Select the best way to display your data with the data visualisation catalogue

Have you ever wondered if your data would be best displayed as a bar chart, line graph, or scatterplot? If so, The Data Visualisation Catalogue might help you decide.

The catalogue helps you to choose the right kind of visual presentation for your data.

The tool, developed by Severino Ribecca, started as a project to create a library of different ways to display information.

Severino initially began the project as a way to develop his knowledge of data visualization and as a reference tool for his own work. Now, the Data Visualisation Catalogue allows users to explore the best ways to graphically present information.

Using the Data Visualisation Catalogue

The various visualization choices allow users to decide on the best chart type for their needs. Users can search the catalogue by function or by list.

The catalogue provides new ideas for how to visually relay information to an audience. Once you select a chart type, it provides:

  • The description, anatomy, and functions of the chart
  • A display of similar charts in the catalog
  • Tools to generate the visualization
  • Examples of the chart
  • A reference guide for using the chart
An example of how the venn diagram is cataloged on the site.

With all of this information, I can decide how I want to create the chart and what tools I might need if I decide to modify how the chart is displayed.


If you have ever been stuck when deciding what type of chart to create to best display your data, the Data Visualization Catalogue will be helpful. The site is beneficial for individuals who are often deciding how best to present information or data.

The tool is incredibly useful and I hope to see more visualization charts for displaying qualitative data in the future.

You may be interested in these posts…

You may support me with a generous cup of coffee.

Which is easier to learn: Python or R?

As many of you know, I am committed to learning and writing in public. That means writing about what I learn, showcasing the tools and resources I use, and showing my work or unfinished products.

My current goal is to learn Python or R. While I am familiar with both, I need to start from scratch to advance my skills. Ideally, I would like to start with the easier language (based on my current skill level and knowledge of other programming tools). But is Python easier to learn than R, or is R the easier language?

Some caveats

I am interested in data analyses, graphics/visualization, and general programming. While I figured that there are some things Python may do better than R or vis versa, I am interested in using the more robust tool.

I am also well versed in Stata and I’m satisfied with my knowledge of the app, but I find that I’m limited by not knowing one of the widely recognized programming languages. I am also aware that Stata has PyStata, a new Python and Stata integration that allows users to use Stata from a Python environment. This feature is an added plus for me to pursue learning Python.

Two important notes

  • One is not necessarily better than the other. I am only trying to decide which tool best fits my needs.

What is Python?

Python is a general programming language that serves multiple purposes. Python is used for web and software development, scripting, and data analysis. Users can access different packages developed by other Python users, including SciKit-learn, SciPy, and NumPy.

Some users suggest that Python is a more general tool than R.

What is R?

R is a language and environment for statistical computing. R has a library of open-source software that makes it a rich program, including tidyverse, dplyr, and tidyr. It is primarily used for statistical analysis. R would be sufficient if I were only interested in data analyses and modeling (e.g., linear and nonlinear regression).

So far, I’ve learned three important things about R:

  • R Syntax is not as readable as Python
  • It is not necessarily a programming language
  • It is mainly for statistics, though the program allows for other possibilities

Python or R?

Both Python and R are free, open-source programming languages. Both have supportive communities that contribute to different packages. Visualization in R is generally better than Python for some users of both applications. I’m familiar with ggplot2 because I’ve come across many excellent R visualizations that have used the package to produce high-quality graphics.

Overall, learning Python is more accessible and friendly for people just getting started with coding. The general advice is to learn Python if you are new to coding; learn R if you are focused mainly on statistics or data analyses.

I was told to decide whether to learn Python or R based on my goals. Since I am not only focused on data analyses, I’ll stick with learning Python and look to strengthen my R skills down the line. Python and R can also communicate with each other, so may look into setting this up once I become proficient with both.

You may be interested in these posts…

You may support me with a generous cup of coffee.