Karthik Ram

> This post was updated on August 20, 2014, with `AntWeb` version
> `0.7.2.99`. Please install an updated version to make sure the code
> works.

![](/assets/blog-images/2014-02-18-antweb/casent0003205_h_1_high.jpg)

Data on more than `10,000` species of ants recorded worldwide are
available through from [California Academy of
Sciences](https://www.calacademy.org/)'
[AntWeb](https://www.antweb.org/), a repository that boasts a wealth of
natural history data, digital images, and specimen records on ant
species from a large community of museum curators.

Digging through some of the earliest announcements of AntWeb, I came
across a Nature News piece titled **"Mashups mix data into global
service"** from January 2006. The article contains this great quote from
[Roderic Page](https://en.wikipedia.org/wiki/Roderic_D.M._Page) *"If you
could pool data from every museum or lab in the world, you could do
amazing things"*. The article also says *"So far, only researchers with
advanced programming skills, working in fields organized enough to have
data online and tagged appropriately, have been able to do this."* In
many ways this really is motivation for why we develop interfaces to
these rich data repositories. Our express intent is to facilitate
researchers explore amazing opportunities that lie within such data by
lowering techinical barriers to use. Right on the heels of our most
recent package ([`ecoengine`](/blog/2014/01/30/ecoengine/)), we are now
happy to first release of an interface to
[AntWeb](https://www.antweb.org/).

A stable version of our R package
[AntWeb](https://docs.ropensci.org/AntWeb/) is now available from CRAN.
The API currently does not require a key for access but larger requests
will be throttled on the server side. It is worth noting that much of
these same data are also ported through the Global Biodiversity
Information Facility and accessible through our
[`gbif`](https://docs.ropensci.org/rgbif/) package. This package
provides a more direct interface to more of the ant specific natural
history data.

## Installing the package

A stable version of the package (`0.7`) is now available on CRAN.

::: {.highlight}
``` {.chroma tabindex="0"}
install.packages("AntWeb")
```
:::

or you can install the latest development version (the master branch is
also always stable & deployable and most up-to-date. Current version is
`0.5.3` at the time of this writing).

::: {.highlight}
``` {.chroma tabindex="0"}
library(devtools)install_github("ropensci/AntWeb")
```
:::

## Searching through the database

As with most of our packages, there are several ways to search through
an API. In the case of AntWeb, you can search by a genus or full species
name or by other taxonomic ranks like sub-phylum.

**Data on ants**

To obtain data on any taxonomic group, you can make a request using the
`aw_data()` function. It's possible to search easily by a taxonomic rank
(e.g. a genus) or by passing a complete scientific name.

**Searching by Genus**

::: {.highlight}
``` {.chroma tabindex="0"}
library(AntWeb)# To get data on an ant genus found widely through Central and South Americadata_genus_only <- aw_data(genus = "acanthognathus")
```
:::

``` {tabindex="0"}
  430 results available for query.
```

::: {.highlight}
``` {.chroma tabindex="0"}
leaf_cutter_ants  <- aw_data(genus = "acromyrmex")
```
:::

``` {tabindex="0"}
  713 results available for query.
```

::: {.highlight}
``` {.chroma tabindex="0"}
unique(leaf_cutter_ants$data$scientific_name)
```
:::

``` {tabindex="0"}
   [1] "acromyrmex versicolor"   "acromyrmex striatus"   [3] "acromyrmex balzani"      "acromyrmex coronatus"   [5] "acromyrmex crassispinus" "acromyrmex heyeri"   [7] "acromyrmex lundii"       "acromyrmex fracticornis"   [9] "acromyrmex niger"        "acromyrmex nigrosetosus"  [11] "acromyrmex rugosus"      "acromyrmex subterraneus"  [13] "acromyrmex alw01"        "acromyrmex alw02"  [15] "acromyrmex alw03"        "acromyrmex alw04"  [17] "acromyrmex octospinosus" "acromyrmex lobicornis"  [19] "acromyrmex silvestrii"   "acromyrmex landolti"  [21] "acromyrmex ambiguus"     "acromyrmex hystrix"  [23] "acromyrmex laticeps"     "acromyrmex indet"  [25] "acromyrmex echinatior"   "acromyrmex volcanus"  [27] "acromyrmex disciger"     "acromyrmex aspersus"  [29] "acromyrmex pubescens"    "acromyrmex moelleri"  [31] "acromyrmex evenkul"      "acromyrmex hispidus"  [33] "acromyrmex nobilis"      "acromyrmex pulvereus"  [35] "acromyrmex lundi"
```

**Searching by species**

::: {.highlight}
``` {.chroma tabindex="0"}
# You can request data on any particular speciesacanthognathus_df <- aw_data(scientific_name = "acanthognathus brevicornis")
```
:::

``` {tabindex="0"}
  2 results available for query.
```

::: {.highlight}
``` {.chroma tabindex="0"}
head(acanthognathus_df)
```
:::

``` {tabindex="0"}
  $count  [1] 2  $call  $call$genus  [1] "acanthognathus"  $call$species  [1] "brevicornis"  $data                                                                url  1 http://antweb.org/api/v2/?occurrenceId=CAS:ANTWEB:casent0280684  2 http://antweb.org/api/v2/?occurrenceId=CAS:ANTWEB:casent0637708    catalogNumber     family  subfamily          genus specificEpithet  1 casent0280684 formicidae myrmicinae Acanthognathus     brevicornis  2 casent0637708 formicidae myrmicinae Acanthognathus     brevicornis               scientific_name typeStatus stateProvince  country  1 acanthognathus brevicornis                          Colombia  2 acanthognathus brevicornis    dateIdentified habitat minimumElevationInMeters  1                                              NA  2     2013-09-12                               NA
```

::: {.highlight}
``` {.chroma tabindex="0"}
# You can also limit queries to observation records that have been geoferencedacanthognathus_df_geo <- aw_data(genus = "acanthognathus", species = "brevicornis", georeferenced = TRUE)
```
:::

It's also possible to search for records around any location by
specifying a search radius.

::: {.highlight}
``` {.chroma tabindex="0"}
data_by_loc <- aw_coords(coord = "37.76,-122.45", r = 2)# This will search for data on a 2 km radius around that latitude/longitude
```
:::

**Image data**

Most specimens in the database have images associated with them. These
include high, medium, and low resolution version of the head, dorsal
side, full profile, and the specimen label. For example we can retrieve
data on a specimen of *Ecitoninaeeciton burchellii* with the following
call:

::: {.highlight}
``` {.chroma tabindex="0"}
# Data and images for Ecitoninaeeciton burchelliieb <- aw_code(occurrenceid ="CAS:ANTWEB:casent0003205")eb$image_data$high[[2]]
```
:::

``` {tabindex="0"}
  NULL
```

If you're primarily interested in ant images and would like to keep up
with recent additions to the database, you can also use the `aw_images`
function. This function takes two arguments: `since`, the number of days
to search backward, and a `type`. Possible options for type are `h` for
head, `d` for dorsal, `p` for profile, and `l` for label. If a type is
not specified, all available images are retrieved.

::: {.highlight}
``` {.chroma tabindex="0"}
# Retrieve only dorsal images for the last five dayshead(aw_images(since = 5, img_type = "d"))
```
:::

``` {tabindex="0"}
                                                                     high  1 https://www.antweb.org/images/casent0914000/casent0914000_d_1_high.jpg  2 https://www.antweb.org/images/antweb1008677/antweb1008677_d_1_high.jpg  3 https://www.antweb.org/images/casent0906977/casent0906977_d_1_high.jpg  4 https://www.antweb.org/images/casent0914012/casent0914012_d_1_high.jpg  5 https://www.antweb.org/images/antweb1008691/antweb1008691_d_1_high.jpg  6 https://www.antweb.org/images/casent0906997/casent0906997_d_1_high.jpg                                                                     med  1 https://www.antweb.org/images/casent0914000/casent0914000_d_1_low.jpg  2 https://www.antweb.org/images/antweb1008677/antweb1008677_d_1_low.jpg  3 https://www.antweb.org/images/casent0906977/casent0906977_d_1_low.jpg  4 https://www.antweb.org/images/casent0914012/casent0914012_d_1_low.jpg  5 https://www.antweb.org/images/antweb1008691/antweb1008691_d_1_low.jpg  6 https://www.antweb.org/images/casent0906997/casent0906997_d_1_low.jpg                                                                     low  1 https://www.antweb.org/images/casent0914000/casent0914000_d_1_med.jpg  2 https://www.antweb.org/images/antweb1008677/antweb1008677_d_1_med.jpg  3 https://www.antweb.org/images/casent0906977/casent0906977_d_1_med.jpg  4 https://www.antweb.org/images/casent0914012/casent0914012_d_1_med.jpg  5 https://www.antweb.org/images/antweb1008691/antweb1008691_d_1_med.jpg  6 https://www.antweb.org/images/casent0906997/casent0906997_d_1_med.jpg                                                                     thumbnail  1 https://www.antweb.org/images/casent0914000/casent0914000_d_1_thumbview.jpg  2 https://www.antweb.org/images/antweb1008677/antweb1008677_d_1_thumbview.jpg  3 https://www.antweb.org/images/casent0906977/casent0906977_d_1_thumbview.jpg  4 https://www.antweb.org/images/casent0914012/casent0914012_d_1_thumbview.jpg  5 https://www.antweb.org/images/antweb1008691/antweb1008691_d_1_thumbview.jpg  6 https://www.antweb.org/images/casent0906997/casent0906997_d_1_thumbview.jpg    img_type                                                catalog_id  1        d https://www.antweb.org/api/v2/?catalogNumber=casent0914000  2        d https://www.antweb.org/api/v2/?catalogNumber=antweb1008677  3        d https://www.antweb.org/api/v2/?catalogNumber=casent0906977  4        d https://www.antweb.org/api/v2/?catalogNumber=casent0914012  5        d https://www.antweb.org/api/v2/?catalogNumber=antweb1008691  6        d https://www.antweb.org/api/v2/?catalogNumber=casent0906997            upload_date  1 2014-08-15 15:11:14  2 2014-08-20 13:52:13  3 2014-08-15 15:11:14  4 2014-08-15 15:11:15  5 2014-08-20 13:52:13  6 2014-08-15 15:11:15
```

It's also possible to retrieve unique lists of any taxonomic rank using
the `aw_unique` function.

::: {.highlight}
``` {.chroma tabindex="0"}
subfamily_list <- aw_distinct(rank = "subfamily")subfamily_list
```
:::

``` {tabindex="0"}
  [Total results on the server]: 67  [Args]:  rank = subfamily  limit = 1000  [Dataset]: [67 x 1]  [Data preview] :  [1] apidae     bethylidae  67 Levels: aenictinae agroecomyrmecinae amblyoponinae ... xylocopinae
```

::: {.highlight}
``` {.chroma tabindex="0"}
genus_list <- aw_distinct(rank = "genus")genus_list
```
:::

``` {tabindex="0"}
  [Total results on the server]: 490  [Args]:  rank = genus  limit = 1000  [Dataset]: [490 x 1]  [Data preview] :  [1] Aenictinae    Amblyoponinae  490 Levels: Acanthognathus Acanthomyrmex Acanthoponera ... Zigrasimecia
```

::: {.highlight}
``` {.chroma tabindex="0"}
species_list <- aw_distinct(rank = "species")species_list
```
:::

``` {tabindex="0"}
  [Total results on the server]: 10547  [Args]:  rank = species  limit = 1000  [Dataset]: [1000 x 1]  [Data preview] :  [1] basicerotini indet  1000 Levels: a abbreviata abdelazizi abdera abdita abditivata ... orizabanum_complex
```

If you work with existing specimens, you can also query directly by a
specimen ID.

::: {.highlight}
``` {.chroma tabindex="0"}
(data_by_code <- aw_code(catalogNumber="inb0003695883"))
```
:::

``` {tabindex="0"}
  [Total results on the server]: 1  [Args]:  catalogNumber = inb0003695883  [Dataset]: [1 x 16]  [Data preview] :                                                       specimens.url  1  http://antweb.org/api/v2/?occurrenceId=CAS:ANTWEB:inb0003695883  NA                                                            <NA>     specimens.catalogNumber specimens.family specimens.subfamily  1            inb0003695883       formicidae          myrmicinae  NA                    <NA>             <NA>                <NA>     specimens.genus specimens.specificEpithet specimens.scientific_name  1   Acanthognathus                teledectus acanthognathus teledectus  NA            <NA>                      <NA>                      <NA>     specimens.typeStatus specimens.stateProvince specimens.country  1                                       Heredia        Costa Rica  NA                 <NA>                    <NA>              <NA>     specimens.dateIdentified                       specimens.habitat  1                2006-11-02 mature wet forest ex sifted leaf litter  NA                     <NA>                                    <NA>     specimens.minimumElevationInMeters specimens.geojson.type  1                                  50                  point  NA                               <NA>                   <NA>     decimal_latitude decimal_longitude  1         10.413477        -84.023636  NA             <NA>              <NA>
```

::: {.highlight}
``` {.chroma tabindex="0"}
# This will return a list with a metadata data.frame and a image data.frame
```
:::

If you have a multiple specimen IDs, as is often the case when working
with research data, you can get data on all of them at the same time.
The function automatically retuns `NULL` values when no data are found
and you can have these removed using `plyr::compact` (this happens
automatically when you use a function call like `ldply`.)

::: {.highlight}
``` {.chroma tabindex="0"}
specimens <- c("casent0908629", "casent0908650", "casent0908637")results <- lapply(specimens, function(x) aw_code(x))names(results) <- specimenslength(results)
```
:::

``` {tabindex="0"}
  [1] 3
```

## Mapping ant specimen data

As with the previous ecoengine package, you can also visualize location
data for any set of species. Adding `georeferenced = TRUE` to a data
retrieval call will filter out any data points without location
information. Once retrieved the data are mapped with the open source
[Leaflet.js](https://leafletjs.com/) and pushed to your default browser.
Maps and associated `geoJSON` files are also saved to a location
specified (or defaults to your `/tmp` folder). This feature is only
available on the development version on GitHub (`0.5.2` or greater; see
above on how to install) and will be available from CRAN in version
`0.6`

::: {.highlight}
``` {.chroma tabindex="0"}
acd <- aw_data(genus = "acanthognathus")aw_map(acd)
```
:::

![Distribution of long trap-jaw ants in Central and South
America](/assets/blog-images/2014-02-18-antweb/antweb_map.png)

## Integration with the rest of our biodiversity suite

Our newest package on CRAN, [`spocc`](https://github.com/ropensci/spocc)
(Species Occurrence Data), currently in review at CRAN, integrates
`AntWeb` among other sources. More details on `spocc` in our next blog
post.

As always please send [suggestions, bug reports, and
ideas](https://github.com/ropensci/AntWeb/issues/new) related to the
AntWeb R package directly to our repo.


Data on more than 10,000 species of ants recorded worldwide are available through from California Academy of Sciences’ AntWeb, a repository that boasts a wealth of natural history data, digital images, and specimen records on ant species from a large community of museum curators.

AntWeb - programmatic interface to ant biodiversity data

Natural history museums have long been valuable repositories of data on
species diversity. These data have been critical for fostering and
shaping the development of fields such as biogeography and systematics.
The importance of these data repositories is becoming increasingly
important, especially in the context of climate change, where a strong
understanding of how species responded to past climate is key to
understanding their responses in the future. Leading the way in opening
up such valuable data is a new effort by the [Berkeley Initiative in
Global Change
Biology](https://globalchange.berkeley.edu/ecoinformatics-engine) called
the [Ecoengine](https://ecoengine.berkeley.edu/).

The engine is a [fast and open API](https://ecoengine.berkeley.edu/)
that provides access to over 3 million specimens and vegetation data
from surveys. Many of these specimens have already been georeferenced
opening up several use cases for such data. In addition, the engine also
provides access to species checklists and sensor data from the Keck
Hydrowatch project from the Eel river field station. Our newest package,
`ecoengine`, provides an easy interface to these data.

### Installing the package

A stable version of the package (`1.1`) is now [available on
CRAN](https://cran.r-project.org/web/packages/ecoengine/index.html). To
install:

::: {.highlight}
``` {.chroma tabindex="0"}
install.packages("ecoengine")# A small bug with factors (to be resolved in a couple of days in 1.1.2) requires strings as factors to be FALSEoptions(stringsAsFactors = FALSE)
```
:::

You can also install a development version of the package that has
additional features (more below), with several new updates that will
appear on the next CRAN version (ETA mid-March). To install the
development version:

::: {.highlight}
``` {.chroma tabindex="0"}
library(devtools)install_github("ropensci/ecoengine")
```
:::

**Observation data**

The workhorse function of the package is `ee_observations()` which
provides a way to query any taxonomic field. Searches for any field are
automatically fuzzy but one can request exact searches by adding
`__exact` (note the two underscores) to any field being searched. So for
example, if you are interested in data on the genus lynx, one can query
those data like so:

::: {.highlight}
``` {.chroma tabindex="0"}
library(ecoengine)lynx_data <- ee_observations(genus = "lynx", progress = FALSE)
```
:::

``` {tabindex="0"}
## Search contains 795 observations (downloading 1 of 1 pages)
```

::: {.highlight}
``` {.chroma tabindex="0"}
lynx_data
```
:::

``` {tabindex="0"}
## [Total results]: 795## [Args]:## country = United States## genus = lynx## georeferenced = FALSE## page_size = 25## page = 1## [Type]: observations## [Number of results]: 25
```

All ecoengine queries are returned as `S3` objects of class `ecoengine`.
The object includes a total number of results available, the arguments
used (so someone else can reconstruct the same query), the type of data
returned (`photos`, `observations`, `checklists`, `sensor`), and the
actual data itself. The results are paginated to prevent large requests
from hanging part way. Each call returns 25 observations (can be
overridden) and one can request more data by passing a page argument
(e.g. `page = 1`). Pages can also be ranges, or "all" to request all
available data for a query. The pagination method allows calls to be
parallelized and reassembled easily, especially when requests may return
tens of thousand of records. In this case there are only `795` records
so we can request everything in one go.

::: {.highlight}
``` {.chroma tabindex="0"}
library(ecoengine)lynx_data <- ee_observations(genus = "lynx", progress = FALSE, page = "all")
```
:::

``` {tabindex="0"}
## Search contains 795 observations (downloading 32 of 32 pages)
```

::: {.highlight}
``` {.chroma tabindex="0"}
# The progress bar is useful on the command line but tends to clutter up# documentation which is why it’s been turned off here. Messages can also be# turned off with quiet = TRUElynx_data
```
:::

``` {tabindex="0"}
## [Total results]: 795## [Args]:## country = United States## genus = lynx## georeferenced = FALSE## page_size = 25## page = all## [Type]: observations## [Number of results]: 795
```

Other fields (all documented and available from `?ee_observations`)
include kingdom, phylum, clss (intentionally misspelled to avoid
conflict with a sql keyword), genus and scientific_name. Any of these
fields can also be searched exactly by adding `__exact` at the end.

We can request georeferenced data only for visualization purposes.

::: {.highlight}
``` {.chroma tabindex="0"}
lynx_data <- ee_observations(genus = "lynx", progress = FALSE, page = "all",    georeferenced = TRUE)
```
:::

``` {tabindex="0"}
## Search contains 714 observations (downloading 29 of 29 pages)
```

and it's easy to examine these data as an interactive map using the
`ee_map` function (the function is currently only available in the dev
version).

::: {.highlight}
``` {.chroma tabindex="0"}
ee_map(lynx_data)
```
:::

This generates an interactive Leaflet JS map and renders to your default
browser. You can easily embed such maps into talks using iFrames and
[Slidify](http://slidify.org/).

![Map of Lynx observations across North
America](/assets/blog-images/2014-01-30-ecoengine/ecoengine_map.png)

The search possibilities are endless and the data can be supplemented by
many other sources in our site (e.g. taxize). Here are a few example
queries.

::: {.highlight}
``` {.chroma tabindex="0"}
pinus <- ee_observations(scientific_name = "Pinus")lynx_data <- ee_observations(genus = "Lynx")# Georeferenced data onlylynx_data <- ee_observations(genus = "Lynx", georeferenced = TRUE)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")chordata <- ee_observations(phylum = "chordata")# Class is clss since the former is a reserved keyword in SQL.aves <- ee_observations(clss = "aves")
```
:::

**Checklist data**

The museum contains checklists of species spanning a long time scale. To
query checklists, use `ee_checklists()`

::: {.highlight}
``` {.chroma tabindex="0"}
all_lists <- ee_checklists()
```
:::

``` {tabindex="0"}
## Returning 57 checklists
```

::: {.highlight}
``` {.chroma tabindex="0"}
head(all_lists[, c("footprint", "subject")])
```
:::

``` {tabindex="0"}
##                                                        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 57 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:

::: {.highlight}
``` {.chroma tabindex="0"}
spiders <- ee_checklists(subject = "Spiders")
```
:::

``` {tabindex="0"}
## Returning 2 checklists
```

::: {.highlight}
``` {.chroma tabindex="0"}
spiders
```
:::

``` {tabindex="0"}
##                  record## 4  bigcb:specieslist:15## 10 bigcb:specieslist:20##                                                         footprint## 4  https://ecoengine.berkeley.edu/api/footprints/hastings-reserve/## 10   https://ecoengine.berkeley.edu/api/footprints/angelo-reserve/##                                                                       url## 4  https://ecoengine.berkeley.edu/api/checklists/bigcb%3Aspecieslist%3A15/## 10 https://ecoengine.berkeley.edu/api/checklists/bigcb%3Aspecieslist%3A20/##                                           source subject## 4  https://ecoengine.berkeley.edu/api/sources/18/ Spiders## 10 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.

::: {.highlight}
``` {.chroma tabindex="0"}
library(plyr)spider_details <- ldply(spiders$url, checklist_details)names(spider_details)
```
:::

``` {tabindex="0"}
##  [1] "url"                              "observation_type"##  [3] "scientific_name"                  "collection_code"##  [5] "institution_code"                 "country"##  [7] "state_province"                   "county"##  [9] "locality"                         "coordinate_uncertainty_in_meters"## [11] "begin_date"                       "end_date"## [13] "kingdom"                          "phylum"## [15] "clss"                             "order"## [17] "family"                           "genus"## [19] "specific_epithet"                 "infraspecific_epithet"## [21] "source"                           "remote_resource"## [23] "earliest_period_or_lowest_system" "latest_period_or_highest_system"
```

::: {.highlight}
``` {.chroma tabindex="0"}
unique(spider_details$scientific_name)
```
:::

``` {tabindex="0"}
##  [1] "holocnemus pluchei"        "oecobius navus"##  [3] "uloborus diversus"         "neriene litigiosa"##  [5] "theridion sp. A"           "tidarren sp."##  [7] "dictyna sp. A"             "dictyna sp. B"##  [9] "mallos sp."                "yorima sp."## [11] "hahnia sanjuanensis"       "cybaeus sp."## [13] "zanomys sp."               "anachemmis sp."## [15] "titiotus sp."              "oxyopes scalaris"## [17] "zora hespera"              "drassinella sp."## [19] "phrurotimpus mateonus"     "scotinella sp."## [21] "castianeira luctifera"     "meriola californica"## [23] "drassyllus insularis"      "herpyllus propinquus"## [25] "micaria utahna"            "trachyzelotes lyonneti"## [27] "ebo evansae"               "habronattus oregonensis"## [29] "metaphidippus sp."         "platycryptus californicus"## [31] "calymmaria sp."            "frontinella communis"## [33] "undetermined sp."          "latrodectus hesperus"## [35] "theridion sp. B"           "agelenopsis oregonensis"## [37] "pardosa spp."              "schizocosa mccooki"## [39] "hololena sp."              "callobius sp."## [41] "pimus sp."                 "aliatypus sp."## [43] "antrodiaetus sp."          "antrodiaetus riversi"## [45] "anyphaena californica"     "aculepeira packardi"## [47] "araneus bispinosus"        "araniella displicata"## [49] "cyclosa conica"            "cyclosa turbinata"## [51] "brommella sp."             "cicurina sp."## [53] "dictyna sp."               "emblyna oregona"## [55] "orodrassus sp."            "sergiolus sp."## [57] "erigone sp."               "pityohyphantes sp."## [59] "tachygyna sp."             "alopecosa kochi"## [61] "oxyopes salticus"          "philodromus sp."## [63] "tibellus oblongus"         "pimoa sp."## [65] "undetermined spp."         "metaphidippus manni"## [67] "thiodina sp."              "diaea livens"## [69] "metellina sp."             "cobanus cambridgei"## [71] "tetragnatha sp."           "tetragnatha versicolor"## [73] "dipoena sp."               "theridion spp."## [75] "misumena vatia"            "misumenops sp."## [77] "tmarus angulatus"          "xysticus sp."## [79] "hyptiotes gertschi"        "mexigonus morosus"
```

**Photo 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. To get all pictures of the California condor from the database:

::: {.highlight}
``` {.chroma tabindex="0"}
condor <- ee_photos(scientific_name = "Gymnogyps californianus", quiet = TRUE, progress = FALSE)
```
:::

::: {.highlight}
``` {.chroma tabindex="0"}
condor
```
:::

``` {tabindex="0"}
## [Total results]: 8## [Args]:## page_size = 25## scientific_name = Gymnogyps californianus## georeferenced = FALSE## page = 1## [Type]: photos## [Number of results]: 8
```

The package also provides functionality to quickly browse photos in the
default browser. By calling `view_photos()` on any `ecoengine` object of
type `photos`, R will render a static html page with thumbnails and
metadata and launch a default browser with additional links.

::: {.highlight}
``` {.chroma tabindex="0"}
view_photos(ee_photos(scientific_name = "Gymnogyps californianus", quiet = TRUE))
```
:::

![Ecoengine photo
viewer](/assets/blog-images/2014-01-30-ecoengine/ecoengine_photo_viewer.png)

**Sensor data**

Sensor data come from the [Keck HydroWatch
Center](http://hydrowatch.cs.berkeley.edu/). Retrieving the data are
simple. Three functions provides all the necessary functionality.

- `ee_list_sensors()` - provides a list of sensors and the data they
  provide.
- `ee_sensor_data()` - retrieves data for any of these sensors (ids
  obtained by the previous function) and a date window.
- `ee_sensor_agg` - provides aggregated data that can be requested in
  any interval (minutes, days, weeks, months, years).

### Searching the engine

The search is [elastic](http://www.elasticsearch.org/) by default. One
can search for any field in `ee_observations()` across all available
resources. For example,

::: {.highlight}
``` {.chroma tabindex="0"}
# 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.
```
:::

**Upcoming features**

In the next CRAN update we will add methods to retrieve all the
vegetation data, the interactive maps, searching the data directly from
the map (for example by drawing bounding boxes) and having a formatted
query returned back to your R prompt for inclusion in a script.

As with all of our packages we welcome contributions to the [GitHub
repository](https://github.com/ropensci/ecoengine) as
[issues](https://github.com/ropensci/ecoengine/issues?page=1&state=open)
or [pull requests](https://github.com/ropensci/ecoengine/pulls).


Natural history museums have long been valuable repositories of data on species diversity. These data have been critical for fostering and shaping the development of fields such as  biogeography and systematics.

Rogue Scholar Beiträge

AntWeb - programmatic interface to ant biodiversity data

Introducing the ecoengine package