grafify (version 0.1)
  • grafify plots graphs and performs ANOVAs with post-hoc comparisons, similar to the R package.
  • Uses ggplot2 with colourblind-friendly palettes and supports linear and mixed-effects models.
  • Available online at Shinyapps.io . Also available as a Shinylive app at mirror 1 and mirror 2 , and as a Windows app.
  • Also hosted on Impaas with support from Imperial College London Department of Computing.
  • Resources: Statistics for biologists and R programming.
  • Privacy: Uploaded user data are processed only during the session and data, graphs or analyses are not retained. For sensitive data, you can install a local version from our GitHub .
  • Usage metrics: With your permission, anonymous analytics (Google Analytics) may be collected to understand usage and allocate computing resources.
  • Developed and maintained by Avinash R Shenoy. Last updated on: 18 May 2026, 17:36
Start here
Click 'Start' to try example data or first upload your data.


Data & variables
Pick X and Y variables, and an optional Grouping factor, then go Graphs tab

Add more variables to graph
Pick optional faceting or shape variables. Click 'Variables Chosen' to proceed.
Graph choice & X-axis options
Choose graph type and optional settings. Press 'grafify my data' when ready.
Available options:
Order of X-axis groups:
Order of levels in the Grouping variable:
After graphing data, go to ANOVAs tab


Linear models for ANOVA
Choose options, and click 'Analyse my data' to proceed. Diagnostic plots, ANOVA table, estimated marginal means and comparisons will appear below.



Residuals plots
Additional plots 1 (Press 'Analyse my data' if graphs do not appear). Graphs will be faceted by levels of Random factor.
Additional plots 2 (Press 'Analyse my data' if graphs do not appear). Random factor will be mapped to symbol shapes or size.













Linear Model
This is the summary of the fitted model. It shows model parameters, such as residual SE, coefficients etc. For simple or ordinar linear models, grafify uses the lm() function from base R. For mixed effects models, grafify uses the lmer() function from the lme4 and lmerTest packages.

                            

Data used for analyses
The table below includes the variables used in the analysis, which were chosen in Boxes 1-3. For Mixed effects analyses, means of replicate values of the response variable, if any, (chosen in Box 2) within levels of the random variable (chosen in Box 9), along with the Median, SD and Counts are shown.
Note: Quick help also available by hovering over icons.