Chapter 6 rvertnet
6.1 What is rvertnet?
rvertnet is an R package for interacting with VertNet.org.
VertNet.org API docs: https://github.com/VertNet/webapp/wiki/The-API-search-function
6.2 Basic example
Load rvertnet
Search for Aves in the state of California, limit to 10 records
metadata
data. A tibble is given back:
Search for Mustela nigripes in the states of Wyoming or South Dakota, limit to 20 records
res <- searchbyterm(specificepithet = "nigripes",
state = "(wyoming OR south dakota)", limit = 20)
res$data
#> # A tibble: 20 x 89
#> kingdom recordedby higherclassific… stateprovince basisofrecord month
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Animal… STENHOUSE… | Chordata | |… New South Wa… PreservedSpe… 6
#> 2 Animal… Dewitt, L | Chordata | |… New South Wa… PreservedSpe… 3
#> 3 Animal… NSW State… | Chordata | |… New South Wa… PreservedSpe… 9
#> 4 Animal… A. Seale <NA> Guam PreservedSpe… 7
#> 5 Animal… <NA> Animalia | Chor… <NA> PreservedSpe… 11
#> 6 Animal… A. C. Alc… Animalia; Chord… <NA> PreservedSpe… 3
#> 7 Animal… A. C. Alc… Animalia; Chord… <NA> PreservedSpe… 3
#> 8 Animal… R. H. Beck Animalia; Chord… California PreservedSpe… 7
#> 9 Animal… R. H. Beck Animalia; Chord… California PreservedSpe… 7
#> 10 Animal… R. H. Beck Animalia; Chord… California PreservedSpe… 7
#> 11 Animal… R. H. Beck Animalia; Chord… California PreservedSpe… 7
#> 12 Animal… R. H. Beck Animalia; Chord… California PreservedSpe… 6
#> 13 Animal… R. H. Beck Animalia; Chord… California PreservedSpe… 4
#> 14 Animal… A. Fish Animalia; Chord… California PreservedSpe… 7
#> 15 Animal… S. Cutler Animalia; Chord… California PreservedSpe… 6
#> 16 Animal… S. Cutler Animalia; Chord… California PreservedSpe… 8
#> 17 Animal… S. Cutler Animalia; Chord… California PreservedSpe… 9
#> 18 Animal… Judith Co… Animalia; Chord… California PreservedSpe… 8
#> 19 Animal… <NA> Animalia; Chord… Illinois PreservedSpe… 1
#> 20 Animal… Robin, D. Procellariiform… Alaska PreservedSpe… 7
#> # … with 83 more variables: decimallongitude <chr>, phylum <chr>,
#> # references <chr>, year <chr>, startdayofyear <chr>, taxonrank <chr>,
#> # specificepithet <chr>, bibliographiccitation <chr>, family <chr>,
#> # countrycode <chr>, geodeticdatum <chr>,
#> # coordinateuncertaintyinmeters <chr>, highergeography <chr>,
#> # accessrights <chr>, verbatimlocality <chr>, verbatimeventdate <chr>,
#> # day <chr>, eventid <chr>, collectioncode <chr>, occurrencestatus <chr>,
#> # locationremarks <chr>, coordinateprecision <chr>, institutioncode <chr>,
#> # scientificname <chr>, class <chr>, vernacularname <chr>,
#> # decimallatitude <chr>, occurrenceid <chr>, language <chr>, license <chr>,
#> # country <chr>, georeferenceverificationstatus <chr>, modified <chr>,
#> # eventdate <chr>, identifiedby <chr>, nomenclaturalcode <chr>,
#> # continent <chr>, genus <chr>, order <chr>, catalognumber <chr>,
#> # enddayofyear <chr>, dateidentified <chr>, individualcount <chr>,
#> # georeferenceprotocol <chr>, georeferencedby <chr>, preparations <chr>,
#> # typestatus <chr>, georeferenceddate <chr>, footprintwkt <chr>,
#> # locality <chr>, disposition <chr>, georeferenceremarks <chr>,
#> # identificationremarks <chr>, othercatalognumbers <chr>, fieldnumber <chr>,
#> # waterbody <chr>, recordnumber <chr>, infraspecificepithet <chr>,
#> # islandgroup <chr>, georeferencesources <chr>, verbatimlongitude <chr>,
#> # sex <chr>, island <chr>, verbatimlatitude <chr>, lifestage <chr>,
#> # scientificnameauthorship <chr>, county <chr>, dynamicproperties <chr>,
#> # occurrenceremarks <chr>, verbatimcoordinatesystem <chr>,
#> # identificationverificationstatus <chr>, identificationqualifier <chr>,
#> # institutionid <chr>, eventremarks <chr>, organismid <chr>, eventtime <chr>,
#> # verbatimcoordinates <chr>, informationwithheld <chr>, collectionid <chr>,
#> # previousidentifications <chr>, locationaccordingto <chr>,
#> # associatedmedia <chr>, datasetname <chr>6.2.1 downstream data manipulation
You can pass the data object directly on to dplyr functions. Here, we get a
table of record counts by species in descending order.
library("dplyr")
out <- searchbyterm(genus = "Ochotona", limit = 800)
out$data %>%
group_by(scientificname) %>%
summarise(count = length(scientificname)) %>%
arrange(desc(count))
#> # A tibble: 20 x 2
#> scientificname count
#> <chr> <int>
#> 1 Ochotona princeps 450
#> 2 Ochotona pallasi 129
#> 3 Ochotona princeps saxatilis 103
#> 4 Ochotona hyperborea 30
#> 5 Ochotona dauurica 21
#> 6 Ochotona collaris 15
#> 7 Ochotona princeps figginsi 14
#> 8 Ochotona princeps taylori 8
#> 9 Ochotona princeps schisticeps 6
#> 10 Ochotona alpina 4
#> 11 Ochotona princeps muiri 4
#> 12 Ochotona hyperborea mantchurica 3
#> 13 Ochotona princeps incana 3
#> 14 Ochotona princeps princeps 3
#> 15 Ochotona princeps murri 2
#> 16 Ochotona princeps brunnescens 1
#> 17 Ochotona princeps jewetti 1
#> 18 Ochotona princeps tutelata 1
#> 19 Ochotona princeps uinta 1
#> 20 Ochotona princeps ventorum 1