Tools to Support Evidence Synthesis

Researchers commonly need to summarize scientific information, a process known as 'evidence synthesis'. The first stage of a synthesis process (such as a systematic review or meta-analysis) is to download a list of references from academic search engines such as 'Web of Knowledge' or 'Scopus'. The traditional approach to systematic review is then to sort these data manually, first by locating and removing duplicated entries, and then screening to remove irrelevant content by viewing titles and abstracts (in that order). 'revtools' provides interfaces for each of these tasks. An alternative approach, however, is to draw on tools from machine learning to visualise patterns in the corpus. In this case, you can use 'revtools' to render ordinations of text drawn from article titles, keywords and abstracts, and interactively select or exclude individual references, words or topics.


revtools v0.3.0

Tools to support literature review and evidence synthesis in R, including import, de-duplication and interactive display of bibliographic data.


For a complete introduction to revtools you can check out the user manual; but to get started now you can download revtools either from this site (development version) or CRAN (stable version) as follows:

install.packages("revtools") # install from CRAN
devtools::install_github("mjwestgate/revtools") # install from GitHub
library(revtools) # load

Once you've installed & loaded revtools, you can use any of the inbuilt apps by loading them and drag-and-dropping the data you want to analyse. All the apps export to csv format so you don't need to use R to investigate their results if you'd prefer not to. The apps available in revtools are:

  • screen_duplicates() to investigate potential duplicates within a dataset
  • screen_titles() to screen articles by title
  • screen_abstracts() to screen articles by abstract
  • screen_topics() to run topic models on bibliographic data

If you're a keen to investigate your data in the R workspace, revtools is designed to make data import as straightforward as possible. It does this by using a single function to import bibliographic data from bib, ris, ciw or csv formats:

file_location <- system.file("extdata",
  "avian_ecology_bibliography.ris",
  package = "revtools")
data <- read_bibliography(file_location)

Then you can pass these data to your apps as you would with any other function:

screen_topics(data) # runs using your data

# you can save progress to the workspace by specifying an object:
result <- screen_topics(data)

# or save to a file within the app, and reload that saved file:
y <- readRDS("saved_object.rds")
screen_topics(y)

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Reference manual

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install.packages("revtools")

0.4.0 by Martin J. Westgate, 3 months ago


https://revtools.net


Report a bug at https://github.com/mjwestgate/revtools/issues


Browse source code at https://github.com/cran/revtools


Authors: Martin J. Westgate [aut, cre]


Documentation:   PDF Manual  


Task views: Meta-Analysis


GPL-3 license


Imports ade4, modeltools, NLP, plotly, shiny, shinydashboard, SnowballC, stringdist, tm, topicmodels, viridisLite


See at CRAN