rOpenSci | A rentrez paper, and how to use the NCBI's new API keys

A rentrez paper, and how to use the NCBI’s new API keys

I am happy to say that the latest issue of The R Journal includes a paper describing rentrez, the rOpenSci package for retrieving data from the National Center for Biotechnology Information (NCBI).

The NCBI is one of the most important sources of biological data. The centre provides access to information on 28 million scholarly articles through PubMed and 250 million DNA sequences through GenBank. More importantly, records in the 50 public databases maintained by the NCBI are strongly cross-referenced. As a result, it is possible to pinpoint searches using almost 2 million taxonomic names or a controlled vocabulary with 270,000 terms. rentrez has been designed to make it easy to search for and download NCBI records and download them from within an R session.

The paper and the package vignette both describe typical usages of rentrez. I though it might be fun to use this post to find out where papers describing R packages are published these days. Although PubMed only covers journals in the biological sciences, searching that database will at least give us an idea of which journals like to publish these sorts of papers. Here we use the entrez_search and entrez_summary functions to get some information on all of the papers published in 2017 with the term ‘R package’ in their title:


pkg_search <- entrez_search(db="pubmed", 
                            term="(R Package[TITLE]) AND (2017[PDAT])", 
pkg_summs <- entrez_summary(db="pubmed", web_history=pkg_search$web_history)
List of  96 esummary records. First record:

esummary result with 42 items:
 [1] uid               pubdate           epubdate          source           
 [5] authors           lastauthor        title             sorttitle        
 [9] volume            issue             pages             lang             
[13] nlmuniqueid       issn              essn              pubtype          
[17] recordstatus      pubstatus         articleids        history          
[21] references        attributes        pmcrefcount       fulljournalname  
[25] elocationid       doctype           srccontriblist    booktitle        
[29] medium            edition           publisherlocation publishername    
[33] srcdate           reportnumber      availablefromurl  locationlabel    
[37] doccontriblist    docdate           bookname          chapter          
[41] sortpubdate       sortfirstauthor  

As you can tell from the output above, you can get a lot of information from these summary records. In this case, we are interested in the journals in which these papers appear. We can use the helper function extract_from_esummary to isolate the ‘source’ of each paper, then use table to count up the frequency of each journal.


journals <- extract_from_esummary(pkg_summs, "source")
journal_freq <-, dnn="journal"), responseName="n.papers")
ggplot(journal_freq, aes(reorder(journal, n.papers), n.papers)) + 
    geom_point(size=2) + 
    coord_flip() + 
    scale_y_continuous("Number of papers") +
    scale_x_discrete("Journal") +
    theme_bw() +
    ggtitle("Venues for papers describing R Packages in 2017") 

Dot plot: destination of papers describing R packages in 2017

So, it looks like Bioinformatics, BMC Bionformatics and Molecular Ecology Resources are popular destinations for papers describing R packages, but these appear in journals all the way across the biological sciences.

The R Journal article describes some more typical uses of rentrez, and also describes some of decisions that went into the design of the package. If this example has whetted your appetite, then please check out the article or the package documentation.

🔗 Thanks

The publication of this paper gives me a chance to thank the many people that have helped make rentrez into a useful package. I was very lucky to have this code included in rOpenSci at an early stage. Being part of the wider project made sure rentrez kept pace with the best-practices for code and documentation developed by the R community and got the package out to a wider audience than would have otherwise been possible. I am thankful to everyone who has filed an issue or contributed code to rentrez. I also have to single out Scott Chamberlain, who has done a great deal to make sure the code meets community standards and is useful to as many people as possible.

🔗 API keys for eUtils

To celebrate the publication of this paper I am going to speed up rentrez by a factor of three!

Well, the timing is coincidental, but the latest release of rentrez does make it possible to send and receive information from the NCBI at a greater rate than was previously possible. The NCBI now gives users the opportunity to register for an access key that will allow them to make up to 10 requests per second (non-registered users are limited to 3 requests per second per IP address). As of the latest release, rentrez supports the use of these access keys while enforcing the appropriate rate limits. For one-off cases, this is as simple as adding the api_key argument to a given function call.

prot_links <- entrez_link(db="protein", dbfrom="gene", id=93100, api_key ="ABCD123")

It most cases you will want to use your key for each of several calls to the NCBI. rentrez makes this easy by allowing you to set an environment variable, ENTREZ_KEY. Once this value is set to your key rentrez will use it for all requests to the NCBI. To set the value for a single R session you can use the function set_entrez_key(). Here we set the value and confirm it is now available as an environment variable.

## [1] "ABCD123"

If you use rentrez often you should edit your .Renviron file (see help(Startup) for a description of this file) to include your key. Doing so will mean all requests you send will take advantage of your API key. Here’s the line to add:


As long as an API key is set by one of these methods, rentrez will allow you to make up to ten requests per second.

🔗 Bugs and use-cases please!

The publication in the R Journal is not the end of development for rentrez. Though the package is now feature-complete and stable, I am very keen to make sure it keeps pace with the API it wraps and squash any bugs that might arise. I also appreciate use-cases that demonstrate how the package can take advantage of NCBI data. So, please, file issues at the project’s repository if you have any questions about it!