litsearchr

an R package to facilitate quasi-automatic search strategy development for systematic reviews



Grames, EM, Stillman, AN, Tingley, MW, Elphick, CS. An automated approach to identifying search terms for systematic reviews using keyword co‐occurrence networks. Methods Ecol Evol. 2019; 00: 1– 10. https://doi.org/10.1111/2041-210X.13268

About litsearchr


The litsearchr package for R facilitates systematic search strategy development by partially automating keyword selection and writing Boolean search strings. It uses the Rapid Automatic Keyword Extraction algorithm to identify potential keywords from a scoping search and selects important keywords based on their importance in a keyword co-occurrence network. After keywords are grouped into concept groups, litsearchr writes Boolean searches in up to 53 languages, with stemming support for English. The searches have been tested and work fully in 15 commonly used search databases; other databases may also work but have not been tested.

Our intent in creating litsearchr is to make the process of designing a search strategy for systematic reviews easier for researchers by identifying the terms commonly used in a field. By partially automating keyword selection, litsearchr reduces investigator bias in keyword selection and increases the repeatability of systematic reviews. It also reduces errors in creating database-specific searches by generating searches that work across a wide range of databases. Our hope is that litsearchr can be used to facilitate systematic reviews and contribute to automating evidence synthesis in ecology and conservation biology.

litsearchr is a work in progress - any and all comments, suggestions, bugs, or questions are welcome! Please email eliza.grames@uconn.edu or open an issue at https://github.com/elizagrames/litsearchr.



litsearchr is part of the metaverse, which is a set of R packages that span the entire scope of evidence synthesis in R, from generating search terms, assembling results, screening articles, visualizing risk of bias, doing a meta-analysis, and presenting results. Check out the metaverse here for other packages like robvis and revtools.

Getting Started


To install litsearchr use devtools::install_github("elizagrames/litsearchr") in R or RStudio. You may need to install the devtools package if it is not already installed. This will also install all the litsearchr dependencies.

Before you get started using litsearchr, we suggest you work through the vignette to see how the functions interact with each other.

Vignette


Identifying Search Terms for a Systematic Review: A Demonstration of the litsearchr Package


Search term selection with litsearchr v0.3.0 for an example systematic review of the effects of fire on black-backed woodpeckers
[[OLD: v0.1.0]] Introduction to litsearchr v0.1.0 with an example of writing a systematic review search strategy for Black-backed Woodpecker occupancy of post-fire forest systems

About the developers


Eliza Grames is a PhD candidate at the University of Connecticut focusing on conservation of forest songbirds and methods of improving and automating evidence synthesis. @ElizaGrames eliza.grames@uconn.edu Website
Andrew Stillman is a PhD candidate at the University of Connecticut focusing on Black-backed Woodpecker occupancy of post-fire forest systems. @ANStillman Website
Chris Elphick is a Professor in Ecology and Evolutionary Biology at the University of Connecticut. @ssts Website
Morgan Tingley is a Professor in Ecology and Evolutionary Biology at the University of Connecticut. @mwtingley Website