iCiteR

Build Status

The iCiteR package is a minimal R package designed to help users retrieve data from the NIH’s iCite API. This includes the relative citation ratio, which you can read about here.

Installation

iCiteR is not yet on CRAN, but will soon be (hopefully!)

In the meantime, you can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("riddlet/iCiteR")

Example

There is just one function that will be of interest to most users: get_metrics. This function takes as input a a vector of pubmed IDs and returns all the information yielded by the iCite API.

Likely, most users will already have the PMIDs that correspond to the articles for which they wish to obtain data. For a given article in pubmed, the ID is also printed on the page

The PMID for the Relative Citation Rate paper on Pubmed
The PMID for the Relative Citation Rate paper on Pubmed

For a given PMID(s), one can get the relative citation rate and all other data returned by the iCite API as follows

library(iCiteR)
get_metrics('27599104')
#>       pmid                          doi
#> 1 27599104 10.1371/journal.pbio.1002541
#>                                                           authors
#> 1 B Ian Hutchins, Xin Yuan, James M Anderson, George M Santangelo
#>   citation_count citations_per_year expected_citations_per_year
#> 1             47           15.66667                    2.930151
#>   field_citation_rate is_research_article    journal nih_percentile
#> 1            6.943039                TRUE PLoS Biol.           94.3
#>   relative_citation_ratio
#> 1                 5.34671
#>                                                                                                             title
#> 1 Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level.
#>   year
#> 1 2016

The function also takes a vector of PMIDS:

get_metrics(c('27599104', '27830815', '28968388', '28968381'))
#>       pmid                          doi
#> 1 27599104 10.1371/journal.pbio.1002541
#> 2 27830815              10.1038/539150a
#> 3 28968388 10.1371/journal.pbio.2002536
#> 4 28968381 10.1371/journal.pbio.2003552
#>                                                                                     authors
#> 1                           B Ian Hutchins, Xin Yuan, James M Anderson, George M Santangelo
#> 2                                                                               Gautam Naik
#> 3                    A Cecile J W Janssens, Michael Goodman, Kimberly R Powell, Marta Gwinn
#> 4 B Ian Hutchins, Travis A Hoppe, Rebecca A Meseroll, James M Anderson, George M Santangelo
#>   citation_count citations_per_year expected_citations_per_year
#> 1             47          15.666667                    2.930151
#> 2              5           1.666667                    3.807898
#> 3              4           2.000000                    3.126775
#> 4              1           0.500000                    2.298696
#>   field_citation_rate is_research_article    journal nih_percentile
#> 1            6.943039                TRUE PLoS Biol.           94.3
#> 2            9.204976               FALSE     Nature           23.4
#> 3            8.482253               FALSE PLoS Biol.           34.2
#> 4            6.235857               FALSE PLoS Biol.           10.9
#>   relative_citation_ratio
#> 1                5.346710
#> 2                0.437687
#> 3                0.639637
#> 4                0.217515
#>                                                                                                             title
#> 1 Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level.
#> 2                                                                     The quiet rise of the NIH's hot new metric.
#> 3                                A critical evaluation of the algorithm behind the Relative Citation Ratio (RCR).
#> 4                          Additional support for RCR: A validated article-level measure of scientific influence.
#>   year
#> 1 2016
#> 2 2016
#> 3 2017
#> 4 2017

If you would rather not have the results in a dataframe, it is possible to obtain an S3 object for the data by using the icite_api function

dat <- icite_api('27599104')

print(dat)
#> $content
#> $content$pmid
#> [1] 27599104
#> 
#> $content$doi
#> [1] "10.1371/journal.pbio.1002541"
#> 
#> $content$authors
#> [1] "B Ian Hutchins, Xin Yuan, James M Anderson, George M Santangelo"
#> 
#> $content$citation_count
#> [1] 47
#> 
#> $content$citations_per_year
#> [1] 15.66667
#> 
#> $content$expected_citations_per_year
#> [1] 2.930151
#> 
#> $content$field_citation_rate
#> [1] 6.943039
#> 
#> $content$is_research_article
#> [1] TRUE
#> 
#> $content$journal
#> [1] "PLoS Biol."
#> 
#> $content$nih_percentile
#> [1] 94.3
#> 
#> $content$relative_citation_ratio
#> [1] 5.34671
#> 
#> $content$title
#> [1] "Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level."
#> 
#> $content$year
#> [1] 2016
#> 
#> 
#> $path
#> [1] "api/pubs/27599104"
#> 
#> $response
#> Response [https://icite.od.nih.gov/api/pubs/27599104]
#>   Date: 2019-06-19 19:49
#>   Status: 200
#>   Content-Type: application/json; charset=UTF-8
#>   Size: 588 B
#> {
#>     "pmid": 27599104, 
#>     "doi": "10.1371/journal.pbio.1002541", 
#>     "authors": "B Ian Hutchins, Xin Yuan, James M Anderson, George M San...
#>     "citation_count": 47, 
#>     "citations_per_year": 15.666667, 
#>     "expected_citations_per_year": 2.930151, 
#>     "field_citation_rate": 6.943039, 
#>     "is_research_article": true, 
#>     "journal": "PLoS Biol.", 
#> ...
#> 
#> attr(,"class")
#> [1] "icite_api"