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Welcome to uRos2019

The 7th Conference on the Use of R in Official Statistics (uRos2019) will take place from 20 to 21 May 2019 at the National Institute of Statistics in the Bucharest, Romania. As satellite event, unconfUROS will be organised on 22th of May 2019.

Over the last two decades R has become the lingua franca for statisticians, methodologists and data scientists worldwide. The reasons why the official statistics community is rapidly embracing R are clear: it has an active worldwide community of users, there is wide support from the industry and it combines a vast amount of functionality for data preparation, methodology, visualisation and application building. Moreover, R-based software is exchanged through strictly enforced technical standards:

“R is probably the most thoroughly validated statistics software on Earth.” – Uwe Ligges, CRAN maintainer (useR!2017).

Finally, R is a truly Free (libre) Open Source software product, making it an ideal environment for sharing, collaboration and industrialising official statistics.

The “use of R in Official Statistics” started as a local conference at the Statistical Office of Romania in 2013, with the first international edition being held at that same office in 2014. In 2018 year, Statistics Netherlands hosted the sixth edition of this event, which bringed together official statisticians, scientists, and prominent members of the R community to share ideas on using and developing R tools in the area of official statistics.


Call for abstracts and tutorials (Submission closed)

We are cordially inviting you to submit papers to and attend the International Conference The Use of R in Official Statistics – uRos2019.

The purpose of the conference is to provide a public forum for researchers from academia and institutes of statistics to present, exchange ideas and discuss developments in state-of-the-art statistical software commonly used in applied economics and statistics. The most focused debates are expected to be the use of R in Official Statistics.
The conference consists of a one-day tutorial session and a one-day scientific conference.
The topics covered by the conference are the following:

  • Sampling and estimation
  • R in organization
  • Data cleaning
  • R in production: data analysis
  • Methods for official statistics
  • Shiny applications
  • Time series
  • Report and GUI programming
  • R in production: automation
  • Big data
  • Dissemination and visualization

The participants are kindly asked to submit the abstract via e-mail to: anamaria.dobre [at] insse.ro
Tutorials will be 1.5 hours in length. Tutorials will be presented during the first day (Monday), during parallel sessions in the morning and in the afternoon.
Authors with an accepted abstract can submit a paper for one of the journal special issues as described below.

Papers for Special Journal Issues

A maximum of ten selected research papers will be published in a special issue of the Romanian Statistical Review and additionally a maximum of five papers will be published in a special issue of Austrian Journal of Statistics. Both publications are subjected to passing the standard peer review process.

Papers should be submitted no later than 15th of June 2019 to the uRos2019 secretariat via email to: anamaria.dobre [at] insse.ro

Please make sure to follow the RSR author guidelines.

About the journal

The Romanian Statistical Review is under the coverage of the Web Of Science Core Collection - Emerging Sources Citation Index (ESCI), a Clarivate Analytics database. It aims to provide a favourable space for exchange of ideas and a challenge at the same time. Any paper that can contribute to the understanding of statistics as a science is welcome. It is open-access without any fees.


Important dates

  • 12 April 2019: Deadline for abstracts submission
  • 25 April 2019: Notification of acceptance
  • 20-21 May 2019: Scientific conference & Tutorial sessions
  • 22 May 2019: unconfUROS
  • 15 June 2019: Deadline for paper submission

Keynote Speakers

Julie Josse

Julie Josse Member of RFoundation and Rforwards, Professor of Statistics at Ecole Polytechnique, France.

Her first employment was in the statistics department of an Agronomy University (Agrocampus Ouest) where she was trained to « the French data analysis school » and had the opportunity to work closely with researchers from other departments and increases her interest in transversal studies. In the meantime, she prepared her PhD which was rewarded by the French Statistical Society as the best PhD in applied statistics. She has specialized in missing data, visualization and the nonparametric analyses of complex data structures. Her work was rewarded by a European Union grant in 2013 to increase her research potential and to spend a year at Stanford University. She spent a year as a researcher in INRIA before joining Polytechnique in 2016. At Polytechnique, she is actually responsible of a master data-sciences for business in collaboration with HEC. She has published over 30 articles and written 2 books in applied statistics. Her experience on dealing with incomplete data is recognized by the community: she organized the MissData conference on missing value in 2015 and she is often invited to give lectures to share her experience. Her vocation is to push methodological innovation to bring useful application of her research to the user in particular in bio-sciences and health. Julie Josse is dedicated to reproducible research with the R statistical software: she has developed packages including FactoMineR, denoiseR, missMDA to transfer her work, she is a member of the R foundation and of Rforwards to increase the participation of minorities in the community.

Abstract: A missing value tour in R

In many application settings, the data have missing features which make data analysis challenging. An abundant literature addresses missing data as well as more than 150 R packages. Funded by the R consortium, we have created the R-miss-tastic platform along with a dedicated task view which aims at giving an overview of main references, contributors, tutorials to offer users keys to analyse their data. This platform highlights that this is an active field of work and that as usual different problems requires designing dedicated methods.
In this presentation, I will share my experience on the topic. I will start by the inferential framework, where the aim is to estimate at best the parameters and their variance in the presence of missing data. Last multiple imputation methods have focused on taking into account the heterogeneity of the data (multi-sources with variables of different natures, etc.). Then I will present recent results in a supervised-learning setting. A striking one is that the widely-used method of imputing with the mean prior to learning can be consistent. That such a simple approach can be relevant may have important consequences in practice.

Giulio Barcaroli

Giulio Barcaroli From 1977 to 2018 he has been working at the Italian National Institute of Statistics (Istat).

Until 1992 he was in the ICT Department. In 1992 he switched to the methodological sector, where he has been responsible for research and development of methods and techniques in the statistical production processes (in particular: computer assisted data collection, automated and assisted coding, editing and imputation of errors and missing data, data integration, sample design and estimation). In addition, he has been responsible for development activities of generalized software tools.
With respect to sample design he is one of the authors of the methodology for stratified sampling design implemented in the R package “SamplingStrata” (of which he is also the maintainer).
During this period he has held a series of positions: head of divisions (the last one: “Methods, Quality and Metadata”), member of Istat Methodologies Committee and of EUROSTAT DIME (Directors of Methodology in the European Statistical System Group).
He actively participated in the “modernization project” of Istat (he is one of the authors of the Istat’s Business Architecture) and also in the ESS standardization activities (three different ESSnet projects).
He has also been actively involved in the field of the use of Big Data sources for statistical purposes. In particular, he has been member of the Istat’s Technical Committee for the Statistical Use of Big Data, and he participated in the European Project “Essnet on Big Data”.
He is author of numerous publications, available at the following links:
https://www.researchgate.net/profile/Giulio_Barcaroli
https://scholar.google.it/citations?user=8G3UuoQAAAAJ&hl=en

Abstract: R at the Italian National Institute of Statistics (Istat): a twelve years story

It was in 2006-07 that a small group of people inside Istat began to consider the adoption of R as a valid alternative to the use of proprietary software like SAS. There were many reasons for that: R was free and open, and was on the research edge, while SAS was expensive and without the same research background. But the odds were not negligible: SAS was deep inside the production processes, hundreds of people had been trained and used SAS in a systematic way, the assistance levels seemed not to be comparable. Notwithstanding this, also because of the general movement towards open source software, Istat decided to at least diminish its dependence on proprietary solutions (in particular SAS-based). Two initiatives were launched: migration of generalized software systems using open technologies (and R was on the top of these), and mass training on R. Some adhered enthusiastically, some other opposed strong resistance. Nowadays R and SAS coexist, with some important advancements: a relevant number of researchers make use of R in daily activities, the great majority of generalized solutions are R-based, and an important project aiming at building a generalized architecture for statistical production will adopt services many of which are functions of R libraries. The overall balance can be said to be positive, and to report this experience, including concrete examples of how it evolved over time, can be useful to other OS Institutes that undergo or could undergo a similar transition.


Tutorials

Mark van der Loo

Edwin de Jonge

Mark van der Loo and Edwin de Jonge, Statistics Netherlands
Statistical data cleaning for official statistics with R

Abstract: This tutorial will demonstrate how data quality can be systematically defined and improved using R. It will focus on data validation (data checking), locating errors, and imputing missing or erroneous values under restrictions. Examples will be drawn from the Structural Business Survey (SBS) where common restrictions include nonnegativity rules and record-wise balance checks. I will present short introduction to the main principles, provide quizzes and discussions for the audience, and give short R-based exercises. A number of R packages related to data validation will be demonstrated, including 'validate' (for data quality checks), 'errorlocate' (for error localization), 'simputation' (for imputation methods), 'rspa' (for value adjustment), and 'lumberjack' (for keeping track of changes in data). Special attention will be paid on how to combine the various data processing steps, and how to analyse and visualize the results. At the end of the course, participants will have insight into some of the methods common in data editing for business surveys as well as an overview of how to implement that with free and open source R and the mentioned packages.
https://github.com/data-cleaning/uRos2019_tutorial

References: MPJ van der Loo, E. de Jonge (2018) Statistical Data Cleaning with applications in R. John Wiley & Sons.

Alexander Kowarik

Bernhard Meindl

Alexander Kowarik and Bernhard Meindl, Statistics Austria
Modern package development in R

Abstract: In this tutorial, the aim is to learn about several tools that help you to improve building an maintaining R packages. Participants will learn about the following topics:
- setup continuous integration using travis
- perform automatic code and style checking
- improve unit tests in your package
- create better documentation
- automatically create nice looking documentation websites.
This tutorial will be "hands-on" so it is advised to bring your noteooks with you.
https://github.com/bernhard-da/uros19_tutorial

Marco Ballin

Giulio Barcaroli

Marco Ballin and Giulio Barcaroli, Italian National Institute of Statistics (Istat)
Use of R package SamplingStrata for the Optimal Stratification of Sampling Frames for Multipurpose Sampling Surveys

Abstract: The aim of this tutorial is to enable the participants to learn how to use the R package “SamplingStrata” in order to optimize the design of stratified samples. The package offers an approach for the determination of the best stratification of a sampling frame, the one that ensures the minimum sample cost under the condition to satisfy precision constraints in a multivariate and multi-domain case. This approach is based on the use of the genetic algorithm: each solution (i.e. a particular partition in strata of the sampling frame) is considered as an individual in a population; the fitness of all individuals is evaluated applying the Bethel algorithm to calculate the sampling size satisfying precision constraints on the target estimates. Functions in the package allows to: (a) prepare necessary inputs and check their validity; (b) perform the optimization step choosing the values of the most important parameters; (c) assign the optimized strata labels to the sampling frame; (d) select a sample from the new frame accordingly to the best allocation; (e) test the compliance of the design to precision constraints.
https://github.com/barcaroli/SamplingStrata-Tutorial-uRos2019

Marcello D’Orazio

Marcello D’Orazio, Italian National Institute of Statistics (Istat),
Integration of data sources in R through statistical matching

Abstract: Statistical matching (aka data fusion) consists in a set of techniques developed to integrate distinct data sources referred to the same target population (typically stemming out from sample surveys) to get insights on the relationship between variables not jointly observed in a single data source. The tutorial will give an idea of the standard setting of statistical matching and the corresponding underlying assumptions; it will show how to perform matching in R by means of facilities provided by the R package StatMatch, including analyses that should precede and follow the matching step.

Mervyn Ó'Lúing

Mervyn Ó'Lúing, Central Statistics Office, Ireland,
Mapping a Table of Data with Esri Shapefiles in R

Abstract: This is an introductory tutorial to mapping opensource data with two shapefiles, plot multiple time series maps and produce web based maps using the "ggplot2" and "tmap" R packages in R. Users will learn how to:
- create a CSV file with GDP data extracted from an official Tanzanian publication,
- create an R data-frame from this data,
- add a geospatial reference to this data-frame,
- download two Esri shapefiles, regions and water bodies, from the Tanzanian National Bureau of Statistics’ (TNBS) website,
- create geospatial data-frames from the Esri shapefiles,
- join the region geospatial data-frame to the GDP data-frame,
- plot the data using the “ggplot2” package, and overlay the region and water bodies geospatial data frames,
- plot multiple time series maps and produce interactive zoomable web based maps using the “tmap” package.


Programme

Download the book of abstracts: uRos2019.pdf
Download the final agenda: Final_agenda_uRos2019_v3.pdf

May 20, Monday
08:30 - 09:30 Registration of participants
09:30 - 10:00 Welcome message
10:00 - 11:00 Keynote speaker 1
11:00 - 11:30 Coffee break
11:30 - 13:00 Parallel tutorial sessions I
13:00 - 14:00 Lunch break and Group photo
14:00 - 15:30 Parallel tutorial sessions II
15:30 - 16:00 Coffee break
16:00 - 17:30 Parallel tutorial and Scientific Sessions
17:30 - 17:40 Go to Conference Room for a Project Presentation
17:40 - 18:00 Project Presentation
19:00 Official dinner - Hanul lui Manuc, Bucharest

May 21, Tuesday
08:30 - 09:00 Walk in
09:00 - 10:00 Keynote speaker 2
10:00 - 10:30 Coffee break
10:30 - 12:30 Scientific session I (parallel sessions)
12:30 - 13:30 Lunch break
13:30 - 15:00 Scientific session II (parallel sessions)
15:00 - 15:30 Coffee break
15:30 - 17:10 Scientific session III (parallel sessions)
17:10 - 17:20 Go to Conference Room for Closing remarks
17:20 - 17:45 Closing remarks

May 22, Wednesday
unconfUROS Hackathon
08:30 - 09:00 Welcoming participants
09:00 Opening, who is who?; Work starts.
12:00 - 13:00 Lunch break
19:30 End of day/Discussions

uRosConf2019 Logo


unconfUROS hackathon

The uRos2019 conference will be followed by a short one-day unconference. This event is a one-day hackathon where we develop applications for official statistics. More information can be found at the unconfUROS github page.

What are we building?

Some ideas have been developed, but please submit your own through the github repository:

https://github.com/uRosConf/unconfUROS2019

Ideas are welcome, also if you do not want to participate actively.

Why participate?

  • Work with and learn from a group of excellent R programmers
  • Free lunch :-)
  • A chance to present the work done on the website of the conference
  • FUN!

What do you expect from participants?

We would like participants to have some experience with R programming and the ability to work with git/GitHub.

How to participate

If you are interested in participating, please contact us by email to anamaria.dobre [at] insse.ro with a short summary of your motivation and your experience in R.


Register

The conference fee is only EUR 100 thanks to the financial support of NIS Romania. Payments are accepted only through bank transfer. Number of accepted participants are limited to 100 on a first come, first served basis.

The Participant registration fee includes:

  • participation Certificate;
  • entrance to all sessions;
  • conference bag;
  • coffee breaks;
  • official dinner on 20th May, 2019.

The payment details for EU and non-EU participants are:
Universitatea Ecologica din Bucuresti
Bd. Vasile Milea, nr. 1G, sector 6, 061341, Bucuresti, Romania
VAT: 10240221
Bank: Groupe Societe Generale - Sucursala Academiei
Bank address: 1-7, Ion Mihalache blvd., sector 1, 011171 Bucharest
IBAN (EURO): RO80BRDE410SV22843654100
SWIFT code: BRDEROBU
Please state the name of the registered conference participant beneficiary of the payment and conference identifier code: UROS2019

In order to confirm the payment of the Conference Fee we are kindly requesting that you send a scanned copy of the Bank Transfer Order, as a attached file (PDF or image file), by e-mail: nicoleta.caragea [at] insse.ro

For registration please follow this link: Google Form


Organisation

Organizing committee

  • Nicoleta Caragea
  • Daniela Elena Stefanescu
  • Viorica Salagean
  • Bogdan Liviu Patarlageanu
  • Vitty Cristian Chiran
  • Mariana Vasile
  • Nicoleta Jalba

Scientific Board

  • Tudorel Andrei (President, National Institute of Statistics - Romania)
  • Matthias Templ (ZHAW school of engineering, Switzerland)
  • Mark van der Loo (Statistics Netherlands)
  • Alexander Kowarik (Statistics Austria)
  • Bogdan Oancea (National Institute of Statistics, University of Bucharest, Romania)
  • Adrian Dusa (University of Bucharest, Romania)
  • Elena Druica (University of Bucharest, Romania)
  • Kazumi Wada (National Statistics Center, Japan)
  • Marcello d’Orazio (FAO, Rome, Italy)
  • Valentin Todorov (UNIDO, Vienna Austria)
  • Nicoleta Caragea (National Institute of Statistics, Ecological University of Bucharest, Romania)
  • Ciprian Alexandru (National Institute of Statistics, Ecological University of Bucharest, Romania)
  • Alina Matei (University of Neuchatel, Switzerland)
  • Bernhard Meindl (Statistics Austria, Austria)
  • Claudiu Herteliu (Bucharest University of Economic Studies, EMOS Board, Romania)
  • Ana-Maria Ciuhu (National Institute of Statistics & Institute for National Economy, Romania)
  • Edwin de Jonge (Statistics Netherlands, The Netherlands)
  • Gergeley Daróczi (System1, Hungary)
  • Kamarul Ariffin Mansor (MARA University of Technology, Malaysia)
  • Marius Nicolae Jula (University of Bucharest, Romania)
  • Matyas Meszaros (Eurostat, Luxemburg)
  • Mihaela Paun (University of Bucharest)
  • Roxana Adam (University of Bucharest & National Institute of Statistics, Romania)

Venue & Contact

Bucharest is the capital city of Romania and, at the same time, the city’s most important and largest: it is a political, financial, banking, commercial, cultural and scientific center. The population of more than two million people ranks Bucharest as the sixth capital city size-wise in the European Union.

We look forward to welcoming you to Bucharest for uRos2019!

General information

  Address of National Institute of Statistics: 
  No.16 Libertatii Bvd., District 5, Bucharest, Romania 
  Telephone center: +4021 3181824; +4021 3181842 
  Fax: +4021 3124875; +4021 3181851; +4021 3181873 
  e-mail: nicoleta.caragea [at] insse.ro 

Accommodation (no guarantee by the conference, only informations)

http://www.ibis.com/gb/hotel-5938-ibis-bucharest-palatul-parlamentului-city-centre/index.shtml
http://parliament-hotel.ro/
http://www.europaroyalebucharest.com/
http://www.hotel-horoscop.ro/english/index_en.html

How to reach from Airport

maps.google.com

Info about Bucharest

http://romaniatourism.com/bucharest.html

Info about public transportation in Bucharest

http://romaniatourism.com/bucharest.html#transportation


Secretariat

If you have any questions, please do not hesitate to contact the secretariat.

Ana Maria CIUHU e-mail: anamaria.dobre [at] insse.ro


Code of Conduct

Organizers, speakers, registered participants, and sponsors of the uRos conference and/or unconfUROS hackathon, as well as persons or organizations following and discussing the conference through (social) media or internet are required to follow this Code of Conduct. We shall refer to the combined group as ‘participants’ of the uRos conference.

Privacy: participants respect each other’s privacy. In particular it is not allowed to systematically gather information on organizers, speakers and (registered) participants, including but not limited to personal life, professional position, political views, social media outings, open source contributions, or pictures.

Open: participants are open to collaboration, whether it’s on projects, working groups, packages, problems, or otherwise. Participants are receptive to constructive comment and criticism, as the experiences and skill sets of other participants contribute to the whole of our efforts. Participants are accepting of anyone who wishes to take part in their activities, fostering an environment where all can participate and everyone can make a difference.

Respectful: participants are respectful of others, their positions, their skills, their commitments, and their efforts. They are respectful of the volunteer efforts that permeate the R community. In the case of disagreement, participants are courteous in raising their issues. Participants are attentive in communications, whether in person or online, and tactful when approaching differing views. They refrain from demeaning, discriminatory, or harassing behaviour and speech.

Considerate: participants are considerate of their peers — fellow uRos participants. They are thoughtful when addressing the efforts of others, keeping in mind that oftentimes the labour was completed simply for the good of the community.

In case of violation, participants may be removed from the premises or blocked from communication with the organization. Where relevant, the proper authorities or organizations will be informed of inappropriate behaviour.

References

This code of conduct is based on he R Community Code of Conduct by the R consortium


GDPR terms and conditions

This document sets out the terms and conditions between organisers and you when you register as a participant for this conference and you are agreeing to comply with these terms and conditions.

You should read this document carefully. Organisers reserves the right to change these terms and conditions at any time.

DECLARATION OF CONSENT

I hereby give my consent for organisers to collect, process and use my personal data for the purposes related to my participation to uRos Conference events and for any other use to which I have consented. The data that can be collected consist of:

  • Name, surname, academic degree, institutional affiliation, e-mail address
  • Photos from the conference that might include my person;
  • Videos from the conference that might include my person.

USE OF PERSONAL DATA

Organisers will not sell your personal data.
The processing of the respective personal data involves communication with the authors in order to publish the studies, publication of the accepted studies in journals and indexation of the journal in international databases.
I hereby give my consent for organisers to use my personal data for the following purposes:

  • to receive email communication about uRos Conference events;
  • to use my images and videos that I may be photographed or filmed by organisers photographer and cameraman in promotional or event related materials by organisers.

The usage of my personal data must comply with the requirements of the Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons about the processing of personal data and on the free movement of such data (“GDPR“).

PERSONAL INFORMATION

You can ask organisers at any time to have an unrestricted access to your personal data and exercise your rights to: consult, modify, erase, export your personal information, by sending a message to Ana Maria CIUHU e-mail: anamaria.dobre [at] insse.ro.
If you’d like to learn more about GDPR and your rights under GDPR, please read the GDPR guide.


Organizers

  • National Institute of Statistics - Romania http://www.insse.ro/
  • Ecological University of Bucharest http://www.ueb.ro/