grafify has three main features:
Two other features including practice datasets (with randomised blocks), and data simulation for power analyses. The first three features are better documented at present.
Easily plot data as scatter/dot plots with boxes, violins or bars with
plot_ functions of 6 broad types.
plot_histogram, and residuals of linear models with
The following 12 categorical (qualitative/discreet) and 5 quantitative (3 sequential and 2 divergent) palettes are implemented in
grafify for making graphs with
scale_colour_grafify functions can be used to apply all
grafify palettes to any
Get ANOVA tables and linear models with these easy wrappers.
Perform post-hoc comparisons based on fitted models for response variables and slopes. Get Estimated Marginal Means, P values, parameter estimates with CI95 with these wrappers.
The best place to see
grafify in action is the vignettes website, which has detailed description of all functions.
Shenoy, A. R. (2021) grafify: an R package for easy graphs, ANOVAs and post-hoc comparisons. Zenodo. http://doi.org/10.5281/zenodo.5136508
grafify is now on CRAN and can be installed by typing
Any updates not yet on CRAN will be made available here first. To install from GitHub you also need to install the
remotes package. Then type
grafify requires the following packages to be installed:
I made this package mainly for exploring data by quickly making graphs of different types. Secondly, to implement linear regressions for ANOVA. I also use it to introduce linear models in my teaching, including the analyses of randomised block designs to new users.
Also visit Statistics for Micro/Immuno Biologists for basic statistics theory and data analyses in R.
Go to this website for function documentations.