This site is all about data harvesting, analysis and communication to increase our understanding of marine system dynamics and improve marine conservation.
This website has just been created, so stay tuned for more posts on topics spinning around harvesting and visualization of marine data, spatio-temporal modelling, statistical and machine learning, tech-related information on communicating marine science, on programming, useful R packages and more.
Last update: 2020/01/10 The following is a checklist of things to consider or do before (re-)submitting a package to CRAN, which I find every now and then helpful when I have go through this progress again. With the release of devtools 2.0 things have also changed a bit from the description provided in the R packages book by Hadley Wickham. So this checklist might be also helpful for you.
I wrote the following R Code for an Integrated Trend Analysis (ITA) during my PhD thesis in 2010, when I attended for the first time the annual meeting of the ICES/HELCOM Working Group on Integrated Assessments of the Baltic Sea (WGIAB). The code helped running a cross-comparison of several Baltic Sea sub-systems (see the 2010 report1). Together with Rabea Diekmann we fine-tuned the code and published it along with a full description on ITA methods in a Book chapter2 in Climate Impacts on the Baltic Sea: From Science to Policy.
This post compares a few change point detection method available in R given different time series dynamics and research questions. Change points or breakpoints are abrupt variations in time series data and may represent transitions between different states. The detection of change points is useful in modelling and prediction of time series and is found in application areas such as medical condition monitoring, speech and image analysis or climate change detection.
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.
© 2018 - This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License
