In my little world of finance, data almost always is a time series. Through both quiet iteration and significant revolutions, the volunteers of R have made analyzing and charting time series pleasant. As a mini-tribute to all those who have...

In my little world of finance, data almost always is a time series. Through both quiet iteration and significant revolutions, the volunteers of R have made analyzing and charting time series pleasant. As a mini-tribute to all those who have...

Regular expressions are a fantastic tool when you’re looking for patterns in time series. I wish I’d realised that sooner. Here’s a timely example: traditionally, when you have two successive quarters of negative GDP growth, you’re in recession. We have a quarterly GDP time series for Australia, and we want to know how many recessions

Handling time series data in R In this blog post I want to write some thoughts about handling time series data in R. In contrast to cross-sectional data, in time series applications each observation has an additional component besides it’s value: the point of time. This requires some additional efforts, for example: x-axis has to

A few days ago a colleague came to me for advice on the interpretation of some data. The dataset was large and included measurements for twenty-six species at several site-year-plot combinations. A substantial amount of effort had clearly been made to ensure every species at every site over several years was documented. I don’t pretend

This guest post is by Tammer Kamel, Founder of Quandl Finding and formatting numerical data for analysis in R or Excel or indeed any application is a pain that all real world data analysts know all too well. In aggregate I have probably spent weeks of my life trying to find data on the web. And several more weeks...

In the natural sciences, it is common to have incomplete or unevenly sampled time series for a given variable. Determining cycles in such series is not directly possible with methods such as Fast Fourier Transform (FFT) and may require some degree of interpolation to fill in gaps. An alternative is the Lomb-Scargle method (or...

Every so often I want to plot some data with pretty upper and lower error bounds, such as temperature data through time, perhaps with the maximum and minimum temperature range or standard error bounds for averaged data. The polygon( ) function can make those sorts of pretty plots. However, I’ll often have chunks of missing