Code and tutorials to visualise your data that is both beautiful *and* statistically valid
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README.md

Raincloud Plots

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Code and tutorials to visualise your data in a way that is both beautiful and statistically valid.

Table of contents

Read the preprint

You can read all about Raincloud Plots in our preprint at PeerJ.

Allen M, Poggiali D, Whitaker K, Marshall TR, Kievit R. (2018) Raincloud plots: a multi-platform tool for robust data visualization. PeerJ Preprints 6:e27137v1 https://doi.org/10.7287/peerj.preprints.27137v1

Give us feedback

We'd love to hear your thoughts and any feedback you have. The best place to leave comments on the preprint and the project in general is at the PeerJ preprint. Just scroll to the bottom of that page and leave us feedback or ask a question.

You can also submit a pull request to this repository if you find a bug! Just check out our contributing guidelines for help getting started.

Citing RainCloudPlots

We really hope you find these tutorials helpful and want to use the code in your next paper or presentation! This repository is made available under the MIT license which means you're welcome to use and remix the contents so long as you credit the creators: Micah Allen, Davide Poggiali, Kirstie Whitaker, Tom Rhys Marshall and Rogier Kievit.

There is a zenodo archive of the codebase and we ask that you cite the preprint along with the version of the code you used when referring to RainCloud plots.

Allen M, Poggiali D, Whitaker K, Marshall TR, Kievit R. (2018) Raincloud plots: a multi-platform tool for robust data visualization. PeerJ Preprints 6:e27137v1 https://doi.org/10.7287/peerj.preprints.27137v1

Allen M, Poggiali D, Whitaker K, Marshall TR, Kievit R. (2018) RainCloudPlots tutorials and codebase (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.1402959

Get in touch with Kirstie Whitaker if you need help citing a particular version of this codebase.

Interactive tutorials online

You don't have to take our word for it, you can use Project Jupyter's binder to interact with the python and R tutorials yourself.

Just follow either of the links below.

  • Binder will open the python tutorial as an online jupyter notebook.
  • Binder will open an online version of RStudio. You'll need to navigate to the R tutorial (/tutorial_R/raincloud_tutorial_r.Rmd) from within RStudio and open the file to run it.

Installing and running on your local machine

Download from GitHub

The easiest way to get the code you need is to download a copy of this repository to your computer. (You can also clone the repository if you're used to using git and GitHub.)

You'll notice that we've included three copies of the sampled data files, one for each language. This is to hopefully make it as easy for you as possible to run the tutorials and edit the code yourself.

Once you have the repository downloaded and unzipped, jump to the instructions for setting up the enviroment you're most comfortable with: python, R or matlab.

Python environment

The code for RainCloud plots is packaged in the ptitprince python package. It can be installed via pip using: pip install ptitprince.

There are a couple of other dependencies for the raincloud plots tutorial: they're listed in the requirements.txt file in this repository. Note in particular that you must have seaborn version 0.9.0 or above to run the tutorial.

The following instructions will help you install all the necessary dependencies and get up and running really easily .

First, if you don't already have it, we recommend that you install anaconda which will automatically install the jupyter notebook for you.

Then, from inside the RainCloudPlots folder type:

conda create --name rainpy python=3.6

activate rainpy

pip install -r requirements.txt

conda install nb_conda

This will create (and activate) a conda environment with all the the specific packages you need to run the tutorial.

A little note about jupyter notebooks and conda environments. If you have installed nb_conda as described above, you'll be able to choose the rainpy kernel in your jupyter notebook once it's open. Don't forget to switch from the default kernel when you open up the notebook.

From inside the tutorial_python direcotry, open up a jupyter notebook server by typing:

jupyter notebook

Double click on raincloud_tutorial_python.ipynb in your browser, make sure you're using the rainpy kernel, and execute the cells in the notebook.

R environment

We recommend that you run the R tutorial through RStudio. You can download the appropriate version of RStudio Desktop for your operating system here.

If you already have RStudio please check that you have knitr version 1.20 or higher.

The following packages will be read in when you run raincloud_tutorial_r.Rmd. They'll be installed if you don't already have them.

packages <- c("cowplot", "readr", "ggplot2" ,
              "dplyr", "lavaan")

From inside the tutorial_R directory, run raincloud_tutorial_r.Rmd.

Matlab environment

You'll need two dependencies to run the matlab tutorial: cbrewer which can be downloaded here and robust_statistical_toolbox which should be downloaded from GitHub in the same way you downloaded this repository.

NOTE: the robust statistical toolbox is made available under the GNU General Public License which is a "copyleft" license. GPL is compatible with MIT (the license for this repository) but supersedes it. Make sure that you are not violating this license when you run the matlab tutorial. 😃

Put both of these dependencies in the tutorial_matlab folder and then, from inside that directory, run raincloud_tutorial_matlab.mlx.