rplos introduction

The rplos package interacts with the API services of PLoS (Public Library of Science) Journals. You used to need an API key to work with this package - that is no longer needed!

This tutorial will go through three use cases to demonstrate the kinds of things possible in rplos.

Search across PLoS papers in various sections of papers
Search for terms and visualize results as a histogram OR as a plot through time
Text mining of scientific literature

Load package from CRAN

install.packages("rplos")

library('rplos')

Search across PLoS papers in various sections of papers

searchplos is a general search, and in this case searches for the term Helianthus and returns the DOI's of matching papers

searchplos(q = "Helianthus", fl = "id", limit = 5)

#> $meta
#>   numFound start maxScore
#> 1      466     0       NA
#> 
#> $data
#>                             id
#> 1 10.1371/journal.pone.0148280
#> 2 10.1371/journal.pone.0111982
#> 3 10.1371/journal.pone.0139188
#> 4 10.1371/journal.pone.0057533
#> 5 10.1371/journal.pone.0045899

Get only full article DOIs

searchplos(q = "*:*", fl = 'id', fq = 'doc_type:full', start = 0, limit = 5)

#> $meta
#>   numFound start maxScore
#> 1   197382     0       NA
#> 
#> $data
#>                             id
#> 1 10.1371/journal.pcbi.1000942
#> 2 10.1371/journal.pone.0130089
#> 3 10.1371/journal.pone.0165670
#> 4 10.1371/journal.pgen.1002909
#> 5 10.1371/journal.pcbi.1004317

Get DOIs for only PLoS One articles

searchplos(q = "*:*", fl = 'id', fq = 'journal_key:PLoSONE', 
           start = 0, limit = 5)

#> $meta
#>   numFound start maxScore
#> 1  1465904     0       NA
#> 
#> $data
#>                                                    id
#> 1 10.1371/journal.pone.0130089/results_and_discussion
#> 2  10.1371/journal.pone.0130089/materials_and_methods
#> 3 10.1371/journal.pone.0130089/supporting_information
#> 4                        10.1371/journal.pone.0130089
#> 5                  10.1371/journal.pone.0130089/title

Get DOIs for full article in PLoS One

searchplos(q = "*:*", fl = 'id',
   fq = list('journal_key:PLoSONE', 'doc_type:full'),
   start = 0, limit = 5)

#> $meta
#>   numFound start maxScore
#> 1   167954     0       NA
#> 
#> $data
#>                             id
#> 1 10.1371/journal.pone.0130089
#> 2 10.1371/journal.pone.0165670
#> 3 10.1371/journal.pone.0162873
#> 4 10.1371/journal.pone.0163015
#> 5 10.1371/journal.pone.0167202

Search for many terms

q <- c('ecology','evolution','science')
lapply(q, function(x) searchplos(x, limit = 2))

#> [[1]]
#> [[1]]$meta
#>   numFound start maxScore
#> 1    36908     0       NA
#> 
#> [[1]]$data
#>                             id
#> 1 10.1371/journal.pone.0059813
#> 2 10.1371/journal.pone.0001248
#> 
#> 
#> [[2]]
#> [[2]]$meta
#>   numFound start maxScore
#> 1    59186     0       NA
#> 
#> [[2]]$data
#>                             id
#> 1 10.1371/journal.pbio.0050030
#> 2 10.1371/journal.pbio.0030245
#> 
#> 
#> [[3]]
#> [[3]]$meta
#>   numFound start maxScore
#> 1   176450     0       NA
#> 
#> [[3]]$data
#>                             id
#> 1 10.1371/journal.pbio.0020122
#> 2 10.1371/journal.pbio.1001166

Search on specific sections

A suite of functions were created as light wrappers around searchplos as a shorthand to search specific sections of a paper.

plosauthor searchers in authors
plosabstract searches in abstracts
plostitle searches in titles
plosfigtabcaps searches in figure and table captions
plossubject searches in subject areas

plosauthor searches across authors, and in this case returns the authors of the matching papers. the fl parameter determines what is returned

plosauthor(q = "Eisen", fl = "author", limit = 5)

#> $meta
#>   numFound start maxScore
#> 1      876     0       NA
#> 
#> $data
#>             author
#> 1 Jonathan A Eisen
#> 2 Jonathan A Eisen
#> 3 Jonathan A Eisen
#> 4 Jonathan A Eisen
#> 5 Jonathan A Eisen

plosabstract searches across abstracts, and in this case returns the id and title of the matching papers

plosabstract(q = 'drosophila', fl = 'id,title', limit = 5)

#> $meta
#>   numFound start maxScore
#> 1     3116     0       NA
#> 
#> $data
#>                             id
#> 1 10.1371/journal.pbio.0040198
#> 2 10.1371/journal.pbio.0030246
#> 3 10.1371/journal.pone.0012421
#> 4 10.1371/journal.pone.0002817
#> 5 10.1371/journal.pbio.1000318
#>                                                                                       title
#> 1                                                                               All for All
#> 2                                               School Students as Drosophila Experimenters
#> 3                                      Host Range and Specificity of the Drosophila C Virus
#> 4           High-Resolution, In Vivo Magnetic Resonance Imaging of Drosophila at 18.8 Tesla
#> 5 Genomic Responses to Abnormal Gene Dosage: The X Chromosome Improved on a Common Strategy

plostitle searches across titles, and in this case returns the title and journal of the matching papers

plostitle(q = 'drosophila', fl = 'title,journal', limit = 5)

#> $meta
#>   numFound start maxScore
#> 1     1930     0       NA
#> 
#> $data
#>        journal                                          title
#> 1 PLoS Biology    Expression in Aneuploid Drosophila S2 Cells
#> 2     PLOS ONE Contribution of Drosophila TRPA1 to Metabolism
#> 3 PLoS Biology    Combinatorial Coding for Drosophila Neurons
#> 4 PLoS Biology    School Students as Drosophila Experimenters
#> 5     PLoS ONE    Quantification of Food Intake in Drosophila

Search terms & visualize results as a histogram OR as a plot through time

plosword allows you to search for 1 to K words and visualize the results as a histogram, comparing number of matching papers for each word

out <- plosword(list("monkey", "Helianthus", "sunflower", "protein", "whale"),
    vis = "TRUE")
out$table

#>   No_Articles       Term
#> 1       11163     monkey
#> 2         466 Helianthus
#> 3        1285  sunflower
#> 4      125467    protein
#> 5        1465      whale

out$plot

plot of chunk plosword1plot

You can also pass in curl options, in this case get verbose information on the curl call.

plosword('Helianthus', callopts = list(verbose = TRUE))

#> Number of articles with search term 
#>                                 466

Visualize terms

plot_throughtime allows you to search for up to 2 words and visualize the results as a line plot through time, comparing number of articles matching through time. Visualize with the ggplot2 package, only up to two terms for now.

library("ggplot2")
plot_throughtime(terms = "phylogeny", limit = 200) + 
  geom_line(size = 2, color = 'black')

plot of chunk throughtime1