JournalAnalysis matches Europe PMC articles to journal-level metrics from Scimago or InCites (JCR). This tutorial walks through a typical workflow: define a search, retrieve and inspect results, summarize abstracts, rank journals, and export a shortlist.

Setup

Load JournalAnalysis and dplyr for the filtering steps later in the tutorial.

library(JournalAnalysis)
library(dplyr)

Define your search

Europe PMC queries use the same boolean syntax as the website search box. You can pass one query or combine several; multiple queries are unioned before filtering.

JournalAnalysis also ships example query strings you can adapt:

query3
#> [1] "microbiome AND (psychiatry OR psychology OR neuroscience) AND (rhesus OR macaque or human or stress or monkey) AND (NOT ecology)"

For this tutorial we use query3, which targets microbiome papers in psychiatry, psychology, and neuroscience-related contexts while excluding ecology papers.

See the Europe PMC search help for field names and operators.

Retrieve publication data

get_publication_data() is the main entry point. It:

Loads journal metrics from scimago or incities (InCites / JCR)
Queries Europe PMC for each search string
Filters articles by year and citation count
Joins articles to journals by ISSN

Start with a modest limit while you refine the query:

pub_data <- get_publication_data(
  journal_source = "scimago",
  queries = query3,
  limit = 200,
  min_year = 2015,
  min_citations = 3,
  n_cores = 1
)
#> 30872 records found, returning 200
#> Removed records published before 2015.
#> Removed records with less than 3 citations.
#> Removed records with NA values for pmid, doi, and authors.
#> 23 records passed the filter.

length(pub_data)
#> [1] 3
names(pub_data)
#> [1] "journals" "articles" "combined"
vapply(pub_data, nrow, integer(1))
#> journals articles combined 
#>       23       23       23

The returned list has three elements:

Element	Description
`pub_data$articles`	Article metadata from Europe PMC
`pub_data$journals`	Journal metrics for matched ISSNs
`pub_data$combined`	Articles joined to journal metadata

Inspect the results

After retrieval, inspect each list element to confirm the query and filters behaved as expected.

dplyr::glimpse(pub_data$articles)
#> Rows: 23
#> Columns: 30
#> $ id                    <chr> "40930373", "41463035", "41490486", "41077197", …
#> $ source                <chr> "MED", "MED", "MED", "MED", "MED", "MED", "MED",…
#> $ pmid                  <chr> "40930373", "41463035", "41490486", "41077197", …
#> $ pmcid                 <chr> "PMC12911324", "PMC12730554", "PMC12799112", "PM…
#> $ doi                   <chr> "10.1016/j.biopsych.2025.08.020", "10.3390/biome…
#> $ title                 <chr> "RNA in Plasma Extracellular Vesicles of Adolesc…
#> $ authorString          <chr> "Korobkova L, Thornton ME, Collin MA, Morin EL, …
#> $ journalTitle          <chr> "Biol Psychiatry", "Biomedicines", "Proc Natl Ac…
#> $ journalVolume         <chr> "100", "13", "123", "91", "70", "195", "21", "17…
#> $ pubYear               <int> 2026, 2025, 2026, 2025, 2025, 2025, 2025, 2025, …
#> $ journalIssn           <chr> "00063223; 18732402;", "22279059", "00278424; 10…
#> $ pageInfo              <chr> "64-75", "3025", "e2426232122", "106626", "227-2…
#> $ pubType               <chr> "research-article; journal article", "review-art…
#> $ isOpenAccess          <chr> "N", "Y", "Y", "Y", "Y", "N", "Y", "Y", "Y", "Y"…
#> $ inEPMC                <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y"…
#> $ inPMC                 <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y"…
#> $ citedByCount          <dbl> 3, 6, 3, 3, 3, 3, 16, 3, 5, 8, 9, 35, 5, 5, 3, 1…
#> $ hasReferences         <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y"…
#> $ hasTextMinedTerms     <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y"…
#> $ hasDbCrossReferences  <chr> "N", "N", "Y", "N", "N", "N", "N", "N", "N", "N"…
#> $ hasPDF                <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y"…
#> $ hasBook               <chr> "N", "N", "N", "N", "N", "N", "N", "N", "N", "N"…
#> $ hasSuppl              <chr> "Y", "N", "Y", "Y", "N", "Y", "Y", "Y", "N", "N"…
#> $ hasLabsLinks          <chr> "N", "N", "Y", "Y", "N", "Y", "Y", "Y", "N", "Y"…
#> $ hasTMAccessionNumbers <chr> "Y", "Y", "N", "Y", "Y", "Y", "Y", "Y", "Y", "Y"…
#> $ firstPublicationDate  <chr> "2025-09-08", "2025-12-10", "2026-01-05", "2025-…
#> $ firstIndexDate        <chr> "2025-09-12", "2026-01-02", "2026-01-06", "2025-…
#> $ issue                 <chr> "1", "12", "2", "5", "7", "4", "5", "1", "3", "1…
#> $ ISSN.1                <chr> "00063223", "22279059", "00278424", "01634453", …
#> $ ISSN.2                <chr> "18732402;", "", "10916490;", "15322742;", "1805…
dplyr::glimpse(pub_data$journals)
#> Rows: 23
#> Columns: 28
#> $ Rank                      <int> 115, 306, 349, 522, 626, 1023, 1213, 1215, 1…
#> $ Sourceid                  <dbl> 17978, 19700182758, 3600148102, 14308, 21121…
#> $ Title                     <chr> "Neuron", "Nature Communications", "Alzheime…
#> $ Type                      <chr> "journal", "journal", "journal", "journal", …
#> $ Issn                      <chr> "10974199, 08966273", "20411723", "15525279,…
#> $ Publisher                 <chr> "Cell Press", "Nature Research", "John Wiley…
#> $ Open.Access               <chr> "No", "Yes", "No", "No", "Yes", "Yes", "Yes"…
#> $ Open.Access.Diamond       <chr> "No", "No", "No", "No", "No", "No", "No", "N…
#> $ SJR                       <dbl> 8.564, 4.904, 4.530, 3.611, 3.283, 2.425, 2.…
#> $ SJR.Best.Quartile         <chr> "Q1", "Q1", "Q1", "Q1", "Q1", "Q1", "Q1", "Q…
#> $ H.index                   <int> 569, 634, 194, 380, 929, 150, 76, 152, 196, …
#> $ Total.Docs...2025.        <int> 348, 11528, 1244, 332, 4305, 532, 473, 289, …
#> $ Total.Docs...3years.      <int> 1058, 26313, 1938, 859, 11040, 1395, 699, 10…
#> $ Total.Refs.               <int> 27520, 755955, 72380, 19345, 236204, 38250, …
#> $ Total.Citations..3years.  <int> 12733, 452297, 17963, 5437, 95587, 10614, 77…
#> $ Citable.Docs...3years.    <int> 778, 25888, 1394, 530, 10095, 1384, 690, 394…
#> $ Citations...Doc...2years. <dbl> 11.25, 16.60, 11.93, 5.88, 8.23, 7.37, 11.57…
#> $ Ref....Doc.               <dbl> 79.08, 65.58, 58.18, 58.27, 54.87, 71.90, 76…
#> $ X.Female                  <dbl> 41.08, 37.59, 49.88, 45.28, 36.99, 45.59, 38…
#> $ Overton                   <int> 2, 91, 6, 1, 78, 2, 0, 7, 0, 0, 1, 1, 0, 0, …
#> $ Country                   <chr> "United States", "United Kingdom", "United S…
#> $ Region                    <chr> "Northern America", "Western Europe", "North…
#> $ Coverage                  <chr> "1988-2026", "2010-2026", "2005-2026", "1969…
#> $ Categories                <chr> "Neuroscience (miscellaneous) (Q1)", "Bioche…
#> $ Areas                     <chr> "Neuroscience", "Biochemistry, Genetics and …
#> $ ISSN                      <chr> "10974199; 08966273", "20411723", "15525279;…
#> $ ISSN.1                    <chr> "10974199", "20411723", "15525279", "0006322…
#> $ ISSN.2                    <chr> "08966273", "", "15525260", "18732402", "002…
dplyr::glimpse(pub_data$combined)
#> Rows: 23
#> Columns: 55
#> $ id                        <chr> "40930373", "41463035", "41490486", "4107719…
#> $ source                    <chr> "MED", "MED", "MED", "MED", "MED", "MED", "M…
#> $ pmid                      <chr> "40930373", "41463035", "41490486", "4107719…
#> $ pmcid                     <chr> "PMC12911324", "PMC12730554", "PMC12799112",…
#> $ doi                       <chr> "10.1016/j.biopsych.2025.08.020", "10.3390/b…
#> $ title                     <chr> "RNA in Plasma Extracellular Vesicles of Ado…
#> $ authorString              <chr> "Korobkova L, Thornton ME, Collin MA, Morin …
#> $ journalTitle              <chr> "Biol Psychiatry", "Biomedicines", "Proc Nat…
#> $ journalVolume             <chr> "100", "13", "123", "91", "70", "195", "21",…
#> $ pubYear                   <int> 2026, 2025, 2026, 2025, 2025, 2025, 2025, 20…
#> $ journalIssn               <chr> "00063223; 18732402;", "22279059", "00278424…
#> $ pageInfo                  <chr> "64-75", "3025", "e2426232122", "106626", "2…
#> $ pubType                   <chr> "research-article; journal article", "review…
#> $ isOpenAccess              <chr> "N", "Y", "Y", "Y", "Y", "N", "Y", "Y", "Y",…
#> $ inEPMC                    <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y",…
#> $ inPMC                     <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y",…
#> $ citedByCount              <dbl> 3, 6, 3, 3, 3, 3, 16, 3, 5, 8, 9, 35, 5, 5, …
#> $ hasReferences             <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y",…
#> $ hasTextMinedTerms         <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y",…
#> $ hasDbCrossReferences      <chr> "N", "N", "Y", "N", "N", "N", "N", "N", "N",…
#> $ hasPDF                    <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y",…
#> $ hasBook                   <chr> "N", "N", "N", "N", "N", "N", "N", "N", "N",…
#> $ hasSuppl                  <chr> "Y", "N", "Y", "Y", "N", "Y", "Y", "Y", "N",…
#> $ hasLabsLinks              <chr> "N", "N", "Y", "Y", "N", "Y", "Y", "Y", "N",…
#> $ hasTMAccessionNumbers     <chr> "Y", "Y", "N", "Y", "Y", "Y", "Y", "Y", "Y",…
#> $ firstPublicationDate      <chr> "2025-09-08", "2025-12-10", "2026-01-05", "2…
#> $ firstIndexDate            <chr> "2025-09-12", "2026-01-02", "2026-01-06", "2…
#> $ issue                     <chr> "1", "12", "2", "5", "7", "4", "5", "1", "3"…
#> $ ISSN.1                    <chr> "00063223", "22279059", "00278424", "0163445…
#> $ ISSN.2                    <chr> "18732402;", "", "10916490;", "15322742;", "…
#> $ Rank                      <int> 522, 3816, 626, 1215, 16730, 10559, 349, 306…
#> $ Sourceid                  <dbl> 14308, 21100840442, 21121, 22428, 19494, 234…
#> $ Title                     <chr> "Biological Psychiatry", "Biomedicines", "Pr…
#> $ Type                      <chr> "journal", "journal", "journal", "journal", …
#> $ Issn                      <chr> "00063223, 18732402", "22279059", "10916490,…
#> $ Publisher                 <chr> "Elsevier Inc.", "Multidisciplinary Digital …
#> $ Open.Access               <chr> "No", "Yes", "Yes", "Yes", "Yes", "No", "No"…
#> $ Open.Access.Diamond       <chr> "No", "No", "No", "No", "No", "No", "No", "N…
#> $ SJR                       <dbl> 3.611, 1.165, 3.283, 2.180, 0.313, 0.561, 4.…
#> $ SJR.Best.Quartile         <chr> "Q1", "Q1", "Q1", "Q1", "Q2", "Q1", "Q1", "Q…
#> $ H.index                   <int> 380, 108, 929, 152, 63, 98, 194, 634, 125, 1…
#> $ Total.Docs...2025.        <int> 332, 3096, 4305, 289, 48, 51, 1244, 11528, 1…
#> $ Total.Docs...3years.      <int> 859, 9523, 11040, 1072, 178, 178, 1938, 2631…
#> $ Total.Refs.               <int> 19345, 215639, 236204, 10607, 1744, 3394, 72…
#> $ Total.Citations..3years.  <int> 5437, 46921, 95587, 3945, 219, 405, 17963, 4…
#> $ Citable.Docs...3years.    <int> 530, 9348, 10095, 394, 177, 176, 1394, 25888…
#> $ Citations...Doc...2years. <dbl> 5.88, 4.74, 8.23, 4.41, 1.28, 2.43, 11.93, 1…
#> $ Ref....Doc.               <dbl> 58.27, 69.65, 54.87, 36.70, 36.33, 66.55, 58…
#> $ X.Female                  <dbl> 45.28, 48.07, 36.99, 46.76, 40.17, 48.56, 49…
#> $ Overton                   <int> 1, 5, 78, 7, 0, 0, 6, 91, 0, 0, 1, 2, 6, 0, …
#> $ Country                   <chr> "United States", "Switzerland", "United Stat…
#> $ Region                    <chr> "Northern America", "Western Europe", "North…
#> $ Coverage                  <chr> "1969-2026", "2013-2026", "1917, 1933, 1945-…
#> $ Categories                <chr> "Biological Psychiatry (Q1)", "Biochemistry,…
#> $ Areas                     <chr> "Neuroscience", "Biochemistry, Genetics and …

PubMed IDs for the matched articles:

pmids <- pub_data$articles$pmid
head(pmids)
#> [1] "40930373" "41463035" "41490486" "41077197" "40809946" "40603695"

Summarize abstracts

get_word_cloud() fetches abstracts for a vector of PubMed IDs and writes a PNG word cloud:

wordcloud_path <- "microbiome_psych_wordcloud.png"

get_word_cloud(
  pubmed_ids = pmids,
  plot_name = wordcloud_path
)
#> There are total 23 PMIDs
#> Warning in tm_map.SimpleCorpus(abstTxt, removePunctuation): transformation
#> drops documents
#> Warning in tm_map.SimpleCorpus(text2.corpus, function(x) removeNumbers(x)):
#> transformation drops documents
#> Warning in tm_map.SimpleCorpus(text2.corpus, tolower): transformation drops
#> documents
#> Warning in tm_map.SimpleCorpus(text2.corpus, removeWords,
#> stopwords("english")): transformation drops documents
#> png 
#>   2

knitr::include_graphics(wordcloud_path)

Rank journals

Filter the matched journals to subject areas of interest, then keep titles at or above the median SJR within that subset.

cats <- "Multidisciplinary|Neuroscience|Psychology|Psychiatry"

best_journals <- pub_data$journals |>
  filter(
    Type == "journal",
    SJR >= median(SJR, na.rm = TRUE),
    grepl(cats, Categories)
  ) |>
  select(Title, Rank, Type, SJR, Country, Categories)

best_journals
#> # A tibble: 8 × 6
#>   Title                                      Rank Type    SJR Country Categories
#>   <chr>                                     <int> <chr> <dbl> <chr>   <chr>     
#> 1 Neuron                                      115 jour…  8.56 United… Neuroscie…
#> 2 Nature Communications                       306 jour…  4.90 United… Biochemis…
#> 3 Alzheimer's and Dementia                    349 jour…  4.53 United… Cellular …
#> 4 Biological Psychiatry                       522 jour…  3.61 United… Biologica…
#> 5 Proceedings of the National Academy of S…   626 jour…  3.28 United… Multidisc…
#> 6 Translational Psychiatry                   1023 jour…  2.42 United… Biologica…
#> 7 Research                                   1213 jour…  2.18 United… Multidisc…
#> 8 Current Neuropharmacology                  2068 jour…  1.64 United… Medicine …

Adjust the category pattern and ranking rule to match your field. For InCites data (journal_source = "incities", or aliases incites / jcr), use columns such as Journal.Impact.Factor instead of SJR.

Export results

Save the ranked journal table for review outside R.

export_path <- file.path(tempdir(), "highest_impact_relevant_journals")
save_as_csv(best_journals, filename = export_path)
list.files(tempdir(), pattern = "highest_impact_relevant_journals", full.names = TRUE)
#> [1] "/tmp/RtmpjiK5KM/highest_impact_relevant_journals.csv"

Next steps

Tighten or broaden the Europe PMC query and re-run get_publication_data()
Compare scimago and incities (InCites / JCR) journal sources for your topic
See ?get_publication_data and the function reference for all parameters

Finding Journals for Your Paper