Browse code

fix and clean

Simone authored on 06/12/2017 17:47:26
Showing49 changed files

... ...
@@ -1,7 +1,7 @@
1 1
 Package: RGMQL
2 2
 Type: Package
3 3
 Title: GenoMetric Query Language for R/Bioconductor
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-Version: 0.99.36
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+Version: 0.99.37
5 5
 Author: Simone Pallotta, Marco Masseroli
6 6
 Maintainer: Simone Pallotta <simonepallotta@hotmail.com>
7 7
 Description: This package brings the GenoMetric Query Language (GMQL)
... ...
@@ -82,7 +82,6 @@ biocViews:
82 82
 Collate: 
83 83
     'AllClasses.R'
84 84
     'AllGenerics.R'
85
-    'GMQL4TFarm.R'
86 85
     'GMQLtoGRanges.R'
87 86
     'GRangesToGMQL.R'
88 87
     'S3Aggregates.R'
... ...
@@ -91,6 +90,7 @@ Collate:
91 90
     'S3Operator.R'
92 91
     'Utils.R'
93 92
     'evaluation-functions.R'
93
+    'filter-extract-function.R'
94 94
     'gmql_cover.R'
95 95
     'gmql_difference.R'
96 96
     'gmql_extend.R'
... ...
@@ -29,7 +29,6 @@ export(SQRT)
29 29
 export(STD)
30 30
 export(SUM)
31 31
 export(UP)
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-export(collect)
33 32
 export(compile_query)
34 33
 export(compile_query_fromfile)
35 34
 export(delete_dataset)
... ...
@@ -86,8 +85,12 @@ importFrom(S4Vectors,aggregate)
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 importFrom(S4Vectors,merge)
87 86
 importFrom(S4Vectors,metadata)
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 importFrom(data.table,fread)
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+importFrom(dplyr,arrange)
89 89
 importFrom(dplyr,bind_cols)
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+importFrom(dplyr,collect)
90 91
 importFrom(dplyr,filter)
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+importFrom(dplyr,group_by)
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+importFrom(dplyr,select)
91 94
 importFrom(glue,glue)
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 importFrom(methods,is)
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 importFrom(methods,isClass)
... ...
@@ -1,20 +1,9 @@
1
-#' Method filter
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-#' 
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-#' Wrapper to GMQL select function
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-#' 
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-#' @name filter
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-#' @rdname filter-GMQLDataset-method
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-#' @aliases filter
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-#' 
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-setGeneric("filter", function(.data, m_predicate = NULL, r_predicate = NULL, 
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-                            semijoin = NULL, ...) standardGeneric("filter"))
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-
12 1
 #' Method cover
13 2
 #' 
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-#' Wrapper to GMQL cover function
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+#' Wrapper to GMQL COVER operator
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 #' 
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 #' @name cover
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-#' @rdname cover-GMQLDataset-method
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+#' @rdname cover
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 #' @aliases cover
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 #' 
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 setGeneric("cover", function(data, ...) standardGeneric("cover"))
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@@ -24,29 +13,20 @@ setGeneric("cover", function(data, ...) standardGeneric("cover"))
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 #' Wrapper to GMQL map function
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 #' 
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 #' @name map
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-#' @rdname map-GMQLDataset-method
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-#' @aliases map
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+#' @rdname map
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+#' @aliases map,GMQLDataset-method
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 #' 
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 setGeneric("map", function(x, y, ...) standardGeneric("map"))
31 20
 
32 21
 
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-#' Method collect
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-#' 
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-#' Wrapper to GMQL materialize function
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-#' 
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-#' @name collect
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-#' @rdname collect-GMQLDataset-method
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-#' @export
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-setGeneric("collect", function(x, dir_out = getwd(), name = "ds1", ...) 
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-                standardGeneric("collect"))
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-
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-
44 22
 #' Method take
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 #' 
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 #' Wrapper to take function
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 #' 
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 #' @name take
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-#' @rdname take-GMQLDataset-method
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+#' @rdname take
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+#' @aliases take,GMQLDataset-method
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+#' 
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 setGeneric("take", function(data, ...) standardGeneric("take"))
51 31
 
52 32
 
... ...
@@ -55,39 +35,8 @@ setGeneric("take", function(data, ...) standardGeneric("take"))
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 #' Wrapper to GMQL extend function
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 #' 
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 #' @name extend
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-#' @rdname extend-GMQLDataset-method
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-#' @aliases extend GMQLDataset-method
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-setGeneric("extend", function(.data, ...) standardGeneric("extend"))
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-
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-#' Method select
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+#' @rdname extend
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+#' @aliases extend,GMQLDataset-method
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 #' 
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-#' Wrapper to GMQL project function
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-#' 
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-#' @name select
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-#' @rdname select-GMQLDataset-method
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-#' @aliases select 
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-setGeneric("select", function(.data, ...) standardGeneric("select"))
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-
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-
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-#' Method arrange
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-#' 
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-#' Wrapper to GMQL order function
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-#' 
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-#' @name arrange
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-#' @rdname arrange-GMQLDataset-method
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-#' @aliases arrange 
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-#' 
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-setGeneric("arrange", function(.data, metadata_ordering = NULL, 
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-                    regions_ordering = NULL, fetch_opt = NULL, num_fetch = 0, 
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-                    reg_fetch_opt = NULL, reg_num_fetch = 0, ...) 
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-                            standardGeneric("arrange"))
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-
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-#' Method group_by
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-#' 
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-#' Wrapper to GMQL group function
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-#' 
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-#' @name group_by
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-#' @rdname group_by-GMQLDataset-method
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-#' @aliases group_by 
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-setGeneric("group_by", function(.data, ...) standardGeneric("group_by"))
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+setGeneric("extend", function(.data, ...) standardGeneric("extend"))
93 42
 
... ...
@@ -158,7 +158,7 @@ take_value.META_AGGREGATES <- function(obj){
158 158
 #' res = extend(exp, m_score = MEDIAN("score"))
159 159
 #' 
160 160
 #' 
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-#' @name SUM
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+#' @name AGGREGATES-Object
162 162
 #' @aliases SUM
163 163
 #' @rdname aggr-class
164 164
 #' @export
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@@ -173,7 +173,7 @@ SUM <- function(value)
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     return(list)
174 174
 }
175 175
 
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-#' @name MIN
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+#' @name AGGREGATES-Object
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 #' @aliases MIN
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 #' @rdname aggr-class
179 179
 #' @export
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@@ -189,7 +189,7 @@ MIN <- function(value)
189 189
 }
190 190
 
191 191
 
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-#' @name MAX
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+#' @name AGGREGATES-Object
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 #' @aliases MAX
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 #' @rdname aggr-class 
195 195
 #' @export
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@@ -204,7 +204,7 @@ MAX <- function(value)
204 204
     return(list)
205 205
 }
206 206
 
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-#' @name AVG
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+#' @name AGGREGATES-Object
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 #' @aliases AVG
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 #' @rdname aggr-class
210 210
 #' @export
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@@ -219,7 +219,7 @@ AVG <- function(value)
219 219
     return(list)
220 220
 }
221 221
 
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-#' @name BAG
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+#' @name AGGREGATES-Object
223 223
 #' @aliases BAG
224 224
 #' @rdname aggr-class
225 225
 #' @export
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@@ -234,7 +234,7 @@ BAG <- function(value)
234 234
     return(list)
235 235
 }
236 236
 
237
-#' @name COUNT
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+#' @name AGGREGATES-Object
238 238
 #' @aliases COUNT
239 239
 #' @rdname aggr-class
240 240
 #' @export
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@@ -252,7 +252,7 @@ as.character.COUNT <- function(obj) {
252 252
 }
253 253
 check.COUNT <- function(obj){}
254 254
 
255
-#' @name STD
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+#' @name AGGREGATES-Object
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 #' @aliases STD
257 257
 #' @rdname aggr-class
258 258
 #' @export
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@@ -268,7 +268,7 @@ STD <- function(value)
268 268
 }
269 269
 
270 270
 
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-#' @name MEDIAN
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+#' @name AGGREGATES-Object
272 272
 #' @aliases MEDIAN
273 273
 #' @rdname aggr-class
274 274
 #' @export
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@@ -283,7 +283,7 @@ MEDIAN <- function(value)
283 283
     return(list)
284 284
 }
285 285
 
286
-#' @name Q1
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+#' @name AGGREGATES-Object
287 287
 #' @aliases Q1
288 288
 #' @rdname aggr-class
289 289
 #' @export
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@@ -298,7 +298,7 @@ Q1 <- function(value)
298 298
     return(list)
299 299
 }
300 300
 
301
-#' @name Q2
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+#' @name AGGREGATES-Object
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 #' @aliases Q2
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 #' @rdname aggr-class
304 304
 #' @export
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@@ -312,7 +312,7 @@ Q2 <- function(value)
312 312
     return(list)
313 313
 }
314 314
 
315
-#' @name Q3
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+#' @name AGGREGATES-Object
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 #' @aliases Q3
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 #' @rdname aggr-class
318 318
 #' @export
... ...
@@ -327,7 +327,7 @@ Q3 <- function(value)
327 327
     return(list)
328 328
 }
329 329
 
330
-#' @name BAGD
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+#' @name AGGREGATES-Object
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 #' @aliases BAGD
332 332
 #' @rdname aggr-class
333 333
 #' @export
... ...
@@ -25,34 +25,48 @@ print.PARAMETER <- function(obj){
25 25
 #' to be used in GMQL cover method
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 #' 
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 #' \itemize{
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-#' \item{ANY: It defines any amount of overlapping regions to be considered.}
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-#' \item{ALL: It defines the minimum (and/or maximum) to the number of samples 
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-#' in the input dataset.}
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+#' \item{ALL: It represents the number of samples in the input dataset.}
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+#' \item{ANY: It represents any amount of overlapping regions to be 
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+#' considered.}
31 31
 #' }
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 #' 
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-#' @return param object
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+#' @return Param object
34 34
 #'
35 35
 #' @examples
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 #' 
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+#' ## This statement initializes and runs the GMQL server for local execution 
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+#' ## and creation of results on disk. Then, with system.file() it defines 
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+#' ## the path to the file "DATASET" in the subdirectory "example"
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+#' ## of the package "RGMQL" and opens such file as a GMQL dataset named "exp" 
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+#' ## using customParser
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+#' 
37 43
 #' init_gmql()
38 44
 #' test_path <- system.file("example", "DATASET", package = "RGMQL")
39 45
 #' exp = read_dataset(test_path)
40 46
 #' 
41
-#' ## This statement produces an output dataset with a single output sample. 
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-#' ## The COVER operation considers all areas defined by a minimum 
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-#' ## of two overlapping regions in the input samples, 
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-#' ## up to maximum amount of overlapping regions.
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+#' ## The following statement produces an output dataset with a single 
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+#' ## output sample. The COVER operation considers all areas defined by 
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+#' ## a minimum of two overlapping regions in the input samples, 
50
+#' ## up to maximum amount of overlapping regions equal to the number 
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+#' ## of input samples.
45 52
 #' 
46 53
 #' res = cover(exp, 2, ALL())
47 54
 #' 
48
-#' ## This statement produces an output dataset with a single output sample. 
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-#' ## The COVER operation considers all areas defined by a minimum 
50
-#' ## of two overlapping regions in the input samples, 
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+#' ## The following statement produces an output dataset with a single 
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+#' ## output sample. The COVER operation considers all areas defined by 
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+#' ## a minimum of two overlapping regions in the input samples, 
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+#' ## up to any amount of overlapping regions.
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+#' 
60
+#' res = cover(exp, 2, ANY())
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+#' 
62
+#' ## The following statement produces an output dataset with a single 
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+#' ## output sample. The COVER operation considers all areas defined by 
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+#' ## a half of maximum amount of overlapping regions in the input samples, 
51 65
 #' ## up to any amount of overlapping regions.
52 66
 #' 
53
-#' res = cover(exp, 2, ANY()+2/3)
67
+#' res = cover(exp, ALL()/2, ANY())
54 68
 #' 
55
-#' @name ALL
69
+#' @name Cover-Param
56 70
 #' @aliases ALL
57 71
 #' @rdname cover-param-class
58 72
 #' @export
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@@ -65,7 +79,7 @@ ALL <- function()
65 79
     return(list)
66 80
 }
67 81
 
68
-#' @name ANY
82
+#' @name Cover-Param
69 83
 #' @aliases ANY
70 84
 #' @rdname cover-param-class
71 85
 #' @export
... ...
@@ -101,7 +101,7 @@ check.DISTAL <- function(value)
101 101
 #' genometric_predicate = list(MD(1), DGE(12000), DOWN()), 
102 102
 #' DF("provider"), region_output = "RIGHT")
103 103
 #'
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-#' @name DL
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+#' @name DISTAL-Object
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 #' @aliases DL
106 106
 #' @rdname distal-class
107 107
 #' @export
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@@ -129,7 +129,7 @@ DG <- function(value)
129 129
     return(list)
130 130
 }
131 131
 
132
-#' @name DLE
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+#' @name DISTAL-Object
133 133
 #' @aliases DLE
134 134
 #' @rdname distal-class
135 135
 #' @export
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@@ -143,7 +143,7 @@ DLE <- function(value)
143 143
     return(list)
144 144
 }
145 145
 
146
-#' @name DGE
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+#' @name DISTAL-Object
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 #' @aliases DGE
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 #' @rdname distal-class
149 149
 #' @export
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@@ -157,7 +157,7 @@ DGE <- function(value)
157 157
     return(list)
158 158
 }
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160
-#' @name MD
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+#' @name DISTAL-Object
161 161
 #' @aliases MD
162 162
 #' @rdname distal-class
163 163
 #' @export
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@@ -172,7 +172,7 @@ MD <- function(value)
172 172
 }
173 173
 
174 174
 
175
-#' @name UP
175
+#' @name DISTAL-Object
176 176
 #' @aliases UP
177 177
 #' @rdname distal-class
178 178
 #' @export
... ...
@@ -190,7 +190,7 @@ as.character.UP <- function(obj) {
190 190
 }
191 191
 
192 192
 
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-#' @name DOWN
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+#' @name DISTAL-Object
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 #' @aliases DOWN
195 195
 #' @rdname distal-class
196 196
 #' @export
... ...
@@ -87,7 +87,7 @@ as.character.OPERATOR <- function(obj) {
87 87
 #' exp = read_dataset(test_path)
88 88
 #' out = select(exp, metadata_update = list(concSq = SQRT("concentration")))
89 89
 #' 
90
-#' @name META
90
+#' @name OPERATOR-Object
91 91
 #' @aliases META
92 92
 #' @rdname operator-class
93 93
 #' @export
... ...
@@ -123,7 +123,7 @@ check.META <- function(type)
123 123
 }
124 124
 
125 125
 
126
-#' @name NIL
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+#' @name OPERATOR-Object
127 127
 #' @aliases NIL
128 128
 #' @rdname operator-class
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 #' @export
... ...
@@ -147,7 +147,7 @@ check.NIL <- function(value)
147 147
     
148 148
 }
149 149
 
150
-#' @name SQRT
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+#' @name OPERATOR-Object
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 #' @aliases SQRT
152 152
 #' @rdname operator-class
153 153
 #' @export
... ...
@@ -4,9 +4,9 @@
4 4
 
5 5
 #' Condition evaluation functions
6 6
 #'
7
-#' These functions is used to support joinBy and/or groupBy function parameter.
7
+#' These functions are used to support joinBy and/or groupBy function parameter.
8 8
 #' It create a list of one element: matrix containing the two coloumn:
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-#' type of condition evaluation and the metadata attribute
9
+#' type of condition evaluation and the metadata attribute name
10 10
 #'
11 11
 #' \itemize{
12 12
 #' \item{FN: It defines a FULL (FULLNAME) evaluation of the input values.
... ...
@@ -26,11 +26,9 @@
26 26
 #' 
27 27
 #' @examples
28 28
 #' 
29
-#' init_gmql()
30
-#' test_path <- system.file("example", "DATASET", package = "RGMQL")
31
-#' r = read_dataset(test_path)
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+#' "where is my example?"
32 30
 #' 
33
-#' @name FN
31
+#' @name Evaluation-Function
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 #' @aliases FN
35 33
 #' @rdname condition_eval_func
36 34
 #' @export
... ...
@@ -51,7 +49,7 @@ FN <- function(...)
51 49
     join_condition_matrix
52 50
 }
53 51
 
54
-#' @name EX
52
+#' @name Evaluation-Function
55 53
 #' @aliases EX
56 54
 #' @rdname condition_eval_func
57 55
 #' @export
... ...
@@ -72,7 +70,7 @@ EX <- function(...)
72 70
     join_condition_matrix
73 71
 }
74 72
 
75
-#' @name DF
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+#' @name Evaluation-Function
76 74
 #' @aliases DF
77 75
 #' @rdname condition_eval_func
78 76
 #' @export
79 77
similarity index 86%
80 78
rename from R/GMQL4TFarm.R
81 79
rename to R/filter-extract-function.R
... ...
@@ -1,44 +1,44 @@
1
-#' FILTER AND EXTRACT
1
+#' Filter and extract function
2 2
 #'
3
-#' This function let user to create a new GRangeslist with fixed information:
4
-#' seqnames,ranges ans strand and a variable part made up by the regions
5
-#' defined as input.
6
-#' The metadata and metadata_prefix are used to filter the data and choose
7
-#' only the samples that match at least one metdatata with its prefix.
8
-#' The input regions are shown for each sample obtained from filtering.
3
+#' This function lets user to create a new GRangesList with fixed information:
4
+#' seqnames, ranges and strand, and a variable part made up by the regions
5
+#' defined as input. The metadata and metadata_prefix are used to filter 
6
+#' the data and choose only the samples that match at least one metdatata 
7
+#' with its prefix. The input regions are shown for each sample obtained 
8
+#' from filtering.
9 9
 #'
10 10
 #' @import xml2
11 11
 #' @importFrom dplyr bind_cols
12 12
 #' @importFrom data.table fread
13 13
 #' @importFrom rtracklayer import
14 14
 #'
15
-#' @param data string GMQL dataset folder path or GrangesList
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+#' @param data string GMQL dataset folder path or GRangesList
16 16
 #' object
17
-#' @param metadata vector of character containing names of metadata
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+#' @param metadata vector of strings containing names of metadata attributes
18 18
 #' to be searched for in metadata files.
19
-#' data will be extracted if at least one condition is satisfied:
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-#' this condition will be logically "ANDed" with prefix filtering (see below)
21
-#' if NULL no filtering action occured
22
-#' (i.e every sample will be taken for regions filtering)
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-#' @param metadata_prefix vector of character that will filter metadata
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+#' Data will be extracted if at least one condition is satisfied:
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+#' this condition is logically "ANDed" with prefix filtering (see below)
21
+#' if NULL no filtering action occures
22
+#' (i.e every sample is taken for region filtering)
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+#' @param metadata_prefix vector of strings that will filter metadata
24 24
 #' containing rispectively every element of this vector.
25
-#' number of elelment in both vector must match
26
-#' @param regions vector of character that will extract only region
27
-#' attribute specified; if NULL no regions will be taken and the output
28
-#' will be only GRanges made up by the first attribute
29
-#' (seqnames,start,end,strand)
25
+#' number of element in both vector must match
26
+#' @param regions vector of strings that extracts only region attribute 
27
+#' specified; if NULL no regions attribute is taken and the output is only 
28
+#' GRanges made up by the region coordinate attributes 
29
+#' (seqnames, start, end, strand)
30 30
 #' @param suffix name for each metadata column of GRanges. by default is the 
31 31
 #' "antibody_target". This string is taken from sample metadata file or from
32 32
 #' metadata() associated. If not present, the column name is the name of 
33 33
 #' selected regions
34 34
 #'
35 35
 #' @details
36
-#' This function works only with datatset or Grangeslist that have the same
36
+#' This function works only with datatset or GRangesList that has the same
37 37
 #' information about regions attribute (but of course different value)
38 38
 #' In case of Grangeslist data input the function will search for metadata
39 39
 #' into metadata() function associated to Grangeslist.
40 40
 #'
41
-#' @return granges with selected regions (if any) in elementMetadata
41
+#' @return Granges with selected regions (if any) in elementMetadata
42 42
 #'
43 43
 #' @examples
44 44
 #'
... ...
@@ -5,14 +5,15 @@
5 5
 #' specified) by “collapsing” the input dataset samples and their regions 
6 6
 #' according to certain rules specified by the input parameters.
7 7
 #' The attributes of the output genomic regions are only the region 
8
-#' coordinates, and Jaccard indexes (JaccardIntersect and JaccardResult).
8
+#' coordinates, and Jaccard indexes (\emph{JaccardIntersect} and 
9
+#' \emph{JaccardResult}).
9 10
 #' Jaccard Indexes are standard measures of similarity of the contributing 
10 11
 #' regions, added as default region attributes.
11 12
 #' The JaccardIntersect index is calculated as the ratio between the lengths 
12 13
 #' of the intersection and of the union of the contributing regions; 
13 14
 #' the JaccardResult index is calculated as the ratio between the lengths 
14 15
 #' of the result and the union of the contributing regions.
15
-#' If aggregate functions are specified, a new attributes is added for 
16
+#' If aggregate functions are specified, a new attribute is added for 
16 17
 #' each aggregate function specified.
17 18
 #' Output metadata are the union of the input ones.
18 19
 #' If \emph{groupby} clause is specified, the input samples are partitioned 
... ...
@@ -24,54 +25,47 @@
24 25
 #' 
25 26
 #' @include AllClasses.R
26 27
 #' @importFrom methods is
27
-#' @importFrom rJava J
28
-#' @importFrom rJava .jnull
29
-#' @importFrom rJava .jarray
28
+#' @importFrom rJava J .jnull .jarray
30 29
 #' 
31 30
 #' @param data GMQLDataset class object
32 31
 #' @param min_acc minimum number of overlapping regions to be considered 
33
-#' during execution
34
-#' Is a integer number, declared also as string.
35
-#' minAcc accept also:
32
+#' during execution. It is an integer number, declared also as string.
33
+#' minAcc accepts also:
36 34
 #' \itemize{
37
-#' \item{PARAMETER class object: \code{\link{ALL}} that represents the number 
35
+#' \item{PARAMETER class object: \code{\link{ALL}}, that represents the number 
38 36
 #' of samples in the input dataset}
39
-#' \item{and expression built using PARAMETER object: (ALL() + N) / K or
40
-#' ALL() / K }
37
+#' \item{an expression built using PARAMETER object: (ALL() + N) / K or
38
+#' ALL() / K, with N and K integer values }
41 39
 #' }
42 40
 #' @param max_acc maximum number of overlapping regions to be considered 
43
-#' during execution
44
-#' Is a integer number, declared also as string.
41
+#' during execution. It is an integer number, declared also as string.
45 42
 #' maxAcc accept also:
46 43
 #' \itemize{
47
-#' \item{PARAMETER class object: \code{\link{ALL}} that represents the number 
44
+#' \item{PARAMETER class object: \code{\link{ALL}}, that represents the number 
48 45
 #' of samples in the input dataset}
49
-#' \item{PARAMETER calss object: \code{\link{ANY}}} that acts as a wildcard, 
50
-#' considering any amount of overlapping.
51
-#' \item{and expression built using PARAMETER object: (ALL() + N) / K or
52
-#' ALL() / K }
46
+#' \item{PARAMETER class object: \code{\link{ANY}}}, that acts as a wildcard, 
47
+#' considering any amount of overlapping regions.
48
+#' \item{an expression built using PARAMETER object: (ALL() + N) / K or
49
+#' ALL() / K, with N and K integer values  }
53 50
 #' }
54
-#' @param groupBy list of evalation function to define condition 
55
-#' evaluation on metadata:
51
+#' @param groupBy list of evalation functions to define evaluation on metadata:
56 52
 #' \itemize{
57
-#' \item{\code{\link{FN}}: Fullname evaluation, two attributes match 
58
-#' if they both end with value and, if they have a further prefixes,
53
+#' \item{\code{\link{FN}}(value): Fullname evaluation, two attributes match 
54
+#' if they both end with \emph{value} and, if they have further prefixes,
59 55
 #' the two prefix sequence are identical}
60
-#' \item{\code{\link{EX}}: Exact evaluation, only attributes exactly 
61
-#' as value will match; no further prefixes are allowed. }
62
-#' \item{\code{\link{DF}}: Default evaluation, the two attributes match 
63
-#' if both end with value.}
56
+#' \item{\code{\link{EX}}(value): Exact evaluation, only attributes exactly 
57
+#' as \emph{value} match; no further prefixes are allowed. }
58
+#' \item{\code{\link{DF}}(value): Default evaluation, the two attributes match 
59
+#' if both end with \emph{value}.}
64 60
 #' }
65
-#' @param ... Additional arguments for use in specific methods.
66
-#' 
67
-#' In this case a series of element in the form \emph{key} = \emph{aggregate}.
68
-#' The \emph{aggregate} is an object of class AGGREGATES
69
-#' The aggregate functions available are: \code{\link{SUM}}, 
61
+#' @param ... a series of expressions separated by comma in the form 
62
+#' \emph{key} = \emph{aggregate}. The \emph{aggregate} is an object of 
63
+#' class AGGREGATES. The aggregate functions available are: \code{\link{SUM}}, 
70 64
 #' \code{\link{COUNT}}, \code{\link{MIN}}, \code{\link{MAX}}, 
71 65
 #' \code{\link{AVG}}, \code{\link{MEDIAN}}, \code{\link{STD}}, 
72 66
 #' \code{\link{BAG}}, \code{\link{BAGD}}, \code{\link{Q1}}, 
73 67
 #' \code{\link{Q2}}, \code{\link{Q3}}.
74
-#' Every aggregate accepts a string value, execet for COUNT, which does not 
68
+#' Every aggregate accepts a string value, except for COUNT, which does not 
75 69
 #' have any value.
76 70
 #' Argument of 'aggregate function' must exist in schema, i.e. among region 
77 71
 #' attributes. Two style are allowed:
... ...
@@ -81,53 +75,60 @@
81 75
 #' }
82 76
 #' "mixed style" is not allowed
83 77
 #'
84
-#' @param variation string identifying the cover GMQL function variation.
85
-#' The admissible string are:
78
+#' @param variation string identifying the cover GMQL operator variation.
79
+#' The admissible strings are:
86 80
 #' \itemize{
87
-#' \item{FLAT: returns the contiguous region that starts from the first end 
88
-#' and stops at the last end of the regions which would contribute 
89
-#' to each region of the \emph{cover}.}
90
-#' \item{SUMMIT: returns regions that start from a position
81
+#' \item{FLAT: It returns the regions that start from the first end and stop 
82
+#' at the last end of the regions which would contribute to each region 
83
+#' of the \emph{cover}.}
84
+#' \item{SUMMIT: It returns regions that start from a position
91 85
 #' where the number of intersecting regions is not increasing afterwards and
92
-#' stops at a position where either the number of intersecting regions 
86
+#' stop at a position where either the number of intersecting regions 
93 87
 #' decreases, or it violates the max accumulation index.}
94
-#' \item{HISTOGRAM: returns the non-overlapping regions contributing to 
95
-#' the cover, each with its accumulation index value, which is assigned to 
96
-#' the AccIndex region attribute.}
88
+#' \item{HISTOGRAM: It returns the non-overlapping regions contributing to 
89
+#' the \emph{cover}, each with its accumulation index value, which is assigned 
90
+#' to the \emph{AccIndex} region attribute.}
97 91
 #' \item{COVER: default value.}
98 92
 #' }
93
+#' Can be all caps or lowercase
99 94
 #' 
100 95
 #' @return GMQLDataset object. It contains the value to use as input 
101 96
 #' for the subsequent GMQLDataset method
102 97
 #' 
103 98
 #' @examples
104 99
 #' 
105
-#' ## This statement produces an output dataset with a single output sample. 
106
-#' ## The COVER operation considers all areas defined by a minimum 
107
-#' ## of two overlapping regions in the input samples, 
108
-#' ## up to any amount of overlapping regions.
100
+#' ## This statement initializes and runs the GMQL server for local execution 
101
+#' ## and creation of results on disk. Then, with system.file() it defines 
102
+#' ## the path to the folder "DATASET" in the subdirectory "example"
103
+#' ## of the package "RGMQL" and opens such file as a GMQL dataset named "exp" 
104
+#' ## using customParser
109 105
 #' 
110 106
 #' init_gmql()
111 107
 #' test_path <- system.file("example","DATASET",package = "RGMQL")
112 108
 #' exp = read_dataset(test_path)
109
+#'   
110
+#' ## the following statement produces an output dataset with a single output 
111
+#' ## sample. The COVER operation considers all areas defined by a minimum 
112
+#' ## of two overlapping regions in the input samples, up to any amount of 
113
+#' ## overlapping regions.
114
+#' 
113 115
 #' res = cover(exp, 2, ANY())
114 116
 #'
115
-#' \dontrun{
116
-#' ## This GMQL statement computes the result grouping the input exp samples 
117
-#' ## by the values of their cell metadata attribute, 
117
+#' ## The following GMQL statement computes the result grouping the input 
118
+#' ## exp samples by the values of their cell metadata attribute, 
118 119
 #' ## thus one output res sample is generated for each cell type; 
119 120
 #' ## output regions are produced where at least 2 and at most 3 regions 
120 121
 #' ## of grouped exp samples overlap, setting as attributes of the resulting 
121 122
 #' ## regions the minimum pvalue of the overlapping regions (min_pvalue) 
122 123
 #' ## and their Jaccard indexes (JaccardIntersect and JaccardResult).
123 124
 #' 
124
-#' test_path <- system.file("example", "DATASET", package = "RGMQL")
125
-#' exp = read_dataset(test_path)
126 125
 #' res = cover(exp, 2, 3, groupBy = list(DF("cell")), 
127 126
 #' min_pValue = MIN("pvalue"))
128
-#' }
129 127
 #' 
130
-#' @aliases cover cover-method
128
+#' @name cover
129
+#' @rdname cover
130
+#' @aliases cover,GMQLDataset-method
131
+#' @aliases cover-method
131 132
 #' @export
132 133
 setMethod("cover", "GMQLDataset",
133 134
             function(data, min_acc, max_acc, groupBy = NULL, 
... ...
@@ -56,6 +56,8 @@
56 56
 #' 
57 57
 #' }
58 58
 #' 
59
+#' @name extend
60
+#' @rdname extend
59 61
 #' @aliases extend-method
60 62
 #' @export
61 63
 setMethod("extend", "GMQLDataset", function(.data, ...)
... ...
@@ -1,32 +1,83 @@
1
+group_by.GMQLDateset <- function(.data, groupBy_meta = NULL, 
2
+    groupBy_regions = NULL, region_aggregates = NULL, meta_aggregates = NULL)
3
+{
4
+    ptr_data = .data@value
5
+    gmql_group(ptr_data, groupBy_meta, groupBy_regions, region_aggregates, 
6
+                meta_aggregates)
7
+}
8
+
1 9
 #' Method group_by
2 10
 #' 
11
+#' @description Wrapper to GMQL GROUP operator
3 12
 #' 
4 13
 #' @importFrom rJava J .jarray .jnull
5
-#' 
14
+#' @importFrom dplyr group_by
6 15
 #' 
7 16
 #' @param .data GMQLDataset object
8
-#'
17
+#' @param groupBy_meta it define condition evaluation on metadata.
18
+#' \itemize{
19
+#' \item{\code{\link{FN}}: Fullname evaluation, two attributes match 
20
+#' if they both end with value and, if they have a further prefixes,
21
+#' the two prefix sequence are identical}
22
+#' \item{\code{\link{EX}}: Exact evaluation, only attributes exactly 
23
+#' as value will match; no further prefixes are allowed. }
24
+#' \item{\code{\link{DF}}: Default evaluation, the two attributes match 
25
+#' if both end with value.}
26
+#' }
27
+#' @param groupBy_regions vector of string made up by schema field attribute
28
+#' @param region_aggregates It accept a series of aggregate function on 
29
+#' region attribute. 
30
+#' All the element in the form \emph{key} = \emph{aggregate}.
31
+#' The \emph{aggregate} is an object of class AGGREGATES
32
+#' The aggregate functions available are: \code{\link{SUM}}, 
33
+#' \code{\link{COUNT}}, \code{\link{MIN}}, \code{\link{MAX}}, 
34
+#' \code{\link{AVG}}, \code{\link{MEDIAN}}, \code{\link{STD}}, 
35
+#' \code{\link{BAG}}, \code{\link{BAGD}}, \code{\link{Q1}}, 
36
+#' \code{\link{Q2}}, \code{\link{Q3}}.
37
+#' Every aggregate accepts a string value, execet for COUNT, which does not 
38
+#' have any value.
39
+#' Argument of 'aggregate function' must exist in schema, i.e. among region 
40
+#' attributes. Two style are allowed:
41
+#' \itemize{
42
+#' \item list of key-value pairs: e.g. sum = SUM("pvalue")
43
+#' \item list of values: e.g. SUM("pvalue")
44
+#' }
45
+#' "mixed style" is not allowed
46
+#' @param meta_aggregates It accept a series of aggregate function on 
47
+#' metadata attribute.
48
+#' All the element in the form \emph{key} = \emph{aggregate}.
49
+#' The \emph{aggregate} is an object of class AGGREGATES
50
+#' The aggregate functions available are: \code{\link{SUM}}, 
51
+#' \code{\link{COUNT}}, \code{\link{MIN}}, \code{\link{MAX}}, 
52
+#' \code{\link{AVG}}, \code{\link{MEDIAN}}, \code{\link{STD}}, 
53
+#' \code{\link{BAG}}, \code{\link{BAGD}}, \code{\link{Q1}}, 
54
+#' \code{\link{Q2}}, \code{\link{Q3}}.
55
+#' Every aggregate accepts a string value, execet for COUNT, which does not 
56
+#' have any value.
57
+#' Argument of 'aggregate function' must exist in schema, i.e. among region 
58
+#' attributes. Two style are allowed:
59
+#' \itemize{
60
+#' \item list of key-value pairs: e.g. sum = SUM("cell")
61
+#' \item list of values: e.g. SUM("cell")
62
+#' }
63
+#' "mixed style" is not allowed
64
+#' 
65
+#' 
9 66
 #' @return GMQLDataset object. It contains the value to use as input 
10 67
 #' for the subsequent GMQLDataset method
11 68
 #'
12 69
 #' @examples
13 70
 #' 
14
-#' 
15
-#' 
16
-#' @aliases group_by group_by-method
71
+#' @name group_by
72
+#' @rdname group_by
73
+#' @aliases group_by,GMQLDataset-method
74
+#' @aliases group_by-method
17 75
 #' @export
18
-setMethod("group_by","GMQLDataset",
19
-          function(.data, groupBy_meta = NULL, groupBy_regions = NULL, 
20
-            region_aggregates = NULL, meta_aggregates = NULL)
21
-          {
22
-              ptr_data = .data@value
23
-              gmql_group(ptr_data, groupBy_meta, groupBy_regions, 
24
-                            region_aggregates, meta_aggregates)
25
-          })
76
+setMethod("group_by","GMQLDataset",group_by.GMQLDateset)
26 77
 
27
-gmql_group <- function(input_data, group_meta, group_reg)
78
+gmql_group <- function(input_data, group_meta, group_reg, region_aggregates, 
79
+                            meta_aggregates)
28 80
 {
29
-    
30 81
     if(!is.null(group_meta))
31 82
     {
32 83
         cond <- .join_condition(group_meta)
... ...
@@ -69,7 +120,7 @@ gmql_group <- function(input_data, group_meta, group_reg)
69 120
     
70 121
     WrappeR <- J("it/polimi/genomics/r/Wrapper")
71 122
     response <- WrappeR$group(join_condition_matrix, metadata_matrix, 
72
-                                groupBy_regions, region_matrix, input_data)
123
+                                group_reg, region_matrix, input_data)
73 124
     error <- strtoi(response[1])
74 125
     data <- response[2]
75 126
     if(error!=0)
... ...
@@ -74,6 +74,8 @@
74 74
 #' out = map(ref,exp, minScore = MIN("score"), 
75 75
 #' joinBy = list(DF("cell_tissue")))
76 76
 #' 
77
+#' @name map
78
+#' @rdname map
77 79
 #' @aliases map-method
78 80
 #' @export
79 81
 setMethod("map", "GMQLDataset",
... ...
@@ -13,7 +13,7 @@
13 13
 #' test_path <- system.file("example","DATASET",package = "RGMQL")
14 14
 #' rd = read_dataset(test_path)
15 15
 #' filtered = filter(rd)
16
-#' aggr = aggregate(filtered, DF("antibody_targer","cell_karyotype"))
16
+#' aggr = aggregate(filtered, list(DF("antibody_targer","cell_karyotype")))
17 17
 #' collect(aggr, dir_out = test_path)
18 18
 #' 
19 19
 #' \dontrun{
... ...
@@ -65,45 +65,61 @@ execute <- function()
65 65
     }
66 66
 }
67 67
 
68
+collect.GMQLDataset <- function(x, dir_out = getwd(), name = "ds1")
69
+{
70
+    ptr_data <- x@value
71
+    gmql_materialize(ptr_data, dir_out, name)
72
+}
68 73
 
69 74
 
70 75
 #' Method collect
71 76
 #'
72
-#' It saves the contents of a dataset that contains samples metadata and 
73
-#' samples regions.
74
-#' It is normally used to persist the contents of any dataset generated 
75
-#' during a GMQL query.
77
+#' @description Wrapper to GMQL MATERIALIZE operator
78
+#' 
79
+#' @description It saves the content of a dataset that contains samples 
80
+#' metadata and regions. It is normally used to persist the content of any 
81
+#' dataset generated during a GMQL query.
76 82
 #' Any dataset can be materialized, but the operation can be time-consuming.
77 83
 #' For best performance, materialize the relevant data only.
78 84
 #'
79 85
 #' @importFrom rJava J
86
+#' @importFrom dplyr collect
80 87
 #' 
81 88
 #' @param x GMQLDataset class object
82 89
 #' @param dir_out destination folder path.
83
-#' by default is current working directory of the R process
84
-#' @param name name of the result dataset
90
+#' by default it is the current working directory of the R process
91
+#' @param name name of the result dataset. by default it is the string "ds1"
85 92
 #' 
86
-#' @param ... Additional arguments for use in specific methods
93
+#' @details 
94
+#' 
95
+#' An error occures if the directory already exist at the destination
96
+#' folder path
87 97
 #' 
88 98
 #' @return None
89 99
 #'
90 100
 #' @examples
101
+#' 
102
+#' ## This statement initializes and runs the GMQL server for local execution 
103
+#' ## and creation of results on disk. Then, with system.file() it defines 
104
+#' ## the path to the folder "DATASET" in the subdirectory "example"
105
+#' ## of the package "RGMQL" and opens such file as a GMQL dataset named 
106
+#' ## "data" using customParser
91 107
 #'
92 108
 #' init_gmql()
93
-#' test_path <- system.file("example","DATASET",package = "RGMQL")
94
-#' r = read_dataset(test_path)
95
-#' s = filter(r)
96
-#' m = aggregate(s, DF("antibody_targer","cell_karyotype"))
97
-#' collect(m, dir_out = test_path)
109
+#' test_path <- system.file("example", "DATASET", package = "RGMQL")
110
+#' data = read_dataset(test_path)
111
+#' 
112
+#' ## The following statement materialize the dataset, previoulsy read, at 
113
+#' ## th specific destination path into local folder "ds1" opportunely created
114
+#' 
115
+#' collect(data, dir_out = test_path)
98 116
 #' 
117
+#' @name collect
118
+#' @rdname collect
119
+#' @aliases collect,GMQLDataset-method
99 120
 #' @aliases collect-method
100 121
 #' @export
101
-setMethod("collect", "GMQLDataset",
102
-            function(x, dir_out = getwd(), name = "ds1")
103
-            {
104
-                ptr_data <- x@value
105
-                gmql_materialize(ptr_data, dir_out, name)
106
-            })
122
+setMethod("collect", "GMQLDataset",collect.GMQLDataset)
107 123
 
108 124
 gmql_materialize <- function(input_data, dir_out, name)
109 125
 {
... ...
@@ -157,9 +173,11 @@ gmql_materialize <- function(input_data, dir_out, name)
157 173
 #' init_gmql()
158 174
 #' test_path <- system.file("example", "DATASET", package = "RGMQL")
159 175
 #' rd = read_dataset(test_path)
160
-#' aggr = aggregate(rd, DF("antibody_target", "cell_karyotype"))
176
+#' aggr = aggregate(rd, list(DF("antibody_target", "cell_karyotype")))
161 177
 #' taken <- take(aggr, rows = 45)
162 178
 #' 
179
+#' @name take
180
+#' @rdname take
163 181
 #' @aliases take-method
164 182
 #' @export
165 183
 setMethod("take", "GMQLDataset",
... ...
@@ -169,7 +187,6 @@ setMethod("take", "GMQLDataset",
169 187
                 gmql_take(ptr_data, rows)
170 188
             })
171 189
 
172
-
173 190
 gmql_take <- function(input_data, rows = 0L)
174 191
 {
175 192
     rows <- as.integer(rows[1])
... ...
@@ -1,60 +1,73 @@
1 1
 #' Method aggregate
2 2
 #' 
3
-#' It builds a dataset consisting of a single sample having as many regions as
4
-#' the number of regions of the input data and as many metadata as the union of
5
-#' the 'attribute-value' tuples of the input samples. 
6
-#' If at least one evaluation function is specified: the samples are then 
3
+#' @description Wrapper to GMQL MERGE operator
4
+#' 
5
+#' @description It builds a dataset consisting of a single sample having as 
6
+#' many regions as the number of regions of all the input dataset samples
7
+#' and as many metadata as the union of the 'attribute-value' tuples of the 
8
+#' input samples. If \emph{groupBy} is specified: the samples are then 
7 9
 #' partitioned in groups, each with a distinct value of the grouping metadata 
8 10
 #' attributes. The operation is separately applied to each group, yielding 
9
-#' one sample in the result for each group. 
10
-#' Samples whose names are not present in the grouping metadata parameter 
11
-#' are disregarded.
11
+#' one sample in the result for each group. Samples whose metadata are 
12
+#' not present in the grouping metadata parameter are disregarded.
12 13
 #'
13 14
 #' @importFrom rJava J .jarray .jnull
14 15
 #' @importFrom S4Vectors aggregate
15 16
 #'
16 17
 #' @param x GMQLDataset class object
17
-#' @param ... Additional arguments for use in specific methods.
18
-#' It accepts a list of evalation function to define evaluation on metadata:
18
+#' @param groupBy list of evalation functions to define evaluation on metadata:
19 19
 #' \itemize{
20
-#' \item{\code{\link{FN}}: Fullname evaluation, two attributes match 
21
-#' if they both end with value and, if they have a further prefixes,
22
-#' the two prefix sequence are identical}
23
-#' \item{\code{\link{EX}}: Exact evaluation, only attributes exactly 
24
-#' as value will match; no further prefixes are allowed. }
25
-#' \item{\code{\link{DF}}: Default evaluation, the two attributes match 
26
-#' if both end with value.}
20
+#' \item{\code{\link{FN}}(value): Fullname evaluation, two attributes match 
21
+#' if they both end with \emph{value} and, if they have further prefixes,
22
+#' the two prefix sequences are identical}
23
+#' \item{\code{\link{EX}}(value): Exact evaluation, only attributes exactly 
24
+#' as \emph{value} match; no further prefixes are allowed. }
25
+#' \item{\code{\link{DF}}(value): Default evaluation, the two attributes match 
26
+#' if both end with \emph{value}.}
27 27
 #' }
28 28
 #' 
29 29
 #' @return GMQLDataset object. It contains the value to use as input 
30 30
 #' for the subsequent GMQLDataset method
31 31
 #'
32 32
 #' @examples
33
-#'
34
-#' # It creates a dataset called merged which contains one sample for each
35
-#' # antibody_target value found within the metadata of the exp dataset sample;
36
-#' # each created sample contains all regions from all 'exp' samples
37
-#' # with a specific value for their antibody_target and cell metadata
38
-#' # attributes.
33
+#' 
34
+#' ## This statement initializes and runs the GMQL server for local execution 
35
+#' ## and creation of results on disk. Then, with system.file() it defines 
36
+#' ## the path to the folder "DATASET" in the subdirectory "example"
37
+#' ## of the package "RGMQL" and opens such file as a GMQL dataset named "exp" 
38
+#' ## using customParser
39 39
 #'
40 40
 #' init_gmql()
41 41
 #' test_path <- system.file("example","DATASET",package = "RGMQL")
42 42
 #' exp = read_dataset(test_path)
43
-#' merged = aggregate(exp, DF("antibody_target","cell"))
44 43
 #'
44
+#' ## This statement creates a dataset called merged which contains one 
45
+#' ## sample for each antibody_target and cell value found within the metadata 
46
+#' ## of the exp dataset sample; each created sample contains all regions 
47
+#' ## from all 'exp' samples with a specific value for their 
48
+#' ## antibody_target and cell metadata
49
+#' ## attributes.
50
+#'
51
+#' merged = aggregate(exp, list(DF("antibody_target","cell")))
52
+#'
53
+#' @name aggregate
54
+#' @rdname aggregate
55
+#' @aliases aggregate,GMQLDataset-method
45 56
 #' @aliases aggregate-method
46 57
 #' @export
47 58
 #' 
48 59
 setMethod("aggregate", "GMQLDataset",
49
-            function(x, ...)
60
+            function(x, groupBy = NULL)
50 61
             {
51 62
                 ptr_data = x@value
52
-                groupBy = list(...)
53 63
                 gmql_merge(ptr_data, groupBy)
54 64
             })
55 65
 
56 66
 gmql_merge <- function(input_data, groupBy)
57 67
 {
68
+    if(!is.list(groupBy))
69
+        stop("groupBy must be list")
70
+    
58 71
     if(!is.null(groupBy) && !length(groupBy) == 0)
59 72
     {
60 73
         cond <- .join_condition(groupBy)
... ...
@@ -1,77 +1,89 @@
1
+arrange.GMQLDataset <- function(.data, metadata_ordering = NULL, 
2
+        regions_ordering = NULL, fetch_opt = NULL, num_fetch = 0, 
3
+        reg_fetch_opt = NULL, reg_num_fetch = 0)
4
+{
5
+    ptr_data <- .data@value
6
+    gmql_order(ptr_data, metadata_ordering, regions_ordering, 
7
+                fetch_opt, num_fetch, reg_fetch_opt, reg_num_fetch)
8
+}
9
+
1 10
 #' Method arrange
2
-#'
3
-#' It is used to order either samples or sample regions or both, according to 
4
-#' a set of metadata and/or region attributes, and/or region coordinates.
5
-#' Order can be specified as ascending / descending for every attribute
6
-#' The number of samples and their regions remain the same 
7
-#' (unless fetching options are specified) but a new ordering metadata 
8
-#' and/or region attribute is added.
11
+#' 
12
+#' @description Wrapper to GMQL ORDER operator
13
+#' 
14
+#' @description It is used to order either samples or sample regions or both, 
15
+#' according to a set of metadata and/or region attributes.
16
+#' Order can be specified as ascending / descending for every attribute. 
17
+#' The number of samples and their regions remain the same as well as 
18
+#' their attributes, (unless fetching options are specified) but a new 
19
+#' ordering metadata and/or region attribute is added.
9 20
 #' Sorted samples or regions have a new attribute "order", 
10 21
 #' added to either metadata, or regions, or both of them as specified in inputs
11 22
 #'
12
-#' @importFrom rJava J
13
-#' @importFrom rJava .jnull
14
-#' @importFrom rJava .jarray
23
+#' @importFrom rJava J .jnull .jarray
24
+#' @importFrom dplyr arrange
15 25
 #' 
16 26
 #' @param .data GMQLDataset class object
17
-#' @param metadata_ordering list of ordering function contains name of 
18
-#' metadata.
19
-#' The function available are: \code{\link{ASC}}, \code{\link{DESC}}
27
+#' @param metadata_ordering list of ordering functions containing name of 
28
+#' metadata attribute.
29
+#' The functions available are: \code{\link{ASC}}, \code{\link{DESC}}
20 30
 #' 
21 31
 #' @param fetch_opt string indicating the option used to fetch the 
22
-#' first k sample:
32
+#' first k samples; it can assume the values:
23 33
 #' \itemize{
24
-#' \item{mtop: it fetch the first k sample}
25
-#' \item{mtopp: it fetch the first k sample in each group.}
26
-#' \item{mtopg: it fetch the percentage of sample.}
34
+#' \item{mtop: it fetches the first k samples}
35
+#' \item{mtopg: it fetches the percentage of samples.}
36
+#' \item{mtopp: it fetches the first k samples in each group.}
37
+#' 
27 38
 #' }
28 39
 #' if NULL, \emph{num_fetch} is not considered 
29 40
 #' 
30
-#' @param num_fetch integer value identifying the number of region to fetch
31
-#' by default is 0, that's means all sample are fetched
41
+#' @param num_fetch integer value identifying the number of samples to fetch;
42
+#' by default it is 0, that means all samples are fetched
32 43
 #' s
33
-#' @param regions_ordering list of ordering function contains 
34
-#' name of region schema value.
35
-#' The function available are: \code{\link{ASC}}, \code{\link{DESC}}.
44
+#' @param regions_ordering list of ordering functions containing name of 
45
+#' region attribute.
46
+#' The functions available are: \code{\link{ASC}}, \code{\link{DESC}}.
36 47
 #' 
37 48
 #' @param reg_fetch_opt string indicating the option used to fetch the 
38
-#' first k regions:
49
+#' first k regions; it can assume the values:
39 50
 #' \itemize{
40
-#' \item{rtop: it fetch the first k regions.}
41
-#' \item{rtopp: it fetch the first k regions in each group.}
42
-#' \item{rtopg: it fetch the percentage of regions.}
51
+#' \item{rtop: it fetches the first k regions.}
52
+#' \item{rtopg: it fetches the first k percentage of regions.}
53
+#' \item{rtopp: it fetches the first k regions in each group.}
43 54
 #' }
44 55
 #' if NULL, \emph{reg_num_fetch} is not considered 
45 56
 #' 
46 57
 #' @param reg_num_fetch integer value identifying the number of region to fetch
47
-#' by default is 0, that's means all regions are fetched
48
-#' @param ... Additional arguments for use in specific methods.
58
+#' by default it is 0, that means all regions are fetched
49 59
 #' 
50 60
 #' @return GMQLDataset object. It contains the value to use as input 
51 61
 #' for the subsequent GMQLDataset method
52 62
 #'
53 63
 #' @examples
54 64
 #' 
55
-#' ## It orders the samples according to the Region_count metadata attribute 
56
-#' ## and takes the two samples that have the highest count. 
57
-#'
65
+#' ## This statement initializes and runs the GMQL server for local execution 
66
+#' ## and creation of results on disk. Then, with system.file() it defines 
67
+#' ## the path to the folder "DATASET" in the subdirectory "example"
68
+#' ## of the package "RGMQL" and opens such file as a GMQL dataset named 
69
+#' ## "data" using customParser
70
+#' 
58 71
 #' init_gmql()
59
-#' test_path <- system.file("example","DATASET",package = "RGMQL")
60
-#' r = read_dataset(test_path)
61
-#' o = arrange(r, list(ASC("Region_Count")),fetch_opt = "mtop",num_fetch = 2)
72
+#' test_path <- system.file("example", "DATASET", package = "RGMQL")
73
+#' data = read_dataset(test_path)
62 74
 #' 
75
+#' ## The following statement orders the samples according to the Region_Count 
76
+#' ## metadata attribute and takes the two samples that have the highest count. 
77
+#'
78
+#' o = arrange(data, list(ASC("Region_Count")), fetch_opt = "mtop", 
79
+#' num_fetch = 2)
63 80
 #' 
64
-#' @aliases sort-method
81
+#' @name arrange
82
+#' @rdname arrange
83
+#' @aliases arrange,GMQLDataset-method
84
+#' @aliases arrange-method
65 85
 #' @export
66
-setMethod("arrange", "GMQLDataset",
67
-            function(.data, metadata_ordering = NULL, regions_ordering = NULL, 
68
-                    fetch_opt = NULL, num_fetch = 0, reg_fetch_opt = NULL, 
69
-                    reg_num_fetch = 0, ...)
70
-            {
71
-                ptr_data <- .data@value
72
-                gmql_order(ptr_data, metadata_ordering, regions_ordering, 
73
-                    fetch_opt, num_fetch, reg_fetch_opt, reg_num_fetch)
74
-            })
86
+setMethod("arrange", "GMQLDataset", arrange.GMQLDataset)
75 87
 
76 88
 gmql_order <- function(data, metadata_ordering, regions_ordering,
77 89
                     fetch_opt, num_fetch, reg_fetch_opt, reg_num_fetch)
... ...
@@ -135,8 +147,8 @@ gmql_order <- function(data, metadata_ordering, regions_ordering,
135 147
 {
136 148
     opt <- tolower(opt)
137 149
     if(!identical("mtop",opt) && !identical("mtopp",opt) && 
138
-                    !identical("mtopg",opt) && !identical("rtop",opt) && 
139
-                    !identical("rtopp",opt) && !identical("rtopg",opt))
150
+        !identical("mtopg",opt) && !identical("rtop",opt) && 
151
+        !identical("rtopp",opt) && !identical("rtopg",opt))
140 152
         stop("option not admissable")
141 153
     opt
142 154
 }
... ...
@@ -1,8 +1,37 @@
1
+select.GMQLDataset <- function(.data, metadata = NULL, metadata_update = NULL, 
2
+                            all_but_meta = FALSE, regions = NULL, 
3
+                            regions_update = NULL, all_but_reg = FALSE) 
4
+{
5
+    data = .data@value
6
+    r_update <- substitute(regions_update)
7
+    if(!is.null(r_update))
8
+    {
9
+        reg_update <- .trasform_update(deparse(r_update))
10
+        reg_update <- paste(reg_update,collapse = "")
11
+    }
12
+    else
13
+        reg_update <- .jnull("java/lang/String")
14
+    
15
+    m_update <- substitute(metadata_update)
16
+    if(!is.null(m_update))
17
+    {
18
+        meta_update <- .trasform_update(deparse(m_update))
19
+        meta_update <- paste(meta_update,collapse = "")
20
+    }
21
+    else
22
+        meta_update <- .jnull("java/lang/String")
23
+    
24
+    gmql_project(data, metadata, meta_update, all_but_meta, regions, 
25
+                    reg_update, all_but_reg)
26
+}
27
+
1 28
 #' Method select
2
-#'
3
-#' It creates, from an existing dataset, a new dataset with all the samples 
4
-#' from input dataset, but keeping for each sample in the input dataset 
5
-#' only those metadata and/or region attributes expressed.
29
+#' 
30
+#' @description Wrapper to GMQL PROJECT operator 
31
+#' 
32
+#' @description It creates, from an existing dataset, a new dataset with all 
33
+#' the samples from input dataset, but keeping for each sample in the input 
34
+#' dataset only those metadata and/or region attributes expressed.
6 35
 #' Region coordinates and values of the remaining metadata remain equal to 
7 36
 #' those in the input dataset. It allows to:
8 37
 #' \itemize{
... ...
@@ -10,9 +39,8 @@
10 39
 #' \item{Update new metadata and/or region attributes in the result}
11 40
 #' }
12 41
 #' 
13
-#' @importFrom rJava J
14
-#' @importFrom rJava .jnull
15
-#' @importFrom rJava .jarray
42
+#' @importFrom rJava J .jnull .jarray
43
+#' @importFrom dplyr select
16 44
 #' 
17 45
 #' @param .data GMQLDataset class object
18 46
 #' 
... ...
@@ -44,9 +72,7 @@
44 72
 #' \item{All basic mathematical operations (+, -, *, /), including parenthesis}
45 73
 #' \item{SQRT, META, NIL constructor object defined by OPERATOR object}
46 74
 #' }
47
-#' 
48
-#' @param ... Additional arguments for use in specific methods.
49
-#' 
75
+#'  
50 76
 #' @return GMQLDataset object. It contains the value to use as input 
51 77
 #' for the subsequent GMQLDataset method
52 78
 #'
... ...
@@ -87,37 +113,12 @@
87 113
 #' 
88 114
 #' }
89 115
 #' 
90
-#'
91
-#' @aliases select, select-method
116
+#' @name select
117
+#' @rdname select
118
+#' @aliases select,GMQLDataset-method
119
+#' @aliases select-method
92 120
 #' @export
93
-setMethod("select", "GMQLDataset",
94
-            function(.data, metadata = NULL, metadata_update = NULL, 
95
-                        all_but_meta = FALSE, regions = NULL, 
96
-                        regions_update = NULL, all_but_reg = FALSE, ...)
97
-            {
98
-                data = .data@value
99
-                r_update <- substitute(regions_update)
100
-                if(!is.null(r_update))
101
-                {
102
-                    reg_update <- .trasform_update(deparse(r_update))
103
-                    reg_update <- paste(reg_update,collapse = "")
104
-                }
105
-                else
106
-                    reg_update <- .jnull("java/lang/String")
107
-                
108
-                m_update <- substitute(metadata_update)
109
-                if(!is.null(m_update))
110
-                {
111
-                    meta_update <- .trasform_update(deparse(m_update))
112
-                    meta_update <- paste(meta_update,collapse = "")
113
-                }
114
-                else
115
-                    meta_update <- .jnull("java/lang/String")
116
-                
117
-                gmql_project(data, metadata, meta_update,
118
-                                all_but_meta, regions, 
119
-                                reg_update, all_but_reg)
120
-            })
121
+setMethod("select", "GMQLDataset",select.GMQLDataset)
121 122
 
122 123
 gmql_project <-function(input_data, metadata, metadata_update, all_but_meta, 
123 124
                             regions, regions_update, all_but_reg)
... ...
@@ -1,3 +1,32 @@
1
+filter.GMQLDateset <- function(.data, m_predicate = NULL, r_predicate = NULL, 
2
+                                    semijoin = NULL)
3
+{
4
+    val <- .data@value
5
+    meta_pred <- substitute(m_predicate)
6
+    if(!is.null(meta_pred))
7
+    {
8
+        predicate <- .trasform(deparse(meta_pred))
9
+        predicate <- paste(predicate,collapse = "")
10
+        predicate <- as.character(glue::glue(predicate))
11
+        
12
+    }
13
+    else
14
+        predicate <- .jnull("java/lang/String")
15
+    
16
+    reg_pred <- substitute(r_predicate)
17
+    if(!is.null(reg_pred))
18
+    {
19
+        region_predicate <- .trasform(deparse(reg_pred))
20
+        region_predicate <- paste(region_predicate,collapse = "")
21
+        region_predicate <- as.character(glue::glue(region_predicate))
22
+        
23
+    }
24
+    else
25
+        region_predicate <- .jnull("java/lang/String")
26
+    
27
+    gmql_select(val, predicate, region_predicate, semijoin)
28
+}
29
+
1 30
 #' Method filter
2 31
 #' 
3 32
 #' It creates a new dataset from an existing one by extracting a subset of 
... ...
@@ -22,7 +51,6 @@
22 51
 #' @param r_predicate logical predicate made up by R logical operation 
23 52
 #' on schema region values. 
24 53
 #' Only !, |, ||, &, && are admitted.
25
-#' @param ... Additional arguments for use in specific methods.
26 54
 #' 
27 55
 #' @param semijoin \code{\link{semijoin}} function to define filter method 
28 56
 #' with semijoin condition (see examples).
... ...
@@ -65,38 +93,12 @@
65 93
 #' semijoin(join_data, TRUE, list(DF("cell"))))
66 94
 #' 
67 95
 #' }
68
-#' 
69
-#' @aliases filter filter-method
96
+#' @name filter
97
+#' @rdname filter
98
+#' @aliases filter,GMQLDataset-method
99
+#' @aliases filter-method
70 100
 #' @export
71
-setMethod("filter", "GMQLDataset",
72
-        function(.data, m_predicate = NULL, r_predicate = NULL, 
73
-                    semijoin = NULL, ...)
74
-        {
75
-            val <- .data@value
76
-            meta_pred <- substitute(m_predicate)
77
-            if(!is.null(meta_pred))
78
-            {
79
-                predicate <- .trasform(deparse(meta_pred))
80
-                predicate <- paste(predicate,collapse = "")
81
-                predicate <- as.character(glue::glue(predicate))
82
-                
83
-            }
84
-            else
85
-                predicate <- .jnull("java/lang/String")
86
-            
87
-            reg_pred <- substitute(r_predicate)
88
-            if(!is.null(reg_pred))
89
-            {
90
-                region_predicate <- .trasform(deparse(reg_pred))
91
-                region_predicate <- paste(region_predicate,collapse = "")
92
-                region_predicate <- as.character(glue::glue(region_predicate))
93
-                
94
-            }
95
-            else
96
-                region_predicate <- .jnull("java/lang/String")
97
-            
98
-            gmql_select(val, predicate, region_predicate, semijoin)
99
-        })
101
+setMethod("filter", "GMQLDataset", filter.GMQLDateset)
100 102
 
101 103
 gmql_select <- function(input_data, predicate, region_predicate, s_join)
102 104
 {
... ...
@@ -194,6 +196,8 @@ semijoin <- function(data, not_in = FALSE, groupBy = NULL)
194 196
 }
195 197
 
196 198
 
199
+
200
+
197 201
 .trasform <- function(predicate=NULL)
198 202
 {
199 203
     predicate <- gsub("&|&&","AND",predicate)
... ...
@@ -6,35 +6,44 @@
6 6
 #' Ordering functions
7 7
 #'
8 8
 #' These functions is used to create a series of metadata as string
9
-#' that require ordering on value; is used only in arrange method.
10
-#' (see example)
9
+#' that require ordering on value; is used only in arrange method
10
+#' (see example).
11 11
 #' 
12 12
 #' \itemize{
13
-#' \item{ASC: It defines a ascending order for input value}
13
+#' \item{ASC: It defines an ascending order for input value}
14 14
 #' \item{DESC: It defines a descending order for input value}
15 15
 #' }
16 16
 #' 
17 17
 #' @param ... series of metatdata as string
18 18
 #'
19
-#' @return ordering object
19
+#' @return Ordering object
20 20
 #' 
21 21
 #' @examples
22 22
 #' 
23
+#' ## This statement initializes and runs the GMQL server for local execution 
24
+#' ## and creation of results on disk. Then, with system.file() it defines 
25
+#' ## the path to the folder "DATASET" in the subdirectory "example"
26
+#' ## of the package "RGMQL" and opens such file as a GMQL dataset named 
27
+#' ## "data" using customParser
28
+#' 
23 29
 #' init_gmql()
24 30
 #' test_path <- system.file("example", "DATASET", package = "RGMQL")
25
-#' r = read_dataset(test_path)
31
+#' data = read_dataset(test_path)
26 32
 #' 
27
-#' ## It orders the samples according to the Region_count metadata attribute 
28
-#' ## and takes the two samples that have the highest count. 
33
+#' ## It orders the samples according to the Region_Count metadata attribute 
34
+#' ## and takes the two samples that have the lowest count. 
29 35
 #'
30
-#' desc = arrange(r,list(ASC("Region_Count")), fetch_opt = "mtop", 
36
+#' asc = arrange(data, list(ASC("Region_Count")), fetch_opt = "mtop", 
31 37
 #' num_fetch = 2)
38
+#' 
39
+#' ## It orders the samples according to the pvalue schema attribute 
40
+#' ## and takes the first five samples that have the lowest pvalue and the 
41
+#' ## first seven regions.
32 42
 #'  
33
-#' asc = arrange(r, list(ASC("Region_Count")),list(DESC("score")),
34
-#' fetch_opt = "mtop", num_fetch = 5, reg_fetch_opt = "rtop", 
35
-#' reg_num_fetch = 7)
43
+#' desc = arrange(data, list(DESC("pvalue")), fetch_opt = "mtop", num_fetch = 5, 
44
+#' reg_fetch_opt = "rtop", reg_num_fetch = 7)
36 45
 #' 
37
-#' @name DESC
46
+#' @name Ordering-functions
38 47
 #' @aliases DESC
39 48
 #' @rdname ordering-class
40 49
 #' @export
... ...
@@ -56,7 +65,7 @@ DESC <- function(...)
56 65
     order_matrix
57 66
 }
58 67
 
59
-#' @name ASC
68
+#' @name Ordering-functions
60 69
 #' @aliases ASC
61 70
 #' @rdname ordering-class
62 71
 #' @export
... ...
@@ -341,25 +341,41 @@ run_query_fromfile <- function(url, fileName, filePath, output_gtf = TRUE)
341 341
 
342 342
 #' Compile GMQL query
343 343
 #'
344
-#' It compiles the GMQL query into repository taken from file or insert as text 
345
-#' string, using the proper GMQL web service available on a remote server
344
+#' It compiles a GMQL query taken from file or inserted as text string, 
345
+#' using the proper GMQL web service available on a remote server
346 346
 #' 
347 347
 #' 
348 348
 #' @import httr
349 349
 #' @param url string url of server: It must contain the server address 
350 350
 #' and base url; service name is added automatically
351 351
 #' @param query string text of the query
352
-#'
352
+#' @param filePath string path of txt file containing a GMQL query
353
+#' 
353 354
 #' @return None
354 355
 #'
355 356
 #' @examples
356 357
 #' 
357
-#' remote_url = "http://genomic.elet.polimi.it/gmql-rest-r/"
358
+#' ## Login to GMQL REST services suite as guest
359
+#' 
360
+#' remote_url = "http://genomic.deib.polimi.it/gmql-rest-r/"
358 361
 #' login_gmql(remote_url)
359
-#' compile_query(remote_url, "DATASET = SELECT() HG19_TCGA_dnaseq;
362
+#' 
363
+#' ## This statement get the query as text string and run the compile 
364
+#' ## web service
365
+#' 
366
+#' compile_query(remote_url, "DATASET = SELECT() Example_Dataset_1;
360 367
 #' MATERIALIZE DATASET INTO RESULT_DS;")
361 368
 #' 
369
+#' ## logout from GMQL REST services suite
370
+#' 
371
+#' logout_gmql(remote_url)
372
+#' 
362 373
 #' \dontrun{
374
+#' 
375
+#' ## This statement defines the path to the file "query1.txt" in the 
376
+#' ## subdirectory "example" of the package "RGMQL" and run the compile 
377
+#' ## web service
378
+#' 
363 379
 #' test_path <- system.file("example", package = "RGMQL")
364 380
 #' test_query <- file.path(test_path, "query1.txt")
365 381
 #' compile_query_fromfile(remote_url, test_query)
... ...
@@ -384,9 +400,6 @@ compile_query <- function(url, query)
384 400
         return(content)
385 401
 }
386 402
 
387
-
388
-#' @param filePath string path of txt files containing a GMQL query
389
-#'
390 403
 #' @name compile_query
391 404
 #' @rdname compile_query
392 405
 #' @export
... ...
@@ -833,7 +846,7 @@ upload_dataset <- function(url,datasetName,folderPath,schemaName=NULL,
833 846
 #' @return None
834 847
 #' 
835 848
 #' @details
836
-#' If no error occur, print "Deleted Dataset", otherwise a specific error 
849
+#' If no error occur, it prints "Deleted Dataset", otherwise a specific error 
837 850
 #' is printed
838 851
 #' 
839 852
 #' @examples
... ...
@@ -842,9 +855,9 @@ upload_dataset <- function(url,datasetName,folderPath,schemaName=NULL,
842 855
 #' 
843 856
 #' ## This dataset does not exist
844 857
 #' 
845
-#' remote_url <- "http://genomic.elet.polimi.it/gmql-rest-r/"
858
+#' remote_url <- "http://genomic.deib.polimi.it/gmql-rest-r/"
846 859
 #' login_gmql(remote_url)
847
-#' delete_dataset(remote_url, "job_test1_test101_20170604_180908_RESULT_DS")
860
+#' delete_dataset(remote_url, "test1_20170604_180908_RESULT_DS")
848 861
 #' 
849 862
 #' }
850 863
 #' 
851 864
deleted file mode 100644
... ...
@@ -1,574 +0,0 @@
1
-##gff-version 2
2
-##source-version rtracklayer 1.36.6
3
-##date 2017-12-04
4
-chr1	rtracklayer	sequence_feature	11873	12227	0	+	.	name "NR_046018"
5
-chr1	rtracklayer	sequence_feature	12612	12721	0	+	.	name "NR_046018"
6
-chr1	rtracklayer	sequence_feature	917444	917497	0	-	.	name "NM_001291366"
7
-chr1	rtracklayer	sequence_feature	934341	934812	0	-	.	name "NM_021170"
8
-chr1	rtracklayer	sequence_feature	934343	934812	0	-	.	name "NM_001142467"
9
-chr1	rtracklayer	sequence_feature	934905	934993	0	-	.	name "NM_021170"
10
-chr1	rtracklayer	sequence_feature	934905	934993	0	-	.	name "NM_001142467"
11
-chr1	rtracklayer	sequence_feature	935071	935167	0	-	.	name "NM_021170"
12
-chr1	rtracklayer	sequence_feature	935071	935552	0	-	.	name "NM_001142467"
13
-chr1	rtracklayer	sequence_feature	935245	935552	0	-	.	name "NM_021170"
14
-chr1	rtracklayer	sequence_feature	1017197	1018367	0	-	.	name "NM_017891"
15
-chr1	rtracklayer	sequence_feature	1019732	1019763	0	-	.	name "NM_017891"
16
-chr1	rtracklayer	sequence_feature	1019860	1019886	0	-	.	name "NM_017891"
17
-chr1	rtracklayer	sequence_feature	1021257	1021392	0	-	.	name "NM_017891"
18
-chr1	rtracklayer	sequence_feature	1022518	1022584	0	-	.	name "NM_017891"
19
-chr1	rtracklayer	sequence_feature	1022881	1022977	0	-	.	name "NM_017891"
20
-chr1	rtracklayer	sequence_feature	1025732	1025808	0	-	.	name "NM_017891"
21
-chr1	rtracklayer	sequence_feature	1026851	1026945	0	-	.	name "NM_017891"
22
-chr1	rtracklayer	sequence_feature	1027370	1027483	0	-	.	name "NM_017891"
23
-chr1	rtracklayer	sequence_feature	1051439	1051736	0	-	.	name "NM_017891"
24
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