Tools to Support Evidence Synthesis
Add line breaks to one or more strings
Combine (potentially overlapping) article sets generated by screening ...
Determine optimal way to divide articles among 2 or more reviewers
Bibliographic data from 20 papers on avian ecology
Description of class 'bibliography'
Methods for class 'bibliography'
Divide a set of articles among two or more reviewers
Create a de-duplicated data.frame
Locate duplicated information within a data.frame
Format a citation
Functions for fuzzy string matching
Construct a document-term matrix (DTM)
rbind two or more data frames with different columns
Import bibliographic data
revtools: Tools to support reviews and evidence synthesis
Load a set of stopwords
Calculate a topic model
Shiny app for screening articles by their abstracts
Shiny app for locating and excluding duplicated entries in a dataset
Shiny app for screening articles by their titles
Shiny app for screening bibliographies using topic models
Description of class 'screen_topics_progress'
Methods for class 'screen_topics_progress'
Lookup table for ris tags
Export imported bibliographic data as .bib or .ris formats
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.