R/S5.aggregate_allBrands_2010.R
In malooney/beerConsumptionBrandChoice:
Defines functions
S5.aggregate_allBrands_2010
#' @export
S5.aggregate_allBrands_2010 <- function( market = "all",
screenPlot = T,
figurePlot = T,
saveData = T,
out_algoRunTime = T ){
startTime <- Sys.time()
old <- options(stringsAsFactors = FALSE)
on.exit(options(old), add = TRUE)
# Load Data -------------------------------------------------------------------
path.local <- try(rprojroot::find_rstudio_root_file(), silent=TRUE)
if(class(path.local) == 'try-error'){
path.local <- getwd()
} else{}
if(!file.exists(paste(path.local, "/data_beerConsumptionBrandChoice/D4.markets.csv", sep=""))) {
stop("file does not exist in project directory. Run Script 4
(S4.explore_2010marketData.R) to generate the file called:
D4.markets.csv")
} else{
D4.markets <- read.csv(paste(path.local, "/data_beerConsumptionBrandChoice/D4.markets.csv", sep=""))
}
if(!file.exists(paste(path.local, "/data_beerConsumptionBrandChoice/D4.brand_intersection_across_markets.csv", sep=""))) {
stop("file does not exist in project directory. Run Script 4
(S4.explore_2010marketData.R) to generate the file called:
D4.brand_intersection_across_markets.csv")
} else{
D4.brand_intersection_across_markets <- read.csv(paste(path.local, "/data_beerConsumptionBrandChoice/D4.brand_intersection_across_markets.csv", sep=""))
}
if(market=="all"){
if(!file.exists(paste(path.local, "/data_beerConsumptionBrandChoice/D4.allBrandsAllMarkets.csv", sep=""))) {
stop("file does not exist in project directory. Run Script 1
(S4.allBrands_2010analysis.R) to generate the file called:
D4.allBrandsAllMarkets.csv")
} else{
D4.allBrandsAllMarkets <- read.csv(paste(path.local, "/data_beerConsumptionBrandChoice/D4.allBrandsAllMarkets.csv", sep=""))
}
if(!file.exists(paste(path.local, "/data_beerConsumptionBrandChoice/D1.main_beer_drug_and_groc_4_2010.rds", sep=""))) {
stop("file does not exist in project directory. Run Script 1
(S1.beer_2010DataCleaning.R) to generate the file called:
D1.main_beer_drug_and_groc_4_2010.rds")
} else{
dta <- readRDS(paste(path.local, "/data_beerConsumptionBrandChoice/D1.main_beer_drug_and_groc_4_2010.rds", sep=""))
}
dta <- dta[, c(1, 2, 7:14, 19, 21, 25:27, 30, 36, 53:56, 71:76)]
# Add volume measures ---------------------------------------------------------
oz <- round(data.frame(oz=dta$VOL_EQ.x* 288))
total_oz <- (oz* dta$UNITS); colnames(total_oz) <- "total_oz"
total_gal <- (0.0078125* total_oz); colnames(total_gal) <- "total_gal"
dollarPerGal <- dta$DOLLARS/ total_gal;
colnames(dollarPerGal) <- "dollarPerGal"
dta_manip <- cbind(dta, oz, total_oz, total_gal,
dollarPerGal)
rm(oz, total_gal, total_oz, dollarPerGal, dta)
# Remove zero data ------------------------------------------------------------
dta_manip <- dplyr::filter(dta_manip, L5 !="ALL BRAND")
dta_manip <- dplyr::filter(dta_manip, dollarPerGal !="Inf")
# Explore Brands, Firms, and percenatges of Brands by Firms -------------------
uniqueBrands <- dta_manip
colnames(uniqueBrands)[20] <- "Brands"
colnames(uniqueBrands)[19] <- "Firms"
colnames(uniqueBrands)[18] <- "Conglomerates"
colnames(dta_manip)[20] <- "Brands"
colnames(dta_manip)[19] <- "Firms"
colnames(dta_manip)[18] <- "Conglomerates"
uniqueBrands_U <- aggregate(UNITS ~ Brands, uniqueBrands, sum)
uniqueFirms_U <- aggregate(UNITS ~ Firms, uniqueBrands, sum)
uniqueConglomerates_U <- aggregate(UNITS ~ Conglomerates, uniqueBrands, sum)
uniqueBrands_Dol <- aggregate(DOLLARS ~ Brands, uniqueBrands, sum)
uniqueFirms_Dol <- aggregate(DOLLARS ~ Firms, uniqueBrands, sum)
uniqueConglomerates_Dol <- aggregate(DOLLARS ~ Conglomerates, uniqueBrands, sum)
uniqueBrands_TotGal <- aggregate(total_gal ~ Brands, uniqueBrands, sum)
uniqueFirms_TotGal <- aggregate(total_gal ~ Firms, uniqueBrands, sum)
uniqueConglomerates_TotGal <- aggregate(total_gal ~ Conglomerates,
uniqueBrands, sum)
uniqueBrands <- dplyr::full_join(uniqueBrands_U, uniqueBrands_Dol,
by = "Brands")
uniqueFirms <- dplyr::full_join(uniqueFirms_U, uniqueFirms_Dol,
by = "Firms")
uniqueConglomerates <- dplyr::full_join(uniqueConglomerates_U,
uniqueConglomerates_Dol,
by = "Conglomerates")
uniqueBrands <- dplyr::full_join(uniqueBrands, uniqueBrands_TotGal,
by = "Brands")
uniqueFirms <- dplyr::full_join(uniqueFirms, uniqueFirms_TotGal,
by = "Firms")
uniqueConglomerates <- dplyr::full_join(uniqueConglomerates,
uniqueConglomerates_TotGal,
by = "Conglomerates")
uniqueBrands <- dplyr::arrange(uniqueBrands, desc(UNITS))
uniqueFirms <- dplyr::arrange(uniqueFirms, desc(UNITS))
uniqueConglomerates <- dplyr::arrange(uniqueConglomerates, desc(UNITS))
uniqueBrands_prct <- data.frame(matrix(nrow=length(uniqueBrands$Brands),
ncol=4))
uniqueBrands_prct[,1] <- uniqueBrands[,1]
for(i in 1:length(uniqueBrands$Brands)){
a <- uniqueBrands[i,2:4]
b <- colSums(uniqueBrands[,2:4])
uniqueBrands_prct[i,2:4] <- a/b
i=i+1
}
names(uniqueBrands_prct) <- names(uniqueBrands)
uniqueFirms_prct <- data.frame(matrix(nrow=length(uniqueFirms$Firms),
ncol=4))
uniqueFirms_prct[,1] <- uniqueFirms[,1]
for(i in 1:length(uniqueFirms$Firms)){
a <- uniqueFirms[i,2:4]
b <- colSums(uniqueFirms[,2:4])
uniqueFirms_prct[i,2:4] <- a/b
i=i+1
}
names(uniqueFirms_prct) <- names(uniqueFirms)
uniqueConglomerates_prct <- data.frame(matrix(nrow=length(uniqueConglomerates$Conglomerates),
ncol=4))
uniqueConglomerates_prct[,1] <- uniqueConglomerates[,1]
for(i in 1:length(uniqueConglomerates$Conglomerates)){
a <- uniqueConglomerates[i,2:4]
b <- colSums(uniqueConglomerates[,2:4])
uniqueConglomerates_prct[i,2:4] <- a/b
i=i+1
}
names(uniqueConglomerates_prct) <- names(uniqueConglomerates)
if(saveData == T){
dta <- list(aggregateBrands=NA, aggregateFirms=NA, aggregateConglomerates=NA)
dta[[1]] <- list(aggregateDataSummaryBrandsAll=uniqueBrands,
aggregateDataSummaryBrandsAll_prct=uniqueBrands_prct)
dta[[2]] <- list(aggregateDataSummaryFirmsALL=uniqueFirms,
aggregateDataSummaryFirmsAll_prct=uniqueFirms_prct)
dta[[3]] <- list(aggregateDataSummaryConglomeratesALL=uniqueConglomerates,
aggregateDataSummaryConglomeratesAll_prct=uniqueConglomerates_prct)
saveRDS(dta,
paste(path.local, "/data_beerConsumptionBrandChoice/D5.aggregate_allBrands_2010_", gsub('[ ,]', '', market), ".rds", sep = ""))
} else{}
} else{
if(!file.exists(paste(path.local, "/data_beerConsumptionBrandChoice/D2.marketData_2010.rds", sep=""))) {
stop("file does not exist in project directory. Run Script 2
(S2.construct_2010marketData.R) to generate the file called:
D2.marketData_2010.rds")
} else{
D2.marketData_2010 <- readRDS(paste(path.local, "/data_beerConsumptionBrandChoice/D2.marketData_2010.rds", sep=""))
}
dta <- D2.marketData_2010[[Market]]
# Add volume measures ---------------------------------------------------------
oz <- round(data.frame(oz=dta$VOL_EQ.x* 288))
total_oz <- (oz* dta$UNITS); colnames(total_oz) <- "total_oz"
total_gal <- (0.0078125* total_oz); colnames(total_gal) <- "total_gal"
dollarPerGal <- dta$DOLLARS/ total_gal;
colnames(dollarPerGal) <- "dollarPerGal"
dta_manip <- cbind(dta, oz, total_oz, total_gal,
dollarPerGal)
rm(oz, total_gal, total_oz, dollarPerGal, dta)
# Remove zero data ------------------------------------------------------------
dta_manip <- dplyr::filter(dta_manip, L5 !="ALL BRAND")
dta_manip <- dplyr::filter(dta_manip, dollarPerGal !="Inf")
# Explore Brands, Firms, and percenatges of Brands by Firms -------------------
uniqueBrands <- dta_manip
colnames(uniqueBrands)[20] <- "Brands"
colnames(uniqueBrands)[19] <- "Firms"
colnames(uniqueBrands)[18] <- "Conglomerates"
colnames(dta_manip)[20] <- "Brands"
colnames(dta_manip)[19] <- "Firms"
colnames(dta_manip)[18] <- "Conglomerates"
uniqueBrands_U <- aggregate(UNITS ~ Brands, uniqueBrands, sum)
uniqueFirms_U <- aggregate(UNITS ~ Firms, uniqueBrands, sum)
uniqueConglomerates_U <- aggregate(UNITS ~ Conglomerates, uniqueBrands, sum)
uniqueBrands_Dol <- aggregate(DOLLARS ~ Brands, uniqueBrands, sum)
uniqueFirms_Dol <- aggregate(DOLLARS ~ Firms, uniqueBrands, sum)
uniqueConglomerates_Dol <- aggregate(DOLLARS ~ Conglomerates, uniqueBrands, sum)
uniqueBrands_TotGal <- aggregate(total_gal ~ Brands, uniqueBrands, sum)
uniqueFirms_TotGal <- aggregate(total_gal ~ Firms, uniqueBrands, sum)
uniqueConglomerates_TotGal <- aggregate(total_gal ~ Conglomerates,
uniqueBrands, sum)
uniqueBrands <- dplyr::full_join(uniqueBrands_U, uniqueBrands_Dol,
by = "Brands")
uniqueFirms <- dplyr::full_join(uniqueFirms_U, uniqueFirms_Dol,
by = "Firms")
uniqueConglomerates <- dplyr::full_join(uniqueConglomerates_U,
uniqueConglomerates_Dol,
by = "Conglomerates")
uniqueBrands <- dplyr::full_join(uniqueBrands, uniqueBrands_TotGal,
by = "Brands")
uniqueFirms <- dplyr::full_join(uniqueFirms, uniqueFirms_TotGal,
by = "Firms")
uniqueConglomerates <- dplyr::full_join(uniqueConglomerates,
uniqueConglomerates_TotGal,
by = "Conglomerates")
uniqueBrands <- dplyr::arrange(uniqueBrands, desc(UNITS))
uniqueFirms <- dplyr::arrange(uniqueFirms, desc(UNITS))
uniqueConglomerates <- dplyr::arrange(uniqueConglomerates, desc(UNITS))
#rm(uniqueBrands_Dol, uniqueBrands_U, uniqueBrands_TotGal)
selectBrands <- subset(uniqueBrands, Brands %in%
D4.brand_intersection_across_markets[,1])
selectFirms <- subset(dta_manip, Brands %in%
D4.brand_intersection_across_markets[,1])
selectFirms <- subset(uniqueFirms, Firms %in%
unique(selectFirms$Firms))
selectConglomerates <- subset(dta_manip, Brands %in%
D4.brand_intersection_across_markets[,1])
selectConglomerates <- subset(uniqueConglomerates, Conglomerates %in%
unique(selectConglomerates$Conglomerates))
prcntBrandRep_units <- (sum(selectBrands[, 2]) /
sum(uniqueBrands$UNITS)) * 100
prcntFirmRep_units <- (sum(selectFirms[, 2]) /
sum(uniqueFirms$UNITS)) * 100
prcntConglomerateRep_units <- (sum(selectConglomerates[, 2]) /
sum(uniqueConglomerates$UNITS)) * 100
prcntBrandRep_dol <- (sum(selectBrands[, 3]) /
sum(uniqueBrands$DOLLARS)) * 100
prcntFirmRep_dol <- (sum(selectFirms[, 3]) /
sum(uniqueFirms$DOLLARS)) * 100
prcntConglomerateRep_dol <- (sum(selectConglomerates[, 3]) /
sum(uniqueConglomerates$DOLLARS)) * 100
prcntBrandRep_gal <- (sum(selectBrands[, 4]) /
sum(uniqueBrands$total_gal)) * 100
prcntFirmRep_gal <- (sum(selectFirms[, 4]) /
sum(uniqueFirms$total_gal)) * 100
prcntConglomerateRep_gal <- (sum(selectConglomerates[, 4]) /
sum(uniqueConglomerates$total_gal)) * 100
if(figurePlot == T){
dir.create(file.path(path.local,
"data_beerConsumptionBrandChoice/plots"),
showWarnings = FALSE)
pdf(paste(path.local, "/data_beerConsumptionBrandChoice/plots/plot.aggregateData_", gsub('[ ,]', '', Market), ".pdf", sep=""), width=11, height = 8.5)
par(mfrow = c(1, 2))
x <- selectBrands[order(selectBrands$UNITS) ,]
dotchart(x$UNITS, labels = x$Brands,
cex=.7,
main = paste("Market: ", Market, "\n Total Units by Brand", sep=""),
xlab = "Units")
x <- selectBrands[order(selectBrands$DOLLARS), ]
dotchart(x$DOLLARS, labels = x$Brands,
cex = 0.7,
main =paste("Market: ", Market, "\n Total Dollars by Brand", sep=""),
xlab = "Dollars")
dev.off()
} else {}
if(screenPlot == T){
x <- selectBrands[order(selectBrands$UNITS) ,]
dotchart(x$UNITS, labels = x$Brands,
main = paste("Market: ", Market, "\n Total Units by Brand",
sep=""), xlab = "Units")
x <- selectBrands[order(selectBrands$DOLLARS), ]
dotchart(x$DOLLARS, labels = x$Brands,
main =paste("Market: ", Market, " \n Total Dollars by Brand",
sep=""), xlab = "Dollars")
} else{}
aggregateDataSummaryBrands <- selectBrands
aggregateDataSummaryFirms <- selectFirms
aggregateDataSummaryConglomerates <- selectConglomerates
if(saveData == T){
dta <- list(aggregateBrands=NA, aggregateFirms=NA, aggregateConglomerates=NA)
dta[[1]] <- list(prcntBrandRep_units=prcntBrandRep_units,
prcntBrandRep_dol=prcntBrandRep_dol,
prcntBrandRep_gal=prcntBrandRep_gal,
aggregateDataSummaryBrands=aggregateDataSummaryBrands,
aggregateDataSummaryBrandsAll=uniqueBrands)
dta[[2]] <- list(prcntFirmRep_units=prcntFirmRep_units,
prcntFirmRep_dol=prcntFirmRep_dol,
prcntFirmRep_gal=prcntFirmRep_gal,
aggregateDataSummaryFirms=aggregateDataSummaryFirms,
aggregateDataSummaryFirmsAll=uniqueFirms)
dta[[3]] <- list(prcntConglomerateRep_units=prcntConglomerateRep_units,
prcntFConglomerateRep_dol=prcntConglomerateRep_dol,
prcntConglomerateRep_gal=prcntConglomerateRep_gal,
aggregateDataSummaryConglomerates=
aggregateDataSummaryConglomerates,
aggregateDataSummaryConglomeratesAll=uniqueConglomerates)
saveRDS(dta,
paste(path.local, "/data_beerConsumptionBrandChoice/D5.aggregate_allBrands_2010_", gsub('[ ,]', '', Market), ".rds", sep = ""))
} else{}
}
endTime <- Sys.time()
if(out_algoRunTime == T) {
hora <- list(starttime=startTime, endTime=endTime)
} else {}
}
malooney/beerConsumptionBrandChoice documentation built on May 23, 2019, 2:48 p.m.
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Defines functions S5.aggregate_allBrands_2010
#' @export
S5.aggregate_allBrands_2010 <- function( market = "all",
screenPlot = T,
figurePlot = T,
saveData = T,
out_algoRunTime = T ){
startTime <- Sys.time()
old <- options(stringsAsFactors = FALSE)
on.exit(options(old), add = TRUE)
# Load Data -------------------------------------------------------------------
path.local <- try(rprojroot::find_rstudio_root_file(), silent=TRUE)
if(class(path.local) == 'try-error'){
path.local <- getwd()
} else{}
if(!file.exists(paste(path.local, "/data_beerConsumptionBrandChoice/D4.markets.csv", sep=""))) {
stop("file does not exist in project directory. Run Script 4
(S4.explore_2010marketData.R) to generate the file called:
D4.markets.csv")
} else{
D4.markets <- read.csv(paste(path.local, "/data_beerConsumptionBrandChoice/D4.markets.csv", sep=""))
}
if(!file.exists(paste(path.local, "/data_beerConsumptionBrandChoice/D4.brand_intersection_across_markets.csv", sep=""))) {
stop("file does not exist in project directory. Run Script 4
(S4.explore_2010marketData.R) to generate the file called:
D4.brand_intersection_across_markets.csv")
} else{
D4.brand_intersection_across_markets <- read.csv(paste(path.local, "/data_beerConsumptionBrandChoice/D4.brand_intersection_across_markets.csv", sep=""))
}
if(market=="all"){
if(!file.exists(paste(path.local, "/data_beerConsumptionBrandChoice/D4.allBrandsAllMarkets.csv", sep=""))) {
stop("file does not exist in project directory. Run Script 1
(S4.allBrands_2010analysis.R) to generate the file called:
D4.allBrandsAllMarkets.csv")
} else{
D4.allBrandsAllMarkets <- read.csv(paste(path.local, "/data_beerConsumptionBrandChoice/D4.allBrandsAllMarkets.csv", sep=""))
}
if(!file.exists(paste(path.local, "/data_beerConsumptionBrandChoice/D1.main_beer_drug_and_groc_4_2010.rds", sep=""))) {
stop("file does not exist in project directory. Run Script 1
(S1.beer_2010DataCleaning.R) to generate the file called:
D1.main_beer_drug_and_groc_4_2010.rds")
} else{
dta <- readRDS(paste(path.local, "/data_beerConsumptionBrandChoice/D1.main_beer_drug_and_groc_4_2010.rds", sep=""))
}
dta <- dta[, c(1, 2, 7:14, 19, 21, 25:27, 30, 36, 53:56, 71:76)]
# Add volume measures ---------------------------------------------------------
oz <- round(data.frame(oz=dta$VOL_EQ.x* 288))
total_oz <- (oz* dta$UNITS); colnames(total_oz) <- "total_oz"
total_gal <- (0.0078125* total_oz); colnames(total_gal) <- "total_gal"
dollarPerGal <- dta$DOLLARS/ total_gal;
colnames(dollarPerGal) <- "dollarPerGal"
dta_manip <- cbind(dta, oz, total_oz, total_gal,
dollarPerGal)
rm(oz, total_gal, total_oz, dollarPerGal, dta)
# Remove zero data ------------------------------------------------------------
dta_manip <- dplyr::filter(dta_manip, L5 !="ALL BRAND")
dta_manip <- dplyr::filter(dta_manip, dollarPerGal !="Inf")
# Explore Brands, Firms, and percenatges of Brands by Firms -------------------
uniqueBrands <- dta_manip
colnames(uniqueBrands)[20] <- "Brands"
colnames(uniqueBrands)[19] <- "Firms"
colnames(uniqueBrands)[18] <- "Conglomerates"
colnames(dta_manip)[20] <- "Brands"
colnames(dta_manip)[19] <- "Firms"
colnames(dta_manip)[18] <- "Conglomerates"
uniqueBrands_U <- aggregate(UNITS ~ Brands, uniqueBrands, sum)
uniqueFirms_U <- aggregate(UNITS ~ Firms, uniqueBrands, sum)
uniqueConglomerates_U <- aggregate(UNITS ~ Conglomerates, uniqueBrands, sum)
uniqueBrands_Dol <- aggregate(DOLLARS ~ Brands, uniqueBrands, sum)
uniqueFirms_Dol <- aggregate(DOLLARS ~ Firms, uniqueBrands, sum)
uniqueConglomerates_Dol <- aggregate(DOLLARS ~ Conglomerates, uniqueBrands, sum)
uniqueBrands_TotGal <- aggregate(total_gal ~ Brands, uniqueBrands, sum)
uniqueFirms_TotGal <- aggregate(total_gal ~ Firms, uniqueBrands, sum)
uniqueConglomerates_TotGal <- aggregate(total_gal ~ Conglomerates,
uniqueBrands, sum)
uniqueBrands <- dplyr::full_join(uniqueBrands_U, uniqueBrands_Dol,
by = "Brands")
uniqueFirms <- dplyr::full_join(uniqueFirms_U, uniqueFirms_Dol,
by = "Firms")
uniqueConglomerates <- dplyr::full_join(uniqueConglomerates_U,
uniqueConglomerates_Dol,
by = "Conglomerates")
uniqueBrands <- dplyr::full_join(uniqueBrands, uniqueBrands_TotGal,
by = "Brands")
uniqueFirms <- dplyr::full_join(uniqueFirms, uniqueFirms_TotGal,
by = "Firms")
uniqueConglomerates <- dplyr::full_join(uniqueConglomerates,
uniqueConglomerates_TotGal,
by = "Conglomerates")
uniqueBrands <- dplyr::arrange(uniqueBrands, desc(UNITS))
uniqueFirms <- dplyr::arrange(uniqueFirms, desc(UNITS))
uniqueConglomerates <- dplyr::arrange(uniqueConglomerates, desc(UNITS))
uniqueBrands_prct <- data.frame(matrix(nrow=length(uniqueBrands$Brands),
ncol=4))
uniqueBrands_prct[,1] <- uniqueBrands[,1]
for(i in 1:length(uniqueBrands$Brands)){
a <- uniqueBrands[i,2:4]
b <- colSums(uniqueBrands[,2:4])
uniqueBrands_prct[i,2:4] <- a/b
i=i+1
}
names(uniqueBrands_prct) <- names(uniqueBrands)
uniqueFirms_prct <- data.frame(matrix(nrow=length(uniqueFirms$Firms),
ncol=4))
uniqueFirms_prct[,1] <- uniqueFirms[,1]
for(i in 1:length(uniqueFirms$Firms)){
a <- uniqueFirms[i,2:4]
b <- colSums(uniqueFirms[,2:4])
uniqueFirms_prct[i,2:4] <- a/b
i=i+1
}
names(uniqueFirms_prct) <- names(uniqueFirms)
uniqueConglomerates_prct <- data.frame(matrix(nrow=length(uniqueConglomerates$Conglomerates),
ncol=4))
uniqueConglomerates_prct[,1] <- uniqueConglomerates[,1]
for(i in 1:length(uniqueConglomerates$Conglomerates)){
a <- uniqueConglomerates[i,2:4]
b <- colSums(uniqueConglomerates[,2:4])
uniqueConglomerates_prct[i,2:4] <- a/b
i=i+1
}
names(uniqueConglomerates_prct) <- names(uniqueConglomerates)
if(saveData == T){
dta <- list(aggregateBrands=NA, aggregateFirms=NA, aggregateConglomerates=NA)
dta[[1]] <- list(aggregateDataSummaryBrandsAll=uniqueBrands,
aggregateDataSummaryBrandsAll_prct=uniqueBrands_prct)
dta[[2]] <- list(aggregateDataSummaryFirmsALL=uniqueFirms,
aggregateDataSummaryFirmsAll_prct=uniqueFirms_prct)
dta[[3]] <- list(aggregateDataSummaryConglomeratesALL=uniqueConglomerates,
aggregateDataSummaryConglomeratesAll_prct=uniqueConglomerates_prct)
saveRDS(dta,
paste(path.local, "/data_beerConsumptionBrandChoice/D5.aggregate_allBrands_2010_", gsub('[ ,]', '', market), ".rds", sep = ""))
} else{}
} else{
if(!file.exists(paste(path.local, "/data_beerConsumptionBrandChoice/D2.marketData_2010.rds", sep=""))) {
stop("file does not exist in project directory. Run Script 2
(S2.construct_2010marketData.R) to generate the file called:
D2.marketData_2010.rds")
} else{
D2.marketData_2010 <- readRDS(paste(path.local, "/data_beerConsumptionBrandChoice/D2.marketData_2010.rds", sep=""))
}
dta <- D2.marketData_2010[[Market]]
# Add volume measures ---------------------------------------------------------
oz <- round(data.frame(oz=dta$VOL_EQ.x* 288))
total_oz <- (oz* dta$UNITS); colnames(total_oz) <- "total_oz"
total_gal <- (0.0078125* total_oz); colnames(total_gal) <- "total_gal"
dollarPerGal <- dta$DOLLARS/ total_gal;
colnames(dollarPerGal) <- "dollarPerGal"
dta_manip <- cbind(dta, oz, total_oz, total_gal,
dollarPerGal)
rm(oz, total_gal, total_oz, dollarPerGal, dta)
# Remove zero data ------------------------------------------------------------
dta_manip <- dplyr::filter(dta_manip, L5 !="ALL BRAND")
dta_manip <- dplyr::filter(dta_manip, dollarPerGal !="Inf")
# Explore Brands, Firms, and percenatges of Brands by Firms -------------------
uniqueBrands <- dta_manip
colnames(uniqueBrands)[20] <- "Brands"
colnames(uniqueBrands)[19] <- "Firms"
colnames(uniqueBrands)[18] <- "Conglomerates"
colnames(dta_manip)[20] <- "Brands"
colnames(dta_manip)[19] <- "Firms"
colnames(dta_manip)[18] <- "Conglomerates"
uniqueBrands_U <- aggregate(UNITS ~ Brands, uniqueBrands, sum)
uniqueFirms_U <- aggregate(UNITS ~ Firms, uniqueBrands, sum)
uniqueConglomerates_U <- aggregate(UNITS ~ Conglomerates, uniqueBrands, sum)
uniqueBrands_Dol <- aggregate(DOLLARS ~ Brands, uniqueBrands, sum)
uniqueFirms_Dol <- aggregate(DOLLARS ~ Firms, uniqueBrands, sum)
uniqueConglomerates_Dol <- aggregate(DOLLARS ~ Conglomerates, uniqueBrands, sum)
uniqueBrands_TotGal <- aggregate(total_gal ~ Brands, uniqueBrands, sum)
uniqueFirms_TotGal <- aggregate(total_gal ~ Firms, uniqueBrands, sum)
uniqueConglomerates_TotGal <- aggregate(total_gal ~ Conglomerates,
uniqueBrands, sum)
uniqueBrands <- dplyr::full_join(uniqueBrands_U, uniqueBrands_Dol,
by = "Brands")
uniqueFirms <- dplyr::full_join(uniqueFirms_U, uniqueFirms_Dol,
by = "Firms")
uniqueConglomerates <- dplyr::full_join(uniqueConglomerates_U,
uniqueConglomerates_Dol,
by = "Conglomerates")
uniqueBrands <- dplyr::full_join(uniqueBrands, uniqueBrands_TotGal,
by = "Brands")
uniqueFirms <- dplyr::full_join(uniqueFirms, uniqueFirms_TotGal,
by = "Firms")
uniqueConglomerates <- dplyr::full_join(uniqueConglomerates,
uniqueConglomerates_TotGal,
by = "Conglomerates")
uniqueBrands <- dplyr::arrange(uniqueBrands, desc(UNITS))
uniqueFirms <- dplyr::arrange(uniqueFirms, desc(UNITS))
uniqueConglomerates <- dplyr::arrange(uniqueConglomerates, desc(UNITS))
#rm(uniqueBrands_Dol, uniqueBrands_U, uniqueBrands_TotGal)
selectBrands <- subset(uniqueBrands, Brands %in%
D4.brand_intersection_across_markets[,1])
selectFirms <- subset(dta_manip, Brands %in%
D4.brand_intersection_across_markets[,1])
selectFirms <- subset(uniqueFirms, Firms %in%
unique(selectFirms$Firms))
selectConglomerates <- subset(dta_manip, Brands %in%
D4.brand_intersection_across_markets[,1])
selectConglomerates <- subset(uniqueConglomerates, Conglomerates %in%
unique(selectConglomerates$Conglomerates))
prcntBrandRep_units <- (sum(selectBrands[, 2]) /
sum(uniqueBrands$UNITS)) * 100
prcntFirmRep_units <- (sum(selectFirms[, 2]) /
sum(uniqueFirms$UNITS)) * 100
prcntConglomerateRep_units <- (sum(selectConglomerates[, 2]) /
sum(uniqueConglomerates$UNITS)) * 100
prcntBrandRep_dol <- (sum(selectBrands[, 3]) /
sum(uniqueBrands$DOLLARS)) * 100
prcntFirmRep_dol <- (sum(selectFirms[, 3]) /
sum(uniqueFirms$DOLLARS)) * 100
prcntConglomerateRep_dol <- (sum(selectConglomerates[, 3]) /
sum(uniqueConglomerates$DOLLARS)) * 100
prcntBrandRep_gal <- (sum(selectBrands[, 4]) /
sum(uniqueBrands$total_gal)) * 100
prcntFirmRep_gal <- (sum(selectFirms[, 4]) /
sum(uniqueFirms$total_gal)) * 100
prcntConglomerateRep_gal <- (sum(selectConglomerates[, 4]) /
sum(uniqueConglomerates$total_gal)) * 100
if(figurePlot == T){
dir.create(file.path(path.local,
"data_beerConsumptionBrandChoice/plots"),
showWarnings = FALSE)
pdf(paste(path.local, "/data_beerConsumptionBrandChoice/plots/plot.aggregateData_", gsub('[ ,]', '', Market), ".pdf", sep=""), width=11, height = 8.5)
par(mfrow = c(1, 2))
x <- selectBrands[order(selectBrands$UNITS) ,]
dotchart(x$UNITS, labels = x$Brands,
cex=.7,
main = paste("Market: ", Market, "\n Total Units by Brand", sep=""),
xlab = "Units")
x <- selectBrands[order(selectBrands$DOLLARS), ]
dotchart(x$DOLLARS, labels = x$Brands,
cex = 0.7,
main =paste("Market: ", Market, "\n Total Dollars by Brand", sep=""),
xlab = "Dollars")
dev.off()
} else {}
if(screenPlot == T){
x <- selectBrands[order(selectBrands$UNITS) ,]
dotchart(x$UNITS, labels = x$Brands,
main = paste("Market: ", Market, "\n Total Units by Brand",
sep=""), xlab = "Units")
x <- selectBrands[order(selectBrands$DOLLARS), ]
dotchart(x$DOLLARS, labels = x$Brands,
main =paste("Market: ", Market, " \n Total Dollars by Brand",
sep=""), xlab = "Dollars")
} else{}
aggregateDataSummaryBrands <- selectBrands
aggregateDataSummaryFirms <- selectFirms
aggregateDataSummaryConglomerates <- selectConglomerates
if(saveData == T){
dta <- list(aggregateBrands=NA, aggregateFirms=NA, aggregateConglomerates=NA)
dta[[1]] <- list(prcntBrandRep_units=prcntBrandRep_units,
prcntBrandRep_dol=prcntBrandRep_dol,
prcntBrandRep_gal=prcntBrandRep_gal,
aggregateDataSummaryBrands=aggregateDataSummaryBrands,
aggregateDataSummaryBrandsAll=uniqueBrands)
dta[[2]] <- list(prcntFirmRep_units=prcntFirmRep_units,
prcntFirmRep_dol=prcntFirmRep_dol,
prcntFirmRep_gal=prcntFirmRep_gal,
aggregateDataSummaryFirms=aggregateDataSummaryFirms,
aggregateDataSummaryFirmsAll=uniqueFirms)
dta[[3]] <- list(prcntConglomerateRep_units=prcntConglomerateRep_units,
prcntFConglomerateRep_dol=prcntConglomerateRep_dol,
prcntConglomerateRep_gal=prcntConglomerateRep_gal,
aggregateDataSummaryConglomerates=
aggregateDataSummaryConglomerates,
aggregateDataSummaryConglomeratesAll=uniqueConglomerates)
saveRDS(dta,
paste(path.local, "/data_beerConsumptionBrandChoice/D5.aggregate_allBrands_2010_", gsub('[ ,]', '', Market), ".rds", sep = ""))
} else{}
}
endTime <- Sys.time()
if(out_algoRunTime == T) {
hora <- list(starttime=startTime, endTime=endTime)
} else {}
}
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