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https://github.com/ashenoy-cmbi/grafify
Easy grafs, ANOVAs and posthoc comparisons.
https://github.com/ashenoy-cmbi/grafify
ggplot2 linear-models post-hoc-comparisons r statistics vignettes
Last synced: 3 months ago
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Easy grafs, ANOVAs and posthoc comparisons.
- Host: GitHub
- URL: https://github.com/ashenoy-cmbi/grafify
- Owner: ashenoy-cmbi
- Created: 2020-12-28T23:17:03.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-02-25T14:39:21.000Z (11 months ago)
- Last Synced: 2024-10-01T16:36:40.599Z (3 months ago)
- Topics: ggplot2, linear-models, post-hoc-comparisons, r, statistics, vignettes
- Language: R
- Homepage: https://grafify-vignettes.netlify.app
- Size: 105 MB
- Stars: 46
- Watchers: 1
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.Rmd
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README
---
output: github_document
---
### Features
`grafify` has three main features:
1. plotting great-looking graphs for quick-n-easy data exploration with few lines of code
2. apply colour blind-friendly palettes to graphs plotted with `grafify` or `ggplot2`
3. use linear models for ANOVA (including those with randomised block designs and repeated-measures) & post-hoc comparisons (estimated marginal means, confidence intervals and *P* values)
The [vignettes website](https://grafify-vignettes.netlify.app) has detailed help on usage and latest [release notes are here](https://grafify-vignettes.netlify.app/release_notes.html).
As an example, you could write this to plot bar/SD graph from a 2-way ANOVA data with randomised blocks:
```{r, eval = TRUE, include=FALSE}
library(grafify)
```
```{r plot1, eval = F, fig.height=4}
plot_4d_scatterbar(data = data_2w_Festing,
xcol = Strain,
ycol = GST,
bars = Treatment,
shapes = Block)
```
```{r echo=FALSE, eval = F}
ggsave("fig1.jpg", height = 4.5)
```
```{r, fig1, echo = F, out.width="90%"}
knitr::include_graphics("man/figures/fig1.jpg")
```
instead of all this!
```{r, 2wanova, eval=FALSE}
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.
#### Graphs
1. Easily plot data as scatter/dot plots with boxes, violins or bars with `plot_` functions of 6 broad types.
a. Two variables (one categorical & one numeric): these graphs either use scatter (or also called jitter) or dot plot geometries: `plot_scatterbar_sd`, `plot_scatterbox`, `plot_scatterviolin` or `plot_dotbar_sd`, `plot_dotbox`, `plot_dotviolin`
b. One-way or two-way ANOVA designs with or without randomised blocks (3 or 4 dimensions or variables): `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`
c. Matched before-after graphs: `plot_befafter_colours`, `plot_befafter_shapes`, `plot_befafter_box`
c. Quantitative X & Y, plus a third variable: `plot_xy_NumGroup`, `plot_xy_CatGroup`
d. Data distributions: `plot_qqline`, `plot_density` `plot_histogram`, and model diagnostics with `plot_qqmodel`, `plot_qq_gam`, `plot_lm_predict` and `plot_gam_predict`
f. Graphs with SD, SEM or CI95 error bars: `plot_point_sd`, `plot_scatterbar_sd`, `plot_3d_scatterbar`
```{r, all-graphs, echo = F, out.width="90%"}
knitr::include_graphics("man/figures/all_graphsv3.2.0.png")
```
#### Colourblind-friendly colour schemes
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.
```{r, image-palettes, echo = F, out.width="90%"}
knitr::include_graphics("man/figures/grafify_palettesv320.png")
```
All palettes are colourblind-friendly. (See Mike Mol's [blog](https://mikemol.github.io/technique/colorblind/2018/02/11/color-safe-palette.html) and Paul Tol's [blog](https://personal.sron.nl/~pault/#sec:qualitative). Additional colour schemes were chosen from [`cols4all` package](https://github.com/mtennekes/cols4all)).
#### `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.
#### Linear models for ANOVA
Get ANOVA tables and linear models with these easy wrappers.
a. linear models for ordinary ANOVA: `simple_anova`, `simple_model`, `ga_model`, `ga_anova`.
b. linear mixed effects for repeated-measures and randomised-block design ANOVA: `mixed_anova`, `mixed_model`, `mixed_anova_slopes`, `mixed_model_slopes`, `ga_model`, `ga_anova`.
c. plot model residuals with `plot_qqmodel` and `plot_qq_gam`.
d. plot model predictions with `plot_lm_predict` and `plot_gam_predict`.
#### Post-hoc comparisons & Estimated Marginal Means (easy wrappers for `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.
a. `posthoc_Pariwise`, `posthoc_Levelwise` & `posthoc_vsRef`
b. `posthoc_Trends_Pairwise`, `posthoc_Trends_Levelwise` & `posthoc_Trends_vsRef`
#### Data simulation
Generating random one-way and two-way data based on mean and SD and residual error.
a. one-way designs: `make_1way_data`, `make_1way_rb_data`
b. two-way designs: `make_2way_data`, `make_2way_rb_data`
#### Data summaries
`table_summary` is simple function to obtain mean, median and SD by groups, and `table_x_reorder` is for working with factors.
### Vignettes
The best place to see `grafify` in action is the [vignettes](https://grafify-vignettes.netlify.app) website, which has detailed description of all functions.
### **Latest version: v4.0.1 on CRAN or GitHub**
Find out about [latest updates here](https://grafify-vignettes.netlify.app/release_notes.html).
### Citing `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
Latest DOI for all versions: [](https://doi.org/10.5281/zenodo.5136507)
### Installation
`grafify` is now on [CRAN](https://CRAN.R-project.org/package=grafify) 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`.
### Motivation behind `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.
### Statistics for Micro/immuno biologists
Also visit Statistics for [Micro/Immuno Biologists](https://microimmunostats.netlify.app) for basic statistics theory and data analyses in R.
### Function references
Go to this [website](https://ashenoy-cmbi.github.io/grafify/index.html) for function documentations.
### Hexsticker
```{r, image-1hex, echo = F, out.width="150px"}
knitr::include_graphics("man/figures/grafify.png")
```
### Status
[](https://github.com/ashenoy-cmbi/grafify/actions)