This application was developed with Nature Methods and you can find the publication here. The BoxPlotR has also been mentioned in this editorial and this blog entry. Nature methods also dedicated a Points of View and a Points of Significance column to box plots. We hope that you find the BoxPlotR useful and we welcome suggestions for additional features by our users. We would like to thank everyone who has made constructive suggestions so far. We will document the addition of new features in the News tab.
This application allows users to generate customized box plots in a number of variants based on their data. A data matrix can be uploaded as a file or pasted into the application. Basic box plots are generated based on the data and can be modified to include additional information. Additional features become available when checking that option. Information about sample sizes can be represented by the width of each box where the widths are proportional to the square roots of the number of observations n. Notches can be added to the boxes. These are defined as +/-1.58*IQR/sqrt(n) which gives roughly 95% confidence that two medians are different. It is also possible to define the whiskers based on the ideas of Spear and Tukey. Additional options of data visualization (violin and bean plots) reveal more information about the underlying data distribution. Plots can be labeled, customized (colors, dimensions, orientation) and exported as eps, pdf and svg files.
BoxPlotR code can be run locally via GitHub. You can also download and install it as a virtual machine (see GitHub and FAQs for details).
Please consider supporting the development and maintenance of BoxPlotR with a donation.
R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2013)
RStudio and Inc. shiny: Web Application Framework for R. R package version 0.5.0 (2013)
Adler, D. vioplot: Violin plot. R package version 0.2 (2005)
Eklund, A. beeswarm: The bee swarm plot, an alternative to stripchart. R package version 0.1.5 (2012)
Kampstra, P. Beanplot: A Boxplot Alternative for Visual Comparison of Distributions. Journal of Statistical Software, Code Snippets 28(1). 1-9 (2008)
Neuwirth, E. RColorBrewer: ColorBrewer palettes. R package version 1.0-5. (2011)
Hadley Wickham and Lisa Stryjewski: 40 years of boxplots
Kristin Potter: Methods for Presenting Statistical Information: The Box Plot
What do the box plots show, explain colours if used.
Download box plot data as .CSV fileThe number of concurrent sessions has been increased to 50.
The shiny server backend has been updated. The number of concurrent sessions has been limited to 15 and the session idle timeout set to 10 minutes. We are currently reworking the code to support the latest R and shiny versions, plus preparing a system upgrade of the server.
There are several recent updates. The jitter of points is now consistent for all samples. When data points are added to the plot, the size and transparence of the points can now be modified with sliders. The link to one of the boxplot references has been updated.
The sample names are now displayed as they are in the input, ie., spaces and special characters are not replaced with underscores.
If you experience problems with this boxplot server, there is an alternative BoxPlotR mirror available at boxplot.bio.ed.ac.uk.
Upgrade to R version 3.1 and shiny-server 1.2
The user can now choose the color of the data points. There is also an additional option for data point display: data points can now be randomly jittered. A small bug in label display was fixed. Log scales can now be used.
A: For EPS files make sure to 'ungroup' all objects so they can be edited independently. In Adobe Illustrator you will also need to use the 'release compound path' command. For PDF files you should 'release clipping mask'. SVG import appears to have problems in Adobe Illustrator and Corel Draw and should be avoided. EPS, PDF and SVG import all work with Inkscape http://www.inkscape.org/.
A: We provide a pre-configured Docker image that automatically installs and configures the environment. First, ensure you have Docker installed. Then, clone the repository and run the following terminal commands inside the repository directory:
docker build -t boxplotr .
docker run -d -p 3838:3838 boxplotr
You can then access the application by navigating your web browser to http://localhost:3838.