Changes between Version 5 and Version 6 of EcomsUdg/RPackage/Functions

Timestamp:

Apr 29, 2013 11:48:44 AM (9 years ago)

Author:

juaco

Comment:

--

EcomsUdg/RPackage/Functions

@@ +1 @@
+* '''__dataInventory.R__'''
+
+Prior to data analysis, a common need is to have an overview of all data available and their structure (variables, dimensions, units, geographical extent, time span ...). The function `dataInventory.R` is intended to perform this task, returning a list of components summarizing the main characteristics of the selected dataset. The function is called in the following way:
+
+{{{
+dataInventory(dataset, print.summary = TRUE)
+}}} 
+
+The arguments are next described:
+
+* `dataset`: a character string indicating the full path to the virtual dataset (a `ncml` file). This can be either a path containing the directory and name of the file, or an appropriate URL in case the dataset is remotely accessed (e.g., via the [https://www.meteo.unican.es/trac/meteo/wiki/SpecsEuporias/DataServer/THREDDS SPECS-EUPORIAS THREDDS]).
+* `print.summary`: logical flag indicating if a summary table is printed on screen, in addition to the output list.
+
+The output of the function consists of a list of variable length depending on the number of variables contained in the dataset, following this structure:
+
+ * `Name of the variable`
+    * `Description`
+    * `Name`
+
+
+* '''__loadSystem4.R__'''
+
 The ''SPECS-EUPORIAS Data Portal'' can be remotely accessed from R via the [mtl:browser:MLToolbox/trunk/MLToolbox_experiments/antonio/system4/r/loadSystem4.R loadSystem4.R] function. Note that this function is part of a more comprehensive R package currently under development. This function automatically cares about the proper location of the right indices for data sub-setting across the different variable dimensions, given a few simple arguments for subset definition. In addition, instead of retrieving a NetCDF file that needs to be opened and read, the requested data is directly loaded into the current R working session, according to a particular structure described below, prior to data analysis and/or representation.