TF1_Data_Prep
Tony Chuo, National Sun Yat-sen Univ. Taiwan
2022-09-28 10:19:29
Data Preparation : Summarize Data by the Unit of Analysis
- Order Items :
Z817,182 - Orders :
X119,328 - Customers :
A32,241
1. Order Items：Z
rm(list=ls(all=T))
pacman::p_load(magrittr, readr, caTools, ggplot2, dplyr, vcd)1.1 Read Data
Z = read_csv("data/ta_feng_all_months_merged.csv") %>%
data.frame %>% setNames(c(
"date","cust","age","area","cat","prod","qty","cost","price"))## Rows: 817741 Columns: 9
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (5): TRANSACTION_DT, CUSTOMER_ID, AGE_GROUP, PIN_CODE, PRODUCT_ID
## dbl (4): PRODUCT_SUBCLASS, AMOUNT, ASSET, SALES_PRICE
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.nrow(Z)## [1] 8177411.2 Data Conversion
Z$date = as.Date(Z$date, format="%m/%d/%Y")
par(cex=0.8)
hist(Z$date,'weeks',freq=T,las=2)
1.3 Age and Zip Code
age.group = c("<25","25-29","30-34","35-39","40-44",
"45-49","50-54","55-59","60-64",">65")
Z$age = c(paste0("a",seq(24,69,5)),"a99")[match(Z$age,age.group,11)]
Z$area = paste0("z",Z$area)
Fig-2:Zip Codes
par(mfrow=c(1,2),cex=0.7)
table(Z$age, useNA='ifany') %>% barplot(main="Age Groups", las=2)
table(Z$area,useNA='ifany') %>% barplot(main="Areas", las=2)
1.4 Handle Outliers
# Quantile of Variables
sapply(Z[,7:9], quantile, prob=c(.99, .999, .9995))## qty cost price
## 99% 6 858.0 1014.00
## 99.9% 14 2722.0 3135.82
## 99.95% 24 3799.3 3999.00# Remove Outliers
Z = subset(Z, qty<=24 & cost<=3800 & price<=4000)
nrow(Z) ## [1] 8171822. Orders, Transaction Records：X
Treat every distinct customer-date combinations as a transaction (order)
Z$tid = group_indices(Z, date, cust) # assign transaction id## Warning: The `...` argument of `group_keys()` is deprecated as of dplyr 1.0.0.
## Please `group_by()` first
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.n_distinct
# No. cust, cat, prod, tid
sapply(Z[c("cust","cat","prod","tid")], n_distinct)## cust cat prod tid
## 32256 2007 23789 1194222.1 Summarise by tid transaction id
X = Z %>% group_by(tid) %>% summarise(
date = min(date), # date
cust = min(cust), # customer id
age = min(age), # age code
area = min(area), # zip code
items = n(), # no. items
pieces = sum(qty), # total quantity
total = sum(price), # total revenue
gross = sum(price - cost) # total gross margin
) %>% data.frame
nrow(X) # 119422 ## [1] 119422Handle Outliers
# Check Quantile & Remove Outliers
sapply(X[,6:9], quantile, prob=c(.999, .9995, .9999))## items pieces total gross
## 99.9% 54 81.0000 9009.579 1824.737
## 99.95% 62 94.2895 10611.579 2179.817
## 99.99% 82 133.0000 16044.401 3226.548# Remove Outliers
X = subset(X, items<=62 & pieces<95 & total<16000) # 119328交易摘要
summary(X) ## tid date cust age
## Min. : 1 Min. :2000-11-01 Length:119328 Length:119328
## 1st Qu.: 29855 1st Qu.:2000-11-29 Class :character Class :character
## Median : 59705 Median :2001-01-01 Mode :character Mode :character
## Mean : 59712 Mean :2000-12-31
## 3rd Qu.: 89581 3rd Qu.:2001-02-02
## Max. :119422 Max. :2001-02-28
## area items pieces total
## Length:119328 Min. : 1.000 Min. : 1.000 Min. : 5.0
## Class :character 1st Qu.: 2.000 1st Qu.: 3.000 1st Qu.: 227.0
## Mode :character Median : 5.000 Median : 6.000 Median : 510.0
## Mean : 6.802 Mean : 9.222 Mean : 851.6
## 3rd Qu.: 9.000 3rd Qu.:12.000 3rd Qu.: 1080.0
## Max. :62.000 Max. :94.000 Max. :15345.0
## gross
## Min. :-1645.0
## 1st Qu.: 21.0
## Median : 68.0
## Mean : 130.9
## 3rd Qu.: 168.0
## Max. : 3389.0每周交易次數
par(cex=0.8)
hist(X$date, "weeks", freq=T, las=2, main="No. Transaction per Week")
3. Customer：A
Summarise by Customer
d0 = max(X$date) + 1
A = X %>% mutate(
days = as.integer(difftime(d0, date, units="days"))
) %>% group_by(cust) %>% summarise(
r = min(days), # recency
s = max(days), # seniority
f = n(), # frequency
m = mean(total), # monetary
rev = sum(total), # total revenue contribution
raw = sum(gross), # total gross profit contribution
age = min(age), # age group
area = min(area), # area code
) %>% data.frame
nrow(A) # 32241## [1] 32241par(mfrow=c(1,2),cex=0.7)
table(A$age, useNA='ifany') %>% barplot(main="Age Groups",las=2)
table(A$area, useNA='ifany') %>% barplot(main="Areas",las=2) 
par(mfrow=c(3,2), mar=c(3,3,4,2))
for(x in c('r','s','f','m'))
hist(A[,x],freq=T,main=x,xlab="",ylab="",cex.main=2)
hist(pmin(A$f,10),0:10,freq=T,xlab="",ylab="",cex.main=2)
hist(log(A$m,10),freq=T,xlab="",ylab="",cex.main=2)
顧客摘要
summary(A) ## cust r s f
## Length:32241 Min. : 1.00 Min. : 1.00 Min. : 1.000
## Class :character 1st Qu.: 9.00 1st Qu.: 56.00 1st Qu.: 1.000
## Mode :character Median : 26.00 Median : 92.00 Median : 2.000
## Mean : 37.45 Mean : 80.78 Mean : 3.701
## 3rd Qu.: 60.00 3rd Qu.:110.00 3rd Qu.: 4.000
## Max. :120.00 Max. :120.00 Max. :85.000
## m rev raw age
## Min. : 8.0 Min. : 8 Min. : -784.0 Length:32241
## 1st Qu.: 365.0 1st Qu.: 707 1st Qu.: 75.0 Class :character
## Median : 705.7 Median : 1750 Median : 241.0 Mode :character
## Mean : 993.1 Mean : 3152 Mean : 484.6
## 3rd Qu.: 1291.0 3rd Qu.: 3968 3rd Qu.: 612.0
## Max. :12636.0 Max. :127686 Max. :20273.0
## area
## Length:32241
## Class :character
## Mode :character
##
##
## Check & Save
is.na(Z) %>% colSums## date cust age area cat prod qty cost price tid
## 0 0 0 0 0 0 0 0 0 0is.na(X) %>% colSums## tid date cust age area items pieces total gross
## 0 0 0 0 0 0 0 0 0is.na(A) %>% colSums## cust r s f m rev raw age area
## 0 0 0 0 0 0 0 0 0A0 = A; X0 = X; Z0 = Z
save(Z0, X0, A0, file="data/tf0.rdata")
💡 Common Exploring Questions：
§ How consuming behavior varies by age and area groups?
§
Between weekday and weekend, are consumers behave in the same ways?
§ What are the best selling products and categories?
§ Compare
the revenue and profit contribution by product (categories).
§
Segmentation, Define Groups of Customers in different ways
§
Compare the characteristics among the groups
§ Targeting, Focus
on some group(s) of customers
§ Objective, Set the goal(s) for
improvement