I just missed another chat on the rOpenSci website:
I want to know the number of publications by people from a certain country, but I dont know how to achieve this…
Fun! Let’s do that. It’s a bit complicated because there is no field like geography of the authors. But there are affiliation fields, from which we can collect data we need.
Installation
You’ll need the GitHub version for the coutry names data, or just use the CRAN version, and get country names elsewhere.
install.packages("devtools")
devtools::install_github("ropensci/rplos")
library("rplos")
Get the data
articles <- searchplos(q='*:*', limit = 5,
fl=c("id","author_affiliate"),
fq=list('article_type:"Research Article"', "doc_type:full"))
Search for country names in affilitation field
(countries <- lapply(articles$data$author_affiliate, function(x){
out <- sapply(isocodes$name, function(z) grepl(z, x))
isocodes$name[out]
}))
#> [[1]]
#> character(0)
#>
#> [[2]]
#> [1] "Jersey" "United States"
#>
#> [[3]]
#> [1] "China" "Germany"
#>
#> [[4]]
#> character(0)
#>
#> [[5]]
#> [1] "Argentina" "United Kingdom"
You can combine this data with the previously collected data:
# Helper function
splitem <- function(x){
if(length(x) == 0) { NA } else {
if(length(x) > 1) paste0(x, collapse = ", ") else x
}
}
articles$data$countries <- sapply(countries, splitem)
head(articles$data)
#> id
#> 1 10.1371/journal.pone.0095870
#> 2 10.1371/journal.pone.0110535
#> 3 10.1371/journal.pone.0110991
#> 4 10.1371/journal.pone.0111234
#> 5 10.1371/journal.pone.0111388
#> author_affiliate
#> 1 Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Gastroenterology, Ren-Ai Branch, Taipei City Hospital, Taipei, Taiwan; Division of Gastroenterology, Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan; Liver Research Unit, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taipei, Taiwan; Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cheng Hsin General Hospital, Taipei, Taiwan
#> 2 Durham Nephrology Associates, Durham, North Carolina, United States of America; Scientific Activities Department, The National Kidney Foundation, Inc., New York, New York, United States of America; Covance Inc., Princeton, New Jersey, United States of America; Departments of Medicine and Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States of America; Department of Family Medicine, University at Buffalo, Buffalo, New York, United States of America; Baylor Health Care System, Baylor Heart and Vascular Institute, Dallas, Texas, United States of America; Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
#> 3 State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Microelectronics and Solid-State electronics, University of Electronic Science and Technology of China, Sichuan, China; Electrical and Computer Engineering, Kaiserslautern University of Technology, Kaiserslautern German Gottlieb-Daimler-Strabe, Kaiserslautern, Germany
#> 4 SB RAS Institute of Chemical Biology and Fundamental Medicine, Novosibirsk, Russia; Pacific Institute of Bioorganic Chemistry, Far East Division, Russian Academy of Sciences, Vladivostok, Russia; Novosibirsk State University, Novosibirsk, Russia
#> 5 CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina; INGEO, Instituto de Geología, Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de San Juan, San Juan, San Juan, Argentina; School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, West Midlands, United Kingdom
#> countries
#> 1 <NA>
#> 2 Jersey, United States
#> 3 China, Germany
#> 4 <NA>
#> 5 Argentina, United Kingdom
Bigger data set
Okay, cool, lets do it on a bigger data set, and this time, we’ll get another variable counter_total_all, which is the combination of page views/pdf downloads for each article. This will allow us to ask Is number of countries included in the authors related to page views?. I have no idea if this question makes sense, but nonetheless, it is a question :)
articles <- searchplos(q='*:*', limit = 1000,
fl=c("id","counter_total_all","author_affiliate"),
fq=list('article_type:"Research Article"', "doc_type:full"))
#> 1
#> 2
Get countries
countries <- lapply(articles$data$author_affiliate, function(x){
out <- sapply(isocodes$name, function(z) grepl(z, x))
isocodes$name[out]
})
df <- articles$data
df$countries <- sapply(countries, splitem)
Let’s remove those rows with 0 countries, since the authors must be from somewhere, so the country name matching must have errored.
df$n_countries <- sapply(countries, length)
df <- df[ df$n_countries > 0, ]
Plot data
library("ggplot2")
ggplot(df, aes(n_countries, as.numeric(counter_total_all))) +
geom_point() +
labs(y="total page views") +
theme_grey(base_size = 16)
Conclusion: meh, maybe, maybe not
Into rplos
We’ll probably add a function like this into rplos, as a convenient way to handle this use case.