Category: tools

Visualizing and interpreting Cohen’s d effect sizes

Visualizing and interpreting Cohen’s d effect sizes

Cohen’s d (wiki) is a statistic used to indicate the standardised difference between two means. Resarchers often use it to compare the averages between groups, for instance to determine that there are higher outcomes values in a experimental group than in a control group.

Researchers often use general guidelines to determine the size of an effect. Looking at Cohen’s d, psychologists often consider effects to be small when Cohen’s d is between 0.2 or 0.3, medium effects (whatever that may mean) are assumed for values around 0.5, and values of Cohen’s d larger than 0.8 would depict large effects (e.g., University of Bath).

The two groups’ distributions belonging to small, medium, and large effects visualized

Kristoffer Magnusson hosts this Cohen’s d effect size comparison tool on his website the R Psychologist, but recently updated the visualization and its interactivity. And the tool looks better than ever:

Moreover, Kristoffer adds some nice explanatons of the numbers and their interpretation in real life situations:

If you find the tool useful, please consider buying Kristoffer a coffee or buying one of his beautiful posters, like the one above, or below:

Frequentisme betekenis testen poster horizontaal image 0

By the way, Kristoffer hosts many other interesting visualization tools (most made with JavaScript’s D3 library) on statistics and statistical phenomena on his website, have a look!

Automatically create perfect .gitignore file for your project

Automatically create perfect .gitignore file for your project

These days, I am often programming in multiple different languages for my projects. I will do some data generation and machine learning in Python. The data exploration and some quick visualizations I prefer to do in R. And if I’m feeling adventureous, I might add some Processing or JavaScript visualizations.

Obviously, I want to track and store the versions of my programs and the changes between them. I probably don’t have to tell you that git is the tool to do so.

Normally, you’d have a .gitignore file in your project folder, and all files that are not listed (or have patterns listed) in the .gitignore file are backed up online.

However, when you are working in multiple languages simulatenously, it can become a hassle to assure that only the relevant files for each language are committed to Github.

Each language will have their own “by-files”. R projects come with .Rdata, .Rproj, .Rhistory and so on, whereas Python projects generate pycaches and what not. These you don’t want to commit preferably.

Enter the stage,

Here you simply enter the operating systems, IDEs, or Programming languages you are working with, and it will generate the appropriate .gitignore contents for you.

Let’s try it out

For my current project, I am working with Python and R in Visual Studio Code. So I enter:

And Voila, I get the perfect .gitignore including all specifics for these programs and languages:

# Created by,python,visualstudiocode
# Edit at,python,visualstudiocode

### Python ###
# Byte-compiled / optimized / DLL files

# C extensions

# Distribution / packaging

# PyInstaller
#  Usually these files are written by a python script from a template
#  before PyInstaller builds the exe, so as to inject date/other infos into it.

# Installer logs

# Unit test / coverage reports

# Translations

# Scrapy stuff:

# Sphinx documentation

# PyBuilder

# pyenv

# pipenv
#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
#   However, in case of collaboration, if having platform-specific dependencies or dependencies
#   having no cross-platform support, pipenv may install dependencies that don't work, or not
#   install all needed dependencies.

# celery beat schedule file

# SageMath parsed files

# Spyder project settings

# Rope project settings

# Mr Developer

# mkdocs documentation

# mypy

# Pyre type checker

### R ###
# History files

# Session Data files

# User-specific files

# Example code in package build process

# Output files from R CMD build

# Output files from R CMD check

# RStudio files

# produced vignettes

# OAuth2 token, see

# knitr and R markdown default cache directories

# Temporary files created by R markdown

### R.Bookdown Stack ###
# R package: bookdown caching files

### VisualStudioCode ###

### VisualStudioCode Patch ###
# Ignore all local history of files

# End of,python,visualstudiocode

Try it out yourself:

Curated Regular Expression Resources

Curated Regular Expression Resources

Regular expression (also abbreviated to regex) really is a powertool any programmer should know. It was and is one of the things I most liked learning, as it provides you with immediate, godlike powers that can speed up your (data science) workflow tenfold.

I’ve covered many regex related topics on this blog already, but thought I’d combine them and others in a nice curated overview — for myself, and for you of course, to use.

If you have any materials you liked, but are missing, please let me know!


Introduction & Learning


Tutorials (interactive)


Corey Shafer

The Coding Train



Corey Shafer


Roger Peng

Testing & Debugging |


Google’s Dataset Search: Direct access to 25 million interesting datasets

Google’s Dataset Search: Direct access to 25 million interesting datasets

I used to keep a repository of links to interesting datasets to learn data science. However, that page I can retire, as Google has launched its new service Dataset Search.

The “world wide web” hosts millions of datasets, on nearly any topic you can think of. Google’s Dataset Search┬áhas indexed almost 25 million of these datasets, giving you a single entry point to search for datasets online. After a year of testing, Dataset Search is now officially out of beta.

After alpha testing, Dataset Search now includes filter based on the types of dataset that you want (e.g., tables, images, text), on whether the dataset is open source/access. For dataset on geographic area’s, you can see the map. The quality of dataset’s descriptions has improved greatly, and the tool now has a mobile version.

Anyone who publishes data can make their datasets discoverable in Dataset Search by describe the properties of their dataset using a special schema on their own web page.

Visualize graph, diagrams, and proces flows with

Visualize graph, diagrams, and proces flows with is a free online tool to create publication-ready diagrams in an interactive fashion. It uses

It uses graphviz-d3-renderer Bower module and adds editor and live preview of code. Try it on Graphviz fiddling website.

Here are some examples:

A diagram of state transitions
A very complex… graph?
Some clusters with subgraphs

The github page hosts more details and you can even follow the development on twitter.

Record2, apparently