'Amatangelo')
head(amatangelo_occur)
#> occurid sitekey sitename speciesid observed sid timeybp
#> 1 849237 Amatan_3034_10 3034_2000 ABIBAL 1 3034 10
#> 2 849238 Amatan_3034_10 3034_2000 ACERUB 0 3034 10
#> 3 849239 Amatan_3034_10 3034_2000 ACESAC 28 3034 10
#> 4 849240 Amatan_3034_10 3034_2000 BETALL 0 3034 10
#> 5 849241 Amatan_3034_10 3034_2000 BETPAP 1 3034 10
#> 6 849242 Amatan_3034_10 3034_2000 CARCAR 0 3034 10
#> datasetname latitude longitude duration spaceextent provider
#> 1 Amatan_WI_Pla_Mod 44.96245 -87.19103 1 5e-04 Amatangelo
#> 2 Amatan_WI_Pla_Mod 44.96245 -87.19103 1 5e-04 Amatangelo
#> 3 Amatan_WI_Pla_Mod 44.96245 -87.19103 1 5e-04 Amatangelo
#> 4 Amatan_WI_Pla_Mod 44.96245 -87.19103 1 5e-04 Amatangelo
#> 5 Amatan_WI_Pla_Mod 44.96245 -87.19103 1 5e-04 Amatangelo
#> 6 Amatan_WI_Pla_Mod 44.96245 -87.19103 1 5e-04 AmatangelogeteteoccurDataset(dataset): Get your occurrence table in long format for one timebin
amatan_wi_occur <- geteteoccurDataset(data_tables, 'Amatan_WI_Pla_Hist')
head(amatan_wi_occur)
#> occurid sitekey sitename speciesid observed sid timeybp
#> 1 851577 Amatan_3034_60 3034_1950 ABIBAL 1 3034 60
#> 2 851578 Amatan_3034_60 3034_1950 ACERUB 1 3034 60
#> 3 851579 Amatan_3034_60 3034_1950 ACESAC 31 3034 60
#> 4 851580 Amatan_3034_60 3034_1950 ACESPI 0 3034 60
#> 5 851581 Amatan_3034_60 3034_1950 BETALL 2 3034 60
#> 6 851582 Amatan_3034_60 3034_1950 BETPAP 8 3034 60
#> datasetname latitude longitude duration spaceextent
#> 1 Amatan_WI_Pla_Hist 44.96245 -87.19103 1 5e-04
#> 2 Amatan_WI_Pla_Hist 44.96245 -87.19103 1 5e-04
#> 3 Amatan_WI_Pla_Hist 44.96245 -87.19103 1 5e-04
#> 4 Amatan_WI_Pla_Hist 44.96245 -87.19103 1 5e-04
#> 5 Amatan_WI_Pla_Hist 44.96245 -87.19103 1 5e-04
#> 6 Amatan_WI_Pla_Hist 44.96245 -87.19103 1 5e-04unmelt2specXsite(table): Put your occurrence table in P/A matrix format
PAtable <- unmelt2specXsite(amatan_wi_occur)
PAtable[1:5,1:5]
#> Amatan_1_60 Amatan_10_60 Amatan_1000_60 Amatan_1002_60
#> ABIBAL NaN NaN NaN NaN
#> ACENEG 0 0 0 0
#> ACERUB 0 0 0 1
#> ACESAC 11 11 37 0
#> ACESPI NaN NaN NaN NaN
#> Amatan_1003_60
#> ABIBAL NaN
#> ACENEG 0
#> ACERUB 0
#> ACESAC 0
#> ACESPI NaNgetlatlon(provider): Get a list of your sites and their coordinates
wing_sites <- getlatlon(data_tables, 'Wing')
head(wing_sites)
#> sitekey sitename latitude longitude
#> 1 Wing_16-4_73m BCR15 43.8527 -107.536
#> 2 Wing_17-0_73m BCR16 43.8527 -107.536
#> 3 Wing_17-9_73m BCR17 43.8524 -107.535
#> 4 Wing_18-0_73m BCR18 43.8524 -107.535
#> 5 Wing_18-1_73m BCR19 43.8523 -107.536
#> 6 Wing_18-2_73m BCR20 43.8523 -107.535getages(provider): Get a list of your sites and their ages
ages <- getages(data_tables, "Behrensmeyer1")
head(ages)
#> sitekey timeybp
#> 1 Behren_D0025_10.474m 10474000
#> 2 Behren_D0027_10.474m 10474000
#> 3 Behren_D0062_10.066m 10066000
#> 4 Behren_GB001_10.768m 10768000
#> 5 Behren_GB002_10.876m 10876000
#> 6 Behren_KL017_10.568m 10568000getsitetraits(provider): Get your site traits matrix
sitetraits <- getsitetraits(data_tables, "Blois")
head(sitetraits)
#> sitekey variablename numvar discvar
#> 1 Blois_1_1k ANN_PRECIP_MM 283.7258
#> 2 Blois_1_2k ANN_PRECIP_MM 283.2045
#> 3 Blois_1_3k ANN_PRECIP_MM 282.3187
#> 4 Blois_1_4k ANN_PRECIP_MM 282.2152
#> 5 Blois_1_5k ANN_PRECIP_MM 275.3530
#> 6 Blois_1_6k ANN_PRECIP_MM 275.3668getspptraits(provider): Get your species trait matrix
spptraits <- getspptraits(data_tables,"Lyons")
head(spptraits)
#> speciesid traitname numvalue discvalue
#> 1 Ago_pac AFR_MO 10.5
#> 2 Ago_pac AFR_MO 10.5
#> 3 Ago_pac AFR_MO 10.5
#> 4 Ago_pac AFR_MO 10.5
#> 5 Ago_pac AFR_MO 10.5
#> 6 Ago_pac AFR_MO 10.5If you use the ETERnity package, please cite accordingly:
The dataset download and load functions all borrowed heavily from portalr.
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Erica M. Christensen, Glenda M. Yenni, Hao Ye, Juniper L. Simonis, Ellen K. Bledsoe, Renata M. Diaz, Shawn D. Taylor, Ethan P. White, and S. K. Morgan Ernest. (2019). portalr: an R package for summarizing and using the Portal Project Data. Journal of Open Source Software, 4(33), 1098, https://doi.org/10.21105/joss.01098