deprecate array w/ warning for narray pkg

array/__init__.r

@@ -2,4 +2,4 @@
 for (n in ls(.pkg))
     assign(n, .pkg[[n]])
 
-#warning("'array' module is deprecated. Use 'narray' package instead.")
+warning("'array' module is deprecated. Use 'narray' package instead.")

data_frame/assemble.r

@@ -1,5 +1,4 @@
 import('../base/operators')
-.ar = import('../array')
 
 #' Creates a data.frame from named vectors
 #'
@@ -13,7 +12,7 @@ assemble = function(...) {
 
     myclasses = sapply(l., class)
 
-    re = as.data.frame(.ar$stack(l., along=2), stringsAsFactors=FALSE)
+    re = as.data.frame(narray::stack(l., along=2), stringsAsFactors=FALSE)
     for (i in seq_along(re)) {
         cc = myclasses[names(re)[i]]
         if (cc == "factor")

plot/matrix.r

@@ -1,5 +1,4 @@
 .b = import('../base')
-.ar = import('../array')
 
 #' Cluster the rows and columns, order factor levels to respect that
 #'
@@ -10,7 +9,7 @@
 #' @param size     A size c(rows,cols) to limit the result clustering to
 #' @param fill     Fill array if data.frame does not have all values
 cluster = function(df, formula, cols=TRUE, rows=TRUE, size=NULL, fill=NA) {
-    mat = .ar$construct(data=df, formula=formula, fun.aggregate=mean)
+    mat = narray::construct(data=df, formula=formula, fun.aggregate=mean)
     indep_vars = all.vars(formula[[3]])
     rname = indep_vars[1] #FIXME: ar$construct should order by std. axis ordering
     cname = indep_vars[2] # order: 1,2 && remove t()

process/idmap/gene.r

@@ -1,7 +1,6 @@
 library(dplyr)
 .b = import('../../base')
 .io = import('../../io')
-.ar = import('../../array')
 .guess_id_type = import('./guess_id_type')$guess_id_type
 .gene_table = import('../../seq/gene_table')$gene_table
 
@@ -31,7 +30,7 @@ gene.character = function(obj, to, from=.guess_id_type(obj),
     .b$match(obj, from=df$from, to=df$to)
 }
 
-gene.default = function(obj, to, from=.guess_id_type(.ar$dimnames(obj, along=1)),
+gene.default = function(obj, to, from=.guess_id_type(narray::dimnames(obj, along=1)),
                         dset="hsapiens_gene_ensembl", summarize=mean) {
     if (to %in% c("hgnc_symbol", "mgi_symbol"))
         to = "external_gene_name"
@@ -48,7 +47,7 @@ gene.default = function(obj, to, from=.guess_id_type(.ar$dimnames(obj, along=1))
             names(obj) = sub("\\.[0-9]+$", "", dimnames(obj)[[1]])
     }
 
-    .ar$summarize(obj, along=1, from=df$from, to=df$to, FUN=summarize)
+    narray::translate(obj, along=1, from=df$from, to=df$to, FUN=summarize)
 }
 
 gene.ExpressionSet = function(obj, to, from=.guess_id_type(rownames(exprs(obj))),

process/idmap/probeset.r

@@ -1,6 +1,5 @@
 library(dplyr)
 .b = import('../../base')
-.ar = import('../../array')
 .guess_id_type = import('./guess_id_type')$guess_id_type
 .probeset_table = import('../../seq/probeset_table')$probeset_table
 
@@ -26,21 +25,21 @@ probeset.numeric = function(obj, to, from=.guess_id_type(names(obj)), summarize=
     lookup = probeset_table()[[from]]
     df = na.omit(data.frame(from=lookup$probe_id, to=lookup[[to]]))
     df = df[!duplicated(df),]
-    .ar$summarize(obj, along=1, from=df$from, to=df$to, FUN=summarize)
+    narray::translate(obj, along=1, from=df$from, to=df$to, FUN=summarize)
 }
 
 probeset.matrix = function(obj, to, from=.guess_id_type(rownames(obj)), summarize=mean) {
     lookup = probeset_table()[[from]]
     df = na.omit(data.frame(from=lookup$probe_id, to=lookup[[to]]))
     df = df[!duplicated(df),]
-    .ar$summarize(obj, along=1, from=df$from, to=df$to, FUN=summarize)
+    narray::translate(obj, along=1, from=df$from, to=df$to, FUN=summarize)
 }
 
 probeset.ExpressionSet = function(obj, to, from=.guess_id_type(rownames(exprs(obj))),  summarize=mean) {
     lookup = probeset_table()[[from]]
     df = na.omit(data.frame(from=lookup$probe_id, to=lookup[[to]]))
     df = df[!duplicated(df),]
-    exprs(obj) = .ar$summarize(exprs(obj), along=1, from=df$from, to=df$to, FUN=summarize)
+    exprs(obj) = narray::translate(exprs(obj), along=1, from=df$from, to=df$to, FUN=summarize)
     obj
 }
 

stats/batch.r

@@ -1,5 +1,3 @@
-.ar = import('../array')
-
 merge = function(method, X, batch, covariate=NULL) {
     if (method == "combat")
         combat(X, batch, covariate)
@@ -32,14 +30,14 @@ none = function(X, batch=NA) {
 
 .list2mat = function(ll) {
     if (is.list(ll))
-        .ar$stack(ll, along=2)
+        narray::stack(ll, along=2)
     else
         ll
 }
 
 .mat2list = function(X, subsets) {
     if (is.matrix(X))
-        .ar$split(X, along=2, subsets=subsets)
+        narray::split(X, along=2, subsets=subsets)
     else
         X
 }