During the past decade, research in hydrology and meteorology was dedicated to new topics and carried out at scales ranging from global phenomena, such as global climate change, to very local scales.  New analytical methods have been developed for these tasks, many of them requiring the analysis of large data sets, e.g. time-series data, raster-based remote sensing data, or output from climate or hydrological models.  The related computational efforts increased substantially and thus, the tasks cannot be performed using standard office software anymore but require new analytical tools.

R is a well-developed, simple and effective programming language and an integrated environment for statistical computing, data analysis and processing, and the generation of well-designed publication-quality graphics. It provides a wide variety of statistical techniques (e.g. linear and non-linear modelling, classical statistical tests, time-series analysis, classification, clustering, raster data processing), and can be extended easily by additional packages. Unlike conventional office software, R is a powerful tool to process large data sets, e.g. long-term climate or hydrological time series of daily values. 
As an open-source integrated suite of software facilities, R is freely available. There exist a number of freely available graphical user interfaces for R, which make writing and executing R code easier and more intuitively. The most popular ones are RStudio and Tinn-R.

The introductory training for beginners in R focuses on the use of the open-source statistical package R (version 3.0.1) for statistical analysis and plotting.  After an introduction into the general use of R, the participants will learn how to use basic statistical functions and plotting functionalities.  The course program is planned as full-day lessons for 5 days. There will be an alternation of short theoretical modules and extended problem-oriented practical exercises on reading, writing, and executing R code, and on the interpretation and visualization of the results. The course program may be adapted to specific examples provided by the participants. Thus, the participants are encouraged to bring time series or other geodata related to their current work.  
Training participants are welcome to work on their own computers during the training. Internet access will be provided.

For details, please see the Course Description.

The course materials are available at the eLearning platform of the CAWa project.

Mrs. Aijan Ainabekova

CAIAG Central Asian Institute for Applied Geosciences
Directorate

Timur Frunze rd. 32/2
720027 Bishkek, Kyrgyz Republic

phone +996 312 555454(218)
email: a.ainabekova@caiag.kg