I am Research Associate in Finance and Geography at the University of Oxford. My research focuses on land registration in the UK, the use of big data and new digital methodologies in social science research, and the economics and financialization of real estate and housing. You can contact me at email@example.com (more)
Feb. 28, 2021
I have found one of the best packages in emacs to be ESS mode which allows me to work seamlessly with R. I much prefer the setup and workflow of ESS than R Studio which I had previously done most of my R coding in. However, I spend most of my time coding in Python in jupyter notebooks. I have tried on several occasions to switch to using jupyter notebooks inside emacs, but it has never quite clicked for me and I’ve always gone back to just working in a browser.
Aug. 25, 2019
I’ve recently encountered a situation where I am working with very large datasets in a constrained environment. As a result, the only practical option has been to store the datasets in a compressed format, and then load them into R to start on the data analysis.
The problem is that when your working with datasets in the 10gb+ range on a normal desktop loading the data into memory (thankfully there is still enough of that available!
May. 23, 2017
I recently encountered a series of memory errors when using pandas in the LSE high powered computing environment. While detailing the problem, isn’t going to be of particular interest to any one I thought that a quick run down of how to monitor memory usage in a jupyter notebook may be of use to a few people out there.
While there are a few different ways of doing this I found that using the package memory profiler was by far the easiest option.