Computing Environments
The data employed in our paper used a conda environment that installed R version 4.2.0 (the most recent R version compatible with the high-performance computing system of the lead author’s university) and the required dependencies simultaneously. Scripts to match voters to school districts were originally run on Linux-based machines. Please note that, broadly speaking, many voter files are large and some use cases may require significant computing power.
Evaluate the size of your data and the computing power available to you before running code. Academic audiences may not be aware that R generally manipulates objects through a copy-on-modify system that requires more memory than is often expected to manipulate large objects (see Ch. 2 of Wickham, 2019). If you work with large datasets, you may need to increase the memory available to R. One (rough) rule of thumb is that you should have at least 2x the size of the object in memory to manipulate it.
We have, additionally, verified that the QOR package and its functions work with a newer R version (4.3.3) on Linux; we anticipate that creators of the relevant dependencies will continue to support new R releases in the future, as these are well-known packages.
Disclaimer
When using data obtained from any level of government, please consult the laws of the specific government(s) in question to ensure compliance. It is important to understand that U.S. states differ widely in their laws regarding the use of voter registration data. Users are solely responsible for ensuring that their use of the data complies with all applicable laws and regulations. The authors of this package do not assume any liability for users’ treatment of any data or their use of the package itself.
Reference List
Text throughout this website cites the following external references:
Cambon J., Hernangómez D., Belanger C., & Possenriede D. (2021). tidygeocoder: An R package for geocoding. Journal of Open Source Software, 6(65), 3544, https://doi.org/10.21105/joss.03544 (R package version 1.0.6)
Pebesma, E., & Bivand, R. (2023). Spatial Data Science: With applications in R. Chapman and Hall/CRC. doi:10.1201/9780429459016, https://r-spatial.org/book/.
Wickham, H. (2019). Advanced R, Second Edition. Chapman & Hall/CRC. Accessed at https://adv-r.hadley.nz/