refactor, doc, example for melt(id.vars=patterns())

Author: tdhock

R/fmelt.R

@@ -182,20 +182,17 @@ melt.data.table = function(data, id.vars, measure.vars, variable.name = "variabl
        value.name = "value", ..., na.rm = FALSE, variable.factor = TRUE, value.factor = FALSE,
        verbose = getOption("datatable.verbose")) {
   if (!is.data.table(data)) stopf("'data' must be a data.table")
-  if (missing(id.vars)) id.vars=NULL
-  id.sub = substitute(id.vars)
-  if (is.call(id.sub)) {
-    eval.result = eval_with_cols(id.sub, names(data))
-    if (!is.null(eval.result)) {
-      id.vars = eval.result
-    }
-  }
-  if (missing(measure.vars)) measure.vars = NULL
-  measure.sub = substitute(measure.vars)
-  if (is.call(measure.sub)) {
-    eval.result = eval_with_cols(measure.sub, names(data))
-    if (!is.null(eval.result)) {
-      measure.vars = eval.result
+  for(type.vars in c("id.vars","measure.vars")){
+    sub.lang <- substitute({
+      if (missing(VAR)) VAR=NULL
+      substitute(VAR)
+    }, list(VAR=as.symbol(type.vars)))
+    sub.result = eval(sub.lang)
+    if (is.call(sub.result)) {
+      eval.result = eval_with_cols(sub.result, names(data))
+      if (!is.null(eval.result)) {
+        assign(type.vars, eval.result)
+      }
     }
   }
   if (is.list(measure.vars)) {

man/melt.data.table.Rd

@@ -19,8 +19,8 @@ multiple columns simultaneously.
 \arguments{
 \item{data}{ A \code{data.table} object to melt.}
 \item{id.vars}{vector of id variables. Can be integer (corresponding id
-column numbers) or character (id column names) vector. If missing, all
-non-measure columns will be assigned to it. If integer, must be positive; see Details. }
+column numbers) or character (id column names) vector, perhaps created using \code{patterns()}. If missing, all
+non-measure columns will be assigned to it. If integer, must be positive; see Details.}
 \item{measure.vars}{Measure variables for \code{melt}ing. Can be missing, vector, list, or pattern-based.
 
   \itemize{
@@ -131,6 +131,7 @@ melt(DT, id.vars=1, measure.vars=c("c_1", "c_2"), na.rm=TRUE) # remove NA
 # melt "f_1,f_2" and "d_1,d_2" simultaneously, retain 'factor' attribute
 # convenient way using internal function patterns()
 melt(DT, id.vars=1:2, measure.vars=patterns("^f_", "^d_"), value.factor=TRUE)
+melt(DT, id.vars=patterns("[in]"), measure.vars=patterns("^f_", "^d_"), value.factor=TRUE)
 # same as above, but provide list of columns directly by column names or indices
 melt(DT, id.vars=1:2, measure.vars=list(3:4, c("d_1", "d_2")), value.factor=TRUE)
 # same as above, but provide names directly: