@@ -64,7 +64,7 @@ Imports:

     glue,

     BiocGenerics

 Depends:

-    R(<= 3.4.2), RGMQLScalaLib

+    R(<= 3.4.2), RGMQLlib

 VignetteBuilder: knitr

 Suggests: 

     BiocStyle,

@@ -74,7 +74,7 @@ exportMethods(setdiff)

 exportMethods(take)

 exportMethods(union)

 import(GenomicRanges)

-import(RGMQLScalaLib)

+import(RGMQLlib)

 import(httr)

 import(xml2)

 importClassesFrom(GenomicRanges,GRangesList)

@@ -96,21 +96,21 @@ export_gmql <- function(samples, dir_out, is_gtf)

     files_sub_dir <- paste0(dir_out,"/files")

     dir.create(files_sub_dir)

-    c = .counter()

+    cnt = .counter()

     #col_names <- .get_schema_names(samples)

     if(to_GTF)

     {

         #write region

         lapply(samples,function(x,dir){

-            sample_name = paste0(dir,"/S_",c(),".gtf")

+            sample_name = paste0(dir,"/S_",cnt(),".gtf")

             g <- rtracklayer::export(x,sample_name,format = "gtf")

         },files_sub_dir)

-        c = .counter(0)

+        cnt = .counter(0)

         meta <- metadata(samples)

         #write metadata

         lapply(meta,function(x,dir){

-            sample_name = paste0(dir,"/S_",c(),".gtf")

+            sample_name = paste0(dir,"/S_",cnt(),".gtf")

             .write_metadata(x,sample_name)

         },files_sub_dir)

     }

@@ -118,18 +118,18 @@ export_gmql <- function(samples, dir_out, is_gtf)

     {

         #write region

         lapply(samples,function(x,dir){

-            sample_name = paste0(dir,"/S_",c(),".gdm")

+            sample_name = paste0(dir,"/S_",cnt(),".gdm")

             region_frame <- data.frame(x)

             write.table(region_frame,sample_name,col.names = FALSE,

                             row.names = FALSE, sep = '\t',quote = FALSE)

         },files_sub_dir)

-        c = .counter(0)

+        cnt = .counter(0)

         meta <- metadata(samples)

         #write metadata

         lapply(meta,function(x,dir){

-            sample_name = paste0(dir,"/S_",c(),".gdm")

+            sample_name = paste0(dir,"/S_",cnt(),".gdm")

             .write_metadata(x,sample_name)

         },files_sub_dir)

     }

@@ -3,7 +3,7 @@ group_by.GMQLDateset <- function(.data, groupBy_meta = conds(),

 {

     ptr_data = value(.data)

     gmql_group(ptr_data, groupBy_meta, groupBy_regions, region_aggregates, 

-                meta_aggregates)

+                    meta_aggregates)

 }

 #' Method group_by

@@ -71,8 +71,8 @@ group_by.GMQLDateset <- function(.data, groupBy_meta = conds(),

 #' test_path <- system.file("example","DATASET",package = "RGMQL")

 #' exp = read_GMQL(test_path)

 #' 

-#' ## This GMQL statement groups samples of the input 'exp' dataset according to 

-#' ## their value of the metadata attribute 'tumor_type' and computes the 

+#' ## This GMQL statement groups samples of the input 'exp' dataset according 

+#' ## to their value of the metadata attribute 'tumor_type' and computes the 

 #' ## maximum value that the metadata attribute size takes inside the samples 

 #' ## belonging to each group. The samples in the output GROUPS_T dataset 

 #' ## have a new _group metadata attribute which indicates which group they 

@@ -89,17 +89,17 @@ group_by.GMQLDateset <- function(.data, groupBy_meta = conds(),

 #' ## grouping attribute 'cell', and adds the metadata aggregate attribute 

 #' ## 'n_samp', which counts the number of samples belonging to the respective 

 #' ## group. It has the following output GROUPS_C dataset samples 

-#' ## (note that now no sample has metadata attribute _group with value equal 0 

-#' ## since all input samples include the metadata attribute cell, 

+#' ## (note that now no sample has metadata attribute _group with value 

+#' ## equal 0 since all input samples include the metadata attribute cell, 

 #' ## with different values, on which the new grouping is based)

 #' 

 #' GROUPS_C = group_by(exp, conds("cell"),

-#' meta_aggregates = list(n_samp AS COUNTSAMP()))

+#' meta_aggregates = list(n_samp = COUNTSAMP()))

 #' 

 #' ## This GMQL statement groups the regions of each 'exp' dataset sample by 

 #' ## region coordinates chr, left, right, strand  (these are implicitly 

-#' ## considered) and the additional region attribute score (which is explicitly 

-#' ## specified), and keeps only one region for each group. 

+#' ## considered) and the additional region attribute score (which is 

+#' ## explicitly specified), and keeps only one region for each group. 

 #' ## In the output GROUPS dataset schema, the new region attributes 

 #' ## avg_pvalue and max_qvalue are added, respectively computed as the 

 #' ## average of the values taken by the pvalue and the maximum of the values 

@@ -108,7 +108,7 @@ group_by.GMQLDateset <- function(.data, groupBy_meta = conds(),

 #' ## Note that the region attributes which are not coordinates or score are 

 #' ## discarded.

 #' 

-#' GROUPS = group_by(exp, group_reg = "score", 

+#' GROUPS = group_by(exp, groupBy_regions = "score", 

 #' region_aggregates = list(avg_pvalue = AVG("pvalue"), 

 #' max_qvalue = MAX("qvalue")))

 #' 

@@ -149,8 +149,8 @@ gmql_group <- function(input_data, group_meta, group_reg, region_aggregates,

         if(!length(group_reg))

             group_reg <- .jnull("java/lang/String")

-        

-        group_reg <- .jarray(group_reg)

+        else

+            group_reg <- .jarray(group_reg,dispatch = TRUE)

     }

     else

         group_reg <- .jnull("java/lang/String")

@@ -69,8 +69,8 @@ init_gmql <- function(output_format = "GTF", remote_processing = FALSE,

 #'

 #' @examples

 #'

-#' ## These statements initializes GMQL with local processing with sample files 

-#' ## output format as tab delimited and then stop it

+#' ## These statements initializes GMQL with local processing with sample 

+#' ## files output format as tab delimited and then stop it

 #' 

 #' init_gmql("tab", FALSE)

 #' 

@@ -102,8 +102,9 @@ stop_gmql <- function()

 #' 

 #' @examples

 #' 

-#' ## These statements initializes GMQL with local processing with sample files 

-#' ## output format as tab delimited and then change processing mode to remote

+#' ## These statements initializes GMQL with local processing with sample 

+#' ## files output format as tab delimited and then change processing mode 

+#' ## to remote

 #' 

 #' init_gmql("tab", remote_processing = FALSE)

 #' 

@@ -100,18 +100,6 @@ select.GMQLDataset <- function(.data, metadata = NULL, metadata_update = NULL,

 #' regions_update = list(new_score = (score / 1000.0) + 100), 

 #' regions = c("score"), all_but_reg = TRUE)

 #' 

-#' 

-#' ## It produces an output dataset that contains the same samples 

-#' ## as the input dataset. 

-#' ## Each output sample only contains, as region attributes, 

-#' ## the four basic coordinates (chr, left, right, strand) and the specified 

-#' ## region attributes 'variant_classification' and 'variant_type', 

-#' ## and as metadata attributes only the specified ones, 

-#' ## i.e. manually_curated_tissue_status and manually_curated_tumor_tag.

-#' 

-#' DS_out = select(data, regions = c("variant_classification", 

-#' "variant_type"), metadata = c("manually_curated_tissue_status", 

-#' "manually_curated_tumor_tag"))

 #'

 #' 

 #' @name select

@@ -61,7 +61,7 @@

 #' 

 #' remote_url = "http://genomic.deib.polimi.it/gmql-rest-r/"

 #' login_gmql(remote_url)

-#' data1 = read_GMQL("public.Example_Dataset1",is_local = FALSE)

+#' data1 = read_GMQL("public.Example_Dataset_1",is_local = FALSE)

 #' 

 #' @name read_GMQL

 #' @rdname read-function

@@ -1,5 +1,5 @@

 #' @importFrom rJava .jpackage .jinit

-#' @import RGMQLScalaLib

+#' @import RGMQLlib

 #' 

 .onLoad <- function(libname, pkgname) {

     .jpackage(pkgname, lib.loc = libname)

@@ -24,7 +24,7 @@ initGMQLscalaAPI <- function(libLoc, mem = "12G") {

     # Starting the java engine

     .jinit(force.init = TRUE)

     if (missing(libLoc)) {

-        libLoc = system.file("extdata", "java", package = "RGMQLScalaLib")

+        libLoc = system.file("extdata", "java", package = "RGMQLlib")

     }

     path = Sys.glob(paste0(libLoc, "/*.jar"))

@@ -664,7 +664,7 @@ show_datasets_list <- function(url)

 #' 

 #' ## It show all sample present into public dataset 'Example_Dataset1'

 #' 

-#' list <- show_samples_list(remote_url, "public.Example_Dataset1")

+#' list <- show_samples_list(remote_url, "public.Example_Dataset_1")

 #' 

 #' @name show_samples_list

 #' @rdname show_samples_list

@@ -712,7 +712,7 @@ show_samples_list <- function(url,datasetName)

 #' 

 #' ## show schema of public dataset 'Example_Dataset1'

 #' 

-#' list <- show_schema(remote_url, "public.Example_Dataset1")

+#' list <- show_schema(remote_url, "public.Example_Dataset_1")

 #' 

 #' @name show_schema

 #' @rdname show_schema

@@ -937,12 +937,12 @@ delete_dataset <- function(url,datasetName)

 #' 

 #' remote_url = "http://genomic.deib.polimi.it/gmql-rest-r"

 #' login_gmql(remote_url)

-#' download_dataset(remote_url, "public.Example_Dataset1", path = getwd())

+#' download_dataset(remote_url, "public.Example_Dataset_1", path = getwd())

 #' 

 #' ## Create GRangesList from public dataset Example_Dataset1 got 

 #' ## from repository

 #' 

-#' download_as_GRangesList(remote_url, "public.Example_Dataset1")

+#' download_as_GRangesList(remote_url, "public.Example_Dataset_1")

 #' }

 #' 

 #' @name download_dataset

@@ -1031,7 +1031,7 @@ download_as_GRangesList <- function(url,datasetName)

 #' ## This statement retrieves metadata for sample 'S_00000' from public 

 #' ## dataset 'Example_Dataset1'

 #' 

-#' sample_metadata(remote_url, "public.Example_Dataset1", "S_00000")

+#' sample_metadata(remote_url, "public.Example_Dataset_1", "S_00000")

 #' 

 #'

 #' @name sample_metadata

@@ -1095,7 +1095,7 @@ sample_metadata <- function(url, datasetName,sampleName)

 #' ## This statement retrieves regions data for sample "S_00000" from public 

 #' ## dataset "Example_Dataset1"

 #'  

-#' sample_region(remote_url, "public.Example_Dataset1", "S_00000")

+#' sample_region(remote_url, "public.Example_Dataset_1", "S_00000")

 #' 

 #' }

 #' 

@@ -40,12 +40,12 @@ If error occurs, a specific error is printed

 remote_url = "http://genomic.deib.polimi.it/gmql-rest-r"

 login_gmql(remote_url)

-download_dataset(remote_url, "public.Example_Dataset1", path = getwd())

+download_dataset(remote_url, "public.Example_Dataset_1", path = getwd())

 ## Create GRangesList from public dataset Example_Dataset1 got 

 ## from repository

-download_as_GRangesList(remote_url, "public.Example_Dataset1")

+download_as_GRangesList(remote_url, "public.Example_Dataset_1")

 }

 }

@@ -75,8 +75,8 @@ init_gmql()

 test_path <- system.file("example","DATASET",package = "RGMQL")

 exp = read_GMQL(test_path)

-## This GMQL statement groups samples of the input 'exp' dataset according to 

-## their value of the metadata attribute 'tumor_type' and computes the 

+## This GMQL statement groups samples of the input 'exp' dataset according 

+## to their value of the metadata attribute 'tumor_type' and computes the 

 ## maximum value that the metadata attribute size takes inside the samples 

 ## belonging to each group. The samples in the output GROUPS_T dataset 

 ## have a new _group metadata attribute which indicates which group they 

@@ -93,17 +93,17 @@ meta_aggregates = list(max_size = MAX("size")))

 ## grouping attribute 'cell', and adds the metadata aggregate attribute 

 ## 'n_samp', which counts the number of samples belonging to the respective 

 ## group. It has the following output GROUPS_C dataset samples 

-## (note that now no sample has metadata attribute _group with value equal 0 

-## since all input samples include the metadata attribute cell, 

+## (note that now no sample has metadata attribute _group with value 

+## equal 0 since all input samples include the metadata attribute cell, 

 ## with different values, on which the new grouping is based)

 GROUPS_C = group_by(exp, conds("cell"),

-meta_aggregates = list(n_samp AS COUNTSAMP()))

+meta_aggregates = list(n_samp = COUNTSAMP()))

 ## This GMQL statement groups the regions of each 'exp' dataset sample by 

 ## region coordinates chr, left, right, strand  (these are implicitly 

-## considered) and the additional region attribute score (which is explicitly 

-## specified), and keeps only one region for each group. 

+## considered) and the additional region attribute score (which is 

+## explicitly specified), and keeps only one region for each group. 

 ## In the output GROUPS dataset schema, the new region attributes 

 ## avg_pvalue and max_qvalue are added, respectively computed as the 

 ## average of the values taken by the pvalue and the maximum of the values 

@@ -112,7 +112,7 @@ meta_aggregates = list(n_samp AS COUNTSAMP()))

 ## Note that the region attributes which are not coordinates or score are 

 ## discarded.

-GROUPS = group_by(exp, group_reg = "score", 

+GROUPS = group_by(exp, groupBy_regions = "score", 

 region_aggregates = list(avg_pvalue = AVG("pvalue"), 

 max_qvalue = MAX("qvalue")))

@@ -77,6 +77,6 @@ dataPeak = read_GMQL(test_path,"NarrowPeakParser")

 remote_url = "http://genomic.deib.polimi.it/gmql-rest-r/"

 login_gmql(remote_url)

-data1 = read_GMQL("public.Example_Dataset1",is_local = FALSE)

+data1 = read_GMQL("public.Example_Dataset_1",is_local = FALSE)

 }

@@ -22,8 +22,9 @@ After invoking collect() it is not possbile to switch the processing mode.

 }

 \examples{

-## These statements initializes GMQL with local processing with sample files 

-## output format as tab delimited and then change processing mode to remote

+## These statements initializes GMQL with local processing with sample 

+## files output format as tab delimited and then change processing mode 

+## to remote

 init_gmql("tab", remote_processing = FALSE)

@@ -33,7 +33,7 @@ login_gmql(remote_url)

 ## This statement retrieves metadata for sample 'S_00000' from public 

 ## dataset 'Example_Dataset1'

-sample_metadata(remote_url, "public.Example_Dataset1", "S_00000")

+sample_metadata(remote_url, "public.Example_Dataset_1", "S_00000")

 }

@@ -39,7 +39,7 @@ login_gmql(remote_url)

 ## This statement retrieves regions data for sample "S_00000" from public 

 ## dataset "Example_Dataset1"

-sample_region(remote_url, "public.Example_Dataset1", "S_00000")

+sample_region(remote_url, "public.Example_Dataset_1", "S_00000")

 }

@@ -89,17 +89,5 @@ regions_update = list(new_score = (score / 1000.0) + 100),

 regions = c("score"), all_but_reg = TRUE)

-## It produces an output dataset that contains the same samples 

-## as the input dataset. 

-## Each output sample only contains, as region attributes, 

-## the four basic coordinates (chr, left, right, strand) and the specified 

-## region attributes 'variant_classification' and 'variant_type', 

-## and as metadata attributes only the specified ones, 

-## i.e. manually_curated_tissue_status and manually_curated_tumor_tag.

-

-DS_out = select(data, regions = c("variant_classification", 

-"variant_type"), metadata = c("manually_curated_tissue_status", 

-"manually_curated_tumor_tag"))

-

 }

@@ -39,6 +39,6 @@ login_gmql(remote_url)

 ## It show all sample present into public dataset 'Example_Dataset1'

-list <- show_samples_list(remote_url, "public.Example_Dataset1")

+list <- show_samples_list(remote_url, "public.Example_Dataset_1")

 }

@@ -37,6 +37,6 @@ login_gmql(remote_url)

 ## show schema of public dataset 'Example_Dataset1'

-list <- show_schema(remote_url, "public.Example_Dataset1")

+list <- show_schema(remote_url, "public.Example_Dataset_1")

 }

@@ -14,8 +14,8 @@ Stop GMQL server

 }

 \examples{

-## These statements initializes GMQL with local processing with sample files 

-## output format as tab delimited and then stop it

+## These statements initializes GMQL with local processing with sample 

+## files output format as tab delimited and then stop it

 init_gmql("tab", FALSE)