I am pleased to announce my CTS.csv file which includes 18 climate monthly time series in one easy to access csv file. This is part of my goal of having a user friendly way for do-it-yourself citizen climate scientists to … Continue reading →

I’ve recently been looking at (http://www.geo.uni-potsdam.de/member-details/show/108.html ‘Martin Trauth’s web page at The University of Potsdam Institute of Earth and Environmental Science’)'s book MATLAB® Recipes for Earth Sciences to try to understand what some of my palaeoceanography colleagues are doing with their data analyses (lots of frequency domain time series techniques and a...

This morning I came across a post which discusses the differences between scala, ruby and python when trying to analyse time series data. Essentially, there is a text file consisting of times in the format HH:MM and we want to get an idea of its distribution. Tom discusses how this would be a bit clunky

If you need to generate synthetic wind speed time series, you may find useful the procedure described in “A Markov method for simulating non-gaussian wind speed time series” by G.M. McNerney and P.S. Veers (Sandia Laboratories, 1985), and “Estimation of extreme wind speeds with very long return periods” by M.D.G Dukes and J.P. Palutikof (Journal

There are various ways to plot data that is represented by a time series in R. The ggplot2 package has scales that can handle dates reasonably easily. Fast Tube by Casper As an example consider a data set on the number of views of the you tube channel ramstatvid. A short snippet of the data is shown

Ever since I first looked at this NYT visualization by Amanda Cox, I’ve always wanted to reproduce this in R. This is a plot that stacks multiple time series onto one another, with the width of the river/ribbon/hourglass representing the strength at each time. The NYT article used box office revenue as the width of