# Changes between Version 57 and Version 58 of udg/ecoms/RPackage/examples

Ignore:
Timestamp:
Jun 17, 2014 5:10:34 PM (8 years ago)
Comment:

--

### Legend:

Unmodified
 v57 = Introduction and usage recommendations In this section a number of examples for data download and visualization/analysis are presented within the R environment, through the loadECOMS function. Currently, there are four different seasonal to annual hindcasts and one observational gridded dataset available at '''ECOMS-UDG'''. All of them are available through the common interface loadECOMS, and therefore the argument values may vary slightly. For instance, arguments members and leadMonth do not apply in the case of the observational gridded dataset WFDEI (see [http://meteo.unican.es/ecoms-udg/RPackage/Examples/globalSelection Example 3]), and hence ignored. Similarly, the output data structure may vary consequently, and forecast data types include the initialization dates and the names of the chosen members, while this information is not included for other types of gridded data. In this section a number of examples for data download and visualization/analysis are presented within the R environment, through the loadECOMS function. Currently, there are four different seasonal to annual hindcasts and one observational gridded dataset available at '''ECOMS-UDG'''. All of them are available through the common interface loadECOMS, and therefore the argument values may vary slightly. For instance, arguments members and leadMonth do not apply in the case of the observational gridded dataset WFDEI, and hence ignored. Similarly, the output data structure may vary consequently, and forecast data types include the initialization dates and the names of the chosen members, while this information is not included for other types of gridded data. The examples given have been kept deliberately simple in order to preserve a moderate output size (<150 Mb) and reasonable execution times (<10 minutes), although larger (and hence more time-consuming) requests can be done. The limitations in data loading depend essentially on two factors: