Do you also like shiny apps and would like to host more than 5 apps as currently permitted at shinyapps.io? Than the solution might be to run your own shiny server using any (virtual) server. In the following I will describe step-by-step how I set up my own Shiny as well as Rstudio Server using Digital Ocean. At the university I already use Rstudio Server extensively in my stats courses, which runs on a physical server at my research institute.
This 2nd part will cover the installation of R, RStudio and the Shiny Server and is based on R’s Ubuntu packages for R tutorial, the DigitalOcean manual, RStudio’s RStudio Server and Shiny Server guides, and Dean Attali’s great blog post. Table of Contents - Part 2 Step 8: Some preparations Step 9: Install R and packages 9.1 R 9.2 R Packages Step 10: Install RStudio Server Step 11: Install and configure Shiny Server 11.
If you have only one GitHub account and work with a single computer you will find plenty of information in the internet on how to link your machine to your account. Also, if you want to set up a secure SSH protocol to connect your computer to GitHub so that you don’t have to supply your username or password at each visit, a good starting point is the Connecting to GitHub with SSH documentation on the GitHub help pages or take a look at some of the many blogs, e.
In this post I will present the oce package, which can be handy for marine data scientist working with oceanographical data. oce provides lots of functions to read oceanographic data, process the data specific to the measuring instrument, and visualize results following oceanographic conventions (using the base graphics). The key function for importing data into R is ?read.oce(), which automatically recognizes the file type. If the recognition does not work, try the individual functions (e.
This post has been stimulated by a discussion with a colleague who asked about the normalization method for the root mean square error (NRMSE) in the INDperform R package, which is based on the indicator testing framework outlined in my article (Otto et al. 2018)1. At the time of writing the article and package I simply used a common approach and didn’t test it much further. But sparked by this discussion I started to test it thoroughly (as you will see below), which will make me revise the package.
INDperform Overview INDperform is an R package that implements a quantitative framework for selecting and validating the performance of state indicators tailored to meet regional conditions and specific management needs as described in Otto et al. (2018) 1 (see also my post on indicators). The package builds upon the tidy data principles and offers functions to identify temporal indicator changes, model relationships to pressures while taking non-linear responses and temporal autocorrelation into account, and to quantify the robustness of these models.
Indicators are useful and versatile tools applied in disciplines such as engineering, chemistry, medicine, economy or sociology. In ecosystem-based management a key role of an indicator is to inform on the current status of the system component as well as the effectiveness of specific management measures to move the component into a different state. In European Union (EU) marine policy, indicator development has recently progressed as part of the implementation of the Marine Strategy Framework Directive (MSFD)1 to aid the achievement of Good Environmental Status (GES) of the EU’s marine waters by 2020.