Editorial

The ‘Editorial’ article from the 2017-1 issue.

Roger Bivand (.na.character)
2017-06-01

This new issue, Volume 9, Issue 1, of the R Journal contains 33 contributed research articles, like the second issue of 2016. Most of the articles present R packages, and cover a very wide range of uses of R. Our journal continues to be critically dependent on its readers, authors, reviewers and editors. Annual submission numbers have grown markedly, but the rate of growth is less than that of the number of CRAN packages. Table 1 shows the outcomes of submitted contributed articles by year of submission. The proportion of submissions reaching publication has been roughly half since 2012.

Table 1: Submission outcomes 2009–2016, by year of submission.
2009 2010 2011 2012 2013 2014 2015 2016
Published 26 26 26 22 31 36 51 58
Rejected 11 14 11 24 29 32 53 64
Under review 0 0 0 0 0 0 0 19
Total 37 40 37 46 60 68 104 141

In order to try to restore some balance to the inflow of submissions, the kinds of articled solicited were clarified in January 2017. Articles introducing CRAN or Bioconductor packages — the most common kind of submission — should now provide broader context. We would like to encourage the submission of reviews and proposals, comparisons and the benchmarking of alternative implementations, and presentations of applications demonstrating how new or existing techniques can be applied in an area of current interest using R.

Table 2: Published contributed articles 2009–2016, by year of publication.
2009 2010 2011 2012 2013 2014 2015 2016
Page count 109 123 123 136 362 358 479 895
Article count 18 18 20 18 35 33 36 62
Average length 6.1 6.8 6.2 7.6 10.3 10.8 13.3 14.4

Not only has the number of submissions increased, but the length of published articles has also increased (see Table 2). The apparent jump from 2012 to 2013 may be associated with the change from a two column to a single column format, but page counts have risen, increasing the workload of reviewers and editors. We only have consistent records of the time taken to process accepted contributed articles for the 2013–2016 period. Again, the excellent work done by our generous reviewers and my very hard-working predecessors and especially Michael Lawrence last year, is evident in holding median times from receipt to publication online to a little over 200 days, as Table 3 shows.

Table 3: Median day count from acknowledgement to acceptance and online publication 2013–2016, by year of publication.
2013 2014 2015 2016
Median 347.0 225.5 212.5 212.0

Using gender (Blevins and Mullen 2015; Mullen 2016) and genderizeR (Wais 2016a,b), it is also possible to use author given names1 to try to monitor author diversity; affiliation location has not yet been successfully examined. Table 4 shows that there remains plenty to do to reflect the strengths of our community adequately2.

Table 4: Authors of published articles 2009–2016, by year of publication; women/men split based on author given names.
2009 2010 2011 2012 2013 2014 2015 2016
Women 5 9 8 6 10 18 27 32
Men 32 30 33 27 62 55 55 121
Unknown 3 5 3 3 7 4 9 10

In addition to re-framing the description of the kinds of articles we invite authors to contribute to our journal, work has been done on our website. Its appearance has been brought into line with that of the main R project website, and articles are reached through “landing” pages containing the abstract and citatation information as well as listings of CRAN and Bioconductor packages cited in the article. So far very few contributed articles associate themselves directly with CRAN Task Views, so these are inferred from cited CRAN packages and listed on the landing pages. Further progress in helping to make work published in our journal more accessible is planned.

I hope you continue to enjoy and benefit from reading work published in our journal.

CRAN packages used

gender, genderizeR

CRAN Task Views implied by cited packages

Note

This article is converted from a Legacy LaTeX article using the texor package. The pdf version is the official version. To report a problem with the html, refer to CONTRIBUTE on the R Journal homepage.

C. Blevins and L. Mullen. Jane, John \(\ldots\) Leslie? A historical method for algorithmic gender prediction. Digital Humanities Quarterly, 9: 2015. URL http://www.digitalhumanities.org/dhq/vol/9/3/000223/000223.html.
L. Mullen. Gender: Predict gender from names using historical data. 2016. URL https://github.com/ropensci/gender. R package version 0.5.1.
K. Wais. Gender Prediction Methods Based on First Names with genderizeR. The R Journal, 8(1): 17–37, 2016a. URL https://journal.r-project.org/archive/2016/RJ-2016-002/index.html.
K. Wais. genderizeR: Gender prediction based on first names. 2016b. URL https://CRAN.R-project.org/package=genderizeR. R package version 2.0.0.

References

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Citation

For attribution, please cite this work as

Bivand, "Editorial", The R Journal, 2017

BibTeX citation

@article{RJ-2017-1-editorial,
  author = {Bivand, Roger},
  title = {Editorial},
  journal = {The R Journal},
  year = {2017},
  note = {https://rjournal.github.io/},
  volume = {9},
  issue = {1},
  issn = {2073-4859},
  pages = {4-5}
}

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