Resources for reproducing some of the visualisations in Eurostat Statistics Explained articles.
The material provided herein can be used to (re)produce some of the statistical outputs (tables and figures) presented in Eurostat Statistics Explained articles. it is used to recreate the figures published in the articles and are made available in the form of either code source files or computing notebooks. The latter will allow you to fetch the open data disseminated on Eurostat online database and interact with it dynamically. See related documentation and publications below.
|General and regional statistics / EU policies||Economy and finance||Population and social conditions|
|Industry and services||Agriculture, forestry and fisheries||International trade|
|Transport||Environment and energy||Science, technology and digital society|
- Run the notebooks (both
binder. We provide the interactive environments with already installed packages to query and access Eurostat database for notebook resources below (current build with commit 1179b50):
- Run the notebooks in
Google colab(you will need a Google login): launch (try for instance this notebook).
The resources are organised according to the thematic structure already adopted for the Statistics Explained articles:
Rnotebook on causes of death statistics,
Pythonnotebook on hours of work,
Rnotebook on population structure and ageing,
Rnotebook on young people and social inclusion,
Pythonnotebook on poverty and social exclusion,
Pythonnotebook on weekly death statistics,
Rsource code for income, consumption and wealth,
Want to contribute? For instance, implement a Statistics Explained article you find very interesting in your favourite language? Please submit your pull requests directly to the master branch!
Found a mistake in the code? Please, report it to us in the issues section.
- Eurostat online database.
- Statistics Explained main page.
Rpackages to access open data:
Pythonmodules to access open data:
- More on JSON-stat format and tools.
- Useful graphic tools galleries, in
binderdocumentation and examples.
- BBC visual and data journalism cookbook for
- World Bank atlas of Sustainable Development Goals 2018 with the source code.
- How Open Are Official Statistics?.
- Barrenada L., Bonamino L., Derayati R., Farias da Silva E., Girardi M., Gojsic D., Hadj Hassen S., Koehler K., Marinetti I., Querido B., Sheeka F., Davies J., Meszaros M., Lehtimäki H. and Grazzini J. (2021): Statistics Coded – Storytelling through literate programming and runnable computing, to appear in Proc. New Techniques and Technologies for Statistics (abstract).
- Luhmann S., Grazzini J., Ricciato F., Meszaros M., Giannakouris K., Museux J.-M. and Hahn M. (2019): Promoting reproducibility-by-design in statistical offices, in Proc. New Techniques and Technologies for Statistics, doi:10.5281/zenodo.3240198.
- Grazzini J., Gaffuri J. and Museux J.-M. (2019): Delivering Official Statistics as Do-It-Yourself services to foster produsers' engagement with Eurostat open data in Proc. New Techniques and Technologies for Statistics, doi:10.5281/zenodo.3240272.
- Project Jupyter et al. (2018): Binder 2.0 - Reproducible, interactive, sharable environments for science at scale, in Proc. Python in Science Conference, doi:10.25080/Majora-4af1f417-011.
- Grazzini J., Museux J.-M. and Hahn M. (2018): Empowering and interacting with statistical produsers: A practical example with Eurostat data as a service, in Proc. Conference of European Statistics Stakeholders, doi:10.5281/zenodo.3240557.
- Lahti L., Huovari J., Kainu M. and Biecek, P. (2017): Retrieval and analysis of Eurostat open data with the eurostat package, The R Journal, 9(1):385-392.