# Changes between Version 34 and Version 35 of udg/ecoms/RPackage/examples

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Timestamp:
May 29, 2013 10:14:19 AM (9 years ago)
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 v34 Data are now ready for analysis into our R session. Note that we have set the standard.vars argument to TRUE, and we have specified a path to the dictionary, a file with extension ''.dic'', whose aim is to translate the original variable stored in the dataset (in this case in Kelvin instead of Celsius). More details on the use of the dictionary are provided [https://www.meteo.unican.es/trac/meteo/wiki/SpecsEuporias/RPackage here]. Data are now ready for analysis into our R session. Note that we have set the standard.vars argument to TRUE, and we have specified a path to the dictionary, a file with extension ''.dic'', whose aim is to translate the original variable stored in the dataset (in this case in Kelvin instead of Celsius). More details on the use of the dictionary are provided in [wiki:SpecsEuporias/RPackage this section]. {{{ }}} The function has been implemented to access seasonal slices (as determined by the season argument. Seasons can be defined in several ways: A single month (e.g. season = 1 for January, as in this example), a standard season (e.g. season=1:3 for JFM, or season=c(12,1,2) for DJF), or any period of consecutive months (e.g. season=1:6, for the first half of the year). Seasons are returned for a given year period (defined by the years argument, e.g. years = 1990:1999 in this example) with a homogeneous forecast lead time (as given by the leadMonth argument; in this example leadMonth = 1 for one-month lead time) with respect to the first month of the selected season. For example, in this particular case the data loaded correspond to the series of January 1990 to January 1999 from the December 1989 to December 1998 runtime forecast. As a result, the length of the time series returned for each of the 280 grid cells is of 310 days (31 days of January * 10 years). Note that it is also possible to work with year-crossing seasons, such as DJF (in this case season=c(12,1,2)). A common task consists of the representation of data, e.g. by mapping the spatial mean for the period considered. Another common task is the representation of time series for selected point locations/grid cells. In this example, we will map the mean temperature field for the period selected (1990-99) preserving the original spatial resolution of the model. Furthermore, we will display time series at two grid points coincident with the locations of two Spanish cities. To this aim, we will make use of some base R functions and also from some contributed packages that can be very useful for climate data handling and representation.