The material provided herein can be used to (re)produce some of the statistical results 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.
 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
RandPython) inbinder. We provide the interactive environments with already installed packages to query and access Eurostat database for notebook resources below (current build with commit 8ad6aec):- Launch
Jupyteralone: - Launch
JupyterLab: - Launch
RStudio:
- Launch
- Run the notebooks in
Google colab(you will need a Google login): launch(try for instance this notebook).
Description
The resources are organised according to the thematic structure already adopted for the Statistics Explained articles:
- general/ for reproducing some of the Stastistics Explained articles on "General and regional statistics, EU policies",
- economy/ for "Economy and finance" articles,
- popul/ for "Population and social conditions" articles:
- icts/ for "Industry and services" articles:
- agric/ for "Agriculture, forestry and fisheries" articles,
- external/ for "International trade" articles:
- transp/ for "Transport",
- envir/ for "Environment and energy" articles,
- science/ for "Science, technology and digital society" articles:
About
| contributors |
|
| version | 0.1 |
| status | since 2019 |
| license | EUPL |
You want to contribute to the development of a Statistics Explained article? Please submit your pull requests to "master" branch!
- Eurostat online database.
- Statistics Explained main page.
Rpackages to access open data:restatapi,rsdmx(via SDMX),eurostat,rjstat(via JSON-stat).Pythonmodules to access open data:pandaSDMX(via SDMX),jsonstat.py,eurostatapiclient,pyrostat(via JSON-stat).- More on JSON-stat format and tools.
- Useful graphic tools galleries, in
RandPython. binderdocumentation and examples.repo2dockerconfiguration files.- BBC visual and data journalism cookbook for
Rgraphics. - World Bank atlas of Sustainable Development Goals 2018 with the source code.
- How Open Are Official Statistics?.
- 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.