This is the introductory page for the R workshop that will be held during the 2016 AmeriDendro conference held in Mendoza, Argentina March 28 - April 1. The R workshop is scheduled to be held on Tuesday March 29 from 6 pm to 9 pm.
The R language and programming environment is increasingly used in dendrochronology. R is the world’s preeminent open-source statistical computing software and its power can be harnessed for tree-ring science through the contribution of add-on packages which are freely available on the internet. There are now several R packages for working with tree-ring data from measuring (measuRing) to standardization and chronology building (dplR) to climate-growth analysis (treeclim, pointRes) as well as working with data from dendrometers (dendrometeR). Although extremely powerful, R has a steep learning curve that has lead some to postpone using it in their own work. In this interactive workshop the authors and maintainers of several R packages will demonstrate the ways in which analysts can work with tree-ring data in R over the entire life cycle of a project in a transparent and reproducible way – from initial measuring of the wood to statistical tests to producing publication-quality graphics. The workshop format will include a demonstration using onboard data sets but will also include a chance for participants to work with their own data in a collaborative environment. Both novice and experienced R users will leave the workshop with new analytic tools for working with tree-ring data in R.
Please install R by visiting www.r-project.org/. We recommend that you use RStudio to interact with and script in R. These documents were all made using R version 3.2.4 (2016-03-10).
You will want the following add on libraries installed as well: dplR, treeclim, measuRing, and pointRes. You can download and install these using the install.packages function from the R prompt:
install.packages("dplR")
install.packages("treeclim")
install.packages("measuRing")
install.packages("pointRes")These documents were all made using the most up-to-date versions of the packages available on the Comprehensive R Archive Network. You can update packages in R via:
update.packages()Updating regularly is good practice.
During the conference the authors of these packages will demonstrate some basic aspects of their use through executable examples with onboard data sets. After a basic introduction of the packages, you will have a chance to work through examples yourself or work on your own analysis.
No prior R experience is necessary but for those who are new to R, we suggest using the resources at swirl as a way of beginning your journey with R.