vignettes/b1_assigning_articles.Rmd
b1_assigning_articles.RmdTwenty one journals in the discipline of plant pathology were selected by the authors as being the primary choice for most plant pathologists when publishing manuscripts.
Using the assumption that journals publish an average 10 articles per issue, for a population of ~1000 articles, using a confidence level of 95% and confidence interval of 10% we need 88 samples. In Issue #3, we decided to select 200 articles to have a large sample of the population.
There are three distinct steps involved:
Set up the journals, years, and page numbers and evaluators and
assign randomly. Create a tibble that holds these data
along with empty fields for the evaluation of the articles. Create a
Google Sheets object to serve as a template for the second
step.
Look up the articles and check that the selected journal, year, page number actually are appropriate and enter the DOI and other comments regarding article selection in Google Sheets. Which is used in the third step.
Import article notes and DOIs from Google Sheets back to R, retrieve as many bib entries as possible using the DOI. Merge the bib entries with the notes and DOI values. Create a new tab in the Google Sheets object for the evaluators to carry out evaluations.
We hand-picked a list of 21 journals that we felt represented plant
pathology research. In this step, we will create a tibble
in R of these journals, assigning them a number so that we can randomise
them.
journal_list <- tibble(
seq(1:21),
c("Australasian Plant Pathology",
"Canadian Journal of Plant Pathology",
"Crop Protection",
"European Journal of Plant Pathology",
"Forest Pathology",
"Journal of General Plant Pathology",
"Journal of Phytopathology",
"Journal of Plant Pathology",
"Virology Journal (Plant Viruses Section)",
"Molecular Plant-Microbe Interactions",
"Molecular Plant Pathology",
"Nematology",
"Physiological and Molecular Plant Pathology",
"Phytoparasitica",
"Phytopathologia Mediterranea",
"Phytopathology",
"Plant Disease",
"Plant Health Progress",
"Plant Pathology",
"Revista Mexicana de Fitopatología",
"Tropical Plant Pathology"))
names(journal_list) <- c("number", "journal")Initially, when we started this work we had four authors working together on it. For the four original authors of the paper we created a list for us to use that will randomly assign articles for us to evaluate for this manuscript.
Later we decided to add more authors and more articles. That work is detailed in this second document.
Now we will create a randomised list of journal articles to assign to each of the four authors for this paper.
Generate a random list of years between 2012 and 2016 and a random list of start pages between 1 and 150 since some journals start numbering at 1 with every issue. Then bind the columns of the randomised list of journals with the randomised years and page start numbers. This then assumes that there is no temporal effect, i.e., the time of year an article is published does not affect whether or not it is reproducible.
year <- sample(2012:2016, 200, replace = TRUE)
contains_page <- sample.int(n = 150, size = 200, replace = TRUE)
journals <- cbind(journals[, -1], year, contains_page, assignees)
journals <- arrange(.data = journals, assignees, journal, year, contains_page)## # A tibble: 21 × 2
## journal n
## <chr> <int>
## 1 Journal of General Plant Pathology 13
## 2 Phytoparasitica 13
## 3 Journal of Phytopathology 12
## 4 Molecular Plant-Microbe Interactions 12
## 5 Plant Pathology 12
## 6 Tropical Plant Pathology 12
## 7 Australasian Plant Pathology 11
## 8 Canadian Journal of Plant Pathology 10
## 9 Nematology 10
## 10 Revista Mexicana de Fitopatología 10
## 11 Virology Journal (Plant Viruses Section) 10
## 12 Journal of Plant Pathology 9
## 13 Phytopathology 9
## 14 Crop Protection 8
## 15 Forest Pathology 8
## 16 Phytopathologia Mediterranea 8
## 17 Plant Disease 8
## 18 Molecular Plant Pathology 7
## 19 Plant Health Progress 7
## 20 Physiological and Molecular Plant Pathology 6
## 21 European Journal of Plant Pathology 5Once this is done, the articles are manually examined for
suitability. Reference articles or off-topic articles are not included.
Notes are provided regarding these cases in
assigned_article_notes. If the selected page number/article
was not suitable, the next sequential article was selected manually.
A variety of information will be collected with each article to be used in the analysis later. It is easiest to enter this using a spreadsheet application, so we will add on the columns for what information we want to collect and save the table as a Google Sheet for manual editing.
to_record <- c(
"doi",
"IF_5year",
"country",
"open",
"repro_inst",
"iss_per_year",
"art_class",
"supl_mats",
"comp_mthds_avail",
"software_avail",
"software_cite",
"analysis_auto",
"data_avail",
"data_annot",
"data_tidy",
"reproducibility_score"
)
journals[to_record] <- ""
template <-
journals %>%
group_by(assignees) %>%
as_tibble()We decided to use Google Sheets so that we could concurrently edit
the file more easily. Once we’re done filling in our evaluations, we
will import the data back to R. Jenny Bryan has created a handy package,
googlesheets4 that we use here. Note that this must be run
interactively for gs4_create() to work. Knitting this .Rmd
file will not generate any Google Sheets, it must be done in an
interactive R session.
Create a Google Sheets workbook to hold worksheets for this project. This first sheet will serve as a template and is thus named “template”.
# Give googlesheets4 permission to access spreadsheets and Google Drive
gs4_auth()
# create Google Sheet for concurrent edits, first sheet: article notes template
gs4_create(
"article_notes",
sheets = template
)This step is completed in Google Sheets, Adam looked up articles and added notes and DOIs to a Google Sheet, “article_notes”.
The Google Sheets file can be found here: https://docs.google.com/spreadsheets/d/19gXobV4oPZeWZiQJAPNIrmqpfGQtpapXWcSxaXRw1-M/edit#gid=1699540381.
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