The Berkeley Ecoengine (http://ecoengine.berkeley.edu) provides an open API to a wealth of museum data contained in the Berkeley natural history museums. This R package provides a programmatic interface to this rich repository of data allowing for the data to be easily analyzed and visualized or brought to bear in other contexts. This vignette provides a brief overview of the package's capabilities.
The API documentation is available at http://ecoengine.berkeley.edu/developers/. As with most APIs it is possible to query all the available endpoints that are accessible through the API itself. Ecoengine has something similar.
library(ecoengine)
ee_about()
| type |
|---|
| wieslander_vegetation_type_mapping |
| wieslander_vegetation_type_mapping |
| wieslander_vegetation_type_mapping |
| wieslander_vegetation_type_mapping |
| data |
| data |
| data |
| data |
| actions |
| meta-data |
| meta-data |
| meta-data |
The data functions in the package include ones that query obervations,
checklists, photos, and vegetation records. These data are all formatted
as a common S3 class called ecoengine. The class includes 4 slots.
- [
Total results on server] A total result count (not necessarily the results in this particular object but the total number available for a particlar query) - [
Args] The arguments (So a reader can replicate the results or rerun the query using other tools.) - [
Type] The type (photos,observation, orchecklist) - [
Number of results retrieved] The data. Data are most often coerced into adata.frame. To access the data simply useresult_object$data.
The default print method for the class will summarize the object.
For the sake of speed, results are paginated at 1000 results per page.
It is possible to request all pages for any query by specifying
page = all in any function that retrieves data. However, this option
should be used if the request is reasonably sized. With larger requests,
there is a chance that the query might become interrupted and you could
lose any data that may have been partially downloaded. In such cases the
recommended practice is to use the returned observations to split the
request. You can always check the number of requests you'll need to
retreive data for any query by running ee_pages(obj) where obj is an
object of class ecoengine.
request <- ee_photos(county = "Santa Clara County", quiet = TRUE, progress = FALSE)
# Use quiet to suppress messages. Use progress = FALSE to suppress progress
# bars which can clutter up documents.
ee_pages(request)
#> [1] 1
# Now it's simple to parallelize this request You can parallelize across
# number of cores by passing a vector of pages from 1 through the total
# available.
The database contains over 2 million records (3427932 total). Many of these have already been georeferenced. There are two ways to obtain observations. One is to query the database directly based on a partial or exact taxonomic match. For example
pinus_observations <- ee_observations(scientific_name = "Pinus", page = 1, quiet = TRUE,
progress = FALSE)
pinus_observations
#> [Total results on the server]: 59543
#> [Args]:
#> country = United States
#> scientific_name = Pinus
#> extra = last_modified
#> georeferenced = FALSE
#> page_size = 1000
#> page = 1
#> [Type]: FeatureCollection
#> [Number of results retrieved]: 1000
For additional fields upon which to query, simply look through the help
for ?ee_observations. In addition to narrowing data by taxonomic
group, it's also possible to add a bounding box (add argument bbox) or
request only data that have been georeferenced (set
georeferenced = TRUE).
lynx_data <- ee_observations(genus = "Lynx", georeferenced = TRUE, quiet = TRUE,
progress = FALSE)
lynx_data
#> [Total results on the server]: 725
#> [Args]:
#> country = United States
#> genus = Lynx
#> extra = last_modified
#> georeferenced = True
#> page_size = 1000
#> page = 1
#> [Type]: FeatureCollection
#> [Number of results retrieved]: 725
# Notice that we only for the first 1000 rows. But since 795 is not a big
# request, we can obtain this all in one go.
lynx_data <- ee_observations(genus = "Lynx", georeferenced = TRUE, page = "all",
progress = FALSE)
#> Search contains 725 observations (downloading 1 of 1 pages)
lynx_data
#> [Total results on the server]: 725
#> [Args]:
#> country = United States
#> genus = Lynx
#> extra = last_modified
#> georeferenced = True
#> page_size = 1000
#> page = all
#> [Type]: FeatureCollection
#> [Number of results retrieved]: 725
Other search examples
animalia <- ee_observations(kingdom = "Animalia")
Artemisia <- ee_observations(scientific_name = "Artemisia douglasiana")
asteraceae <- ee_observationss(family = "asteraceae")
vulpes <- ee_observations(genus = "vulpes")
Anas <- ee_observations(scientific_name = "Anas cyanoptera", page = "all")
loons <- ee_observations(scientific_name = "Gavia immer", page = "all")
plantae <- ee_observations(kingdom = "plantae")
# grab first 10 pages (250 results)
plantae <- ee_observations(kingdom = "plantae", page = 1:10)
chordata <- ee_observations(phylum = "chordata")
# Class is clss since the former is a reserved keyword in SQL.
aves <- ee_observations(clss = "aves")
Additional Features
As of July 2014, the API now allows you exclude or request additional fields from the database, even if they are not directly exposed by the API. The list of fields are:
id, record, source, remote_resource, begin_date, end_date,
collection_code, institution_code, state_province, county,
last_modified, original_id, geometry,
coordinate_uncertainty_in_meters, md5, scientific_name,
observation_type, date_precision, locality,
earliest_period_or_lowest_system, latest_period_or_highest_system,
kingdom, phylum, clss, order, family, genus,
specific_epithet, infraspecific_epithet, minimum_depth_in_meters,
maximum_depth_in_meters, maximum_elevation_in_meters,
minimum_elevation_in_meters, catalog_number, preparations, sex,
life_stage, water_body, country, individual_count,
associated_resources
To request additional fields
Just pass then in the extra field with multiple ones separated by
commas.
aves <- ee_observations(clss = "aves", extra = "kingdom,genus")
#> Search contains 237673 observations (downloading 1 of 238 pages)
names(aves$data)
#> [1] "longitude" "latitude"
#> [3] "type" "url"
#> [5] "record" "observation_type"
#> [7] "scientific_name" "country"
#> [9] "state_province" "begin_date"
#> [11] "end_date" "source"
#> [13] "remote_resource" "locality"
#> [15] "coordinate_uncertainty_in_meters" "recorded_by"
#> [17] "kingdom" "genus"
#> [19] "last_modified"
Similarly use exclude to exclude any fields that might be returned by
default.
aves <- ee_observations(clss = "aves", exclude = "source,remote_resource")
#> Search contains 237673 observations (downloading 1 of 238 pages)
names(aves$data)
#> [1] "longitude" "latitude"
#> [3] "type" "url"
#> [5] "record" "observation_type"
#> [7] "scientific_name" "country"
#> [9] "state_province" "begin_date"
#> [11] "end_date" "locality"
#> [13] "coordinate_uncertainty_in_meters" "recorded_by"
#> [15] "last_modified"
Mapping observations
The development version of the package includes a new function
ee_map() that allows users to generate interactive maps from
observation queries using Leaflet.js.
lynx_data <- ee_observations(genus = "Lynx", georeferenced = TRUE, page = "all",
quiet = TRUE)
ee_map(lynx_data)
The ecoengine also contains a large number of photos from various sources. It's easy to query the photo database using similar arguments as above. One can search by taxa, location, source, collection and much more.
photos <- ee_photos(quiet = TRUE, progress = FALSE)
photos
#> [Total results on the server]: 74468
#> [Args]:
#> page_size = 1000
#> georeferenced = 0
#> page = 1
#> [Type]: photos
#> [Number of results retrieved]: 1000
The database currently holds 74468 photos. Photos can be searched by
state province, county, genus, scientific name, authors along with date
bounds. For additional options see ?ee_photos.
charles_results <- ee_photos(authors = "Charles Webber", quiet = TRUE, progress = FALSE)
charles_results
#> [Total results on the server]: 4907
#> [Args]:
#> page_size = 1000
#> authors = Charles Webber
#> georeferenced = FALSE
#> page = 1
#> [Type]: photos
#> [Number of results retrieved]: 1000
# Let's examine a couple of rows of the data
charles_results$data[1:2, ]
#> # A tibble: 2 x 18
#> url
#> <chr>
#> 1 https://ecoengine.berkeley.edu/api/photos/CalPhotos%3A0024%2B3291%2B2018%2B
#> 2 https://ecoengine.berkeley.edu/api/photos/CalPhotos%3A0024%2B3291%2B1998%2B
#> # ... with 17 more variables: record <chr>, authors <chr>, locality <chr>,
#> # county <chr>, photog_notes <chr>, begin_date <dttm>, end_date <dttm>,
#> # collection_code <chr>, scientific_name <chr>, url <chr>,
#> # license <chr>, media_url <chr>, remote_resource <chr>, source <chr>,
#> # geometry.type <chr>, longitude <chr>, latitude <chr>
view_photos(charles_results)
This will launch your default browser and render a page with thumbnails
of all images returned by the search query. You can do this with any
ecoengine object of type photos. Suggestions for improving the photo
browser are welcome.
Other photo search examples
# All the photos in the CDGA collection
all_cdfa <- ee_photos(collection_code = "CDFA", page = "all", progress = FALSE)
# All Racoon pictures
racoons <- ee_photos(scientific_name = "Procyon lotor", quiet = TRUE, progress = FALSE)
There is a wealth of checklists from all the source locations. To get all available checklists from the engine, run:
all_lists <- ee_checklists()
#> Returning 52 checklists
head(all_lists[, c("footprint", "subject")])
#> footprint
#> 1 https://ecoengine.berkeley.edu/api/footprints/angelo-reserve/
#> 2 https://ecoengine.berkeley.edu/api/footprints/angelo-reserve/
#> 3 https://ecoengine.berkeley.edu/api/footprints/angelo-reserve/
#> 4 https://ecoengine.berkeley.edu/api/footprints/hastings-reserve/
#> 5 https://ecoengine.berkeley.edu/api/footprints/angelo-reserve/
#> 6 https://ecoengine.berkeley.edu/api/footprints/hastings-reserve/
#> subject
#> 1 Mammals
#> 2 Mosses
#> 3 Beetles
#> 4 Spiders
#> 5 Amphibians
#> 6 Ants
Currently there are 52 lists available. We can drill deeper into any
list to get all the available data. We can also narrow our checklist
search to groups of interest (see unique(all_lists$subject)). For
example, to get the list of Spiders:
spiders <- ee_checklists(subject = "Spiders")
#> Found 1 checklists
spiders
#> record
#> 4 bigcb:specieslist:15
#> footprint
#> 4 https://ecoengine.berkeley.edu/api/footprints/hastings-reserve/
#> url
#> 4 https://ecoengine.berkeley.edu/api/checklists/bigcb%3Aspecieslist%3A15/
#> source subject
#> 4 https://ecoengine.berkeley.edu/api/sources/18/ Spiders
Now we can drill deep into each list. For this tutorial I'll just retrieve data from the the two lists returned above.
library(plyr)
#> -------------------------------------------------------------------------
#> You have loaded plyr after dplyr - this is likely to cause problems.
#> If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
#> library(plyr); library(dplyr)
#> -------------------------------------------------------------------------
#>
#> Attaching package: 'plyr'
#> The following objects are masked from 'package:dplyr':
#>
#> arrange, count, desc, failwith, id, mutate, rename, summarise,
#> summarize
#> The following object is masked from 'package:purrr':
#>
#> compact
spider_details <- ldply(spiders$url, checklist_details)
names(spider_details)
#> [1] "url" "observation_type"
#> [3] "scientific_name" "collection_code"
#> [5] "institution_code" "country"
#> [7] "state_province" "county"
#> [9] "locality" "begin_date"
#> [11] "end_date" "kingdom"
#> [13] "phylum" "clss"
#> [15] "order" "family"
#> [17] "genus" "specific_epithet"
#> [19] "infraspecific_epithet" "source"
#> [21] "remote_resource" "earliest_period_or_lowest_system"
#> [23] "latest_period_or_highest_system"
unique(spider_details$scientific_name)
#> [1] "Holocnemus pluchei" "Oecobius navus"
#> [3] "Uloborus diversus" "Neriene litigiosa"
#> [5] "Theridion " "Tidarren "
#> [7] "Dictyna " "Mallos "
#> [9] "Yorima " "Hahnia sanjuanensis"
#> [11] "Cybaeus " "Zanomys "
#> [13] "Anachemmis " "Titiotus "
#> [15] "Oxyopes scalaris" "Zora hespera"
#> [17] "Drassinella " "Phrurotimpus mateonus"
#> [19] "Scotinella " "Castianeira luctifera"
#> [21] "Meriola californica" "Drassyllus insularis"
#> [23] "Herpyllus propinquus" "Micaria utahna"
#> [25] "Trachyzelotes lyonneti" "Ebo evansae"
#> [27] "Habronattus oregonensis" "Metaphidippus "
#> [29] "Platycryptus californicus" "Calymmaria "
#> [31] "Frontinella communis" "Undetermined "
#> [33] "Latrodectus hesperus"
Our resulting dataset now contains 33 unique spider species.
The search is elastic by default. One can search for any field in
ee_observations() across all available resources. For example,
# The search function runs an automatic elastic search across all resources
# available through the engine.
lynx_results <- ee_search(query = "genus:Lynx")
lynx_results[, -3]
# This gives you a breakdown of what's available allowing you dig deeper.
| field | results |
|---|---|
| California | 470 |
| Nevada | 105 |
| Alaska | 82 |
| British Columbia | 47 |
| Arizona | 36 |
| Baja California Sur | 25 |
| Montana | 19 |
| Baja California | 16 |
| New Mexico | 14 |
| Oregon | 13 |
Similarly it's possible to search through the observations in a detailed manner as well.
all_lynx_data <- ee_search_obs(query = "Lynx", page = "all", progress = FALSE)
#> Search contains 1033 observations (downloading 2 of 2 pages)
all_lynx_data
#> [Total results on the server]: 1033
#> [Args]:
#> q = Lynx
#> page_size = 1000
#> page = all
#> [Type]: observations
#> [Number of results retrieved]: 1000
Footprints
ee_footprints() provides a list of all the footprints.
footprints <- ee_footprints()
footprints[, -3] # To keep the table from spilling over
| name |
|---|
| Angelo Reserve |
| Sagehen Reserve |
| Hastings Reserve |
| Blue Oak Ranch Reserve |
Data sources
ee_sources() provides a list of data sources for the specimens
contained in the museum.
source_list <- ee_sources()
unique(source_list$name)
| name |
|---|
| MVZ Birds Observations |
| MVZ Birds Eggs and Nests |
| MVZ Herp Collection |
| MVZ Herp Observations |
| MVZ Hildebrand Collection |
| MVZ Mammals |
| BIGCB Species Checklists |
| MVZ Mammals Observations |
| Essig Museum of Entymology |
| UC Museum for Paleontology |
devtools::session_info()
#> Session info -------------------------------------------------------------
#> setting value
#> version R version 3.3.3 (2017-03-06)
#> system x86_64, darwin13.4.0
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> tz America/Los_Angeles
#> date 2017-08-15
#> Packages -----------------------------------------------------------------
#> package * version date source
#> assertthat 0.2.0 2017-04-11 CRAN (R 3.3.2)
#> backports 1.1.0 2017-05-22 CRAN (R 3.3.2)
#> base * 3.3.3 2017-03-07 local
#> bindr 0.1 2016-11-13 cran (@0.1)
#> bindrcpp 0.2 2017-06-17 CRAN (R 3.3.2)
#> broom 0.4.2 2017-02-13 CRAN (R 3.3.2)
#> cellranger 1.1.0 2016-07-27 CRAN (R 3.3.0)
#> codetools 0.2-15 2016-10-05 CRAN (R 3.3.3)
#> colorspace 1.3-2 2016-12-14 CRAN (R 3.3.2)
#> coyote 0.3 2017-01-06 Github (karthik/coyote@acad466)
#> curl 2.8.1 2017-07-21 CRAN (R 3.3.2)
#> data.table 1.10.4 2017-02-01 CRAN (R 3.3.2)
#> datasets * 3.3.3 2017-03-07 local
#> devtools * 1.13.2 2017-06-02 CRAN (R 3.3.3)
#> digest 0.6.12 2017-01-27 CRAN (R 3.3.2)
#> dplyr * 0.7.2 2017-07-20 CRAN (R 3.3.2)
#> ecoengine * 1.11 2017-07-01 local
#> evaluate 0.10.1 2017-06-24 CRAN (R 3.3.3)
#> forcats 0.2.0 2017-01-23 CRAN (R 3.3.2)
#> foreign 0.8-69 2017-06-21 CRAN (R 3.3.2)
#> formatR 1.5 2017-04-25 CRAN (R 3.3.2)
#> ggplot2 * 2.2.1.9000 2017-05-04 Github (hadley/ggplot2@f4398b6)
#> glue 1.1.1 2017-06-21 CRAN (R 3.3.2)
#> graphics * 3.3.3 2017-03-07 local
#> grDevices * 3.3.3 2017-03-07 local
#> grid 3.3.3 2017-03-07 local
#> gtable 0.2.0 2016-02-26 CRAN (R 3.3.0)
#> haven 1.1.0 2017-07-09 CRAN (R 3.3.2)
#> hms 0.3 2016-11-22 CRAN (R 3.3.2)
#> htmltools 0.3.6 2017-04-28 CRAN (R 3.3.3)
#> httr 1.2.1 2016-07-03 CRAN (R 3.3.0)
#> jsonlite 1.5 2017-06-01 CRAN (R 3.3.2)
#> knitr 1.16 2017-05-18 CRAN (R 3.3.3)
#> lattice 0.20-35 2017-03-25 CRAN (R 3.3.2)
#> lazyeval 0.2.0 2016-06-12 CRAN (R 3.3.0)
#> lubridate 1.6.0 2016-09-13 CRAN (R 3.3.0)
#> magrittr 1.5 2014-11-22 CRAN (R 3.3.0)
#> memoise 1.1.0 2017-04-21 CRAN (R 3.3.2)
#> methods * 3.3.3 2017-03-07 local
#> mnormt 1.5-5 2016-10-15 CRAN (R 3.3.0)
#> modelr 0.1.1 2017-07-24 CRAN (R 3.3.2)
#> munsell 0.4.3 2016-02-13 CRAN (R 3.3.0)
#> nlme 3.1-131 2017-02-06 CRAN (R 3.3.2)
#> pander * 0.6.1 2017-08-06 CRAN (R 3.3.2)
#> parallel 3.3.3 2017-03-07 local
#> pkgconfig 2.0.1 2017-03-21 cran (@2.0.1)
#> plyr * 1.8.4 2016-06-08 CRAN (R 3.3.0)
#> psych 1.7.5 2017-05-03 CRAN (R 3.3.3)
#> purrr * 0.2.3 2017-08-02 CRAN (R 3.3.2)
#> R6 2.2.2 2017-06-17 CRAN (R 3.3.2)
#> Rcpp 0.12.12 2017-07-15 CRAN (R 3.3.2)
#> readr * 1.1.1 2017-05-16 CRAN (R 3.3.3)
#> readxl 1.0.0 2017-04-18 CRAN (R 3.3.3)
#> reshape2 1.4.2 2016-10-22 CRAN (R 3.3.0)
#> rlang 0.1.2 2017-08-09 CRAN (R 3.3.2)
#> rmarkdown * 1.6 2017-06-15 CRAN (R 3.3.2)
#> rprojroot 1.2 2017-01-16 cran (@1.2)
#> rvest 0.3.2 2016-06-17 CRAN (R 3.3.0)
#> scales 0.4.1 2016-11-09 CRAN (R 3.3.2)
#> stats * 3.3.3 2017-03-07 local
#> stringi 1.1.5 2017-04-07 CRAN (R 3.3.2)
#> stringr 1.2.0 2017-02-18 cran (@1.2.0)
#> tibble * 1.3.3 2017-05-30 Github (tidyverse/tibble@b2275d5)
#> tidyr * 0.6.3 2017-05-15 CRAN (R 3.3.2)
#> tidyverse * 1.1.1 2017-01-27 CRAN (R 3.3.2)
#> tools 3.3.3 2017-03-07 local
#> utils * 3.3.3 2017-03-07 local
#> whisker 0.3-2 2013-04-28 CRAN (R 3.3.0)
#> withr 2.0.0 2017-07-28 CRAN (R 3.3.2)
#> xml2 1.1.1 2017-01-24 CRAN (R 3.3.2)
Please send any comments, questions, or ideas for new functionality or improvements to <karthik.ram@berkeley.edu>. The code lives on GitHub under the rOpenSci account. Pull requests and bug reports are most welcome.
Karthik Ram
Aug, 2017
Santa Clara, California