I am Economics Lead at Zalando SE building and evaluating large scale decision-making systems. I am also a visiting research fellow at the Institute for Quantitative Social Science, Harvard University developing statistical software and applications for the social and physical sciences. I previously held posts at City, University of London, the Hertie School of Governance, Yonsei University, and the London School of Economics where in 2012 I completed a PhD in quantitative political science. My academic research focuses on the international political economy of public financial and monetary institutions, as well as applied social science statistics. My work has been published in peer reviewed journals including the British Journal of Political Science, Journal of Common Market Studies, Journal of Peace Research, International Studies Quarterly, Journal of European Public Policy, Review of International Political Economy, Political Science Research and Methods, and Journal of Statistical Software. I have co-authored a number of pieces on European banking union for the Bruegel Policy Contribution series. I published a book on reproducible computational research methods for Chapman and Hall.
For more details, please see my CV.
I'm always looking for ways to download data from the internet into R. Though I prefer to host and access plain-text data sets (CSV is my personal favourite) from GitHub (see my short paper on the topic) sometimes it's convenient to get data stored on Dropbox . There has been a change in the way Dropbox URLs work and I just added some functionality to the repmis R package. So I though that I'ld write a quick post on how to directly download data from Dropbox into R. The download method is different depending on whether or not your plain-text data is in a Dropbox Public folder or not. Dropbox Public Folder Dropbox is trying to do away with its public folders. New users need to actively create a Public folder. Regardless, sometimes you may want to download data from one. It used to be that files in Public folders were accessible through non-secure (http) URLs. It's easy to download these into R, just use the read.table command, where the URL is the file name