Maëlle Salmon

In the [second post of the series where we obtained data
fromeBird](/blog/2018/08/21/birds-radolfzell/) wedetermined what birds
were observed in the county of Constance, and wecomplemented this
knowledge with some taxonomic and trait information in[the fourth post
of theseries](/blog/2018/09/04/birds-taxo-traits/). Now,we could be
curious about the occurrence of these birds in *scientificwork*. In this
post, we will query the scientific literature and an openscientific data
repository for species names: what have these birds beenstudied for?
Read on if you want to learn how to use R packages allowingto do so!

## Getting a list of 50 species from occurrence data

For more details about the following code, refer to the [previous postof
the series](/blog/2018/08/21/birds-radolfzell/).The single difference is
our adding a step to keep only data for themost recent years.

::: {.highlight}
``` {.chroma tabindex="0"}
# polygon for filteringlandkreis_konstanz <- osmdata::getbb("Landkreis Konstanz",                             format_out = "sf_polygon")crs <- sf::st_crs(landkreis_konstanz)# get and filter dataf_out_ebd <- "ebird/ebd_lk_konstanz.txt"library("magrittr")ebd <- auk::read_ebd(f_out_ebd) %>%  sf::st_as_sf(coords = c("longitude", "latitude"),                 crs = crs) in_indices <- sf::st_within(ebd, landkreis_konstanz)ebd <- dplyr::filter(ebd, lengths(in_indices) > 0)ebd <- as.data.frame(ebd)ebd <- dplyr::filter(ebd, approved, lubridate::year(observation_date) > 2010)
```
:::

For the sake of simplicity, we shall only use the 50 species observedthe
most often.

::: {.highlight}
``` {.chroma tabindex="0"}
species <- ebd %>%  dplyr::count(common_name, sort = TRUE) %>%  head(n = 50) %>%  dplyr::pull(common_name)
```
:::

The species are Carrion Crow, Eurasian Blackbird, Mallard, EurasianCoot,
Great Tit, Great Crested Grebe, Mute Swan, Great Cormorant,Eurasian Blue
Tit, Gray Heron, Black-headed Gull, Common Chaffinch,Common Chiffchaff,
Tufted Duck, European Starling, White Wagtail,European Robin, Little
Grebe, Common Wood-Pigeon, Red-crested Pochard,Ruddy Shelduck, Graylag
Goose, Red Kite, Common Buzzard, EurasianBlackcap, Great Spotted
Woodpecker, Eurasian Magpie, Gadwall, CommonPochard, Eurasian Nuthatch,
Green-winged Teal, House Sparrow, EurasianJay, Yellow-legged Gull,
Yellowhammer, Eurasian Green Woodpecker, EaredGrebe, Eurasian Reed
Warbler, Barn Swallow, Northern Shoveler, EurasianMoorhen, Black
Redstart, Great Egret, White Stork, Eurasian Wren,Long-tailed Tit,
Common House-Martin, Eurasian Kestrel, EuropeanGoldfinch and European
Greenfinch[(`glue::glue_collapse(species, sep = ", ", last = " and ")`)](https://twitter.com/LucyStats/status/1031938964796657665?s=19).

## Querying the scientific literature

Just like rOpenSci has a taxonomic
toolbelt([`taxize`](https://github.com/ropensci/taxize)) and a
speciesoccurrence data toolbelt
([`spocc`](https://github.com/ropensci/spocc)),it has a super package
for querying the scientific
literature:[`fulltext`](https://github.com/ropensci/fulltext)! This
packagesupports search for "PLOS via the rplos package, Crossref via
thercrossref package, Entrez via the rentrez package, arXiv via the
aRxivpackage, and BMC, Biorxiv, EuroPMC, and Scopus via internal
helperfunctions".

We shall use `fulltext` to retrieve the titles and abstracts
ofscientific articles mentioning each species, and will use `tidytext`
tocompute the most prevalent words in these works.

We first define a function retrieving the titles and abstracts of
worksobtained as result when querying one species name.

We use `dplyr::bind_rows` because we want all results for one species
atonce, while `fulltext` returns a list of data.frames with one
data.frameby data source.

::: {.highlight}
``` {.chroma tabindex="0"}
.get_papers <- function(species){  species %>%    tolower() %>%    fulltext::ft_search() %>%    fulltext::ft_get() %>%    fulltext::ft_collect() %>%    fulltext::ft_chunks(c("title", "abstract")) %>%    fulltext::ft_tabularize() %>%    dplyr::bind_rows()}.get_papers(species[1]) %>%  dplyr::pull(title)
```
:::

    ##  [1] "Great spotted cuckoo nestlings have no antipredatory effect on magpie or carrion crow host nests in southern Spain"                                ##  [2] "Donor-Control of Scavenging Food Webs at the Land-Ocean Interface"                                                                                 ##  [3] "Formal comment to Soler et al.: Great spotted cuckoo nestlings have no antipredatory effect on magpie or carrion crow host nests in southern Spain"##  [4] "Socially Driven Consistent Behavioural Differences during Development in Common Ravens and Carrion Crows"                                          ##  [5] "Behavioral Responses to Inequity in Reward Distribution and Working Effort in Crows and Ravens"                                                    ##  [6] "Early Duplication of a Single MHC IIB Locus Prior to the Passerine Radiations"                                                                     ##  [7] "Investigating the impact of media on demand for wildlife: A case study of Harry Potter and the UK trade in owls"                                   ##  [8] "New Caledonian Crows Rapidly Solve a Collaborative Problem without Cooperative Cognition"                                                          ##  [9] "Nest Predation Deviates from Nest Predator Abundance in an Ecologically Trapped Bird"                                                              ## [10] "Dietary Compositions and Their Seasonal Shifts in Japanese Resident Birds, Estimated from the Analysis of Volunteer Monitoring Data"

If we were working on a scientific study, we'd add a few more
filters,e.g. having the species mentioned in the abstract, and not
onlysomewhere in the paper which is probably the way the
differentliterature search providers define a match. But we're not, so
we cankeep our query quite free! My favourite paper involving the
Carrion Crowis ["Investigating the impact of media on demand for
wildlife: A casestudy of Harry Potter and the UK trade
inowls"](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0182368)because
it's a fun and important scientific question, and is supportedby open
data (by the way you can access CITES trade data (internationaltrade in
endangered species) in R
using[`cites`](https://github.com/ecohealthalliance/cites/) and
CITESSpeciesplus database
using[`rcites`](https://docs.ropensci.org/rcites//)).

We then apply this function to all 50 species and keep each article
onlyonce.

::: {.highlight}
``` {.chroma tabindex="0"}
get_papers <- ratelimitr::limit_rate(.get_papers,                                     rate = ratelimitr::rate(1, 2))all_papers <- purrr::map_df(species, get_papers)nrow(all_papers)
```
:::

    ## [1] 522

::: {.highlight}
``` {.chroma tabindex="0"}
all_papers <- unique(all_papers)nrow(all_papers)
```
:::

    ## [1] 378

Now, we get the most common words from titles and abstracts. For that
wefirst append the title to the abstract which is a quick hack.

::: {.highlight}
``` {.chroma tabindex="0"}
library("tidytext")library("rcorpora")stopwords <- corpora("words/stopwords/en")$stopWordsall_papers %>%  dplyr::group_by(title, abstract) %>%  dplyr::summarise(text = paste(title, abstract)) %>%  dplyr::ungroup() %>%  unnest_tokens(word, text) %>%  dplyr::filter(!word %in% stopwords) %>%  dplyr::count(word, sort = TRUE) -> words
```
:::

So, what are the most common words in these papers?

::: {.highlight}
``` {.chroma tabindex="0"}
head(words, n = 10) 
```
:::

    ##           word   n## 1      species 754## 2        birds 514## 3        virus 270## 4        avian 268## 5         bird 262## 6        study 243## 7     breeding 231## 8         wild 227## 9  populations 217## 10  population 213

Not too surprising, and obviously less entertaining than looking
atindividual species' results. Maybe a wordcloud can give us a better
ideaof the wide area of topics of studies involving our 50 most
frequentbird species. We use the
[`wordcloud`package](https://cran.r-project.org/web/packages/wordcloud/index.html).

::: {.highlight}
``` {.chroma tabindex="0"}
library("wordcloud")with(words, wordcloud(word, n, max.words = 100))
```
:::

![wordcloud of titles and abstracts of
scientificpapers](/img/blog-images/2018-09-11-birds-science/wordcloud-1.png)

We see that topics include ecological words such as "foraging" but
alsoepidemiological questions since "influenza" and "h5n1" come up. Now,
howinformative as this wordcloud can be, it's a bit ugly, so we'll
prettifyit using the
[`wordcloud2`package](https://github.com/Lchiffon/wordcloud2) instead,
and thesilhouette of a bird
[fromPhylopic](http://phylopic.org/image/6209c9be-060e-4d7f-bc74-a75f3ccf4629/).

::: {.highlight}
``` {.chroma tabindex="0"}
bird <- words %>%  head(n = 100) %>%  wordcloud2::wordcloud2(figPath = "bird.png",                        color = "black", size = 1.5)# https://www.r-graph-gallery.com/196-the-wordcloud2-library/htmlwidgets::saveWidget(bird,                        "tmp.html",                        selfcontained = F)
```
:::

I wasn't able to `webshot` the resulting html despite increasing
the`delay` parameter so I screenshot it by hand!

::: {.highlight}
``` {.chroma tabindex="0"}
magick::image_read("screenshot.png")
```
:::

<figure>
<img src="/img/blog-images/2018-09-11-birds-science/wordcloud2-1.png"
width="1366" alt="wordcloud shaped as a bird" />
<figcaption aria-hidden="true"><p>wordcloud shaped as a
bird</p></figcaption>
</figure>

The result is a bit kitsch, doesn't include the word "species", oneneeds
to know it's the silhouette of a bird to recognize it, and we'dneed to
work a bit on not reshaping the silhouette, but it's fun as itis.

## Querying scientific open data

There are quite a few scientific open data repositories out there,
amongwhich the giant [DataONE](https://www.dataone.org/) that has an
APIinterfaced with an R package. We shall use it to perform a
searchsimilar to the previous section, but looking at the data indexed
onDataONE. Since DataONE specializes in ecological and environmental
data,we expect to find rather ecological data.

We first define a function to retrieve metadata of datasets for
onespecies. It looks the species names in the abstract.

::: {.highlight}
``` {.chroma tabindex="0"}
.get_meta <- function(species){    cn <- dataone::CNode("PROD")  search <- list(q = glue::glue("abstract:{species}"),                        fl = "id,title,abstract",                        sort = "dateUploaded+desc")    result <- dataone::query(cn, solrQuery = search,                           as="data.frame")    if(nrow(result) == 0){    NULL  }else{    # otherwise one line by version  result <- unique(result)    tibble::tibble(species = species,                 title = result$title,                 abstract = result$abstract)  }}
```
:::

Note that DataONE searching could be more precise: one can choose
tosearch from a given data source only for instance. See the
[searchingDataONEvignette](https://github.com/DataONEorg/rdataone/blob/master/vignettes/searching-dataone.Rmd).

::: {.highlight}
``` {.chroma tabindex="0"}
get_meta <- ratelimitr::limit_rate(.get_meta,                                     rate = ratelimitr::rate(1, 2))all_meta <- purrr::map_df(species, get_meta)nrow(all_meta)
```
:::

    ## [1] 266

::: {.highlight}
``` {.chroma tabindex="0"}
length(unique(all_meta$species))
```
:::

    ## [1] 35

35 species are represented.

::: {.highlight}
``` {.chroma tabindex="0"}
all_meta <- unique(all_meta[,c("title", "abstract")])nrow(all_meta)
```
:::

    ## [1] 104

We then extract the most common words.

::: {.highlight}
``` {.chroma tabindex="0"}
all_meta %>%  dplyr::group_by(title, abstract) %>%  dplyr::summarise(text = paste(title, abstract)) %>%  dplyr::ungroup() %>%  unnest_tokens(word, text) %>%  dplyr::filter(!word %in% stopwords) %>%  dplyr::count(word, sort = TRUE) -> data_wordshead(data_words, n = 10)
```
:::

    ## # A tibble: 10 x 2##    word           n##    <chr>      <int>##  1 data         153##  2 species      120##  3 birds         94##  4 breeding      87##  5 feeding       75##  6 population    65##  7 bird          60##  8 genetic       58##  9 study         56## 10 effects       54

Data is the most common word which is quite logical for metadata
ofactual datasets. Let's also have a look at a regular wordcloud.

::: {.highlight}
``` {.chroma tabindex="0"}
with(data_words, wordcloud(word, n, max.words = 100))
```
:::

![wordcloud of titles and abstracts of
scientificmetadata](/img/blog-images/2018-09-11-birds-science/wordcloud3-1.png)

As expected, the words seem more focused on ecology than when looking
atscientific papers. DataONE is a gigantic data catalogue, where one
could

- study the results of such queries (e.g. meta studies of number of,say,
  versions by datasets)

- or find data to integrate to a new study. If you want to
  *download*data from DataONE, refer to the [download
  datavignette](https://github.com/DataONEorg/rdataone/blob/master/vignettes/download-data.Rmd).

## Conclusion

In this post, we used the rOpenSci `fulltext` package, and the
DataONE`dataone` package, to search for bird species names in scientific
papersand scientific open datasets. We were able to draw
wordcloudsrepresenting the diversity of topics of studies in which the
birds hadbeen mentioned or studied. Such a search could be fun to do for
yourfavourite bird(s)! And in general, following the same approach you
couldanswer your own specific research question.

### Scientific literature access

As a reminder, the pipeline to retrieve abstracts and titles of
worksmentioning a bird species was quite smooth:

::: {.highlight}
``` {.chroma tabindex="0"}
species %>%    tolower() %>%    fulltext::ft_search() %>%    fulltext::ft_get() %>%    fulltext::ft_collect() %>%    fulltext::ft_chunks(c("title", "abstract")) %>%    fulltext::ft_tabularize() %>%    dplyr::bind_rows()
```
:::

`fulltext` gives you a lot of power! Other rOpenSci accessing
literaturedata include
[`europepmc`](https://github.com/ropensci/europepmc), RInterface to
Europe PMC RESTful Web
Service;[`jstor`](https://github.com/ropensci/jstor);[`suppdata`](https://github.com/ropensci/suppdata)
for extractingsupplemental information, and [muchmore](/packages/).

### Scientific data access... and publication with R

In this post we used the
[`dataone`package](https://github.com/DataONEorg/rdataone) to access
data fromDataONE. That same package allows uploading data to DataONE.
TherOpenSci suite features
the[`rfigshare`](https://github.com/ropensci/rfigshare) package for
gettingdata from, and publishing data to,
[Figshare](https://figshare.com/).For preparing your own data and its
documentation for publication, checkout the [`EML`
package](https://github.com/ropensci/EML) for writingmetadata respecting
the Ecological Metadata Standard, and the [unconf`dataspice`
project](https://github.com/ropenscilabs/dataspice) forsimpler metadata
entry.

Explore more of our packages suite, including and beyond access
toscientific literature &data and data publication,[here](/packages/).

### No more birding? No, your turn!

This was the last post of this series, that hopefully provided
anoverview of how rOpenSci packages can help you learn more about
birds,and can support your workflow. As a reminder, in this series we
saw

- [How to identify spots for birding using open
  geographicaldata](/blog/2018/08/14/where-to-bird/).Featuring
  `opencage` for geocoding, `bbox` for bounding boxcreation, `osmdata`
  for OpenStreetMap's Overpass API querying,`osmplotr` for map drawing
  using OpenStreetMap's data.

- [How to obtain bird occurrence data
  inR](/blog/2018/08/21/birds-radolfzell/).Featuring `rebird` for
  interaction with the eBird's API, and `auk`for munging of the whole
  eBird dataset.

- [How to extract text from old natural
  historydrawings](/blog/2018/08/28/birds-ocr/).Featuring `magick` for
  image manipulation, `tesseract` for OpticalCharacter Recognition,
  `cld2` and `cld3` for language detection, and`taxize::gnr_resolve` for
  taxonomic name resolution.

- [How to complement an occurrence dataset with taxonomy and
  traitinformation](/blog/2018/09/04/birds-taxo-traits/).Featuring
  `taxize`, taxonomic toolbelt for R, and `traits`,providing access to
  species traits data.

- How to query the scientific literature and scientific open
  datarepositories. This is the post you've just read!

That's a wrap! But now, don't *you* hesitate to explore our
packagessuite for your own needs, and to share about your use cases of
rOpenScipackages as a birder or not via [our friendly
discussionforum](https://discuss.ropensci.org/c/usecases)! Happy
birding!


In the second post of the series where we obtained data fromeBird wedetermined what birds were observed in the county of Constance, and wecomplemented this knowledge with some taxonomic and trait information inthe fourth post of theseries.

Rogue Scholar Beiträge

What have these birds been studied for? Querying science outputs with R