- In R
- In Python
- API
- Web
2018-02-09
In R
Getting started
Installation
# install.packages("devtools")
# devtools::install_github('iobis/robis')
install.packages("robis")
library(robis)
Help and issues
- Help:
help(package = "robis") - Manual:
- Issues: https://github.com/iobis/robis/issues
Additional libraries
library(ggplot2)
ggplot2: A very popular plotting system for R
http://ggplot2.org/
library(leaflet)
Leaflet: R package for one of the most popular open-source JavaScript libraries for interactive maps.
https://rstudio.github.io/leaflet/
Occurrence
occurrence(scientificname = NULL, year = NULL, obisid = NULL, aphiaid = NULL, groupid = NULL, resourceid = NULL, nodeid = NULL, areaid = NULL, startdate = NULL, enddate = NULL, startdepth = NULL, enddepth = NULL, geometry = NULL, qc = NULL, fields = NULL, verbose = FALSE)
ptevol <- occurrence("Pterois volitans") # red lionfish
## Retrieved 2000 records of 2436 (82%) Retrieved 2436 records of 2436 (100%)
colnames(ptevol)
## [1] "id" "decimalLongitude" ## [3] "decimalLatitude" "depth" ## [5] "institutionCode" "collectionCode" ## [7] "catalogNumber" "individualCount" ## [9] "datasetName" "phylum" ## [11] "order" "family" ## [13] "genus" "scientificName" ## [15] "originalScientificName" "scientificNameAuthorship" ## [17] "obisID" "resourceID" ## [19] "species" "qc" ## [21] "aphiaID" "speciesID" ## [23] "scientificNameID" "class" ## [25] "eventDate" "yearcollected" ## [27] "basisOfRecord" "locality" ## [29] "accessRights" "collectionID" ## [31] "habitat" "higherClassification" ## [33] "higherGeography" "language" ## [35] "locationRemarks" "modified" ## [37] "occurrenceID" "recordedBy" ## [39] "recordNumber" "rights" ## [41] "rightsHolder" "specificEpithet" ## [43] "stateProvince" "type" ## [45] "identifiedBy" "geodeticDatum" ## [47] "bibliographicCitation" "minimumDepthInMeters" ## [49] "maximumDepthInMeters" "coordinateUncertaintyInMeters" ## [51] "eventID" "footprintWKT" ## [53] "occurrenceRemarks" "occurrenceStatus" ## [55] "waterBody" "fieldNumber" ## [57] "footprintSRS" "references" ## [59] "taxonRank" "county" ## [61] "lifestage" "dynamicProperties" ## [63] "dateIdentified" "vernacularName" ## [65] "coordinatePrecision" "eventTime" ## [67] "associatedMedia" "datasetID" ## [69] "identificationRemarks" "eventRemarks" ## [71] "ownerInstitutionCode" "taxonomicStatus" ## [73] "island" "islandGroup" ## [75] "materialSampleID" "continent"
Filter by year
occurrence("Pterois volitans", year = 1991,
fields = c("id", "decimalLongitude", "decimalLatitude", "year"))
## Retrieved 11 records of 11 (100%)
## id decimalLongitude decimalLatitude year ## 1 8317355 153.6300 -27.60000 NA ## 2 10918348 55.6000 -20.86667 NA ## 3 702259173 153.6670 -28.40000 NA ## 4 702263624 153.0330 -30.80000 NA ## 5 702428002 145.8500 -5.16667 NA ## 6 703286139 153.6330 -28.39166 NA ## 7 703690415 117.7808 -19.99333 NA ## 8 703690793 116.9650 -19.76167 NA ## 9 703690899 116.7983 -19.62833 NA ## 10 703691118 118.7017 -18.94334 NA ## 11 703691119 117.7317 -20.01917 NA
Filter by date
occurrence("Pterois volitans", startdate = '1980-03-01', enddate = '1980-10-30',
fields = c("id", "decimalLongitude", "decimalLatitude", "eventDate"))
## Retrieved 3 records of 3 (100%)
## id decimalLongitude decimalLatitude eventDate ## 1 868932 158.1986 7.008333 1980-09-05 07:00:00 ## 2 399697683 -177.8700 -29.240000 1980-08-06 10:00:00 ## 3 702370348 116.0660 -20.283000 1980-05-31 10:00:00
Filter by depth
occurrence("Pterois volitans", startdepth = 100, enddepth = 200,
fields = c("id", "decimalLongitude", "decimalLatitude", "depth"))
## Retrieved 10 records of 10 (100%)
## id decimalLongitude decimalLatitude depth ## 1 703597136 127.6836 -12.391670 116.0 ## 2 703597267 127.9425 -12.925975 100.0 ## 3 703597268 127.9511 -12.750695 102.5 ## 4 703597370 134.9856 -8.843335 107.0 ## 5 703598330 113.3336 -23.551390 120.0 ## 6 703690903 114.9292 -20.414165 122.5 ## 7 703691121 117.5433 -19.020835 131.0 ## 8 703691470 118.1525 -18.963335 119.5 ## 9 703691925 117.7925 -19.168335 101.5 ## 10 732957007 120.1400 13.880000 131.5
Filter by polygon
With http://iobis.org/maptool) we can create a WKT for the Perhentian and Redang Islands and use this to query all occurrences.
islands <- occurrence(geometry =
paste("POLYGON ((102.68921 6.05862","102.57111 5.95346",
"103.07785 5.49980","103.25226 5.62555",
"103.07648 5.87970","102.68921 6.05862))",sep=", "))
## Retrieved 532 records of 532 (100%)
Filter by polygon
leafletmap(islands)
Visualize data through time
islands$year <- as.numeric(format(as.Date(islands$eventDate), "%Y")) ggplot(islands[!is.na(islands$year),], aes(x = year, fill = phylum)) + geom_histogram(binwidth = 5) + scale_fill_brewer(palette = "Paired")
Visualize multiple facets
lag <- occurrence("Lagis", resourceid=c(4312, 222))
ggplot() +
geom_histogram(data = lag, aes(x = yearcollected), binwidth = 2) +
facet_grid(resourceID ~ species)
## Retrieved 668 records of 668 (100%)
Filter by dataset
datasets <- dataset(q="Adriatic-INTERREG")
## Retrieved 6 records of 6 (100%)
datasets$name
## [1] "Meiobenthos North Adriatic-INTERREG-FVG-Projects" ## [2] "Mesozooplankton North Adriatic-INTERREG Project" ## [3] "Microzooplankton North Adriatic-INTERREG Project" ## [4] "Microphytobenthos North Adriatic-INTERREG Project" ## [5] "Macrobenthos North Adriatic-INTERREG-FVG Project" ## [6] "Phytoplankton North Adriatic-INTERREG-FVG Project"
Filter by dataset
occ <- occurrence(resourceid = datasets$id, year = 1998,
fields = c("id", "decimalLongitude", "decimalLatitude", "resourceID"))
## Retrieved 1553 records of 1553 (100%)
head(occ)
## id decimalLongitude decimalLatitude resourceID ## 1 719561828 13.56467 45.69350 3494 ## 2 719561980 13.59617 45.66383 3494 ## 3 719561996 13.56467 45.69350 3494 ## 4 719561997 13.56467 45.69350 3494 ## 5 719561998 13.56467 45.69350 3494 ## 6 719561999 13.56467 45.69350 3494
Filter by quality control
Vandepitte L., Bosch S., Tyberghein L., Waumans F., Vanhoorne B., Hernandez F., De Clerck O., & Mees J. (2015) Fishing for data and sorting the catch: assessing the data quality, completeness and fitness for use of data in marine biogeographic databases. Database, 2015. http://dx.doi.org/10.1093/database/bau125
?qcflags
Categories of quality control flags:
- Data format
- Taxonomy
- Completeness
- Geography
- Depth
- Outliers
Some filters: quality control
- 3. Taxonomy: is the taxon level genus or lower?
- 4. Geography (lat/lon): are the latitude/longitude values different from zero?
- 5. Geography (lat/lon): are the latitude/longitude values within their possible boundaries? (world coordinates)
- 7. Completeness (date/time): is the sampling year (start/end) completed and valid?
qc3457 <- occurrence(resourceid = datasets$id, qc = c(3,4,5,7), year = 1998)
## Retrieved 1005 records of 1005 (100%)
Visualize QC
- 28. Species outliers (geography): is the observation within three IQRs from the first & third quartile distance to the geographic centroid of this taxon?
acistu <- occurrence("Acipenser sturio")
## Retrieved 66 records of 66 (100%)
acistu$qcnum <- qcflags(acistu$qc, c(28))
colors <- c("#ee3300", "#86b300")[acistu$qcnum + 1]
popup <- paste0(acistu$datasetName, "<br/>", acistu$catalogNumber,
"<br/><a href=\"http://www.iobis.org/explore/#/dataset/",
acistu$resourceID, "\">OBIS dataset page</a>")
Visualize QC
leaflet() %>% addProviderTiles("CartoDB.Positron") %>%
addCircleMarkers(
popup = popup, radius = 3.5, weight = 0, fillColor = colors, fillOpacity = 1,
lat = acistu$decimalLatitude, lng = acistu$decimalLongitude)
Visualize QC
acistu$qctxt <- c("Not ok", "Ok")[acistu$qcnum + 1]
ggplot() +
geom_polygon(data=map_data("world"),aes(x=long,y=lat,group=group),fill="#dddddd")+
geom_point(data=acistu, aes(x=decimalLongitude, y=decimalLatitude, color=qctxt))
Visualize environmental data
env <- obistools::lookup_xy(acistu) envacistu <- cbind(acistu[complete.cases(env),], env[complete.cases(env),]) ggplot(envacistu, aes(x=sssalinity, y=sstemperature, color=qctxt)) + geom_point()
checklist: Generate checklists
checklist(scientificname = NULL, year = NULL, obisid = NULL, aphiaid = NULL, groupid = NULL, resourceid = NULL, areaid = NULL, startdate = NULL, enddate = NULL, startdepth = NULL, enddepth = NULL, geometry = NULL, qc = NULL, fields = NULL, verbose = FALSE)
checklist: Generate checklists
checklist(year=1800)[1:3,]
## Retrieved 16 records of 16 (100%)
## id valid_id parent_id rank_name tname ## 1 402913 402913 402912 Species Anguilla anguilla ## 2 408772 408772 408771 Subspecies Auxis thazard thazard ## 3 424201 424201 424193 Species Clupea harengus ## tauthor worms_id redlist status records datasets phylum ## 1 (Linnaeus, 1758) 126281 TRUE CR 4 1 Chordata ## 2 (Lacepède, 1800) 293552 NA <NA> 1 1 Chordata ## 3 Linnaeus, 1758 126417 TRUE LC 2 1 Chordata ## order family genus species class ## 1 Anguilliformes Anguillidae Anguilla Anguilla anguilla Actinopterygii ## 2 Perciformes Scombridae Auxis Auxis thazard Actinopterygii ## 3 Clupeiformes Clupeidae Clupea Clupea harengus Actinopterygii
dataset: Dataset metadata
dataset(id = NULL, scientificname = NULL, q = NULL, obisid = NULL,
aphiaid = NULL, groupid = NULL, areaid = NULL, nodeid = NULL,
startdate = NULL, enddate = NULL, startdepth = NULL, enddepth = NULL,
geometry = NULL, verbose = FALSE)
dataset: Dataset metadata
as.data.frame(dataset(id = 1))
## Retrieved 1 records of 1 (100%)
## id digirname website ## 1 1 BATS http://www.vims.edu/bio/zooplankton/BATS ## citation ## 1 Steinberg, D.K. and L.P. Madin (2003) Zooplankton Census. Bermuda Atlantic Time-series Study ## species_cnt taxon_cnt record_cnt imis_dasid ## 1 23 25 635 3802 ## name ## 1 Bermuda Atlantic Time-series Study Zooplankton Census
taxon and taxon_common
taxon(obisid = NULL, aphiaid = NULL, scientificname = NULL, verbose = FALSE) taxon_common(obisid, verbose = FALSE)
tax <- taxon(scientificname = "Pterois volitans") tax
## id valid_id parent_id rank_name tname tauthor ## 1 501083 501083 501069 Species Pterois volitans (Linnaeus, 1758) ## worms_id gisd records datasets phylum order family ## 1 159559 TRUE 2436 51 Chordata Scorpaeniformes Scorpaenidae ## genus species class ## 1 Pterois Pterois volitans Actinopterygii
taxon_common(tax$valid_id)
## name language ## 1 lionfish English ## 2 red lionfish English ## 3 turkeyfish English
area: OBIS areas
head(area())
## Retrieved 535 records of 535 (100%)
## id name type ## 1 309 The Sundarbans prot ## 2 24 Bosnia and Herzegovina eez ## 3 322 Shiretoko prot ## 4 478 EBSA No 2: Ua puakaoa seamounts ebsa ## 5 371 Rivercess and Sinoe Sea Turtle Breeding Ground (Liberia) ebsa ## 6 127 Malaysia eez
node: OBIS nodes
node()[1:5,]
## Retrieved 29 records of 29 (100%)
## id description name url ## 1 1 OBIS Argentina AR-OBIS http://arobis.cenpat-conicet.gob.ar/ ## 2 2 AfrOBIS AfrOBIS http://afrobis.csir.co.za/ ## 3 3 Antarctic OBIS Antarctic OBIS http://www.scarmarbin.be/ ## 4 15 ArCOD ArCOD http://www.arcodiv.org/ ## 5 28 Arctic OBIS Arctic OBIS <NA> ## theme ## 1 Argentina ## 2 Sub-Saharan (S-Africa) ## 3 Antarctica ## 4 Arctic ## 5 Arctic
group: Taxonomic groups
head(group())
## Retrieved 85 records of 85 (100%)
## id name ## 1 1 Acarina ## 2 2 Amoebozoa ## 3 3 Amphipoda ## 4 4 Apusozoa ## 5 5 Ascidiacea ## 6 6 Aves
In Python
Getting started
Installation
# from pypi pip install pyobis # dev pip install git+git://github.com/sckott/pyobis.git#egg=pyobis
Help and issues: https://github.com/sckott/pyobis
Occurrence
from pyobis import occurrences
occurrences.search(scientificname = 'Mola mola')
occurrences.search(scientificname = 'Mola mola', offset=0, limit=10)
occurrences.search(geometry='POLYGON((30.1 10.1, 10 20, 20 40, 40 40, 30.1 10.1))',
limit=20)
occurrences.search(aphiaid=key, year="2013", limit=20)
res = occurrences.download(year = 2001, scientificname = 'Orcinus') res.uuid res.status() res.fetch()
Other methods
- checklist - Create a checklist for a geometry, timeperiod, taxonomic group, …
- taxa - Lookup taxonomic names
- groups - List of taxonomic groups
- resources - Lookup datasets metadata
- nodes - Lookup OBIS nodes metadata
API
Documentation
REST API with same methods as implemented in robis.
Web
Explore Portal
Mapper
