Crossref API in R#
by Cyrus Gomes, Vincent F. Scalfani, and Adam M. Nguyen
Documentation:
Crossref API Documentation: https://api.crossref.org/swagger-ui/index.html
These recipe examples were tested on March 24 2023.
See the bottom of the document for information on R and package versions.
From our testing, we have found that the crossref metadata across publishers and even journals can vary considerably. As a result, it can be easier to work with one journal at a time when using the crossref API (e.g., particulary when trying to extract selected data from records).
Setup#
Importing the necessary libraries and setting up the base api:
If you haven’t already, run “install.packages(‘Package’)” in your R Console to install package you need for this tutorial, substituting Package in the code for the name of the package you need, e.g. tidyverse, purrr, etc..
library(tidyverse) #essential packages
library(purrr) #character manipulation
library(httr) #GET() API requests
library(jsonlite) #converting to JSON
# Set base URL to Crossref API
base_url <- "https://api.crossref.org/works/"
1. Basic CrossRef API call#
Setup basic parameters#
email <- "your_email@ua.edu" #Change this to your email
mailto <- "?mailto="
doi <- "10.1186/1758-2946-4-12" #example
complete_data <- paste(base_url, doi, mailto, email, sep="") #combines it all in one line
complete_data
## [1] "https://api.crossref.org/works/10.1186/1758-2946-4-12?mailto=your_email@ua.edu"
Request data from CrossRef#
raw_api_data <- GET(complete_data)$content # fetches content from the data
head(raw_api_data,n=100)
## [1] 7b 22 73 74 61 74 75 73 22 3a 22 6f 6b 22 2c 22 6d 65 73 73 61 67 65 2d 74
## [26] 79 70 65 22 3a 22 77 6f 72 6b 22 2c 22 6d 65 73 73 61 67 65 2d 76 65 72 73
## [51] 69 6f 6e 22 3a 22 31 2e 30 2e 30 22 2c 22 6d 65 73 73 61 67 65 22 3a 7b 22
## [76] 69 6e 64 65 78 65 64 22 3a 7b 22 64 61 74 65 2d 70 61 72 74 73 22 3a 5b 5b
The raw data is in hexadecimal and is hard to read.
Converting the data to JSON format (which is more readable)#
api_data <- fromJSON(rawToChar(raw_api_data), flatten = TRUE)
print(api_data)
## $status
## [1] "ok"
##
## $`message-type`
## [1] "work"
##
## $`message-version`
## [1] "1.0.0"
##
## $message
## $message$indexed
## $message$indexed$`date-parts`
## [,1] [,2] [,3]
## [1,] 2023 3 7
##
## $message$indexed$`date-time`
## [1] "2023-03-07T09:15:56Z"
##
## $message$indexed$timestamp
## [1] 1.678181e+12
##
##
## $message$`reference-count`
## [1] 16
##
## $message$publisher
## [1] "Springer Science and Business Media LLC"
##
## $message$issue
## [1] "1"
##
## $message$license
## content-version delay-in-days URL
## 1 tdm 0 http://creativecommons.org/licenses/by/2.0
## start.date-parts start.date-time start.timestamp
## 1 2012, 7, 6 2012-07-06T00:00:00Z 1.341533e+12
##
## $message$`content-domain`
## $message$`content-domain`$domain
## list()
##
## $message$`content-domain`$`crossmark-restriction`
## [1] FALSE
##
##
## $message$`short-container-title`
## [1] "J Cheminform"
##
## $message$`published-print`
## $message$`published-print`$`date-parts`
## [,1] [,2]
## [1,] 2012 12
##
##
## $message$DOI
## [1] "10.1186/1758-2946-4-12"
##
## $message$type
## [1] "journal-article"
##
## $message$created
## $message$created$`date-parts`
## [,1] [,2] [,3]
## [1,] 2012 7 6
##
## $message$created$`date-time`
## [1] "2012-07-06T12:14:34Z"
##
## $message$created$timestamp
## [1] 1.341577e+12
##
##
## $message$source
## [1] "Crossref"
##
## $message$`is-referenced-by-count`
## [1] 36
##
## $message$title
## [1] "The Molecule Cloud - compact visualization of large collections of molecules"
##
## $message$prefix
## [1] "10.1186"
##
## $message$volume
## [1] "4"
##
## $message$author
## given family sequence affiliation
## 1 Peter Ertl first NULL
## 2 Bernhard Rohde additional NULL
##
## $message$member
## [1] "297"
##
## $message$`published-online`
## $message$`published-online`$`date-parts`
## [,1] [,2] [,3]
## [1,] 2012 7 6
##
##
## $message$reference
## key doi-asserted-by first-page DOI volume
## 1 336_CR1 publisher 77 10.1007/s10822-011-9487-0 26
## 2 336_CR2 publisher 2174 10.1021/ci2001428 26
## 3 336_CR3 publisher 8732 10.1021/ja902302h 131
## 4 336_CR4 publisher 156 10.2174/157340908785747410 4
## 5 336_CR5 publisher 322 10.2174/157340908786786010 4
## 6 336_CR6 publisher 47 10.1021/ci600338x 47
## 7 336_CR7 publisher 366 10.1002/minf.201000019 29
## 8 336_CR8 publisher 4443 10.1021/jo8001276 73
## 9 336_CR9 <NA> <NA> <NA> <NA>
## 10 336_CR10 <NA> <NA> <NA> <NA>
## 11 336_CR11 publisher D255 10.1093/nar/gkp965 38
## 12 336_CR12 publisher 177 10.1021/ci049714+ 45
## 13 336_CR13 publisher D1100 10.1093/nar/gkr777 40
## 14 336_CR14 publisher 347 10.1016/j.cbpa.2010.02.018 14
## 15 336_CR15 publisher 374 10.1021/ci0255782 43
## 16 336_CR16 <NA> <NA> <NA> <NA>
## author year
## 1 E Martin 2011
## 2 SR Langdon 2011
## 3 LC Blum 2009
## 4 J Dubois 2008
## 5 JL Medina-Franco 2008
## 6 A Schuffenhauer 2007
## 7 S Langdon 2010
## 8 AH Lipkus 2008
## 9 <NA> <NA>
## 10 <NA> <NA>
## 11 Y Wang 2009
## 12 JJ Irwin 2004
## 13 A Gaulton 2012
## 14 ME Welsch 2010
## 15 P Ertl 2003
## 16 <NA> <NA>
## unstructured
## 1 Martin E, Ertl P, Hunt P, Duca J, Lewis R: Gazing into the crystal ball; the future of computer-aided drug design. J Comp-Aided Mol Des. 2011, 26: 77-79.
## 2 Langdon SR, Brown N, Blagg J: Scaffold diversity of exemplified medicinal chemistry space. J Chem Inf Model. 2011, 26: 2174-2185.
## 3 Blum LC, Reymond J-C: 970 Million druglike small molecules for virtual screening in the chemical universe database GDB-13. J Am Chem Soc. 2009, 131: 8732-8733. 10.1021/ja902302h.
## 4 Dubois J, Bourg S, Vrain C, Morin-Allory L: Collections of compounds - how to deal with them?. Cur Comp-Aided Drug Des. 2008, 4: 156-168. 10.2174/157340908785747410.
## 5 Medina-Franco JL, Martinez-Mayorga K, Giulianotti MA, Houghten RA, Pinilla C: Visualization of the chemical space in drug discovery. Cur Comp-Aided Drug Des. 2008, 4: 322-333. 10.2174/157340908786786010.
## 6 Schuffenhauer A, Ertl P, Roggo S, Wetzel S, Koch MA, Waldmann H: The Scaffold Tree - visualization of the scaffold universe by hierarchical scaffold classification. J Chem Inf Model. 2007, 47: 47-58. 10.1021/ci600338x.
## 7 Langdon S, Ertl P, Brown N: Bioisosteric replacement and scaffold hopping in lead generation and optimization. Mol Inf. 2010, 29: 366-385. 10.1002/minf.201000019.
## 8 Lipkus AH, Yuan Q, Lucas KA, Funk SA, Bartelt WF, Schenck RJ, Trippe AJ: Structural diversity of organic chemistry. A scaffold analysis of the CAS Registry. J Org Chem. 2008, 73: 4443-4451. 10.1021/jo8001276.
## 9 mib 2010.10, Molinspiration Cheminformatics: \n http://www.molinspiration.com\n \n ,
## 10 Bernhard R: Avalon Cheminformatics Toolkit. \n http://sourceforge.net/projects/avalontoolkit/\n \n ,
## 11 Wang Y, Bolton E, Dracheva S, Karapetyan K, Shoemaker BA, Suzek TO, Wang J, Xiao J, Zhang J, Bryant SH: An overview of the PubChem BioAssay resource. Nucleic Acids Res. 2009, 38: D255-D266.
## 12 Irwin JJ, Shoichet BK: ZINC − a free database of commercially available compounds for virtual screening. J Chem Inf Model. 2004, 45: 177-182.
## 13 Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP: ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012, 40: D1100-D1107. 10.1093/nar/gkr777.
## 14 Welsch ME, Snyder SA, Stockwell BR: Privileged scaffolds for library design and drug discovery. Curr Opin Chem Biol. 2010, 14: 347-361. 10.1016/j.cbpa.2010.02.018.
## 15 Ertl P: Cheminformatics analysis of organic substituents: Identification of the most common substituents, calculation of substituent properties, and automatic identification of drug-like bioisosteric groups. J Chem Inf Comp Sci. 2003, 43: 374-380. 10.1021/ci0255782.
## 16 TagCrowd: \n http://tagcrowd.com
## journal-title
## 1 J Comp-Aided Mol Des
## 2 J Chem Inf Model
## 3 J Am Chem Soc
## 4 Cur Comp-Aided Drug Des
## 5 Cur Comp-Aided Drug Des
## 6 J Chem Inf Model
## 7 Mol Inf
## 8 J Org Chem
## 9 <NA>
## 10 <NA>
## 11 Nucleic Acids Res
## 12 J Chem Inf Model
## 13 Nucleic Acids Res
## 14 Curr Opin Chem Biol
## 15 J Chem Inf Comp Sci
## 16 <NA>
##
## $message$`container-title`
## [1] "Journal of Cheminformatics"
##
## $message$`original-title`
## list()
##
## $message$language
## [1] "en"
##
## $message$link
## URL
## 1 http://link.springer.com/content/pdf/10.1186/1758-2946-4-12.pdf
## 2 http://link.springer.com/article/10.1186/1758-2946-4-12/fulltext.html
## 3 http://link.springer.com/content/pdf/10.1186/1758-2946-4-12.pdf
## content-type content-version intended-application
## 1 application/pdf vor text-mining
## 2 text/html vor text-mining
## 3 application/pdf vor similarity-checking
##
## $message$deposited
## $message$deposited$`date-parts`
## [,1] [,2] [,3]
## [1,] 2019 6 24
##
## $message$deposited$`date-time`
## [1] "2019-06-24T14:22:07Z"
##
## $message$deposited$timestamp
## [1] 1.561386e+12
##
##
## $message$score
## [1] 1
##
## $message$resource
## $message$resource$primary
## $message$resource$primary$URL
## [1] "https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-4-12"
##
##
##
## $message$subtitle
## list()
##
## $message$`short-title`
## list()
##
## $message$issued
## $message$issued$`date-parts`
## [,1] [,2] [,3]
## [1,] 2012 7 6
##
##
## $message$`references-count`
## [1] 16
##
## $message$`journal-issue`
## $message$`journal-issue`$issue
## [1] "1"
##
## $message$`journal-issue`$`published-print`
## $message$`journal-issue`$`published-print`$`date-parts`
## [,1] [,2]
## [1,] 2012 12
##
##
##
## $message$`alternative-id`
## [1] "336"
##
## $message$URL
## [1] "http://dx.doi.org/10.1186/1758-2946-4-12"
##
## $message$relation
## named list()
##
## $message$ISSN
## [1] "1758-2946"
##
## $message$`issn-type`
## value type
## 1 1758-2946 electronic
##
## $message$subject
## [1] "Library and Information Sciences"
## [2] "Computer Graphics and Computer-Aided Design"
## [3] "Physical and Theoretical Chemistry"
## [4] "Computer Science Applications"
##
## $message$published
## $message$published$`date-parts`
## [,1] [,2] [,3]
## [1,] 2012 7 6
##
##
## $message$`article-number`
## [1] "12"
Replace the above DOI value with a different DOI to customize.
Let’s see the reference part of CrossRef in a convenient tabular way#
api_data$message$`reference`[1:7,1:7] # prints the table of data
## key doi-asserted-by first-page DOI volume
## 1 336_CR1 publisher 77 10.1007/s10822-011-9487-0 26
## 2 336_CR2 publisher 2174 10.1021/ci2001428 26
## 3 336_CR3 publisher 8732 10.1021/ja902302h 131
## 4 336_CR4 publisher 156 10.2174/157340908785747410 4
## 5 336_CR5 publisher 322 10.2174/157340908786786010 4
## 6 336_CR6 publisher 47 10.1021/ci600338x 47
## 7 336_CR7 publisher 366 10.1002/minf.201000019 29
## author year
## 1 E Martin 2011
## 2 SR Langdon 2011
## 3 LC Blum 2009
## 4 J Dubois 2008
## 5 JL Medina-Franco 2008
## 6 A Schuffenhauer 2007
## 7 S Langdon 2010
Selecting specific data#
Getting the Journal Title
#Get Journal Title
api_data$message$`container-title` # prints the title
## [1] "Journal of Cheminformatics"
Getting the Article Title
api_data$message$`title`
## [1] "The Molecule Cloud - compact visualization of large collections of molecules"
Getting the Article Author Names
for (au in api_data$message$author){
article_authors <- paste(api_data$message$author$given, api_data$message$author$family)
}
article_authors
## [1] "Peter Ertl" "Bernhard Rohde"
Getting the Bibliography References and saving them to a list
#Get bibliography references and save to list
bib_refs <- list() #declare a list
for (ref in api_data$message$reference){
bib_refs <- append(x = bib_refs, api_data$message$reference$unstructured)
}
bib_refs[0:5]
## [[1]]
## [1] "Martin E, Ertl P, Hunt P, Duca J, Lewis R: Gazing into the crystal ball; the future of computer-aided drug design. J Comp-Aided Mol Des. 2011, 26: 77-79."
##
## [[2]]
## [1] "Langdon SR, Brown N, Blagg J: Scaffold diversity of exemplified medicinal chemistry space. J Chem Inf Model. 2011, 26: 2174-2185."
##
## [[3]]
## [1] "Blum LC, Reymond J-C: 970 Million druglike small molecules for virtual screening in the chemical universe database GDB-13. J Am Chem Soc. 2009, 131: 8732-8733. 10.1021/ja902302h."
##
## [[4]]
## [1] "Dubois J, Bourg S, Vrain C, Morin-Allory L: Collections of compounds - how to deal with them?. Cur Comp-Aided Drug Des. 2008, 4: 156-168. 10.2174/157340908785747410."
##
## [[5]]
## [1] "Medina-Franco JL, Martinez-Mayorga K, Giulianotti MA, Houghten RA, Pinilla C: Visualization of the chemical space in drug discovery. Cur Comp-Aided Drug Des. 2008, 4: 322-333. 10.2174/157340908786786010."
Save JSON data to a file#
This is particularly useful for downstream testing or returning to results in the future (e.g., no need to keep requesting the data from crossref, save the results to a file)
#Save JSON data to a file
jsonData <- toJSON(api_data)
write (jsonData, "output.json")
Load JSON data from a file#
loaded_data <- fromJSON("output.json")
loaded_data #Load Json data from file
## $status
## [1] "ok"
##
## $`message-type`
## [1] "work"
##
## $`message-version`
## [1] "1.0.0"
##
## $message
## $message$indexed
## $message$indexed$`date-parts`
## [,1] [,2] [,3]
## [1,] 2023 3 7
##
## $message$indexed$`date-time`
## [1] "2023-03-07T09:15:56Z"
##
## $message$indexed$timestamp
## [1] 1.678181e+12
##
##
## $message$`reference-count`
## [1] 16
##
## $message$publisher
## [1] "Springer Science and Business Media LLC"
##
## $message$issue
## [1] "1"
##
## $message$license
## content-version delay-in-days URL
## 1 tdm 0 http://creativecommons.org/licenses/by/2.0
## start.date-parts start.date-time start.timestamp
## 1 2012, 7, 6 2012-07-06T00:00:00Z 1.341533e+12
##
## $message$`content-domain`
## $message$`content-domain`$domain
## list()
##
## $message$`content-domain`$`crossmark-restriction`
## [1] FALSE
##
##
## $message$`short-container-title`
## [1] "J Cheminform"
##
## $message$`published-print`
## $message$`published-print`$`date-parts`
## [,1] [,2]
## [1,] 2012 12
##
##
## $message$DOI
## [1] "10.1186/1758-2946-4-12"
##
## $message$type
## [1] "journal-article"
##
## $message$created
## $message$created$`date-parts`
## [,1] [,2] [,3]
## [1,] 2012 7 6
##
## $message$created$`date-time`
## [1] "2012-07-06T12:14:34Z"
##
## $message$created$timestamp
## [1] 1.341577e+12
##
##
## $message$source
## [1] "Crossref"
##
## $message$`is-referenced-by-count`
## [1] 36
##
## $message$title
## [1] "The Molecule Cloud - compact visualization of large collections of molecules"
##
## $message$prefix
## [1] "10.1186"
##
## $message$volume
## [1] "4"
##
## $message$author
## given family sequence affiliation
## 1 Peter Ertl first NULL
## 2 Bernhard Rohde additional NULL
##
## $message$member
## [1] "297"
##
## $message$`published-online`
## $message$`published-online`$`date-parts`
## [,1] [,2] [,3]
## [1,] 2012 7 6
##
##
## $message$reference
## key doi-asserted-by first-page DOI volume
## 1 336_CR1 publisher 77 10.1007/s10822-011-9487-0 26
## 2 336_CR2 publisher 2174 10.1021/ci2001428 26
## 3 336_CR3 publisher 8732 10.1021/ja902302h 131
## 4 336_CR4 publisher 156 10.2174/157340908785747410 4
## 5 336_CR5 publisher 322 10.2174/157340908786786010 4
## 6 336_CR6 publisher 47 10.1021/ci600338x 47
## 7 336_CR7 publisher 366 10.1002/minf.201000019 29
## 8 336_CR8 publisher 4443 10.1021/jo8001276 73
## 9 336_CR9 <NA> <NA> <NA> <NA>
## 10 336_CR10 <NA> <NA> <NA> <NA>
## 11 336_CR11 publisher D255 10.1093/nar/gkp965 38
## 12 336_CR12 publisher 177 10.1021/ci049714+ 45
## 13 336_CR13 publisher D1100 10.1093/nar/gkr777 40
## 14 336_CR14 publisher 347 10.1016/j.cbpa.2010.02.018 14
## 15 336_CR15 publisher 374 10.1021/ci0255782 43
## 16 336_CR16 <NA> <NA> <NA> <NA>
## author year
## 1 E Martin 2011
## 2 SR Langdon 2011
## 3 LC Blum 2009
## 4 J Dubois 2008
## 5 JL Medina-Franco 2008
## 6 A Schuffenhauer 2007
## 7 S Langdon 2010
## 8 AH Lipkus 2008
## 9 <NA> <NA>
## 10 <NA> <NA>
## 11 Y Wang 2009
## 12 JJ Irwin 2004
## 13 A Gaulton 2012
## 14 ME Welsch 2010
## 15 P Ertl 2003
## 16 <NA> <NA>
## unstructured
## 1 Martin E, Ertl P, Hunt P, Duca J, Lewis R: Gazing into the crystal ball; the future of computer-aided drug design. J Comp-Aided Mol Des. 2011, 26: 77-79.
## 2 Langdon SR, Brown N, Blagg J: Scaffold diversity of exemplified medicinal chemistry space. J Chem Inf Model. 2011, 26: 2174-2185.
## 3 Blum LC, Reymond J-C: 970 Million druglike small molecules for virtual screening in the chemical universe database GDB-13. J Am Chem Soc. 2009, 131: 8732-8733. 10.1021/ja902302h.
## 4 Dubois J, Bourg S, Vrain C, Morin-Allory L: Collections of compounds - how to deal with them?. Cur Comp-Aided Drug Des. 2008, 4: 156-168. 10.2174/157340908785747410.
## 5 Medina-Franco JL, Martinez-Mayorga K, Giulianotti MA, Houghten RA, Pinilla C: Visualization of the chemical space in drug discovery. Cur Comp-Aided Drug Des. 2008, 4: 322-333. 10.2174/157340908786786010.
## 6 Schuffenhauer A, Ertl P, Roggo S, Wetzel S, Koch MA, Waldmann H: The Scaffold Tree - visualization of the scaffold universe by hierarchical scaffold classification. J Chem Inf Model. 2007, 47: 47-58. 10.1021/ci600338x.
## 7 Langdon S, Ertl P, Brown N: Bioisosteric replacement and scaffold hopping in lead generation and optimization. Mol Inf. 2010, 29: 366-385. 10.1002/minf.201000019.
## 8 Lipkus AH, Yuan Q, Lucas KA, Funk SA, Bartelt WF, Schenck RJ, Trippe AJ: Structural diversity of organic chemistry. A scaffold analysis of the CAS Registry. J Org Chem. 2008, 73: 4443-4451. 10.1021/jo8001276.
## 9 mib 2010.10, Molinspiration Cheminformatics: \n http://www.molinspiration.com\n \n ,
## 10 Bernhard R: Avalon Cheminformatics Toolkit. \n http://sourceforge.net/projects/avalontoolkit/\n \n ,
## 11 Wang Y, Bolton E, Dracheva S, Karapetyan K, Shoemaker BA, Suzek TO, Wang J, Xiao J, Zhang J, Bryant SH: An overview of the PubChem BioAssay resource. Nucleic Acids Res. 2009, 38: D255-D266.
## 12 Irwin JJ, Shoichet BK: ZINC − a free database of commercially available compounds for virtual screening. J Chem Inf Model. 2004, 45: 177-182.
## 13 Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP: ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012, 40: D1100-D1107. 10.1093/nar/gkr777.
## 14 Welsch ME, Snyder SA, Stockwell BR: Privileged scaffolds for library design and drug discovery. Curr Opin Chem Biol. 2010, 14: 347-361. 10.1016/j.cbpa.2010.02.018.
## 15 Ertl P: Cheminformatics analysis of organic substituents: Identification of the most common substituents, calculation of substituent properties, and automatic identification of drug-like bioisosteric groups. J Chem Inf Comp Sci. 2003, 43: 374-380. 10.1021/ci0255782.
## 16 TagCrowd: \n http://tagcrowd.com
## journal-title
## 1 J Comp-Aided Mol Des
## 2 J Chem Inf Model
## 3 J Am Chem Soc
## 4 Cur Comp-Aided Drug Des
## 5 Cur Comp-Aided Drug Des
## 6 J Chem Inf Model
## 7 Mol Inf
## 8 J Org Chem
## 9 <NA>
## 10 <NA>
## 11 Nucleic Acids Res
## 12 J Chem Inf Model
## 13 Nucleic Acids Res
## 14 Curr Opin Chem Biol
## 15 J Chem Inf Comp Sci
## 16 <NA>
##
## $message$`container-title`
## [1] "Journal of Cheminformatics"
##
## $message$`original-title`
## list()
##
## $message$language
## [1] "en"
##
## $message$link
## URL
## 1 http://link.springer.com/content/pdf/10.1186/1758-2946-4-12.pdf
## 2 http://link.springer.com/article/10.1186/1758-2946-4-12/fulltext.html
## 3 http://link.springer.com/content/pdf/10.1186/1758-2946-4-12.pdf
## content-type content-version intended-application
## 1 application/pdf vor text-mining
## 2 text/html vor text-mining
## 3 application/pdf vor similarity-checking
##
## $message$deposited
## $message$deposited$`date-parts`
## [,1] [,2] [,3]
## [1,] 2019 6 24
##
## $message$deposited$`date-time`
## [1] "2019-06-24T14:22:07Z"
##
## $message$deposited$timestamp
## [1] 1.561386e+12
##
##
## $message$score
## [1] 1
##
## $message$resource
## $message$resource$primary
## $message$resource$primary$URL
## [1] "https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-4-12"
##
##
##
## $message$subtitle
## list()
##
## $message$`short-title`
## list()
##
## $message$issued
## $message$issued$`date-parts`
## [,1] [,2] [,3]
## [1,] 2012 7 6
##
##
## $message$`references-count`
## [1] 16
##
## $message$`journal-issue`
## $message$`journal-issue`$issue
## [1] "1"
##
## $message$`journal-issue`$`published-print`
## $message$`journal-issue`$`published-print`$`date-parts`
## [,1] [,2]
## [1,] 2012 12
##
##
##
## $message$`alternative-id`
## [1] "336"
##
## $message$URL
## [1] "http://dx.doi.org/10.1186/1758-2946-4-12"
##
## $message$relation
## named list()
##
## $message$ISSN
## [1] "1758-2946"
##
## $message$`issn-type`
## value type
## 1 1758-2946 electronic
##
## $message$subject
## [1] "Library and Information Sciences"
## [2] "Computer Graphics and Computer-Aided Design"
## [3] "Physical and Theoretical Chemistry"
## [4] "Computer Science Applications"
##
## $message$published
## $message$published$`date-parts`
## [,1] [,2] [,3]
## [1,] 2012 7 6
##
##
## $message$`article-number`
## [1] "12"
2. CrossRef API call with a Loop#
Setup API parameters#
base_url <- "https://api.crossref.org/works/"
email <- "your_email@ua.edu" #Change this to your email
mailto <- "?mailto="
doi <- "10.1186/1758-2946-4-12" #example
Create a List of DOIs#
#Create List of DOIs
doi_list <- list('10.1021/acsomega.1c03250',
'10.1021/acsomega.1c05512',
'10.1021/acsomega.8b01647',
'10.1021/acsomega.1c04287',
'10.1021/acsomega.8b01834')
Request metadata for each DOI from CrossRef API and save to a list#
doi_metadata <- list(list()) # a list of lists is used to hold the data from the 5 DOIs
i <- 1
for (doi in doi_list){
doi_metadata[[i]] <-fromJSON(rawToChar(GET(paste(base_url, doi, mailto, email, sep=""))$content), flatten = TRUE)
i <- i+1
Sys.sleep(1) # important to add a delay between API calls
}
# doi_metadata ## not shown here as it is long.
Let’s select some specific data
# Get Article titles
for (item in 1:length(doi_metadata)){
print(doi_metadata[[item]]$message$title)
}
## [1] "Navigating into the Chemical Space of Monoamine Oxidase Inhibitors by Artificial Intelligence and Cheminformatics Approach"
## [1] "Impact of Artificial Intelligence on Compound Discovery, Design, and Synthesis"
## [1] "How Precise Are Our Quantitative Structure–Activity Relationship Derived Predictions for New Query Chemicals?"
## [1] "Applying Neuromorphic Computing Simulation in Band Gap Prediction and Chemical Reaction Classification"
## [1] "QSPR Modeling of the Refractive Index for Diverse Polymers Using 2D Descriptors"
#Get Author affiliations for each Article
for (item in 1:length(doi_metadata)){
for (au in 1:length(doi_metadata[[item]]$message$author)){
print(doi_metadata[[item]]$message$author$affiliation[[1]]$name)
}
}
## [1] "Department of Pharmaceutical Chemistry and Analysis, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi 682041, India"
## [1] "Department of Pharmaceutical Chemistry and Analysis, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi 682041, India"
## [1] "Department of Pharmaceutical Chemistry and Analysis, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi 682041, India"
## [1] "Department of Pharmaceutical Chemistry and Analysis, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi 682041, India"
## [1] "Department of Pharmaceutical Chemistry and Analysis, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi 682041, India"
## [1] "Department of Pharmaceutical Chemistry and Analysis, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi 682041, India"
## [1] "Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115 Bonn, Germany"
## [2] "Data Science and AI, Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, SE-431 83 Gothenburg, Sweden"
## [1] "Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115 Bonn, Germany"
## [2] "Data Science and AI, Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, SE-431 83 Gothenburg, Sweden"
## [1] "Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115 Bonn, Germany"
## [2] "Data Science and AI, Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, SE-431 83 Gothenburg, Sweden"
## [1] "Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115 Bonn, Germany"
## [2] "Data Science and AI, Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, SE-431 83 Gothenburg, Sweden"
## [1] "Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115 Bonn, Germany"
## [2] "Data Science and AI, Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, SE-431 83 Gothenburg, Sweden"
## [1] "Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115 Bonn, Germany"
## [2] "Data Science and AI, Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, SE-431 83 Gothenburg, Sweden"
## [1] "Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India"
## [1] "Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India"
## [1] "Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India"
## [1] "Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India"
## [1] "Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India"
## [1] "Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India"
## [1] "Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States"
## [1] "Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States"
## [1] "Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States"
## [1] "Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States"
## [1] "Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States"
## [1] "Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States"
## [1] "Department of Pharmacoinformatics, National Institute of Pharmaceutical Educational and Research (NIPER), Chunilal Bhawan, 168, Manikata Main Road, 700054 Kolkata, India"
## [1] "Department of Pharmacoinformatics, National Institute of Pharmaceutical Educational and Research (NIPER), Chunilal Bhawan, 168, Manikata Main Road, 700054 Kolkata, India"
## [1] "Department of Pharmacoinformatics, National Institute of Pharmaceutical Educational and Research (NIPER), Chunilal Bhawan, 168, Manikata Main Road, 700054 Kolkata, India"
## [1] "Department of Pharmacoinformatics, National Institute of Pharmaceutical Educational and Research (NIPER), Chunilal Bhawan, 168, Manikata Main Road, 700054 Kolkata, India"
## [1] "Department of Pharmacoinformatics, National Institute of Pharmaceutical Educational and Research (NIPER), Chunilal Bhawan, 168, Manikata Main Road, 700054 Kolkata, India"
## [1] "Department of Pharmacoinformatics, National Institute of Pharmaceutical Educational and Research (NIPER), Chunilal Bhawan, 168, Manikata Main Road, 700054 Kolkata, India"
3. Crossref API call for journal information#
Setup API parameters#
jbase_url <- "https://api.crossref.org/journals/" # the base url for api calls
email <- "your_email@ua.edu" # Change this to be your email
mailto <- "?mailto="
issn <- "1471-2105" # issn for the journal BMC Bioinformatics
Request journal data from crossref API#
jour_data <- fromJSON(rawToChar(GET(paste(jbase_url, issn, mailto, email, sep=""))$content), flatten = TRUE)
jour_data
:tags: ["output_scroll"]
## $status
## [1] "ok"
##
## $`message-type`
## [1] "journal"
##
## $`message-version`
## [1] "1.0.0"
##
## $message
## $message$`last-status-check-time`
## [1] 1.679633e+12
##
## $message$counts
## $message$counts$`current-dois`
## [1] 1306
##
## $message$counts$`backfile-dois`
## [1] 10498
##
## $message$counts$`total-dois`
## [1] 11804
##
##
## $message$breakdowns
## $message$breakdowns$`dois-by-issued-year`
## [,1] [,2]
## [1,] 2010 861
## [2,] 2019 762
## [3,] 2008 745
## [4,] 2009 729
## [5,] 2011 722
## [6,] 2006 633
## [7,] 2021 622
## [8,] 2014 619
## [9,] 2007 613
## [10,] 2012 609
## [11,] 2013 607
## [12,] 2015 603
## [13,] 2020 585
## [14,] 2017 585
## [15,] 2022 582
## [16,] 2016 550
## [17,] 2018 536
## [18,] 2005 414
## [19,] 2004 209
## [20,] 2023 102
## [21,] 2003 66
## [22,] 2002 40
## [23,] 2001 9
## [24,] 2000 1
##
##
## $message$publisher
## [1] "Springer (Biomed Central Ltd.)"
##
## $message$coverage
## $message$coverage$`affiliations-current`
## [1] 0
##
## $message$coverage$`similarity-checking-current`
## [1] 1
##
## $message$coverage$`descriptions-current`
## [1] 0
##
## $message$coverage$`ror-ids-current`
## [1] 0
##
## $message$coverage$`funders-backfile`
## [1] 0.1704134
##
## $message$coverage$`licenses-backfile`
## [1] 0.561726
##
## $message$coverage$`funders-current`
## [1] 0.6906585
##
## $message$coverage$`affiliations-backfile`
## [1] 0
##
## $message$coverage$`resource-links-backfile`
## [1] 0.5611545
##
## $message$coverage$`orcids-backfile`
## [1] 0.1299295
##
## $message$coverage$`update-policies-current`
## [1] 1
##
## $message$coverage$`ror-ids-backfile`
## [1] 0
##
## $message$coverage$`orcids-current`
## [1] 0.4701378
##
## $message$coverage$`similarity-checking-backfile`
## [1] 0.9561821
##
## $message$coverage$`references-backfile`
## [1] 0.9400838
##
## $message$coverage$`descriptions-backfile`
## [1] 0
##
## $message$coverage$`award-numbers-backfile`
## [1] 0.1554582
##
## $message$coverage$`update-policies-backfile`
## [1] 0.660983
##
## $message$coverage$`licenses-current`
## [1] 1
##
## $message$coverage$`award-numbers-current`
## [1] 0.6117917
##
## $message$coverage$`abstracts-backfile`
## [1] 0.3347304
##
## $message$coverage$`resource-links-current`
## [1] 1
##
## $message$coverage$`abstracts-current`
## [1] 0.9785605
##
## $message$coverage$`references-current`
## [1] 0.9984686
##
##
## $message$title
## [1] "BMC Bioinformatics"
##
## $message$subjects
## ASJC name
## 1 2604 Applied Mathematics
## 2 1706 Computer Science Applications
## 3 1312 Molecular Biology
## 4 1303 Biochemistry
## 5 1315 Structural Biology
##
## $message$`coverage-type`
## $message$`coverage-type`$all
## $message$`coverage-type`$all$`last-status-check-time`
## [1] 1.679633e+12
##
## $message$`coverage-type`$all$affiliations
## [1] 0
##
## $message$`coverage-type`$all$abstracts
## [1] 0.4059641
##
## $message$`coverage-type`$all$orcids
## [1] 0.1675703
##
## $message$`coverage-type`$all$licenses
## [1] 0.6102169
##
## $message$`coverage-type`$all$references
## [1] 0.9465435
##
## $message$`coverage-type`$all$funders
## [1] 0.2279736
##
## $message$`coverage-type`$all$`similarity-checking`
## [1] 0.9610302
##
## $message$`coverage-type`$all$`award-numbers`
## [1] 0.2059471
##
## $message$`coverage-type`$all$`ror-ids`
## [1] 0
##
## $message$`coverage-type`$all$`update-policies`
## [1] 0.698492
##
## $message$`coverage-type`$all$`resource-links`
## [1] 0.6097086
##
## $message$`coverage-type`$all$descriptions
## [1] 0
##
##
## $message$`coverage-type`$backfile
## $message$`coverage-type`$backfile$`last-status-check-time`
## [1] 1.679633e+12
##
## $message$`coverage-type`$backfile$affiliations
## [1] 0
##
## $message$`coverage-type`$backfile$abstracts
## [1] 0.3347304
##
## $message$`coverage-type`$backfile$orcids
## [1] 0.1299295
##
## $message$`coverage-type`$backfile$licenses
## [1] 0.561726
##
## $message$`coverage-type`$backfile$references
## [1] 0.9400838
##
## $message$`coverage-type`$backfile$funders
## [1] 0.1704134
##
## $message$`coverage-type`$backfile$`similarity-checking`
## [1] 0.9561821
##
## $message$`coverage-type`$backfile$`award-numbers`
## [1] 0.1554582
##
## $message$`coverage-type`$backfile$`ror-ids`
## [1] 0
##
## $message$`coverage-type`$backfile$`update-policies`
## [1] 0.660983
##
## $message$`coverage-type`$backfile$`resource-links`
## [1] 0.5611545
##
## $message$`coverage-type`$backfile$descriptions
## [1] 0
##
##
## $message$`coverage-type`$current
## $message$`coverage-type`$current$`last-status-check-time`
## [1] 1.679633e+12
##
## $message$`coverage-type`$current$affiliations
## [1] 0
##
## $message$`coverage-type`$current$abstracts
## [1] 0.9785605
##
## $message$`coverage-type`$current$orcids
## [1] 0.4701378
##
## $message$`coverage-type`$current$licenses
## [1] 1
##
## $message$`coverage-type`$current$references
## [1] 0.9984686
##
## $message$`coverage-type`$current$funders
## [1] 0.6906585
##
## $message$`coverage-type`$current$`similarity-checking`
## [1] 1
##
## $message$`coverage-type`$current$`award-numbers`
## [1] 0.6117917
##
## $message$`coverage-type`$current$`ror-ids`
## [1] 0
##
## $message$`coverage-type`$current$`update-policies`
## [1] 1
##
## $message$`coverage-type`$current$`resource-links`
## [1] 1
##
## $message$`coverage-type`$current$descriptions
## [1] 0
##
##
##
## $message$flags
## $message$flags$`deposits-abstracts-current`
## [1] TRUE
##
## $message$flags$`deposits-orcids-current`
## [1] TRUE
##
## $message$flags$deposits
## [1] TRUE
##
## $message$flags$`deposits-affiliations-backfile`
## [1] FALSE
##
## $message$flags$`deposits-update-policies-backfile`
## [1] TRUE
##
## $message$flags$`deposits-similarity-checking-backfile`
## [1] TRUE
##
## $message$flags$`deposits-award-numbers-current`
## [1] TRUE
##
## $message$flags$`deposits-resource-links-current`
## [1] TRUE
##
## $message$flags$`deposits-ror-ids-current`
## [1] FALSE
##
## $message$flags$`deposits-articles`
## [1] TRUE
##
## $message$flags$`deposits-affiliations-current`
## [1] FALSE
##
## $message$flags$`deposits-funders-current`
## [1] TRUE
##
## $message$flags$`deposits-references-backfile`
## [1] TRUE
##
## $message$flags$`deposits-ror-ids-backfile`
## [1] FALSE
##
## $message$flags$`deposits-abstracts-backfile`
## [1] TRUE
##
## $message$flags$`deposits-licenses-backfile`
## [1] TRUE
##
## $message$flags$`deposits-award-numbers-backfile`
## [1] TRUE
##
## $message$flags$`deposits-descriptions-current`
## [1] FALSE
##
## $message$flags$`deposits-references-current`
## [1] TRUE
##
## $message$flags$`deposits-resource-links-backfile`
## [1] TRUE
##
## $message$flags$`deposits-descriptions-backfile`
## [1] FALSE
##
## $message$flags$`deposits-orcids-backfile`
## [1] TRUE
##
## $message$flags$`deposits-funders-backfile`
## [1] TRUE
##
## $message$flags$`deposits-update-policies-current`
## [1] TRUE
##
## $message$flags$`deposits-similarity-checking-current`
## [1] TRUE
##
## $message$flags$`deposits-licenses-current`
## [1] TRUE
##
##
## $message$ISSN
## [1] "1471-2105" "1471-2105"
##
## $message$`issn-type`
## value type
## 1 1471-2105 print
## 2 1471-2105 electronic
4. Crossref API - Get article DOIs for a journal#
Setup API parameters#
jbase_url <- "https://api.crossref.org/journals/" # the base url for api calls
email <- "your_email@ua.edu" # Change this to be your email
mailto = "&mailto="
issn <- "1471-2105" # issn for the journal BMC Bioinformatics
journal_works2014 <- "/works?filter=from-pub-date:2014,until-pub-date:2014&select=DOI" # query to get DOIs for 2014
Request DOI data from crossref API#
doi_data = fromJSON(rawToChar(GET(paste(jbase_url, issn, journal_works2014, mailto, email, sep=""))$content), flatten = TRUE)
doi_data
## $status
## [1] "ok"
##
## $`message-type`
## [1] "work-list"
##
## $`message-version`
## [1] "1.0.0"
##
## $message
## $message$facets
## named list()
##
## $message$`total-results`
## [1] 619
##
## $message$items
## DOI
## 1 10.1186/1471-2105-15-s10-p5
## 2 10.1186/1471-2105-15-166
## 3 10.1186/1471-2105-15-83
## 4 10.1186/1471-2105-15-s10-p28
## 5 10.1186/1471-2105-15-s10-p6
## 6 10.1186/1471-2105-15-16
## 7 10.1186/1471-2105-15-s11-i1
## 8 10.1186/1471-2105-15-s1-s2
## 9 10.1186/s12859-014-0358-2
## 10 10.1186/1471-2105-15-s10-p35
## 11 10.1186/1471-2105-15-s7-s1
## 12 10.1186/1471-2105-15-s10-p32
## 13 10.1186/1471-2105-15-s4-s1
## 14 10.1186/1471-2105-15-s9-s12
## 15 10.1186/s12859-014-0415-x
## 16 10.1186/1471-2105-15-s13-s1
## 17 10.1186/1471-2105-15-305
## 18 10.1186/s12859-014-0411-1
## 19 10.1186/1471-2105-15-s10-p33
## 20 10.1186/1471-2105-15-s3-a2
##
## $message$`items-per-page`
## [1] 20
##
## $message$query
## $message$query$`start-index`
## [1] 0
##
## $message$query$`search-terms`
## NULL
By default, 20 results are displayed. Crossref allows up to 1000 returned results using the rows parameter. To get all 619 results, we can increase the number of returned rows.
rows <- "&rows=700"
doi_data_all <- fromJSON(rawToChar(GET(paste(jbase_url, issn, journal_works2014, rows, mailto, email, sep=""))$content), flatten = TRUE)
Extract DOIs#
dois_list = list()
for (i in doi_data_all$message$items){
dois_list <- append(x = dois_list, doi_data_all$message$items$DOI)
}
length(dois_list)
## [1] 619
#display the first 20
print (dois_list[0:20])
## [[1]]
## [1] "10.1186/1471-2105-15-s7-s7"
##
## [[2]]
## [1] "10.1186/1471-2105-15-84"
##
## [[3]]
## [1] "10.1186/s12859-014-0411-1"
##
## [[4]]
## [1] "10.1186/1471-2105-15-177"
##
## [[5]]
## [1] "10.1186/1471-2105-15-s10-p24"
##
## [[6]]
## [1] "10.1186/1471-2105-15-s1-s2"
##
## [[7]]
## [1] "10.1186/1471-2105-15-s10-p32"
##
## [[8]]
## [1] "10.1186/1471-2105-15-193"
##
## [[9]]
## [1] "10.1186/1471-2105-15-192"
##
## [[10]]
## [1] "10.1186/1471-2105-15-s11-i1"
##
## [[11]]
## [1] "10.1186/1471-2105-15-88"
##
## [[12]]
## [1] "10.1186/1471-2105-15-331"
##
## [[13]]
## [1] "10.1186/1471-2105-15-s11-s10"
##
## [[14]]
## [1] "10.1186/1471-2105-15-s1-s8"
##
## [[15]]
## [1] "10.1186/1471-2105-15-s14-s1"
##
## [[16]]
## [1] "10.1186/1471-2105-15-s10-p33"
##
## [[17]]
## [1] "10.1186/1471-2105-15-s10-p35"
##
## [[18]]
## [1] "10.1186/s12859-014-0358-2"
##
## [[19]]
## [1] "10.1186/1471-2105-15-s10-p6"
##
## [[20]]
## [1] "10.1186/1471-2105-15-115"
What if we have more than 1000 results in a single query?
For example, if we wanted the DOIs from BMC Bioinformatics for years 2014 through 2016?
jbase_url <- "https://api.crossref.org/journals/" # the base url for api calls
email <- "your_email@ua.edu" # Change this to be your email
mailto <- "&mailto="
issn <- "1471-2105" # issn for the journal BMC Bioinformatics
journal_works2014_2016 <- "/works?filter=from-pub-date:2014,until-pub-date:2016&select=DOI" # query to get DOIs for 2014-2016
doi_data2 <- fromJSON(paste(jbase_url, issn, journal_works2014_2016, mailto, email, sep=""), flatten = TRUE)
doi_data2
## $status
## [1] "ok"
##
## $`message-type`
## [1] "work-list"
##
## $`message-version`
## [1] "1.0.0"
##
## $message
## $message$facets
## named list()
##
## $message$`total-results`
## [1] 1772
##
## $message$items
## DOI
## 1 10.1186/s12859-015-0538-8
## 2 10.1186/1471-2105-16-s18-s5
## 3 10.1186/s12859-016-1086-6
## 4 10.1186/s12859-016-1343-8
## 5 10.1186/1471-2105-15-s7-s7
## 6 10.1186/1471-2105-15-84
## 7 10.1186/s12859-014-0411-1
## 8 10.1186/1471-2105-15-177
## 9 10.1186/1471-2105-16-s15-p13
## 10 10.1186/s12859-015-0699-5
## 11 10.1186/1471-2105-15-s10-p24
## 12 10.1186/s12859-016-1297-x
## 13 10.1186/s12859-015-0791-x
## 14 10.1186/1471-2105-15-s1-s2
## 15 10.1186/s12859-015-0834-3
## 16 10.1186/s12859-016-1009-6
## 17 10.1186/1471-2105-15-s10-p32
## 18 10.1186/s12859-016-1129-z
## 19 10.1186/1471-2105-15-193
## 20 10.1186/1471-2105-15-192
##
## $message$`items-per-page`
## [1] 20
##
## $message$query
## $message$query$`start-index`
## [1] 0
##
## $message$query$`search-terms`
## NULL
Here we see that the total results is over 1000 (total-results: 1772). An additional parameter that we can use with crossref API is called “offset”. The offset option allows us to select sets of records and define a starting position (e.g., the first 1000, and then the second set of up to 1000.)
doi_list2 <- list() #declare a list
rows <- "&rows=1000"
temp <- fromJSON(paste(jbase_url, issn, journal_works2014_2016, mailto, email, sep=""), flatten = TRUE)
numResults<- temp$message$`total-results`
Sys.sleep(1)
end <- as.integer(numResults/1000)+1
for (n in 0:end){ # list(range(int(numberOfResults/1000)+1)) = [0,1]
query <- fromJSON(paste(jbase_url, issn, journal_works2014_2016, rows, "&offset=", as.character(1000*n), mailto, email, sep=""), flatten = TRUE)
Sys.sleep(1)
for (doi in query$message$items){
doi_list2 <- append(x = doi_list2, query$message$items$DOI)
}
}
length(doi_list2)
## [1] 1772
#how results 1000 through 1020
print(doi_list2[1000:1020])
## [[1]]
## [1] "10.1186/1471-2105-15-s11-s2"
##
## [[2]]
## [1] "10.1186/1471-2105-15-228"
##
## [[3]]
## [1] "10.1186/1471-2105-15-s2-s7"
##
## [[4]]
## [1] "10.1186/s12859-014-0349-3"
##
## [[5]]
## [1] "10.1186/1471-2105-15-327"
##
## [[6]]
## [1] "10.1186/1471-2105-15-s1-s14"
##
## [[7]]
## [1] "10.1186/s12859-015-0768-9"
##
## [[8]]
## [1] "10.1186/1471-2105-15-s1-s4"
##
## [[9]]
## [1] "10.1186/s12859-016-1253-9"
##
## [[10]]
## [1] "10.1186/1471-2105-15-314"
##
## [[11]]
## [1] "10.1186/s12859-016-1147-x"
##
## [[12]]
## [1] "10.1186/s12859-016-1386-x"
##
## [[13]]
## [1] "10.1186/1471-2105-15-s10-p13"
##
## [[14]]
## [1] "10.1186/1471-2105-16-s15-p7"
##
## [[15]]
## [1] "10.1186/s12859-014-0361-7"
##
## [[16]]
## [1] "10.1186/s12859-014-0429-4"
##
## [[17]]
## [1] "10.1186/1471-2105-15-s10-p8"
##
## [[18]]
## [1] "10.1186/1471-2105-15-48"
##
## [[19]]
## [1] "10.1186/s12859-016-1342-9"
##
## [[20]]
## [1] "10.1186/s12859-015-0746-2"
##
## [[21]]
## [1] "10.1186/s12859-016-1351-8"
R Session Info#
sessionInfo()
## R version 4.2.1 (2022-06-23 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] jsonlite_1.8.4 httr_1.4.5 lubridate_1.9.2 forcats_1.0.0
## [5] stringr_1.5.0 dplyr_1.1.0 purrr_1.0.1 readr_2.1.4
## [9] tidyr_1.3.0 tibble_3.1.8 ggplot2_3.4.1 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] bslib_0.4.2 compiler_4.2.1 pillar_1.8.1 jquerylib_0.1.4
## [5] tools_4.2.1 digest_0.6.31 timechange_0.2.0 evaluate_0.20
## [9] lifecycle_1.0.3 gtable_0.3.1 pkgconfig_2.0.3 rlang_1.0.6
## [13] cli_3.6.0 rstudioapi_0.14 curl_5.0.0 yaml_2.3.7
## [17] xfun_0.37 fastmap_1.1.0 withr_2.5.0 knitr_1.42
## [21] hms_1.1.2 generics_0.1.3 vctrs_0.5.2 sass_0.4.5
## [25] grid_4.2.1 tidyselect_1.2.0 glue_1.6.2 R6_2.5.1
## [29] fansi_1.0.4 rmarkdown_2.20 tzdb_0.3.0 magrittr_2.0.3
## [33] ellipsis_0.3.2 scales_1.2.1 htmltools_0.5.4 colorspace_2.1-0
## [37] utf8_1.2.3 stringi_1.7.12 munsell_0.5.0 cachem_1.0.7