In vallenderlab/JournalAnalysis: Explore and Identify Journals to Publish in.

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Packages to Load

First, you need to load the JournalAnalysis package in order to load variables and functions.

library(JournalAnalysis)
library(dplyr)

Decide on a query and generate pubmed data

Since we are interested in the microbiome in the context of psychiatry, our query is as such.

query3 <- "microbiome AND (psychiatry OR psychology OR neuroscience) AND (rhesus OR macaque or human or stress or monkey) AND (NOT ecology)"
pub_data <- get_publication_data(journal_source = "scimago", queries = query3, limit = 30000, min_citations = 5)

The pub_data objects includes $articles, $journals, and $combined data.

View the resulting pubmed ids

# View the resulting pubmed ids
pmids <- pub_data$articles$pmid
pmids

Retrieve abstracts from pmids and create a wordcloud from the pubmed abstracts

get_word_cloud(pubmed_ids = pmids, plot_name = "microbiome_psych_wordcloud.png")

knitr::include_graphics('./microbiome_psych_wordcloud.png')

Accessing and analyzing journal data

View unique journals based on the articles retrieved with the below command.

pub_data$journals

Now, you can do some further analysis of journals based on SJR, which is.

# Decide on categorical words to keep
cats <- "Multidisciplinary|Neuroscience|Psychology|Psychiatry"

Select "best" journals based on median or any other statistical method. It may be valuable to do some clustering although the journals are ranked into quartiles. We chose the median SJR to look at any journals above the median and qualify them as the upper tier.

# Only keep journals above the median that are journals and include the categories above.
best_journals <- filter(pub_data$journals, SJR >= median(pub_data$journals$SJR) & grepl(cats, Categories) & Type == "journal")

After filtering the journal data, you can now select specific columns to view and save your tibble as a csv file.

# Decide on columns to keep
best_journals <- best_journals %>% select(Title, Rank, Type, SJR, Country, Categories)

best_journals

# Save as a csv file
save_as_csv(best_journals, filename = "highest_impact_relevant_journals")