UPDATE: robvis now exists as a web-app, aimed at those who are not familiar with R or who want to explore the package’s functionality before installing it locally.
The robvis package takes the summary table from risk-of-bias assessments, converts it to tidy data, and produces summary plots formatted according to the assessment tool used.
First ensure you have the devtools package installed:
Then, to install:
To update the package, run the install_github("mcguinlu/robvis") command again.
To load your own data from a .csv file:
To help users explore robvis, we have included example datasets in the package, one for each of the tool templates that currently exist within the package. The data_rob2 dataset (view it here), which contains example risk-of-bias assessments performed using the RoB2.0 tool for randomized controlled trials, is used to create the plots in subsequent sections.
The package contains two plotting functions:
Returns a ggplot object displaying a weighted barchart of the risk of bias of included studies across the domains of the specified tool.
Returns a ggplot object displaying a “traffic light plot”, displaying the risk of bias judgment in each domain for each study.
Outputs a list of the risk of bias assessment tools for which a template currently exists in rob_summary(). We expect this list to be updated in the near future to include tools such as ROBIS (tool for assessing risk of bias in systematic reviews).
rob_tools()
[1] "ROB2"
[1] "ROBINS-I"
[1] "QUADAS-2"
[1] "ROB1"The colour argument of both plotting functions allows users to select from two predefined colour schemes (“cochrane” or “colourblind”) or to define their own palette by providing a vector of hex codes.
For example, to use the predefined “colourblind” palette:
And to define your own colour scheme:
summary_rob <- rob_summary(data = data_rob2, tool = "ROB2", colour = c("#f442c8","#bef441","#000000"))
summary_rob
By default, the rob_summary() function creates a barplot weighted by some measure of a study’s precision. This can be prevented using the “weighted” argument. For example, compare the following two plots:
Finally, because the output (summary_rob and trafficlight_rob in the examples above) is a ggplot2 object, it is easy to adjust the plot to your own preferences.
For example, to add a title to the unweighted RoB2.0 plot created above:
Please note that the ‘robvis’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
This project is licensed under the MIT License - see the LICENSE.md file for details.
rob_summary() function was based on code forwarded by a colleague. I recently discovered that this code was adapted from that presented in the wonderful “Doing Meta-Analysis in R” guide, so I would like to acknowledge the authors here.ggplot2 coding issues.robvis hex sticker.