Introduction RStudio Server desktop app options Site-specific-browser (SBB) tools Electron Unite / Coherence X Introduction In my work as an R consultant/scientist, I often work on/with RStudio Server (soon to be RStudio Workbench) instances. These have several advantages:
The workload is executed on the server and not your local machine The environment can be centrally managed for many users and prevents OS-related issues on Windows/macOS/Linux user machines Session keep running in the background even if the local machine is powered off Often RStudio Server instances are way quicker than local RStudio Desktop installations However, there is also (at least) one downside: it runs in a browser (tab).
Reproducibility is important. More important than ever. However, making a project reproducible is not as trivial as it sounds.
Introduction The problem The mlr use case The solution Code Introduction Continuous integration checking for R packages is usually done on Travis CI because the R community has established a community driven build framework for R. In case you are not aware, there are also other tools that try to simplify the CI tasks for R even more.
This works great for simple checking of small R packages but once it comes to packages that have a lot of dependencies, developers sometimes run into troubles regarding build time.
This guide reflects my view on how to setup a working Arch Linux system tailored towards data science, R and spatial analysis. If you have suggestions for modifications, please open an issue at https://github.com/rbind/pat-s_web/. Enjoy the power of Linux!
Usually, this calculation is done by setting all predictors to their mean value, predict the response, change the desired predictor to a new value and predict the response again. These actions results in two log odds values, respectively, which are transformed into odds by exponentiating them. Finally, the odds ratio can be calculated from these two odds values.