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    Introduction to R from TeO-1

    Introduction to survey data processing using the R language - TeO-1

    For now, this training kit is available in French only.

    This kit is intended for trainers who wish to use teaching materials with beginners in quantitative survey data processing (allow approximately 6 to 12 hours of training in total).

    It includes a simplified and anonymized dataset from the TeO-1 survey prepared by INED's Survey Department for educational purposes.

    The objectives of the training course are as follows:

    • Be able to present the use of files from public statistics to reproduce tables and graphs from scientific publications.
    • Raise awareness among social science data users about the reuse of real survey data and introduce teaching methods for the reuse of this data based on research issues.

    The training will help students reproduce a set of graphs from an issue of Population and Societies. Beauchemin, C., Hamel, C., Lesné, M., Simon, P. and the TeO team survey (2010).  Les discriminations : une question de minorités visibles.  Population & Sociétés,  466(4), 1-4.  https://doi.org/10.3917/popsoc.466.0001. 

    This reproducibility will be achieved using R software, mainly through the Tidyverse ecosystem.

    All the elements can be found below:

    This kit was developed by INED's Statistical Methods Service (SMS).

    Issues of integration and discrimination feature prominently in public debate. However, in 2008, France still lacked national statistics that would enable these phenomena to be studied. The TeO survey was designed to fill this gap. It was conducted jointly by INSEE and INED in order to study issues of immigration, integration and discrimination. It follows in the footsteps of the 1992 Geographical Mobility and Social Integration Survey (MGIS) and seeks to improve statistical knowledge of immigration and integration issues. The TeO survey aimed to understand the extent to which national or migratory origin, combined with other factors common to French society as a whole (age, gender, level of education, social category, place of residence, etc.), contributes to explaining inequalities in access to housing, employment, leisure activities, public services and other resources of social life.