statistics-coded

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

Quick launch

• Run the notebooks (both R and Python) in binder. We provide the interactive environments with already installed packages to query and access Eurostat database for notebook resources below (current build with commit ab6acdd):
  • Launch Jupyter alone:
  • Launch JupyterLab:
  • Launch RStudio:
• 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:

• economy/ for "Economy and finance" articles:

  • Python notebook on trade, investment and employment as aspects of globalisation,
  • Python notebook on comparative price levels of consumer goods and services,
  • Python notebook on government finance statistics,
  • R notebook on regional household income (R markdown version),
  • ...

• popul/ for "Population and social conditions" articles:

  • JavaScript notebook on health statistics at regional level,
  • R notebook on causes of death statistics,
  • Python notebook on hours of work,
  • R notebook on population structure and ageing,
  • R notebook on young people and social inclusion,
  • Python notebook on poverty and social exclusion,
  • Python notebook on weekly death statistics,
  • R source code for income, consumption and wealth,
  • JavaScript notebook on labour market statistics at regional level,
  • R notebook on women and men use of time,
  • R notebook on enforcement of immigration legislations (R markdown version)
  • ...

• icts/ for "Industry and services" articles:

  • R notebook on tourism statistics,
  • SQL source code for structure of multinational enterprise groups in the EU,
  • ...

• agric/ for "Agriculture, forestry and fisheries" articles,

• external/ for "International trade" articles:

  • R notebook on EU international trade in transport services,
  • ...

• transp/ for "Transport",

• envir/ for "Environment and energy" articles:

  • Python notebook on employment and growth statistics,
  • ...

• science/ for "Science, technology and digital society" articles:

  • Python notebook on social media use by enterprises,
  • R demo notebook for Digital economy and society statistics - households and individuals,
  • ...

About

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.

contributors
version	0.1
status	since 2019
license	EUPL

Resources

• Eurostat online database.
• Statistics Explained main page.
• R packages to access open data: restatapi, rsdmx (via SDMX), eurostat, rjstat (via JSON-stat).
• Python modules 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 R and Python.
• binder documentation and examples.
• repo2docker configuration files.
• BBC visual and data journalism cookbook for R graphics.
• World Bank atlas of Sustainable Development Goals 2018 with the source code.

References

• 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, doi:10.5281/zenodo.4580956.
• 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.