Most software developers understand the advantages of packaging up their code: it makes their functions testable, reliable and reusable. Not to mention making their future work much easier and more efficient. Here at Methods, we have realized the benefits of building R and Python packages to bundle up and test our code for collaboration both internally and with clients. This post will help readers in the data science community see how easy it is to get started developing and testing their own Python package using the pytest framework and GitLab CI.

Highlights from rstudio::conf 2018

The second-annual rstudio::conf was held in San Diego at the end of January, bringing together a wide range of speakers, topics, and attendees. Covering all of it would require several people and a lot of space, but I’d like to highlight two broad topics that received a lot of coverage: new tools for shiny and enhanced modeling capabilities for R. Shiny Several speakers introduced a collection of new tools for enhancing the capabilities of Shiny developers: asynchronous processing, simplified functional testing, and load testing are all coming to the shiny world.
Logistic regression produces result that are typically interpreted in one of two ways: Predicted probabilities Odds ratios Odds are the ratio of the probability that something happens to the probabilty it doesn’t happen. $\Omega(X) = \frac{p(y=1|X)}{1-p(y=1|X)}$ An odds ratio is the ratio of two odds, each calculated at a different score for $$X$$. There are strengths and weaknesses to either choice. Predictored probabilities are intuitive, but require assuming a value for every covariate.