library(tinytex)

foragedata <- read.csv("QVI_transaction_data.csv", stringsAsFactors = FALSE) foragebehaviour <- read.csv("QVI_purchase_behaviour.csv") foragedata <- data.table(foragedata) Error in data.table(foragedata) : could not find function "data.table" library(data.table) package �data.table� was built under R version 4.0.5data.table 1.14.2 using 2 threads (see ?getDTthreads). Latest news: r-datatable.com library(ggplot2) package �ggplot2� was built under R version 4.0.5 library(ggmosaic) package �ggmosaic� was built under R version 4.0.5Registered S3 method overwritten by 'htmlwidgets': method from
print.htmlwidget tools:rstudio library(readr) package �readr� was built under R version 4.0.5 library(tidyverse) package �tidyverse� was built under R version 4.0.5Registered S3 methods overwritten by 'dbplyr': method from print.tbl_lazy
print.tbl_sql
-- Attaching packages ------------------------------------------------------------- tidyverse 1.3.1 -- v tibble 3.1.4 v dplyr 1.0.7 v tidyr 1.1.4 v stringr 1.4.0 v purrr 0.3.4 v forcats 0.5.1 package �tibble� was built under R version 4.0.5package �tidyr� was built under R version 4.0.5package �purrr� was built under R version 4.0.5package �dplyr� was built under R version 4.0.5package �stringr� was built under R version 4.0.5package �forcats� was built under R version 4.0.5-- Conflicts ---------------------------------------------------------------- tidyverse_conflicts() -- x dplyr::between() masks data.table::between() x tidyr::expand() masks Matrix::expand() x dplyr::filter() masks stats::filter() x dplyr::first() masks data.table::first() x dplyr::lag() masks stats::lag() x dplyr::last() masks data.table::last() x tidyr::pack() masks Matrix::pack() x dplyr::recode() masks arules::recode() x purrr::transpose() masks data.table::transpose() x tidyr::unpack() masks Matrix::unpack() foragedata <- read.csv("QVI_transaction_data.csv", stringsAsFactors = FALSE) foragebehaviour <- read.csv("QVI_purchase_behaviour.csv") foragedata <- data.table(foragedata) foragebehaviour <- data.table(foragebehaviour) str(foragedata) Classes ‘data.table’ and 'data.frame': 264836 obs. of 8 variables: $ DATE : int 43390 43599 43605 43329 43330 43604 43601 43601 43332 43330 ... $ STORE_NBR : int 1 1 1 2 2 4 4 4 5 7 ... $ LYLTY_CARD_NBR: int 1000 1307 1343 2373 2426 4074 4149 4196 5026 7150 ... $ TXN_ID : int 1 348 383 974 1038 2982 3333 3539 4525 6900 ... $ PROD_NBR : int 5 66 61 69 108 57 16 24 42 52 ... $ PROD_NAME : chr "Natural Chip Compny SeaSalt175g" "CCs Nacho Cheese 175g" "Smiths Crinkle Cut Chips Chicken 170g" "Smiths Chip Thinly S/Cream&Onion 175g" ... $ PROD_QTY : int 2 3 2 5 3 1 1 1 1 2 ... $ TOT_SALES : num 6 6.3 2.9 15 13.8 5.1 5.7 3.6 3.9 7.2 ...

• attr(*, ".internal.selfref")=

head(foragedata) foragedata$DATE <- as.Date(foragedata$DATE, origin = "1899-12-30") head(foragedata) foragedata[ , .N, PROD_NAME] productWords <- data.table(unlist(strsplit(unique(foragedata[, PROD_NAME]), " "))) setnames(productWords, 'words') productwords <- productWords[grepl("\d", words) == FALSE,] productWords <- productWords[grepl("[:alpha:]", words), ] productWords[, .N, words][order(N, decreasing = TRUE)]

foragedata[, SALSA := grepl("salsa", tolower(PROD_NAME))] foragedata <- foragedata[SALSA == FALSE, ][, SALSA := NULL]

summary(foragedata) DATE STORE_NBR LYLTY_CARD_NBR TXN_ID PROD_NBR
Min. :2018-07-01 Min. : 1.0 Min. : 1000 Min. : 1 Min. : 1.00
1st Qu.:2018-09-30 1st Qu.: 70.0 1st Qu.: 70015 1st Qu.: 67569 1st Qu.: 26.00
Median :2018-12-30 Median :130.0 Median : 130367 Median : 135183 Median : 53.00
Mean :2018-12-30 Mean :135.1 Mean : 135531 Mean : 135131 Mean : 56.35
3rd Qu.:2019-03-31 3rd Qu.:203.0 3rd Qu.: 203084 3rd Qu.: 202654 3rd Qu.: 87.00
Max. :2019-06-30 Max. :272.0 Max. :2373711 Max. :2415841 Max. :114.00
PROD_NAME PROD_QTY TOT_SALES
Length:246742 Min. : 1.000 Min. : 1.700
Class :character 1st Qu.: 2.000 1st Qu.: 5.800
Mode :character Median : 2.000 Median : 7.400
Mean : 1.908 Mean : 7.321
3rd Qu.: 2.000 3rd Qu.: 8.800
Max. :200.000 Max. :650.000
foragedata[PROD_QTY == 200] foragedata[LYLTY_CARD_NBR == 226000,] summary(foragedata) DATE STORE_NBR LYLTY_CARD_NBR TXN_ID PROD_NBR
Min. :2018-07-01 Min. : 1.0 Min. : 1000 Min. : 1 Min. : 1.00
1st Qu.:2018-09-30 1st Qu.: 70.0 1st Qu.: 70015 1st Qu.: 67569 1st Qu.: 26.00
Median :2018-12-30 Median :130.0 Median : 130367 Median : 135183 Median : 53.00
Mean :2018-12-30 Mean :135.1 Mean : 135531 Mean : 135131 Mean : 56.35
3rd Qu.:2019-03-31 3rd Qu.:203.0 3rd Qu.: 203084 3rd Qu.: 202654 3rd Qu.: 87.00
Max. :2019-06-30 Max. :272.0 Max. :2373711 Max. :2415841 Max. :114.00
PROD_NAME PROD_QTY TOT_SALES
Length:246742 Min. : 1.000 Min. : 1.700
Class :character 1st Qu.: 2.000 1st Qu.: 5.800
Mode :character Median : 2.000 Median : 7.400
Mean : 1.908 Mean : 7.321
3rd Qu.: 2.000 3rd Qu.: 8.800
Max. :200.000 Max. :650.000
foragedata[, .N, by = DATE] allDates <- data.table(seq(as.Date("2018/07/01"),

• as.Date("2019/06/30"), by = "day"))

setnames(allDates, "DATE") foragedata_by_day <- merge(allDates, foragedata[, .N, by = DATE], all.x = TRUE) theme_set(theme_bw()) theme_update(plot.title = element_text(hjust = 0.5)) ggplot(foragedata_by_day, aes(x = DATE, y = N)) +

• geom_line(col = "blue") +
• labs(x = "Day", y = "Number of transactions",
• title = "Transactions over time") +
• scale_x_date(breaks = "1 month") +
• theme(axis.text.x = element_text(angle = 90, vjust = 0.5))

ggplot(foragedata_by_day[month(DATE) == 12, ],

• aes(x = DATE, y = N)) +
• geom_line(col = "blue") +
• labs(x = "Day", y = "Number of transactions",
• title = "Transactions over time") +
• scale_x_date(breaks = "1 day") +
• theme(axis.text.x = element_text(angle = 90, vjust = 0.5))

foragedata[, PACK_SIZE := parse_number(PROD_NAME)] foragedata[, .N, PACK_SIZE][order(PACK_SIZE)] hist(foragedata[, PACK_SIZE],

• col = "green",border = "black" ,
• xlab = "PACK SIZE",
• ylab = "Total no of chips purchased",
• main = "NO. OF CHIPS PURCHASED ACCORDING TO THEIR PACK SIZES")

foragedata[, BRAND := toupper(substr(PROD_NAME, 1, regexpr(pattern = ' ', PROD_NAME) - 1))] foragedata[, .N, by = BRAND][order(-N)] foragedata[BRAND == "RED", BRAND := "RRD"] foragedata[BRAND == "SNBTS", BRAND := "SUNBITES"] foragedata[BRAND == "INFZNS", BRAND := "INFUZIONS"] foragedata[BRAND == "WW", BRAND := "WOOLWORTHS"] foragedata[BRAND == "SMITH", BRAND := "SMITHS"] foragedata[BRAND == "NCC", BRAND := "NATURAL"] foragedata[BRAND == "DORITO", BRAND := "DORITOS"] foragedata[BRAND == "GRAIN", BRAND := "GRNWVES"] foragedata[, .N, by = BRAND][order(BRAND)] str(foragebehaviour) Classes ‘data.table’ and 'data.frame': 72637 obs. of 3 variables: $ LYLTY_CARD_NBR : int 1000 1002 1003 1004 1005 1007 1009 1010 1011 1012 ... $ LIFESTAGE : chr "YOUNG SINGLES/COUPLES" "YOUNG SINGLES/COUPLES" "YOUNG FAMILIES" "OLDER SINGLES/COUPLES" ... $ PREMIUM_CUSTOMER: chr "Premium" "Mainstream" "Budget" "Mainstream" ...

• attr(*, ".internal.selfref")=

summary(foragebehaviour) LYLTY_CARD_NBR LIFESTAGE PREMIUM_CUSTOMER
Min. : 1000 Length:72637 Length:72637
1st Qu.: 66202 Class :character Class :character
Median : 134040 Mode :character Mode :character
Mean : 136186
3rd Qu.: 203375
Max. :2373711
foragebehaviour[, .N, by = PREMIUM_CUSTOMER][order(-N)] data <- merge(foragedata, foragebehaviour, all.x = TRUE) str(data) Classes ‘data.table’ and 'data.frame': 246742 obs. of 12 variables: $ LYLTY_CARD_NBR : int 1000 1002 1003 1003 1004 1005 1007 1007 1009 1010 ... $ DATE : Date, format: "2018-10-17" "2018-09-16" "2019-03-07" ... $ STORE_NBR : int 1 1 1 1 1 1 1 1 1 1 ... $ TXN_ID : int 1 2 3 4 5 6 7 8 9 10 ... $ PROD_NBR : int 5 58 52 106 96 86 49 10 20 51 ... $ PROD_NAME : chr "Natural Chip Compny SeaSalt175g" "Red Rock Deli Chikn&Garlic Aioli 150g" "Grain Waves Sour Cream&Chives 210G" "Natural ChipCo Hony Soy Chckn175g" ... $ PROD_QTY : int 2 1 1 1 1 1 1 1 1 2 ... $ TOT_SALES : num 6 2.7 3.6 3 1.9 2.8 3.8 2.7 5.7 8.8 ... $ PACK_SIZE : num 175 150 210 175 160 165 110 150 330 170 ... $ BRAND : chr "NATURAL" "RRD" "GRNWVES" "NATURAL" ... $ LIFESTAGE : chr "YOUNG SINGLES/COUPLES" "YOUNG SINGLES/COUPLES" "YOUNG FAMILIES" "YOUNG FAMILIES" ... $ PREMIUM_CUSTOMER: chr "Premium" "Mainstream" "Budget" "Budget" ...

• attr(*, ".internal.selfref")=
• attr(*, "sorted")= chr "LYLTY_CARD_NBR"

write.csv(data, "QVI_data.csv") sales <- data[, .(SALES = sum(TOT_SALES)), . (LIFESTAGE,PREMIUM_CUSTOMER)] sales[order(-SALES)] p <- ggplot(data = sales) +

• geom_mosaic(aes(weight = SALES, x = product(PREMIUM_CUSTOMER, LIFESTAGE) , fill = PREMIUM_CUSTOMER)) +
• labs(x = "Lifestage", y = "Premium Customer", title = "Proportion of Sales") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, size = 10))

p + geom_text(data = ggplot_build(p)$data[[1]],

• aes(x = (xmin + xmax)/2 , y = (ymin + ymax)/2,
• label = as.character(paste(round(.wt/sum(.wt),3)*100, '%'))))

customers <- data[, .(CUSTOMERS = uniqueN(LYLTY_CARD_NBR)), .(LIFESTAGE, PREMIUM_CUSTOMER)][order(-CUSTOMERS)] customers labels <- c("A", "b", "c", "D", "e", "f", "G") p <- ggplot(data = customers) +

• geom_mosaic(aes(weight = CUSTOMERS, x = product(PREMIUM_CUSTOMER, LIFESTAGE), fill = PREMIUM_CUSTOMER)) +
• labs(x = "Lifestage", y = "Premium Customer", title = "Proportion of Customers") +
• theme(axis.text.x = element_text(angle = 90, vjust = 0.5, size = 10)) + scale_x_productlist(labels = labels)

p +

• geom_text(data = ggplot_build(p)$data[[1]],
• aes(x = (xmin + xmax)/2 , y = (ymin + ymax)/2,
• label = as.character(paste(round(.wt/sum(.wt),3)*100, '%'))))

avg_units <- data[, .(AVG = sum(PROD_QTY)/uniqueN(LYLTY_CARD_NBR)), .(LIFESTAGE, PREMIUM_CUSTOMER)][order(-AVG)] avg_units ggplot(data = avg_units,

• aes(weight = AVG, x = LIFESTAGE, fill = PREMIUM_CUSTOMER)) +
• geom_bar(position = position_dodge()) +
• labs(x = "Lifestage", y = "Avg units per transaction",
• title = "Units Per Customer") +
• theme(axis.text.x = element_text(angle = 90, vjust = 0.75, size = 10))

avg_price <- data[, .(AVG = sum(TOT_SALES)/sum(PROD_QTY)), .(LIFESTAGE, PREMIUM_CUSTOMER)][order(-AVG)] avg_price ggplot(data = avg_price,

• aes(weight = AVG, x = LIFESTAGE, fill = PREMIUM_CUSTOMER)) +
• geom_bar(position = position_dodge()) +
• labs(x = "Lifestage", y = "Avg price per unit",
• title = "Price Per Unit") +
• theme(axis.text.x = element_text(angle = 90, vjust = 0.5))

pricePerUnit <- data[, price := TOT_SALES/PROD_QTY] t.test(data[LIFESTAGE %in% c("YOUNG SINGLES/COUPLES", "MIDAGE SINGLES/COUPLES") & PREMIUM_CUSTOMER == "Mainstream", price],

• data[LIFESTAGE %in% c("YOUNG SINGLES/COUPLES", "MIDAGE SINGLES/COUPLES") & PREMIUM_CUSTOMER != "Mainstream", price], alternative = "greater")

  Welch Two Sample t-test

data: data[LIFESTAGE %in% c("YOUNG SINGLES/COUPLES", "MIDAGE SINGLES/COUPLES") & PREMIUM_CUSTOMER == "Mainstream", price] and data[LIFESTAGE %in% c("YOUNG SINGLES/COUPLES", "MIDAGE SINGLES/COUPLES") & PREMIUM_CUSTOMER != "Mainstream", price] t = 37.624, df = 54791, p-value < 2.2e-16 alternative hypothesis: true difference in means is greater than 0 95 percent confidence interval: 0.3187234 Inf sample estimates: mean of x mean of y 4.039786 3.706491

segment <- data[LIFESTAGE == "YOUNG SINGLES/COUPLES" &

• PREMIUM_CUSTOMER == "Mainstream",]

others <- data[!(LIFESTAGE == "YOUNG SINGLES/COUPLES" &

• PREMIUM_CUSTOMER == "Mainstream"),]

qtySegment <- segment[, sum(PROD_QTY)] qtyOthers <- others[, sum(PROD_QTY)] qtySegmentBrand <- segment[, .(TargetSegment =

• sum(PROD_QTY)/qtySegment), by = BRAND]

qtyOthersBrand <- others[, .(Others =

• sum(PROD_QTY)/qtyOthers), by = BRAND]

brandProportions <- merge(qtySegmentBrand, qtyOthersBrand)[, AffinityToBrand := TargetSegment/Others] brandProportions[order(-AffinityToBrand)] qtySegmentPack <- segment[, .(TargetSegment =

• sum(PROD_QTY)/qtySegment), by = PACK_SIZE]

qtyOtherPack <- others[, .(Others =

• sum(PROD_QTY)/qtyOthers), by = PACK_SIZE]

packProportions <- merge(qtySegmentPack, qtyOtherPack)[, AffinityToPack := TargetSegment/Others] packProportions[order(-AffinityToPack)] segment1 <- data[LIFESTAGE == "MIDAGE SINGLES/COUPLES" &

• PREMIUM_CUSTOMER == "Mainstream",]

others1 <- data[!(LIFESTAGE == "MIDAGE SINGLES/COUPLES" &

• PREMIUM_CUSTOMER == "Mainstream"),]

qtySegment1 <- segment1[, sum(PROD_QTY)] qtyOthers1 <- others1[, sum(PROD_QTY)] qtySegmentBrand1 <- segment1[, .(TargetSegment =

• sum(PROD_QTY)/qtySegment1), by = BRAND]

qtyOthersBrand1 <- others1[, .(Others =

• sum(PROD_QTY)/qtyOthers1), by = BRAND]

brandProportions1 <- merge(qtySegmentBrand1, qtyOthersBrand1)[, AffinityToBrand := TargetSegment/Others] brandProportions1[order(-AffinityToBrand)] qtySegmentPack1 <- segment1[, .(TargetSegment =

• sum(PROD_QTY)/qtySegment1), by = PACK_SIZE]

qtyOtherPack1 <- others1[, .(Others =

• sum(PROD_QTY)/qtyOthers1), by = PACK_SIZE]

packProportions1 <- merge(qtySegmentPack1, qtyOtherPack1)[, AffinityToPack := TargetSegment/Others] packProportions1[order(-AffinityToPack)]