shinyDigitise builds upon the popular metaDigitise package and provides users with an interactive UI to help extraction of data from five different plot types. The main functions used in shinyDigitise are called from the metaDigitise package, which allows users to extract descriptive statistics from various plot types.
A large share of the effect sizes in any meta-analysis are read off figures in the primary literature, and that step is usually undocumented, unarchived, and impossible to check. Importantly, both packages allow for replotting and checking of data extraction from graphs — which all contribute to increased reproducibility. The app walks you through calibration and point selection, keeps the calibration data alongside the extracted values, and can redraw your extraction over the original image so errors are visible rather than silent.
Now published in Research Synthesis Methods.
- Five plot types. Mean/error, boxplot, xy mean/error, histogram, and scatterplot.
- Guided, six-stage workflow. The app walks you through each stage in order.
- Live orientation and calibration. Flip or rotate a figure with the rotation angle shown on screen, then set the axes by double-clicking.
- Zoom and adjust on the fly. Click and drag a box over any area to zoom, and adjust point size and group label positions as you work.
- Replot and check. Extractions can be redrawn over the source figure, so mistakes are caught rather than archived.
- Deliberately manual. Semi-automated point detection makes users complacent and introduces errors that are hard to spot; extraction here is your judgement, recorded so others can check it.
- Output you can archive. Results are returned as an object and saved as a
.csvin the same folder as the images.
devtools::install_github("EIvimeyCook/ShinyDigitise")If you plan on using shinyDigitise to extract data from xy mean graphs, you need the dev version of metaDigitise.
devtools::install_github("joelpick/metaDigitise")There is a known bug with some versions of RStudio (rstudio/rstudio#12649). Be sure to update RStudio to the latest version.
Video tutorial here.
library(shinyDigitise)
df <- shinyDigitise("folder where your images are located")or — where you specify the folder through a menu system within SD:
df <- shinyDigitise()The app will walk you through each stage. If a directory is provided, you'll skip straight to the file selection phase. If not, you'll be asked to select a file from the image folder.
- Choose plot type — mean/error, boxplot, xy mean/error, histogram, or scatterplot.
- Orientate figure — flip or rotate the graph (rotation angle is shown on screen).
- Calibrate axes — click calibrate mode on, and, depending on the plot type shown, double click on the axes in the relevant order and add the variable name and values. These will then appear on the plot (the size will depend on the point size slider).
- Add groups — clicking add group will cause a pop up to appear to enter data (name and sample size). This will then appear in the table. Clicking the group will allow you to either double click points on the graph (you have to press the click points button first) or delete the group. Lastly, you can select the type of error shown (if a mean/error graph).
- Comments — add a comment to your data.
- Finished! The app will close when you've reached the end of your graphs.
To view the extracted data just simply call the object you've created or view
the resulting .csv file (which will be saved into the same folder as the
images).
Tip. You can adjust point size and group name positions on the fly, as well as zoom in on the image by clicking and dragging a box over the desired area.
Please file issues and feature requests at https://github.com/EIvimeyCook/shinyDigitise/issues. Pull requests are welcome.
- metaDigitise — the underlying extraction engine
- metRscreen — title and abstract screening
- DataExtraction — data and code for the accompanying paper
Ivimey-Cook, E. R., Noble, D. W. A., Nakagawa, S., Lajeunesse, M. J., & Pick, J. L. (2023). Advice for improving the reproducibility of data extraction in meta-analysis. Research Synthesis Methods, 14(6), 911–915. https://doi.org/10.1002/jrsm.1663
A machine-readable CITATION.cff is included, so GitHub's
"Cite this repository" button gives formatted APA and BibTeX.
Released under the MIT License.