Hmw III: SVD methods and Elections Data

Important

• Due : May 29 2025
• Work in pairs
• Deliver your work through a github repository
• Present your work (15 minutes) on 2025-06-03

This homework is about

1. Using Matrix Factorization methods in Data analysis
2. Investigating voting patterns in Paris

I. Voting Data

Voting data per polling station can be obtained from a variety of websites.

• An example
• Another example
• Yet another one
• Opendatasoft API
• https://opendata.paris.fr
• https://data.smartidf.services/pages/data/
• https://data.opendatasoft.com

Note

Many datasets are available in several formats. When possible, use parquet. parquet files can be uploaded using package arrow.

Data concerning polling stations can also be gathered from various sources.

• https://github.com/datagouv/bureau-vote
• https://opendata.paris.fr

You first task will be to design an extraction pipeline to obtain the voting data you will analyse. You will gather data corresponding to different types of elections (Municipales, Régionales, Législatives, Européennes, Présidentielles) that took place since Year 2000.

title	rounds	year
Local	2	2008
Parliament	2	2012
Parliament	2	2017
Local	2	2020
European P	1	2024

II. Data cleaning

Some data cleaning may be necessary, for example

• Some parties changed their names during the last 25 years. Defining a mapping can facilitate the comparison of results from different elections
• Check that the names of bulletins nuls, bulletins blancs, … are consistent across the different datasets.

Note

Design a cleaning pipeline. Save the cleaned data.

III. Applying Matrix Factorization Methods (SVD)

For one election round, the outcome is summarized by a tibble where rows (individuals) are polling stations and variables/columns are the number of votes obtained by the different condidates/parties.

Perform PCA on different elections. Visualize and describe the result (attention, this is data analysis, not political science).

You may also perform CCA to compare different elections.

Feel free to combine different methods.

IV. Clustering

Perform clustering on the outcomes of the Principal Component Analyses.

References

• Advanced R Programming
  • Functional programming
  • S3
  • Meta programming
• Packages
• Programming with/for ggplot2
• Programming with dplyr
• tidyeval helpers
• Cheatsheets

Grading criteria

Criterion	Points	Details
Documentation/Report	45%	English/French
Presentation	40%
Data gathering/cleaning pipelines	15%