Accessing and processing climate data with meteospain and meteoland R packages
The main aims of the course are to provide participants with (a) the ability to access and download daily/monthly climate data from different weather station networks around Spain, using the R package meteospain; (b) the basis to understand and correctly apply the weather interpolation and radiation estimation procedures available in the R package meteoland.
Introductory R Course
The aim of this course is to learn the basics of writing code in R and to become acquainted with some of the most useful R packages. The first part explains R programming basic concepts (object types, conditional execution and loops). The second part of the course delves into the tidyverse, with emphasis in data transformation (dplyr) and visualization (ggplot2)..
Process-based forest modelling using the medfate R package
The main aims of the course are (a) to provide participants with the basis to understand the formulation and parameterization of biophysical, physiological and ecological processes in the models of forest water balance, carbon balance and forest dynamics included in the R package medfate; (b) to show how to use ‘medfate’ and companion R packages to prepare model inputs, perform simulations and inspect the results.
Back-up and synchronization strategies to keep copies of your computer files
We will briefly discuss some of the tools that are available for syncing folders and/or backing-up your files safely and securely. Although the talk will certainly not cover all available back-up possibilities out there, it is intended to give you a brief introduction to the strategies you can implement to save your day and carry on with your research.
Introduction to R Spatial
The aim of this course is to become familiar working with spatial data in R. In the first half of the course we will show how we can load and inspect vector and raster data from within your R session. This also includes basic vector operations, like e.g. spatial joins and filters, raster algebra (merge, extract…) as well as some basic visualization techniques. In the second half of the course, we will illustrate the use of spatial data in statistical analysis like e.g. raster interpolation, point pattern analysis and krigging.