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2a0c510ade
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Rplots.pdf
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Rplots.pdf
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target.r
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target.r
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library(plyr)
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library(dplyr)
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library(ggdistribute)
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library(ggplot2)
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library(ggExtra)
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library(scales)
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library(gridExtra)
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library(stringr);
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library(DBI);
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prod_plot <- function(priceg, mold, colgrp, branding, outlier, xfact, yfact,xtrans, ytrans, lprice, uprice, pqty ) {
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sql = paste("SELECT * FROM rlarp.rlang_plot('",mold,"','",priceg,"','",colgrp,"','",branding,"',",lprice,",",uprice,") x",sep="");
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con <- dbConnect(RPostgres::Postgres(),dbname = 'ubm',
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host = 'usmidlnx01',
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port = 5030,
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user = 'report',
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password = 'report')
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d <- dbGetQuery(con, sql)
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dbDisconnect(con)
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#-----each graph is composed of 2 pieces when doing the facet() pivot, these 2 pieces make up the plot defition-----
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d$f7 <- substring(d$mold,1,7)
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#d$dim1 <- trimws(paste(d$f7,d$v1ds));
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#d$dim1 <- trimws(paste(d$f7,d$colgrp,d$brnd,d$package,d$suffix,d$kit));
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d$dim1 <- trimws(paste(d$base_item,d$colgrp,d$brnd));
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d$dim2 <- trimws(paste(d$chgrp));
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d$plot <- trimws(paste(d$dim1,d$dim2));
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d$sub <- trimws(paste("v1:",d$coltier,".",substring(d$brnd,1,1),".",d$package,".",d$suffix,".",d$kit));
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#d$sub <- trimws(paste(d$oseas));
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#d$sub <- trimws(paste(d$geo));
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d$qty = d$qty/1000;
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#-----need to include credits------
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d$volmin = 0.0001;
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d$season = factor(d$oseas);
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#-----build widths for how many scenarios are present----------------------------------------------------------------
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dim1 <- data.frame(unique(d$dim1));
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var.dim1 = nrow(dim1);
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dim2 <- data.frame(unique(d$dim2));
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var.dim2 = nrow(dim2);
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#-----------need to do an aggregate to consolidate to single customer point
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d <- subset(d,chgrp != "X", promo != "Excess and Obsolete");
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#-------------------------eliminate outliers-------------------------------------------------------------------------
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dx <- boxplot.stats(d$price, coef = outlier);
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ex <- data.frame(dx$out);
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#ex; #list the excluded outlier prices
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colnames(ex)[1] = "price";
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outl <- inner_join(d,ex, by = "price");
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outl;
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d <- anti_join(d,ex, by = "price");
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#---------switch to log axis if there are still outliers with a coefficient 3----------------------------------------
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var.trans = "identity"
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if (nrow(data.frame(boxplot.stats(d$price, coef = 3)$out)) >= 1){
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var.trans = "log2"
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};
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glob <- ddply(d, .(), summarise,
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Volume=round(sum(qty),0),
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Sales=round(sum(sales),0),
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WeightedAvg=round(sum(sales)/sum(qty),4),
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Mean=round(mean(price),4),
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StdDev=round(sd(price),4),
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Target=round(mean(target_price),4),
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AnyMax=round(max(c(price,target_price)),4),
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AnyMin=round(min(c(price,target_price)),4),
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PriceMin = round(min(price),4),
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PriceMax = round(max(price),4),
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VolumeMin = round(min(pmax(qty,volmin)),4),
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VolumeMax = round(max(qty),1),
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VolumeSD=round(sd(pmax(qty,volmin)),4)
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);
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#targets <- ddply(d, .(dim1, dim2, plot,mold,chan,colgrp, brnd), summarise,
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targets <- ddply(d, .(dim2, v1ds, dim1, plot,mold,chan,colgrp, brnd), summarise,
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Volume=round(sum(qty),0),
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Sales=round(sum(sales),0),
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WeightedAvg=round(sum(sales)/sum(qty*1000),4),
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Mean=round(mean(price),4),
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StdDev=round(sd(price),4),
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Target=round(mean(target_price),4),
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HexCol = min(hex)
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);
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seas <- ddply(d, .(dim1, dim2, plot, oseas), summarise,
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Volume=round(sum(qty),0),
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Sales=round(sum(sales),0),
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WeightedAvg=round(sum(sales)/sum(qty*1000),4),
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Mean=round(mean(price),4),
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StdDev=round(sd(price),4),
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Target=round(mean(target_price),4)
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);
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#-----------------blank dataframe in case there is no data for a scenario-----------------
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blank <- glob
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blank$customer = 'NO DATA'
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blank$oseas = 2020
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blank$season = '2020'
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blank$qty = blank$VolumeSD
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blank$price = blank$Mean
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#blank;
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yr1 <- subset(seas, oseas == 2020);
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yr2 <- subset(seas, oseas == 2021);
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dir_t <- subset(targets, chan == "DIR");
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drp_t <- subset(targets, chan == "DRP");
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whs_t <- subset(targets, chan == "WHS");
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anno <- data.frame(unique(d[c("plot","dim2","dim1","mold","colgrp","brnd")]));
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anno <- data.frame(anno,qty=c(Inf),price=c(Inf),hjustvar = c(1),vjustvar = c(1));
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anno <- merge(x = anno, y = yr1[ , c("plot","Mean","WeightedAvg", "StdDev","Volume")], by = "plot", all.x=TRUE);
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names(anno)[names(anno)=="Mean"] <- "yr1_mn";
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names(anno)[names(anno)=="WeightedAvg"] <- "yr1_wa";
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names(anno)[names(anno)=="StdDev"] <- "yr1_sd";
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names(anno)[names(anno)=="Volume"] <- "yr1_vo";
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anno <- merge(x = anno, y = yr2[ , c("plot","Mean","WeightedAvg", "StdDev","Volume")], by = "plot", all.x=TRUE);
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names(anno)[names(anno)=="Mean"] <- "yr2_mn";
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names(anno)[names(anno)=="WeightedAvg"] <- "yr2_wa";
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names(anno)[names(anno)=="StdDev"] <- "yr2_sd";
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names(anno)[names(anno)=="Volume"] <- "yr2_vo";
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anno <- merge(x = anno, y = dir_t[ , c("plot","Target")], by = "plot", all.x=TRUE);
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names(anno)[names(anno)=="Target"] <- "t_dir";
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anno <- merge(x = anno, y = drp_t[ , c("plot","Target")], by = "plot", all.x=TRUE);
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names(anno)[names(anno)=="Target"] <- "t_drp";
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anno <- merge(x = anno, y = whs_t[ , c("plot","Target")], by = "plot", all.x=TRUE);
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names(anno)[names(anno)=="Target"] <- "t_whs";
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csv <- anno;
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csv <- subset(csv, select = c(mold, dim2, colgrp, brnd, yr1_mn, yr2_mn, yr1_wa, yr2_wa, t_dir, t_drp, t_whs));
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csv$t_dir_rev = csv$t_dir;
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csv$t_drp_rev = csv$t_drp;
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csv$t_whs_rev = csv$t_whs;
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names(csv)[names(csv)=="dim2"] <- "chgrp";
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csv;
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#write.csv(csv, file = paste("//home/ptrowbridge/pt_share/",file_name,"_TRG.csv",sep=""), row.names = FALSE);
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p=ggplot(d, aes(x=qty, y=price, color=v1ds)) +
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#scale_color_manual(values=c("#F44336", "#E91E63", "#9C27B0","#673ab7","#3f51b5","#2196f3","#03a9f4","#00bcd4","#009688","#4caf50","#8bc34a","#8bc34a","#ffeb3b","#ffc107")) +
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geom_point(size=2) +
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geom_text(data = anno,
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aes(
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x=qty,y=price,
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color = NULL,
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hjust=hjustvar,vjust=vjustvar,
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label=paste(
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" mean | wavg | stdd | vol \n",
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"-------|--------|--------|---------\n",
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"PY(black): ",
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#----------mean-------------------------------
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str_pad(
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format(round(yr1_mn, 4), nsmall = 4),
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width = 6,
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side = "both",
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pad = " "),
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"|",
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#----------weighted average-------------------
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str_pad(
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format(round(yr1_wa, 4), nsmall = 4),
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width = 6,
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side = "both",
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pad = " "
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),
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#----------standard deviation-----------------
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"|",
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str_pad(
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format(round(yr1_sd, 4), nsmall = 4),
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width = 6,
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side = "both",
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pad = " "),
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"|",
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#----------volume-----------------------------
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str_pad(
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format(round(yr1_vo/1000, 4), nsmall = 4,width = 7),
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width = 6,
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side = "both",
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pad = " "),
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"\n",
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"CY(green): ",
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#----------mean-------------------------------
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str_pad(
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format(round(yr2_mn, 4), nsmall = 4),
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width = 6,
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side = "both",
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pad = " "),
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"|",
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#----------weighted average-------------------
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str_pad(
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format(round(yr2_wa, 4), nsmall = 4),
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width = 6,
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side = "both",
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pad = " "
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),
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#----------standard deviation-----------------
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"|",
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str_pad(
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format(round(yr2_sd, 4), nsmall = 4),
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width = 6,
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side = "both",
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pad = " "),
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"|",
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#----------volume-----------------------------
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str_pad(
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format(round(yr2_vo/1000, 4), nsmall = 4,width = 7),
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width = 6,
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side = "both",
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pad = " "),
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"\n",
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#format(round(yr2_mn, 4), nsmall = 4),"|",format(round(yr2_wa, 4), nsmall = 4),"|",format(round(yr2_sd, 4), nsmall = 4),"|",format(round(yr2_vo/1000, 4), nsmall = 4,width = 7),"\n",
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" \n",
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" dir (b) | drp (y) | whs (r) \n",
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"-----------|-----------|-----------\n",
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"Targets: ",
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str_pad(
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format(round(t_dir, 4), nsmall = 4),
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width = 9,
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side ="both",
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pad=" "),
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"|",
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str_pad(
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format(round(t_drp, 4), nsmall = 4),
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width = 9,
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side = "both",
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pad = " "),
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"|",
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str_pad(
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format(round(coalesce(t_whs,0), 4), nsmall = 4),
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width = 10,
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side = "both",
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pad = " ")
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)
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),
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family="Courier",
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size = 3,
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#use check_overlap to avoid doubling up the price info print, it will print over top of itself based on the color=sub count of uniques
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check_overlap=TRUE
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) +
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geom_text(aes(label=customer),size=3, vjust = 2, hjust = 0, check_overlap=TRUE) +
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facet_grid(dim2~dim1) +
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#facet_grid(chgrp~plot) +
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#facet_wrap(plot) +
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geom_hline(data=yr1, aes(yintercept=Mean),linetype="dashed", size=.5, colour="black") +
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#geom_hline(data=yr1, aes(yintercept=Mean - StdDev),linetype="dashed", size=.5, colour="black") +
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#geom_hline(data=yr1, aes(yintercept=Mean - StdDev * 2),linetype="dashed", size=.5, colour="black") +
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geom_hline(data=yr1, aes(yintercept=WeightedAvg),linetype="solid", size=.5, colour="black") +
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geom_vline(aes(xintercept = pqty/1000) ,linetype = "dashed",size = .5, colour = "orange") +
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#geom_vline(aes(xintercept = pqty/1000*8) ,linetype = "dashed",size = .5, colour = "grey") +
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geom_vline(aes(xintercept = pqty/1000*8) ,linetype = "dashed",size = .5, colour = "grey") +
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#geom_hline(data=yr2, aes(yintercept=Mean),linetype="dashed", size=.5, colour="green") +
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#geom_hline(data=yr2, aes(yintercept=Mean - StdDev),linetype="dashed", size=.5, colour="green") +
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#geom_hline(data=yr2, aes(yintercept=Mean - StdDev * 2),linetype="dashed", size=.5, colour="green") +
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#geom_hline(data=yr2, aes(yintercept=WeightedAvg),linetype="solid", size=.5, colour="green") +
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geom_hline(data=drp_t, aes(yintercept=Target, color=v1ds),linetype="solid", size=.5) +
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geom_hline(data=dir_t, aes(yintercept=Target, color=v1ds),linetype="solid", size=.5) +
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geom_hline(data=whs_t, aes(yintercept=Target, color=v1ds),linetype="solid", size=.5) +
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#scale_y_continuous(breaks=seq(0, 10, round(glob$StdDev * .5,2))) +
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scale_y_continuous(
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#breaks=seq(glob$PriceMin, glob$PriceMax, round(glob$StdDev * .5,4)),
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breaks = pretty_breaks(n=20),
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limits = c(glob$AnyMin, glob$AnyMax), trans = ytrans
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) +
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scale_x_continuous(
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#breaks=seq(glob$VolumeMin, glob$VolumeMax, round(glob$VolumeSD * 1.0,4)),
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#breaks = pretty_breaks(n=10),
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limits = c(glob$VolumeMin, glob$VolumeMax*1.1), trans = xtrans
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) +
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#scale_x_continuous(trans='log2') +
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#scale_x_continuous(breaks=seq(0,1000,round(glob$VolumeSD * 1,2)), trans = 'log2') +
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#geom_label(colour = "white", fontface = "bold") +
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#geom_text(aes(label=ds$ship_group),position = position_dodge(width=.9), size=2) +
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theme(legend.position="none");
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cp_pvt = p + theme_bw();
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#targets;
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options(
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repr.plot.width=var.dim1*xfact,
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repr.plot.height=var.dim2*yfact
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);
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cp_pvt;
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};
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prod_plot(
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".*" # price group
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,"XTG154" # base part
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,".*" # color tier
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,".*" # branding
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,300 # outlier coefficent
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,5 # width factor
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,4 # high factor
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,"log2" # volume scale type
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,"log2" # price scale typec
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,.01 # filter min price
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,30 # filter max price
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,4590 # pallet quantity
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);
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