Code here written by Erica Krimmel. Please see Use Case: Search based on data quality flag for context.
# Load core libraries; install these packages if you have not already
library(ridigbio)
library(tidyverse)
# Load library for making nice HTML output
library(kableExtra)In this use case for the iDigBio API we look at how to search for specimen records that have a specific data quality flag. See here for more information about iDigBio’s data quality flags.
First, let’s find all the specimen records for the data quality flag we are interested in. Do this using the idig_search_records function from the ridigbio package. You can learn more about this function from the iDigBio API documentation and ridigbio documentation.
In this example, we want to start by searching for specimens flagged with “rev_geocode_flip” which means that iDigBio has swapped the values of the latitude and longitude fields in order to place the coordinate point in the country stated by the record. For example, iDigBio ingests a record with the coordinates “-87.646166, 41.89542” that says it was collected in the United States, but the verbatim coordinates actually plot to Antarctica. If the latitude and longitude are flipped, then the coordinates plot to the United States, so iDigBio assumes that this is what the data provider meant.
# Edit the fields (e.g. `flags` or `institutioncode`) and values (e.g.
# "rev_geocode_flip" or "fmnh") in `list()` to adjust your query and the fields
# (e.g. `uuid`) in `fields` to adjust the columns returned in your results
records <- idig_search_records(rq = list(flags = "rev_geocode_flip",
institutioncode = "fmnh"),
fields = c("uuid",
"institutioncode",
"collectioncode",
"country",
"data.dwc:country",
"stateprovince",
"county",
"locality",
"geopoint",
"data.dwc:decimalLongitude",
"data.dwc:decimalLatitude"),
limit = 100000) %>%
# Rename fields to more easily reflect their provenance (either from the
# data provider directly or modified by the data aggregator)
rename(provider_lon = `data.dwc:decimalLongitude`,
provider_lat = `data.dwc:decimalLatitude`,
provider_country = `data.dwc:country`,
aggregator_lon = `geopoint.lon`,
aggregator_lat = `geopoint.lat`,
aggregator_country = country,
aggregator_stateprovince = stateprovince,
aggregator_county = county,
aggregator_locality = locality) %>%
# Reorder columns for easier viewing
select(uuid, institutioncode, collectioncode, provider_lat, aggregator_lat,
provider_lon, aggregator_lon, provider_country, aggregator_country,
aggregator_stateprovince, aggregator_county, aggregator_locality)Here is what our query result data looks like:
| uuid | institutioncode | collectioncode | provider_lat | aggregator_lat | provider_lon | aggregator_lon | provider_country | aggregator_country | aggregator_stateprovince | aggregator_county | aggregator_locality |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 032387ec-d2c0-4e31-9217-06142b99ab45 | fmnh | mammals | -87.646166 | 41.89542 | 41.89542 | -87.64617 | USA | united states | illinois | cook co | NA |
| 04dba613-bb9a-4281-8dba-eb4bf59cd777 | fmnh | mammals | -88.107013 | 41.86614 | 41.86614 | -88.10701 | USA | united states | illinois | dupage co. | wheaton |
| 05679624-d82c-4488-bd4b-ab13f40abb0b | fmnh | mammals | 75 | 38.00000 | 38 | 75.00000 | China | china | xinjiang uygur | kashi pref | taxkorgan tajik aut co, near little kara kul, ‘kara su’ river |
| 0bdf0231-dae7-4de5-a43b-c756e96cb74e | fmnh | mammals | -87.818397 | 42.03420 | 42.034196 | -87.81840 | USA | united states | illinois | cook co. | oak park, pheasent and harlem |
| 0de28396-f117-4a0f-bca7-0d08cc58dc5a | fmnh | mammals | -88.140531 | 41.79461 | 41.79461 | -88.14053 | USA | united states | illinois | dupage co. | naperville, 1520 maple knoll ct. |
| 0f85a79f-2f5b-4d0d-8770-6e3f262c2834 | fmnh | mammals | -88.090019 | 41.71987 | 41.719872 | -88.09002 | USA | united states | illinois | will co. | naperville, 25 w. 540 royce rd. |
| 109555a6-3fcf-43ec-ae83-450ea6e85e5e | fmnh | fishes | -80.8500 | -6.45000 | -6.4500 | -80.85000 | Peru | peru | NA | NA | lobos de tierra bay |
| 1252e5dc-1fe6-4d78-a775-ba4c0ae5af67 | fmnh | mammals | -88.067012 | 41.87753 | 41.877529 | -88.06701 | USA | united states | illinois | dupage co. | glen ellyn, roosevelt & park |
| 1561e1ce-23b9-43ab-a59c-2b299037f5b2 | fmnh | mammals | -87.973949 | 41.75198 | 41.751975 | -87.97395 | USA | united states | illinois | dupage co. | darien |
| 1ac87a63-1df9-48c4-984b-85f52d8d1f95 | fmnh | mammals | -88.050341 | 41.74697 | 41.746975 | -88.05034 | USA | united states | illinois | dupage co. | woodridge |
| 1f734bc6-130c-48d2-b47f-26cacfa5c722 | fmnh | mammals | 31.3999996 | 24.86667 | 24.8666706 | 31.40000 | Egypt | egypt | matruh | NA | salum, sidi omar |
| 22717ba0-9ec5-4e1c-88fc-26452b9cdb22 | fmnh | mammals | 75 | 38.00000 | 38 | 75.00000 | China | china | xinjiang uygur | kashi pref | taxkorgan tajik aut co, near little kara kul, ‘kara su’ river |
| 27236f31-f92b-4f42-8c76-be4f38599fc7 | fmnh | mammals | -88.050341 | 41.74697 | 41.746975 | -88.05034 | USA | united states | illinois | dupage co. | woodridge |
| 2c475317-1113-4dca-a32c-9f7673026a98 | fmnh | mammals | 31.3999996 | 24.86667 | 24.8666706 | 31.40000 | Egypt | egypt | matruh | NA | salum, sidi omar |
| 30fe1434-1e75-45e2-97d4-84520e0d1f90 | fmnh | mammals | -88.107013 | 41.86614 | 41.86614 | -88.10701 | USA | united states | illinois | dupage co. | wheaton |
| 334800d8-7f2f-472a-92ac-d242e6b4f2bc | fmnh | mammals | -88.090019 | 41.71987 | 41.719872 | -88.09002 | USA | united states | illinois | will co. | naperville, 25 w. 540 royce rd. |
| 33551039-8928-43fe-be46-26a2fb0f0150 | fmnh | invertebrate zoology | -73 | -41.67000 | -41.67 | -73.00000 | Chile | chile | NA | NA | chaica, senode reloncavi, llongothue |
| 37c644b4-b1d8-4ab4-8a82-306502700307 | fmnh | mammals | -89.97818 | 42.08053 | 42.080535 | -89.97818 | USA | united states | illinois | carroll co. | mt. carroll, 1 mile south of mount carroll |
| 38e73860-a1ee-4585-8687-945cdec490ca | fmnh | mammals | -87.646166 | 41.89542 | 41.89542 | -87.64617 | USA | united states | illinois | cook co | NA |
| 39c28ede-a1a2-4654-b530-62b926b522c6 | fmnh | mammals | -88.087747 | 42.63685 | 42.636849 | -88.08775 | USA | united states | wisconsin | kenosha co | kansasville, 23000 burlington rd., 53139 |
| 3dc63226-5b38-47f0-b02d-ce6c1e99baea | fmnh | mammals | -88.087747 | 42.63685 | 42.636849 | -88.08775 | USA | united states | wisconsin | kenosha co | kansasville, 23000 burlington rd., 53139 |
| 419092db-710b-4823-9ada-cef1dc27d413 | fmnh | mammals | -87.67913 | 41.96874 | 41.968745 | -87.67913 | USA | united states | illinois | cook co. | chicago, damen and lawrence |
| 422e0874-3c59-4e97-8838-ab0faed00b16 | fmnh | mammals | -87.968099 | 42.27394 | 42.273935 | -87.96810 | USA | united states | illinois | lake co. | libertyville, 911 creastfield ave. |
| 46e7dca6-bd0f-4710-a2ae-066e47a96e59 | fmnh | invertebrate zoology | -73.0000 | -41.66670 | -41.6667 | -73.00000 | Chile | chile | NA | NA | llangothie, senode, relocnavi, chaica |
| 4b6340c2-8d61-4f06-8539-0c174cd03f3b | fmnh | mammals | 75 | 38.00000 | 38 | 75.00000 | China | china | xinjiang uygur | kashi pref | taxkorgan tajik aut co, near little kara kul, ‘subashi’ pass |
| 4c5a8228-8b47-4c9b-b7b7-8a4748061691 | fmnh | mammals | -88.058783 | 41.79092 | 41.790922 | -88.05878 | USA | united states | illinois | dupage co. | lisle, 5321 westview, 60532 |
| 4f4ecf74-48cd-4d44-bbec-117ce36cc805 | fmnh | mammals | -89.869212 | 42.25056 | 42.250559 | -89.86921 | USA | united states | illinois | stephenson co. | near pearl city-loran/nw |
| 4f56899f-6bfd-482a-89e8-d47f31ca6b73 | fmnh | mammals | -88.107013 | 41.86614 | 41.86614 | -88.10701 | USA | united states | illinois | dupage co. | wheaton |
| 5c836443-dfbc-4298-a0b9-499f587117b9 | fmnh | mammals | -88.056212 | 41.88147 | 41.881469 | -88.05621 | USA | united states | illinois | dupage co. | glen ellyn, 735 cresent blvd. |
| 6385b5e2-4219-4154-a4b1-aee2e297f0ee | fmnh | mammals | -88.050341 | 41.74697 | 41.746975 | -88.05034 | USA | united states | illinois | dupage co. | woodridge |
| 65b73f92-d0a6-4d95-b2ac-e9f9dcf703fc | fmnh | mammals | -87.646166 | 41.89542 | 41.89542 | -87.64617 | USA | united states | illinois | cook co | NA |
| 6b14ca0a-5a3c-4078-9629-385a0fbb0768 | fmnh | mammals | -88.060564 | 41.84333 | 41.843331 | -88.06056 | USA | united states | illinois | dupage co. | glen ellyn, willowbrook nature trail |
| 6ee726f3-18a8-402f-bdd1-5b8da939dfba | fmnh | mammals | -88.107013 | 41.86614 | 41.86614 | -88.10701 | USA | united states | illinois | dupage co. | wheaton |
| 7204a2b2-512b-4431-a95b-c0ed166a0633 | fmnh | mammals | 29.75 | 24.83333 | 24.833334 | 29.75000 | Egypt | egypt | matruh | NA | siwa oasis, el malfa swamp |
| 7437a46e-f784-4b2b-ba13-b98254b5255b | fmnh | mammals | 29.75 | 24.83333 | 24.833334 | 29.75000 | Egypt | egypt | matruh | NA | el malfa, siwa, 110 km w |
| 763c9b19-74a7-43a3-9a5b-4684fef8a585 | fmnh | mammals | -88.174751 | 41.76673 | 41.766727 | -88.17475 | USA | united states | illinois | dupage co. | naperville, river and aurora |
| 79ff24fc-5a16-4adc-8270-a4576176666c | fmnh | mammals | -88.058356 | 41.87121 | 41.871205 | -88.05836 | USA | united states | illinois | dupage co. | glen ellyn, montclaire and turner |
| 7e51a7c1-de80-4e19-82e8-231cbd440fb7 | fmnh | mammals | -88.107013 | 41.86614 | 41.86614 | -88.10701 | USA | united states | illinois | dupage co. | wheaton |
| 7e8abc27-d38a-47a6-937b-978822afc72f | fmnh | mammals | -87.73599 | 41.79169 | 41.79169 | -87.73599 | USA | united states | illinois | cook co. | chicago, 5555 s. kolmar ave |
| 7f5970d1-b69b-4255-a780-aed284ba1ac8 | fmnh | mammals | -89.4903273 | 45.59772 | 45.5977178 | -89.49033 | USA | united states | wisconsin | NA | oneida, sec 29, town 36 n, range 8e |
| 7fdb9011-93c3-4d11-a6c2-96e3a4764d19 | fmnh | mammals | 31.3999996 | 24.86667 | 24.8666706 | 31.40000 | Egypt | egypt | matruh | NA | salum, sidi omar |
| 823e6998-3bc1-43b1-ab51-3f83b945219d | fmnh | mammals | -87.670626 | 42.02282 | 42.022825 | -87.67063 | USA | united states | illinois | cook co. | chicago, 1550 w. juneway terrace, 60626 |
| 836d1a77-3eed-4785-8f3c-7f2bfb33d8ed | fmnh | mammals | -88.087113 | 41.86226 | 41.862257 | -88.08711 | USA | united states | illinois | dupage co. | wheaton, blanchard and illinois |
| 83d136cc-a41e-4128-a682-64aa6dba9e51 | fmnh | mammals | -88.090019 | 41.71987 | 41.719872 | -88.09002 | USA | united states | illinois | will co. | naperville, 25 w. 540 royce rd. |
| 920b9297-a114-4474-aa66-ddaaf6e5ca36 | fmnh | mammals | 29.75 | 24.83333 | 24.833334 | 29.75000 | Egypt | egypt | matruh | NA | siwa oasis, el malfa swamp |
| 92535d43-dcaf-42b9-8e0d-6236a746847d | fmnh | mammals | 75 | 38.00000 | 38 | 75.00000 | China | china | xinjiang uygur | kashi pref | taxkorgan tajik aut co, near little kara kul, ‘kara su’ river |
| 9757887b-ebef-485e-9acc-cd3ed0aa88e4 | fmnh | mammals | -88.060564 | 41.84333 | 41.843331 | -88.06056 | USA | united states | illinois | dupage co. | glen ellyn, willowbrook nature trail |
| 97d7edc5-17e3-44b1-8fa8-b2ccb11a9ab2 | fmnh | mammals | -87.92895 | 41.83281 | 41.832808 | -87.92895 | USA | united states | illinois | dupage co. | oak brook, kimberly and charlatan |
| 9e1b6b23-7b91-4f95-9a95-8c7ef0a232c8 | fmnh | mammals | -87.963927 | 44.52909 | 44.529095 | -87.96393 | USA | united states | wisconsin | brown co. | green bay, 1660 e. shore dr. 54302 |
| 9e925438-7d3f-4c68-8e5b-3406ba816543 | fmnh | mammals | -88.011741 | 41.84293 | 41.842926 | -88.01174 | USA | united states | illinois | dupage co. | lombard, 190 oakton dr. |
If a data provider wants to fix these records in a local collection management system, it might be useful to have them in a CSV format rather than only in R. Here is how we can save our results as a CSV:
# Save `records` as a CSV for reintegration into a local collection management
# system
write_csv(records, "records.csv")It is important for you as a data provider or data user to review the results of the data quality flags and confirm that iDigBio’s interpretation matches your expectations. For example, coordinates representing marine localities and localities in or near Antarctica are prone to misinterpretation.