grafify
has three main features:
grafify
or ggplot2
The vignettes website has detailed help on usage and latest release notes are here.
As an example, you could write this to plot bar/SD graph from a 2-way ANOVA data with randomised blocks:
plot_4d_scatterbar(data = data_2w_Festing,
xcol = Strain,
ycol = GST,
bars = Treatment,
shapes = Block)
instead of all this!
ggplot2::ggplot(data = data_2w_Festing,
aes(x = Strain,
y = GST,
group = interaction(Strain,
Treatment)))+
stat_summary(geom = "bar",
aes(fill = Treatment),
position = position_dodge(width = 0.8),
fun = "mean")+
geom_point(aes(shape = Block),
size = 3, stroke = 1,
position = position_jitterdodge(jitter.width = .2,
dodge.width = .8))+
stat_summary(geom = "errorbar",
width = .2, size = 1,
fun.data = "mean_sdl",
fun.args = list(mult = 1),
position = position_dodge(width = 0.8))+
scale_shape_manual(values = 21:22)+
theme_classic(base_size = 21)+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
scale_fill_manual(values = c(as.vector(graf_palettes$okabe_ito[1:2])))
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_scatterbar_sd
, plot_scatterbox
, plot_scatterviolin
or plot_dotbar_sd
, plot_dotbox
, plot_dotviolin
plot_3d_point_sd
, plot_3d_scatterbar
, plot_3d_scatterbox
, plot_3d_scatterviolin
or plot_4d_point_sd
, plot_4d_scatterbar
, plot_4d_scatterbox
, plot_4d_scatterviolin
plot_befafter_colours
, plot_befafter_shapes
, plot_befafter_box
plot_xy_NumGroup
, plot_xy_CatGroup
plot_qqline
, plot_density
plot_histogram
, and model diagnostics with plot_qqmodel
, plot_qq_gam
, plot_lm_predict
and plot_gam_predict
plot_point_sd
, plot_scatterbar_sd
, plot_3d_scatterbar
The following 12 categorical (qualitative/discreet) and 5 quantitative (3 sequential and 2 divergent) palettes are implemented in grafify
for making graphs with plot_
functions.
In addition, scale_fill_grafify
and scale_colour_grafify
functions can be used to apply all grafify
palettes to any ggplot2
object.
All palettes are colourblind-friendly. (See Mike Mol’s blog and Paul Tol’s blog. Additional colour schemes were chosen from cols4all
package).
grafify
theme & adding log-scales
The theme_grafify
function is a modification of theme_classic
and enables graphs plotted with ggplot2
to have a grafify
-like appearance.
plot_logscales
lets you take any ggplot2
object and transform Y, X or both axes into log2
or log10
values, and latter will also show log10 tick marks.
Get ANOVA tables and linear models with these easy wrappers.
simple_anova
, simple_model
, ga_model
, ga_anova
.mixed_anova
, mixed_model
, mixed_anova_slopes
, mixed_model_slopes
, ga_model
, ga_anova
.plot_qqmodel
and plot_qq_gam
.plot_lm_predict
and plot_gam_predict
.emmeans
)
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.
posthoc_Pariwise
, posthoc_Levelwise
& posthoc_vsRef
posthoc_Trends_Pairwise
, posthoc_Trends_Levelwise
& posthoc_Trends_vsRef
The best place to see grafify
in action is the vignettes website, which has detailed description of all functions.
Find out about latest updates here.
grafify
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 install.packages("grafify")
.
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 remotes::install_github("ashenoy-cmbi/grafify@*release")
.
grafify
requires the following packages to be installed: car
, emmeans
, ggplot2
, Hmisc
, lme4
, lmerTest
, magrittr
, mgcv
, patchwork
, purrr
, stats
, tidyr
.
grafify
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.