R FOR HYDROLOGISTS SEASONALITY AND TREND DECOMPOSITION If you don’t have the data, please first get it from the first tutorial here. Also, you need to install and load the ggplot2 package. if(!require(ggplot2)){install.packages(ggplot2, dep=T)} Answers to these exercises are available here. Time series decomposition is a mathematical procedure which transforms a time series into multiple different […]

# ggplot2

## R FOR HYDROLOGISTS – Correlation and Information Theory Measurements: Part 3: Exercises

R FOR HYDROLOGISTS CORRELATION AND INFORMATION THEORY MEASUREMENTS – PART 3 Before we begin, if you don’t have the data, first get it from the first tutorial here. You will also need to Install and load the ggplot2 and reshape2 packages. if(!require(ggplot2)){install.packages(ggplot2, dep=T)} if(!require(reshape2)){install.packages(reshape2, dep=T)} Answers to these exercises are available here. The mutual information quantifies […]

## R FOR HYDROLOGISTS: Correlation and Information Theory Measurements – Part 2: Exercises

R FOR HYDROLOGISTS CORRELATION AND INFORMATION THEORY MEASUREMENTS (Part 2) Proposed back in the 40’s by Shannon Information theory provide a framework for the analysis of randomness in time-series, and information gain when comparing statistical models of inference. Information theory is based on probability theory and statistics. It often concerns itself with measures of information […]

## R FOR HYDROLOGISTS – Part 1: Correlation and Information Theory Measurements

R FOR HYDROLOGISTS CORRELATION AND INFORMATION THEORY MEASUREMENTS (Part 1) In this tutorial, we will show you how to apply tools, such as the correlation, auto-correlation, entropy, and mutual information as an introductory exercise in the analysis of time series dynamics. The first measurement that we will calculate will be the linear correlation. This statistic […]