Edwin De Jonge
Methodologist Data Scientist
Breda, North Brabant, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolEdwin De Jonge is a methodologist and data scientist at Statistics Netherlands with 14+ years of experience applying computational statistics, complexity science and visualization to national-scale data problems. He combines a theoretical physics background with practical software engineering skills across R, Python, Julia, Scala and C/C++, and is co-author of multiple R packages and a Wiley book on data cleaning. Edwin has tackled extreme-scale projects—whole-population network analysis (18M nodes, ~1e9 links), mobile-phone–based daytime population estimation, and big-data visualization and cleaning pipelines—bringing numerical methods and uncertainty visualization into exploratory analysis. An experienced trainer for R, Python and Julia, he also contributes performance and robustness improvements to high-profile open-source tools (notably optimizing file I/O in readr via memory-mapping). Colleagues consult him on network analysis, machine learning and reproducible data-processing workflows, where his blend of research curiosity and production-hardened coding yields pragmatic, auditable solutions.
15 years of coding experience
9 years of employment as a software developer
MSc, Physics, MSc, Physics at Radboud UniversityGymnasium B, Physics,Mathematics,Chemistry,Dutch,English,French,Greek,Economics, Gymnasium B, Physics,Mathematics,Chemistry,Dutch,English,French,Greek,Economics at Dominicus College Nijmegen
Dutch, English, German, French
Github Skills (12)
file-handling10
file-processing10
c-language10
file-access10
cprogramming-language10
fileio10
memory-mapping10
rcpp9
parsing8
text-parsing8
r7
csv7
Programming languages (13)
Github contributions (5)
tidyverse/readr
Sep 2014 - Sep 2014
Read flat files (csv, tsv, fwf) into R
Role in this project:
Back-end Developer
Back-end DeveloperContributions:8 commits, 1 comment in 4 days
Contributions summary:Edwin focused on optimizing and securing file reading functionalities within the `readr` library, leveraging memory mapping techniques using `boost::interprocess`. They implemented and refined `MMapSource` class for efficient file access and updated the `read_file` function. The user also addressed compilation issues, ensuring cross-platform compatibility with Windows, and added exception handling for improved robustness. These changes enhanced the library's performance and reliability.
csvflat-filerfwfparsing
data-cleaning/errorlocate
Jul 2015 - Jun 2022
Find and replace erroneous fields in data using validation rules
Contributions:287 commits, 285 pushes, 16 branches in 7 years
validation-rulesinvalidationrdata-cleaning
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