[This article was first published on Rstats – quantixed, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.I’ve posted about publication lag times previously. The “lag” refers to the time from submitting a paper and it appearing in a journal.Publication lag times are still a frustration for researchers. Although preprints circumvent the delay in sharing science with others, publication is still king when it comes to evaluation. Contracts are short and publication delays can be long…I recently saw a post comparing median publication lag times for genetics journals. This motivated me to update my code and rerun the analysis for cell biology journals to see what, if anything, has changed.MethodologyI wrote an R package PubMedLagR which uses {rentrez} to retrieve the publication data from PubMed. Once we have this data, it is a matter of producing some plots which I have covered previously. To ensure that we are only looking at research papers, we use "journal article"[pt] as a search term, and also remove from the data anything else (Reviews, Commentaries etc.). Feel free to use it to look at other journals.CaveatsBefore we get started, there are some caveats. The analysis is only as good as the PubMed data. Not all journals submit their date information to PubMed, while for others it is incomplete (as we’ll see below). There are inaccuracies for sure. I found a paper that was supposedly submitted on 1970-01-01, more than 40 years before the journal started. Also, it’s well known that some journals “restart the clock” on a paper by rejecting it and allowing resubmission, then only counting the resubmitted version. So, comparison between journals is a little tricky, but it allows us to look at trends.Let’s dive inWe can use the following code to grab the data we are interested in.library(PubMedLagR)jrnl_list