Systematic Maps are, according to the Environmental Evidence Journal, “overviews of the quantity and quality of evidence in relation to a broad (open) question of policy or management relevance.” In simple terms, this means that documents are categorized according to the type, location, and publication information available for each work within a particular topic. Systematic maps are often used for environmental research, where it is particularly important to track the location of study sites. The spatial nature of a systematic map, particularly for environmental research, means that academics often use some kind of geographic map to analyze and present their information. Understanding the academic community’s familiarity with the R programming language, we built a webapp using R Shiny that could automate certain parts of creating a systematic map for environmental research.
In evidence synthesis it is relatively easy to find published literature on a given topic, and to record what the search string was used in which database, how many records were found, how many were used or not used and why. It is much more difficult to be systematic in searching and recording grey literature.
This function dynamically generates an analysis report (in html, pdf, or docx format) based on a model object. The report includes information about the model that was fitted, the distribution of the observed outcomes, the estimate of the average outcome based on the fitted model, tests and statistics that are informative about potential (residual) heterogeneity in the outcomes, checks for outliers and/or influential studies, and tests for funnel plot asymmetry. A forest plot and a funnel plot are also provided. References for all methods/analysis steps are also added to the report and cited appropriately. Additional functionality for reports based on meta-regression models will be incorporated soon. The function is part of the metafor package.
robvis is an R package that allows users to quickly visualise risk-of-bias assessments performed as part of a systematic review. It allows users to created weighted bar-plots of the distribution of risk-of-bias judgements within each bias domain, in addition to “traffic light” plots of the specific domain-level judgements for each study. The resulting figures are formatted according the risk-of-bias assessment tool use to perform the assessments (currently supported tools are ROB-2, ROBINS-I and QUADAS-2). An associated Shiny app provides a user-friendly interface for the tool.
This project provides an easy-to-use template for web visualisations of environmental evidence maps. Thalloo is a combination of map components and a Jekyll theme that enable quick, simple, and customisable deployment of a web-based tool to display evidence maps. The framework has the following features: i) Visual clustering and display of categorical data. Given a display category (e.g. crop, commodity), and a custom colour palette, points are displayed on a map. Depending on the zoom level and extent, points are clustered dynamically for best display. Any cluster can be selected to see the full metadata about the evidence points it contains; ii) Filtering. Data can be filtered by property in real time, using multiple filters within a property, and using multiple properties to filter; iii) Slicing of dimensionality. Given continuous data (e.g. publication year, time, or an effect size), the map allows real time ‘slicing’ of the dataset along one or many dimensions; iv) Abstract and funding logos. Provide attribution to your funders and partner institutions by including their logos at the top of your map view.