PubMed is an online repository of references and abstracts of publications in the fields of medicine and life sciences. PubMed is a free resource that is developed and maintained by the National Center for Biotechnology Information (NCBI), at the U.S. National Library of Medicine (NLM), located at the National Institutes of Health (NIH). PubMed homepage is located at the following URL: Alternatively, PubMed can be programmatically queried via the NCBI Entrez E-utilities interface.

easyPubMed is an R interface to the Entrez Programming Utilities aimed at allowing an easy and smooth programmatic access to PubMed. The package is suitable for batch downloading large volumes of records (via the batch_pubmed_download() function), and comes with a set of functions to perform basic processing of the PubMed query output. easyPubMed can request and handle PubMed records in either XML or TXT format. This vignette covers the key functions of the package and provides informative examples.


To install easyPubMed from CRAN, you can run the following line of code


Before using the functions included in easyPubMed, make sure to load the library.


Dev Version

A dev version of this software may be found on GitHub. If you are interested in trying it out, you can install it using the devtoolslibrary as follows.


Getting started - Retrieving data from PubMed

The first section of this tutorial covers how to use easyPubMed for querying Entrez and how to retrieve or download PubMed records from the Entrez History Server.

A simple PubMed query via easyPubMed

Performing a standard PubMed search via easyPubMed is a two-step process:

  • the PubMed query step

  • the data retrieval step

PubMed is queried via the get_pubmed_ids() function, which takes a Query string as argument. The standard PubMed synthax applies, i.e. you can use the same tags-filters as in the web search engine. A PubMed query by the get_pubmed_ids() function results in:

  • the query results are posted on the Entrez History Server ready for retrieval

  • the function returns a list containing all information to access and download resuts from the server

Data can be retrieved from the History Server via the fetch_pubmed_data() function. The records can be requested in either XML or TXT format. Here following you can find a very simple example.

my_query <- 'Damiano Fantini[AU] AND "2018"[PDAT]'
my_entrez_id <- get_pubmed_ids(my_query)
my_abstracts_txt <- fetch_pubmed_data(my_entrez_id, format = "abstract")
## [1] ""                                                                                 
## [2] "1. Urol Oncol. 2018 Nov 13. pii: S1078-1439(18)30381-8. doi:"                     
## [3] "10.1016/j.urolonc.2018.09.017. [Epub ahead of print]"                             
## [4] ""                                                                                 
## [5] "Molecular footprints of muscle-invasive bladder cancer in smoking and nonsmoking "
## [6] "patients."

Here, the PubMed records were retrieved in the Abstract format. The formats supported by Entrez and easyPubMed are the following: “asn.1”, “xml”, “medline”, “uilist”, “abstract”. The following example shows how to retrieve PubMed records in XML format.

Note!!! Unlike before, the fetch_pubmed_data() function returns its output as a character-class object, AND NOT as an XMLInternalDocument and XMLAbstractDocument-class object. To access fields in such object, we recommend using the custom_grep() function included in easyPubMed. For example, it is possible to extract the title of each Article as follows.

my_abstracts_xml <- fetch_pubmed_data(pubmed_id_list = my_entrez_id)
## [1] "character"
my_titles <- custom_grep(my_abstracts_xml, "ArticleTitle", "char")

# use gsub to remove the tag, also trim long titles
TTM <- nchar(my_titles) > 75
my_titles[TTM] <- paste(substr(my_titles[TTM], 1, 70), "...", sep = "")

# Print as a data.frame (use kable)
## [1] "Molecular footprints of muscle-invasive bladder cancer in smoking and ..."
## [2] "Genomic classification and risk stratification of bladder cancer."        
## [3] "The BBN model: a mouse bladder cancer model featuring basal-subtype ge..."
## [4] "Resetting the epigenetic balance of Polycomb and COMPASS function at e..."
## [5] "APOBEC-mediated mutagenesis in urothelial carcinoma is associated with..."
## [6] "A Carcinogen-induced mouse model recapitulates the molecular alteratio..."

Downloading and saving records as XML or TXT files

Instead of retrieving PubMed records as character- or XML-class objects, it is also possible to download all records returned by a PubMed query, and save them as txt or xml files on the local machine. Downloaded records will be saved locally as one or more files with a common user-defined prefix followed by a sequential number and the txt or xml extension. If a destination folder is not specified, the current directory will be used as target directory for the download. The batch_pubmed_download() function is suitable for downloading very large volumes of PubMed records.

Note that we included an argument (namely, encoding) to force the encoding of the retrieved records. Here, we recommend “UTF8”. However, you can select different encodings (depending on the local platform). As an example, here we are specifying encoding="ASCII".

new_query <- 'Bladder[TIAB] AND Northwestern[AD] AND Chicago[AD] AND "2018"[PDAT]' 
out.A <- batch_pubmed_download(pubmed_query_string = new_query, 
                               format = "xml", 
                               batch_size = 20,
                               dest_file_prefix = "easyPM_example",
                               encoding = "ASCII")
# this variable stores the name of the output files
## [1] "easyPM_example01.txt" "easyPM_example02.txt" "easyPM_example03.txt"

Analyzing PubMed records

The second section of this tutorial covers those easyPubMed functionalities aimed at transforming and analyzing PubMed records. easyPubMed comes with a set of dedicated functions that perform these tasks and manipulate PubMed results. These functions will be covered in the following section.

Extracting Affiliation data from a single PubMed record

To convert a whole set ofPubMed records (raw input from fetch_pubmed_data(), or batch_pubmed_download()) into a list of individual records (actually, a character vector of Pubmed records), the articles_to_list() function is used. This function identifies and splits the input at each occurrence of a field. Individual records are returned as strings, and as elements of a character vector. Each element will include its XML tags.

my_PM_list <- articles_to_list(pubmed_data = my_abstracts_xml)
## [1] "character"
print(substr(my_PM_list[4], 1, 510))
## [1] "<PubmedArticle> <MedlineCitation Status=\"In-Data-Review\" Owner=\"NLM\"> <PMID Version=\"1\">29785026</PMID> <DateRevised> <Year>2019</Year> <Month>01</Month> <Day>07</Day> </DateRevised> <Article PubModel=\"Print-Electronic\"> <Journal> <ISSN IssnType=\"Electronic\">1546-170X</ISSN> <JournalIssue CitedMedium=\"Internet\"> <Volume>24</Volume> <Issue>6</Issue> <PubDate> <Year>2018</Year> <Month>Jun</Month> </PubDate> </JournalIssue> <Title>Nature medicine</Title> <ISOAbbreviation>Nat. Med.</ISOAbbreviation> </Journal"

Affiliations or other fields of interest can be extracted from a specific record using the custom_grep() function, that combines regular expressions (regexpr, gsub) and substring extraction (substr). The fields extracted from the record will be returned as elements of a list or a character vector.

curr_PM_record <- my_PM_list[1]
custom_grep(curr_PM_record, tag = "PubDate")
## [[1]]
## [1] " <Year>2018</Year> <Month>Nov</Month> <Day>13</Day> "
custom_grep(curr_PM_record, tag = "LastName", format = "char")
## [1] "Fantini" "Seiler"  "Meeks"

easyPubMed implements (out-of-the-box) a tool for extracting data from a PubMed record: the article_to_df() function. This function accepts a string as input (typically, an element of the vector returned by an articles_to_list() call) and returns a data.frame. Each row corresponds to a different author and/or paper; columns are features corresponding to the following fields: c("pmid", "doi", "title", "abstract", "year", "month", "day", "jabbrv", "journal", "lastname", "firstname", "address", "email"). One of these fields corresponds to the Article Abstract text (column n. 2). If the full text Abstract is not required, it is possible to limit the number of chars retrieved from this field by setting the max_chars argument.

# Select a single PubMed record from the internal dataset, NUBL_1618
curr_PM_record <- easyPubMed::EPMsamples$NUBL_1618$rec_lst[[37]]
my.df <- article_to_df(curr_PM_record, max_chars = 18)

# Fields extracted from the PubMed record
## [1] "pmid"     "doi"      "title"    "abstract" "year"     "month"
# Trim long strings and then display some content: each row corresponds to one author
my.df$title <- substr(my.df$title, 1, 15)
my.df$address <- substr(my.df$address, 1, 19)
my.df$jabbrv <- substr(my.df$jabbrv, 1, 10)

# Visualize
my.df[,c("pmid", "title", "jabbrv", "firstname", "address")] 
##       pmid           title     jabbrv firstname             address
## 1 29136502 Molecular Lands Cancer Cel  Joshua J Department of Urolo
## 2 29136502 Molecular Lands Cancer Cel    Seth P Scott Department of

When affiliation info are identical for multiple authors, they are usually omitted in the PubMed record. In these cases, addresses may be imputed for all authors in the data.frame by setting the autofill argument to TRUE.

my.df2 <- article_to_df(curr_PM_record, autofill = TRUE)

# Trim long strings and then display some content: each row corresponds to one author
my.df2$title <- substr(my.df2$title, 1, 15)
my.df2$jabbrv <- substr(my.df2$jabbrv, 1, 10)
my.df2$address <- substr(my.df2$address, 1, 19)

# Visualize
my.df2[,c("pmid", "title", "jabbrv", "firstname", "address")]
##       pmid           title     jabbrv firstname             address
## 1 29136502 Molecular Lands Cancer Cel  Joshua J Department of Urolo
## 2 29136502 Molecular Lands Cancer Cel    Seth P Scott Department of

We can recusively process PubMed records using a loop or sapply() or lapply(). Here’s an example. For more info, check ?lapply or ?

xx <- lapply(my_PM_list, article_to_df, autofill = TRUE, max_chars = 50)
full_df <-, xx)

full_df[seq(1, nrow(full_df), by = 10), c("pmid", "lastname", "jabbrv")] 
##        pmid      lastname       jabbrv
## 1  30446446       Fantini Urol. Oncol.
## 11 29785026       Fantini    Nat. Med.
## 21 29785026 Ntziachristos    Nat. Med.
## 31 29435122         Rimar   Oncotarget
## 41 29367767     Rademaker     Oncogene
## 51 29367767      Collings     Oncogene

Automatic Data Extraction from XML PubMed Records

To retrieve author information and publication data from multiple XML records at once, it is possible to use the table_articles_byAuth() function. This function relies on the funcions discussed above and returns a data.frame including all the fields extracted in the previous example. The function accepts five arguments.

  • pubmed_data: an XML file or an XML object with PubMed records

  • max_chars and autofill: same as discussed in the previous example

  • included_authors: one of the following options c(“first”, “last”, “all”). The function can return data corresponding to the first, the last or all the authors for each PubMed record.

  • dest_file: if not NULL, the function attempts writing its output to the selected file. Existing files will be overwritten.

Approach num. 1: download records first

new_query <- 'Bladder[TIAB] AND Northwestern[AD] AND Chicago[AD] AND "2018"[PDAT]' 
out.B <- batch_pubmed_download(pubmed_query_string = new_query, 
                               dest_file_prefix = "NUBL_18_", 
                               encoding = "ASCII")
## [1] "PubMed data batch 1 / 1 downloaded..."
# Retrieve the full name of the XML file downloaded in the previous step
new_PM_file <- out.B[[1]]
new_PM_df <- table_articles_byAuth(pubmed_data = new_PM_file, 
                                   included_authors = "first", 
                                   max_chars = 0, 
                                   encoding = "ASCII")

# Printing a sample of the resulting data frame
new_PM_df$address <- substr(new_PM_df$address, 1, 28)
new_PM_df$jabbrv <- substr(new_PM_df$jabbrv, 1, 9)
sid <- seq(5, nrow(new_PM_df), by = 10)

new_PM_df[sid, c("pmid", "year", "jabbrv", "lastname", "address")]
##        pmid year    jabbrv     lastname                      address
## 5  30475828 2018  PLoS ONE Delos Santos Loyola University Health Sys
## 15 29990467 2018  J. Urol.      Andreev Arbor Research Collaborative
## 25 29435122 2018 Oncotarge       Glaser Department of Urology, North
## 35 29215524 2018 Obstet Gy         Wong Department of Obstetrics and

Approach num. 2: fetch records on-the-fly!

new_query <- 'Bladder[TIAB] AND Northwestern[AD] AND Chicago[AD] AND "2018"[PDAT]' 
new_query <- get_pubmed_ids(new_query)
fetched_data <- fetch_pubmed_data(new_query, encoding = "ASCII")
new_PM_df <- table_articles_byAuth(pubmed_data = fetched_data, 
                                   included_authors = "first", 
                                   max_chars = 0, 
                                   encoding = "ASCII")

# Printing a sample of the resulting data frame
new_PM_df$address <- substr(new_PM_df$address, 1, 28)
new_PM_df$jabbrv <- substr(new_PM_df$jabbrv, 1, 9)
sid <- seq(5, nrow(new_PM_df), by = 10)

new_PM_df[sid, c("pmid", "year", "jabbrv", "lastname", "address")] 
##        pmid year    jabbrv            lastname
## 5  30475828 2018  PLoS ONE        Delos Santos
## 15 29990467 2018  J. Urol.             Andreev
## 25 29463971 2018 Curr Urol             Hannick
## 35 29243301 2018 J. Vet. I               Segev
## 45 28713003 2018 J Pediatr           Snow-Lisy
## 55 29951597 2018 JCO Preci               Panda
## 65 27590617 2017   Surgery                 Liu
## 75 27188581 2018   Sci Rep Braundmeier-Fleming
## 85 26534943 2016 J. Pharma               Searl
##                         address
## 5  Loyola University Health Sys
## 15 Arbor Research Collaborative
## 25 Department of Urology, Loyol
## 35 Koret School of Veterinary M
## 45 Ann and Robert H. Lurie Chil
## 55 Rutgers Cancer Institute of 
## 65 Division of Research and Opt
## 75 Carl R. Woese Institute for 
## 85 Department of Pharmacology,

More info, other examples and vignettes, and Advanced Guides


Feedback and Citation

Thank you very much for using easyPubMed and/or reading this vignette. Please, feel free to contact me (author/maintainer) for feedback, questions and suggestions: my email is <damiano.fantini(at)gmail(dot)com>. More info about easyPubMed are available at the following URL:

easyPubMed Copyright (C) 2017-2019 Damiano Fantini. This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

!!Note!! If you are using easyPubMed for a scientific publication, please name the package in the Materials and Methods section of the paper. Thanks! Also, I am always open to collaborations. If you have an idea you would like to discuss or develop based on what you read in this Vignette, feel free to contact me via email. Thank you.


## R version 3.5.2 (2018-12-20)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.1 LTS
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas/
## LAPACK: /usr/lib/x86_64-linux-gnu/
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## other attached packages:
## [1] easyPubMed_2.13
## loaded via a namespace (and not attached):
##  [1] compiler_3.5.2  magrittr_1.5    htmltools_0.3.6 tools_3.5.2    
##  [5] yaml_2.2.0      Rcpp_1.0.1      stringi_1.4.3   rmarkdown_1.12 
##  [9] knitr_1.22      stringr_1.4.0   digest_0.6.18   xfun_0.5       
## [13] evaluate_0.13