--- end ---






This is stuff I had put together, then reworked and did something differently. 
The graph parts might still be useful 


~~~~~~~

## ALTERNATE --- UNUSED, BUT GOOD THOUGHT PROCESS ##

## Filter to just iTunes--"DA"
DT.grid.apprvd_titles <- DT.weekly_avg.apprvd_titles[storeid == 1 & transac_type_abbr == "DA"]

## Convert week_number to factor and paidunits to the average of the cut group
DT.grid.apprvd_titles[, week_number := factor(week_number)]
DT.grid.apprvd_titles[, paidunits_avg_of_group_in_range := as.num.as.char(cut2(paidunits, cuts=cuts.paidunits, levels.mean=TRUE, digits=2))]

DT.grid.apprvd_titles <- dcast.data.table(DT.grid.apprvd_titles, releaseid ~ week_number, value.var="paidunits_avg_of_group_in_range")
Weeklycols <- c(setdiff(names(DT.grid.apprvd_titles), "releaseid"))
DT.grid.apprvd_titles[, (Weeklycols) := lapply(.SD, removeNA, replace=0L), .SDcols=Weeklycols]
DT.grid.apprvd_titles[, (Weeklycols) := lapply(.SD, Hmisc::cut2, cuts=cuts, levels.mean=TRUE, digits=1), .SDcols=Weeklycols]
releaseid_ordering <- DT.grid.apprvd_titles[order(get(tail(names(DT.grid.apprvd_titles),1)), decreasing=TRUE)]$releaseid
DT.grid.apprvd_titles[, releaseid := factor(as.character(releaseid), levels=releaseid_ordering)]
DT.grid.apprvd_titles <- reshape2::melt(DT.grid.apprvd_titles, id.var="releaseid", var="week_number", val="paidunits")
DT.grid.apprvd_titles[, paidunits := factor(paidunits)]
setkeyIfNot(DT.grid.apprvd_titles, releaseid)
DT.grid.apprvd_titles[, levels(paidunits), verbose=FALSE]
DT.grid.apprvd_titles

releaseid_above_3 <- DT.weekly_avg.apprvd_titles[week_number == max(week_number) & paidunits >=3 & transac_type_abbr == "DA" & storeid == 1, unique(releaseid)]
ggplot(data=DT.grid.apprvd_titles[releaseid %in% releaseid_above_3], aes(x=week_number, y=releaseid)) + geom_tile(aes(fill=paidunits)) + coord_flip()

P.weekly_counts__apprvd_titles <- ggHeatColumns(DT.grid.apprvd_titles, heat="value")
# f <- printToPDF(P.weekly_counts__apprvd_titles, height=40, width=20, math_fonts=FALSE)
f <- plots.p("weekly_counts__apprvd_titles.pdf")
pdf(file=f, width=20, height=60)
print(P.weekly_counts__apprvd_titles)
dev.off()
.o(f)

P <- ggHeatColumns(DT.weekly_avg.apprvd_titles[storeid==1 & transac_type_abbr == "DA"], id.var="releaseid", colsToPlot="royaltydollar", heat="value")
print(P)

DT.perc_of_weeks.apprvd_titles <-  {
    DT.weekly_avg.apprvd_titles[storeid == 1][paidunits >= 2][,transac_type_abbr := factor(transac_type_abbr)] [order(transac_type_abbr, releaseid), wdiff := diffNA(week_number, fill=.01)*100, by=kCols.tr][, perc_of_weeks := (sum(round(wdiff) %in% c(1, 48) / .N)  ) , by=kCols.tr][, list(perc_of_weeks=unique(perc_of_weeks)), keyby=kCols.tr]
}

ggplot(DT.perc_of_weeks.apprvd_titles, aes(x=perc_of_weeks)) + geom_bar() + facet_grid(transac_type_abbr ~ .)



summary(DT.weekly_avg.apprvd_titles)

gg_barChart_bygrp(DT.plot, x="week_number", y="royaltydollar", fill="group", grp="transac_type_abbr")
ggBarchart2(DT.plot, x="week_number", y="royaltydollar", fill="group")


formnumb(DT.counts.owned_and_tvt[storeid == 1], round=FALSE)
formnumb(DT.counts.apprvd_titles[storeid == 1], round=FALSE)

qMaxDate(tbl="fact_analytics", schema="production"


  function (input = "", sep = "auto", sep2 = "auto", nrows = -1L,
    header = "auto", na.strings = "NA", stringsAsFactors = FALSE,
    verbose = getOption("datatable.verbose"), autostart = 30L,
    skip = -1L, select = NULL, drop = NULL, colClasses = NULL,
    integer64 = getOption("datatable.integer64"), showProgress = getOption("datatable.showProgress"),
    data.table = getOption("datatable.fread.datatable"))