R Notebook-ANZ
rm(list=ls())
library(readxl)## Warning: package 'readxl' was built under R version 3.6.3library(dplyr)## Warning: package 'dplyr' was built under R version 3.6.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(tidyverse)## Warning: package 'tidyverse' was built under R version 3.6.3## -- Attaching packages -------------------------------------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v stringr 1.4.0
## v tidyr 1.1.2 v forcats 0.5.0
## v readr 1.3.1## Warning: package 'ggplot2' was built under R version 3.6.3## Warning: package 'tibble' was built under R version 3.6.3## Warning: package 'tidyr' was built under R version 3.6.3## Warning: package 'readr' was built under R version 3.6.3## Warning: package 'purrr' was built under R version 3.6.3## Warning: package 'stringr' was built under R version 3.6.3## Warning: package 'forcats' was built under R version 3.6.3## -- Conflicts ----------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(visdat)## Warning: package 'visdat' was built under R version 3.6.3library(ggplot2)
library(cluster)## Warning: package 'cluster' was built under R version 3.6.3library(factoextra)## Warning: package 'factoextra' was built under R version 3.6.3## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBaANZ_dataset <- read_excel("D:/ANZ/ANZ synthesised transaction dataset.xlsx")## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i = sheet, :
## Expecting numeric in C3052 / R3052C3: got 'THE DISCOUNT CHEMIST GROUP'## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i = sheet, :
## Expecting numeric in C4360 / R4360C3: got 'LAND WATER & PLANNING East Melbourne'attach(ANZ_dataset)
#View(ANZ_dataset)
ANZ_dataset$date<- as.Date(date)
str(ANZ_dataset)## tibble [12,043 x 23] (S3: tbl_df/tbl/data.frame)
## $ status : chr [1:12043] "authorized" "authorized" "authorized" "authorized" ...
## $ card_present_flag: num [1:12043] 1 0 1 1 1 NA 1 1 1 NA ...
## $ bpay_biller_code : num [1:12043] NA NA NA NA NA NA NA NA NA NA ...
## $ account : chr [1:12043] "ACC-1598451071" "ACC-1598451071" "ACC-1222300524" "ACC-1037050564" ...
## $ currency : chr [1:12043] "AUD" "AUD" "AUD" "AUD" ...
## $ long_lat : chr [1:12043] "153.41 -27.95" "153.41 -27.95" "151.23 -33.94" "153.10 -27.66" ...
## $ txn_description : chr [1:12043] "POS" "SALES-POS" "POS" "SALES-POS" ...
## $ merchant_id : chr [1:12043] "81c48296-73be-44a7-befa-d053f48ce7cd" "830a451c-316e-4a6a-bf25-e37caedca49e" "835c231d-8cdf-4e96-859d-e9d571760cf0" "48514682-c78a-4a88-b0da-2d6302e64673" ...
## $ merchant_code : num [1:12043] NA NA NA NA NA NA NA NA NA NA ...
## $ first_name : chr [1:12043] "Diana" "Diana" "Michael" "Rhonda" ...
## $ balance : num [1:12043] 35.39 21.2 5.71 2117.22 17.95 ...
## $ date : Date[1:12043], format: "2018-08-01" "2018-08-01" ...
## $ gender : chr [1:12043] "F" "F" "M" "F" ...
## $ age : num [1:12043] 26 26 38 40 26 20 43 43 27 40 ...
## $ merchant_suburb : chr [1:12043] "Ashmore" "Sydney" "Sydney" "Buderim" ...
## $ merchant_state : chr [1:12043] "QLD" "NSW" "NSW" "QLD" ...
## $ extraction : chr [1:12043] "2018-08-01T01:01:15.000+0000" "2018-08-01T01:13:45.000+0000" "2018-08-01T01:26:15.000+0000" "2018-08-01T01:38:45.000+0000" ...
## $ amount : num [1:12043] 16.25 14.19 6.42 40.9 3.25 ...
## $ transaction_id : chr [1:12043] "a623070bfead4541a6b0fff8a09e706c" "13270a2a902145da9db4c951e04b51b9" "feb79e7ecd7048a5a36ec889d1a94270" "2698170da3704fd981b15e64a006079e" ...
## $ country : chr [1:12043] "Australia" "Australia" "Australia" "Australia" ...
## $ customer_id : chr [1:12043] "CUS-2487424745" "CUS-2487424745" "CUS-2142601169" "CUS-1614226872" ...
## $ merchant_long_lat: chr [1:12043] "153.38 -27.99" "151.21 -33.87" "151.21 -33.87" "153.05 -26.68" ...
## $ movement : chr [1:12043] "debit" "debit" "debit" "debit" ...summary(ANZ_dataset)## status card_present_flag bpay_biller_code account
## Length:12043 Min. :0.000 Min. :0 Length:12043
## Class :character 1st Qu.:1.000 1st Qu.:0 Class :character
## Mode :character Median :1.000 Median :0 Mode :character
## Mean :0.803 Mean :0
## 3rd Qu.:1.000 3rd Qu.:0
## Max. :1.000 Max. :0
## NA's :4326 NA's :11160
## currency long_lat txn_description merchant_id
## Length:12043 Length:12043 Length:12043 Length:12043
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## merchant_code first_name balance date
## Min. :0 Length:12043 Min. : 0.24 Min. :2018-08-01
## 1st Qu.:0 Class :character 1st Qu.: 3158.59 1st Qu.:2018-08-24
## Median :0 Mode :character Median : 6432.01 Median :2018-09-16
## Mean :0 Mean : 14704.20 Mean :2018-09-15
## 3rd Qu.:0 3rd Qu.: 12465.94 3rd Qu.:2018-10-09
## Max. :0 Max. :267128.52 Max. :2018-10-31
## NA's :11160
## gender age merchant_suburb merchant_state
## Length:12043 Min. :18.00 Length:12043 Length:12043
## Class :character 1st Qu.:22.00 Class :character Class :character
## Mode :character Median :28.00 Mode :character Mode :character
## Mean :30.58
## 3rd Qu.:38.00
## Max. :78.00
##
## extraction amount transaction_id country
## Length:12043 Min. : 0.10 Length:12043 Length:12043
## Class :character 1st Qu.: 16.00 Class :character Class :character
## Mode :character Median : 29.00 Mode :character Mode :character
## Mean : 187.93
## 3rd Qu.: 53.66
## Max. :8835.98
##
## customer_id merchant_long_lat movement
## Length:12043 Length:12043 Length:12043
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## The error obtained while reading the file is because of unexpected values in biller_code column.
#convert character to factor columns
fact_cols <- names(dplyr::select_if(subset(ANZ_dataset,select = -c(account,long_lat,merchant_id,first_name,extraction,transaction_id,customer_id,merchant_long_lat)), is_character))
ANZ_dataset[,fact_cols] <- lapply(ANZ_dataset[,fact_cols], factor)
str(ANZ_dataset)## tibble [12,043 x 23] (S3: tbl_df/tbl/data.frame)
## $ status : Factor w/ 2 levels "authorized","posted": 1 1 1 1 1 2 1 1 1 2 ...
## $ card_present_flag: num [1:12043] 1 0 1 1 1 NA 1 1 1 NA ...
## $ bpay_biller_code : num [1:12043] NA NA NA NA NA NA NA NA NA NA ...
## $ account : chr [1:12043] "ACC-1598451071" "ACC-1598451071" "ACC-1222300524" "ACC-1037050564" ...
## $ currency : Factor w/ 1 level "AUD": 1 1 1 1 1 1 1 1 1 1 ...
## $ long_lat : chr [1:12043] "153.41 -27.95" "153.41 -27.95" "151.23 -33.94" "153.10 -27.66" ...
## $ txn_description : Factor w/ 6 levels "INTER BANK","PAY/SALARY",..: 5 6 5 6 6 3 6 5 5 1 ...
## $ merchant_id : chr [1:12043] "81c48296-73be-44a7-befa-d053f48ce7cd" "830a451c-316e-4a6a-bf25-e37caedca49e" "835c231d-8cdf-4e96-859d-e9d571760cf0" "48514682-c78a-4a88-b0da-2d6302e64673" ...
## $ merchant_code : num [1:12043] NA NA NA NA NA NA NA NA NA NA ...
## $ first_name : chr [1:12043] "Diana" "Diana" "Michael" "Rhonda" ...
## $ balance : num [1:12043] 35.39 21.2 5.71 2117.22 17.95 ...
## $ date : Date[1:12043], format: "2018-08-01" "2018-08-01" ...
## $ gender : Factor w/ 2 levels "F","M": 1 1 2 1 1 2 1 1 1 2 ...
## $ age : num [1:12043] 26 26 38 40 26 20 43 43 27 40 ...
## $ merchant_suburb : Factor w/ 1609 levels "Abbotsford","Aberdeen",..: 53 1380 1380 215 902 NA 709 893 1602 NA ...
## $ merchant_state : Factor w/ 8 levels "ACT","NSW","NT",..: 4 2 2 4 4 NA 7 7 8 NA ...
## $ extraction : chr [1:12043] "2018-08-01T01:01:15.000+0000" "2018-08-01T01:13:45.000+0000" "2018-08-01T01:26:15.000+0000" "2018-08-01T01:38:45.000+0000" ...
## $ amount : num [1:12043] 16.25 14.19 6.42 40.9 3.25 ...
## $ transaction_id : chr [1:12043] "a623070bfead4541a6b0fff8a09e706c" "13270a2a902145da9db4c951e04b51b9" "feb79e7ecd7048a5a36ec889d1a94270" "2698170da3704fd981b15e64a006079e" ...
## $ country : Factor w/ 1 level "Australia": 1 1 1 1 1 1 1 1 1 1 ...
## $ customer_id : chr [1:12043] "CUS-2487424745" "CUS-2487424745" "CUS-2142601169" "CUS-1614226872" ...
## $ merchant_long_lat: chr [1:12043] "153.38 -27.99" "151.21 -33.87" "151.21 -33.87" "153.05 -26.68" ...
## $ movement : Factor w/ 2 levels "credit","debit": 2 2 2 2 2 2 2 2 2 2 ...vis_dat(ANZ_dataset)
bpay_biller_code and merchant_code has 92.67% missingness in data and hence can be removed From the vismiss plot, we can observe that for the rest of the columns containing missing values, all values missing are from the same rows and all these columns have 35.92% of missing data (4326 rows)
ANZ_dataset <- subset(ANZ_dataset, select = -c(bpay_biller_code,merchant_code))
lapply(ANZ_dataset,function(x) { length(which(is.na(x)))})## $status
## [1] 0
##
## $card_present_flag
## [1] 4326
##
## $account
## [1] 0
##
## $currency
## [1] 0
##
## $long_lat
## [1] 0
##
## $txn_description
## [1] 0
##
## $merchant_id
## [1] 4326
##
## $first_name
## [1] 0
##
## $balance
## [1] 0
##
## $date
## [1] 0
##
## $gender
## [1] 0
##
## $age
## [1] 0
##
## $merchant_suburb
## [1] 4326
##
## $merchant_state
## [1] 4326
##
## $extraction
## [1] 0
##
## $amount
## [1] 0
##
## $transaction_id
## [1] 0
##
## $country
## [1] 0
##
## $customer_id
## [1] 0
##
## $merchant_long_lat
## [1] 4326
##
## $movement
## [1] 0head(ANZ_dataset)ANZ_dataset %>% filter(is.na(card_present_flag)) %>% filter(status=="posted") %>% count()ANZ_dataset %>% filter(status=="posted") %>% count()We can verify from above code that the missing values for the 5 columns are when the status is “posted” Data does not need to be cleansed as merchant details will be NA for posted transactions ##Explanatory analysis There are 100 customer out of which 20 has the same customer first name
unique(ANZ_dataset$first_name)## [1] "Diana" "Michael" "Rhonda" "Robert" "Kristin"
## [6] "Tonya" "Fernando" "Isaiah" "Ricky" "Jeffrey"
## [11] "Patrick" "Karen" "Ruth" "Kimberly" "Joseph"
## [16] "Tiffany" "Emily" "Christine" "Ryan" "Michelle"
## [21] "Richard" "Jessica" "Ronald" "Kaitlyn" "Lori"
## [26] "Virginia" "Andrew" "Susan" "Luis" "Gregory"
## [31] "Barry" "Daniel" "Renee" "Amy" "Christopher"
## [36] "Marissa" "Eric" "Natasha" "Edward" "Craig"
## [41] "Sandra" "Debra" "Michele" "Antonio" "Tyler"
## [46] "Lucas" "Jonathan" "Matthew" "James" "Mackenzie"
## [51] "Linda" "Dustin" "Heather" "Derek" "Scott"
## [56] "Charles" "Tim" "Melissa" "Darren" "Jacqueline"
## [61] "Cindy" "Stephanie" "Rachael" "Mary" "Maria"
## [66] "Timothy" "Nathaniel" "Elizabeth" "Paul" "Sarah"
## [71] "Alexander" "Donald" "Kenneth" "Ashley" "Catherine"
## [76] "Billy" "Abigail" "Brian" "David" "Robin"Michael is the most common first name, and has 6 different account users
ANZ_dataset %>% group_by(first_name)%>% summarise(unique(account)) %>% count() %>% filter(n>1) ## `summarise()` regrouping output by 'first_name' (override with `.groups` argument)#Defining Age Group
ANZ_dataset1 <-mutate(ANZ_dataset,Age_group = ifelse(age <= 30 ,'teenage',ifelse(age >30 & age <50, 'midage','old')))
#monthly salary
ANZ_dataset2<-ANZ_dataset1 %>% group_by(account,first_name,format(date, "%m"),format(date, "%Y"),movement)%>% summarise(sum(amount))## `summarise()` regrouping output by 'account', 'first_name', 'format(date, "%m")', 'format(date, "%Y")' (override with `.groups` argument)ANZ_dataset2<- ANZ_dataset2 %>% rename( month = "format(date, \"%m\")" ,year = "format(date, \"%Y\")",total_amount="sum(amount)" )
#Total number of Transactions per month excluding credit
AnZ2 <-ANZ_dataset1 %>% filter(movement!='credit')%>% group_by(account,first_name,format(date, "%m/%Y"),Age_group,gender)%>% count()
#outlier analysis
boxplot(AnZ2$n ~ AnZ2$Age_group,xlab = "Age Group",ylab = "Number of Transactions", main = "Monthy Transactions by age group")
out <-boxplot.stats(AnZ2$n)$out
out_ind <- which(AnZ2$n %in% c(out))
AnZ2[out_ind, ]boxplot(AnZ2$n ~ AnZ2$gender,xlab = "Gender",ylab = "Number of Transactions", main = "Monthy Transactions by Gender")
ANZ_cred<-ANZ_dataset1 %>%filter(movement=='credit')%>% group_by(account,first_name,format(date, "%m/%Y")) %>%summarise(sum(amount)) %>%rename(credit="sum(amount)") ## `summarise()` regrouping output by 'account', 'first_name' (override with `.groups` argument)ANZ_debit<-ANZ_dataset1 %>%filter(movement=='debit')%>% group_by(account,first_name,format(date, "%m/%Y")) %>%summarise(sum(amount)) %>%rename(debit="sum(amount)") ## `summarise()` regrouping output by 'account', 'first_name' (override with `.groups` argument)ANZ_sal<-merge(ANZ_cred,ANZ_debit)
ANZ_sal2<-ANZ_sal %>% mutate(avg_savings_monthly = credit - debit) %>% rename(date ="format(date, \"%m/%Y\")")
ANZ_sal2 %>% ggplot(mapping = aes(x=date,y=avg_savings_monthly,label=first_name))+geom_point()+ geom_text()
There seems to be a number of outliers in below 30 age group. Further analysis shows that Diana and Michael have large number of transactions consistently (all 3 months) in their age group while Jeffery,Richard,Kimberly,Jessica and Rhonda has large number of transactions only in a particular month. Tonya has large number of transactions for 2 months From the boxplot, we can observe that Females tend to spend higher than males and has greater extent of distribution compared to males.
#########cluster analysis
ANZ_sal3<-ANZ_sal2%>% group_by(first_name,account) %>% summarise(credit=sum(credit),debit = sum(debit),savings=sum(avg_savings_monthly))## `summarise()` regrouping output by 'first_name' (override with `.groups` argument)str(ANZ_sal3)## tibble [100 x 5] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ first_name: chr [1:100] "Abigail" "Alexander" "Amy" "Andrew" ...
## $ account : chr [1:100] "ACC-2528867619" "ACC-1890605467" "ACC-414431115" "ACC-721712940" ...
## $ credit : num [1:100] 25305 21897 9782 9389 15385 ...
## $ debit : num [1:100] 4407 5801 4981 1456 6757 ...
## $ savings : num [1:100] 20899 16096 4801 7933 8628 ...
## - attr(*, "groups")= tibble [80 x 2] (S3: tbl_df/tbl/data.frame)
## ..$ first_name: chr [1:80] "Abigail" "Alexander" "Amy" "Andrew" ...
## ..$ .rows : list<int> [1:80]
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## ..- attr(*, ".drop")= logi TRUEnames(dplyr::select_if(ANZ_sal3,is.numeric))## [1] "first_name" "credit" "debit" "savings"mydata <- scale(ANZ_sal3[,c("credit","debit","savings")])
rownames(mydata) <- ANZ_sal3$account
library(factoextra)
fit <- kmeans(mydata, 3)
fviz_cluster(fit, data = mydata)
## Map
library(ggmap)## Warning: package 'ggmap' was built under R version 3.6.3## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.## Please cite ggmap if you use it! See citation("ggmap") for details.library(tmap)## Warning: package 'tmap' was built under R version 3.6.3library(splitstackshape)## Warning: package 'splitstackshape' was built under R version 3.6.3str(ANZ_dataset1)## tibble [12,043 x 22] (S3: tbl_df/tbl/data.frame)
## $ status : Factor w/ 2 levels "authorized","posted": 1 1 1 1 1 2 1 1 1 2 ...
## $ card_present_flag: num [1:12043] 1 0 1 1 1 NA 1 1 1 NA ...
## $ account : chr [1:12043] "ACC-1598451071" "ACC-1598451071" "ACC-1222300524" "ACC-1037050564" ...
## $ currency : Factor w/ 1 level "AUD": 1 1 1 1 1 1 1 1 1 1 ...
## $ long_lat : chr [1:12043] "153.41 -27.95" "153.41 -27.95" "151.23 -33.94" "153.10 -27.66" ...
## $ txn_description : Factor w/ 6 levels "INTER BANK","PAY/SALARY",..: 5 6 5 6 6 3 6 5 5 1 ...
## $ merchant_id : chr [1:12043] "81c48296-73be-44a7-befa-d053f48ce7cd" "830a451c-316e-4a6a-bf25-e37caedca49e" "835c231d-8cdf-4e96-859d-e9d571760cf0" "48514682-c78a-4a88-b0da-2d6302e64673" ...
## $ first_name : chr [1:12043] "Diana" "Diana" "Michael" "Rhonda" ...
## $ balance : num [1:12043] 35.39 21.2 5.71 2117.22 17.95 ...
## $ date : Date[1:12043], format: "2018-08-01" "2018-08-01" ...
## $ gender : Factor w/ 2 levels "F","M": 1 1 2 1 1 2 1 1 1 2 ...
## $ age : num [1:12043] 26 26 38 40 26 20 43 43 27 40 ...
## $ merchant_suburb : Factor w/ 1609 levels "Abbotsford","Aberdeen",..: 53 1380 1380 215 902 NA 709 893 1602 NA ...
## $ merchant_state : Factor w/ 8 levels "ACT","NSW","NT",..: 4 2 2 4 4 NA 7 7 8 NA ...
## $ extraction : chr [1:12043] "2018-08-01T01:01:15.000+0000" "2018-08-01T01:13:45.000+0000" "2018-08-01T01:26:15.000+0000" "2018-08-01T01:38:45.000+0000" ...
## $ amount : num [1:12043] 16.25 14.19 6.42 40.9 3.25 ...
## $ transaction_id : chr [1:12043] "a623070bfead4541a6b0fff8a09e706c" "13270a2a902145da9db4c951e04b51b9" "feb79e7ecd7048a5a36ec889d1a94270" "2698170da3704fd981b15e64a006079e" ...
## $ country : Factor w/ 1 level "Australia": 1 1 1 1 1 1 1 1 1 1 ...
## $ customer_id : chr [1:12043] "CUS-2487424745" "CUS-2487424745" "CUS-2142601169" "CUS-1614226872" ...
## $ merchant_long_lat: chr [1:12043] "153.38 -27.99" "151.21 -33.87" "151.21 -33.87" "153.05 -26.68" ...
## $ movement : Factor w/ 2 levels "credit","debit": 2 2 2 2 2 2 2 2 2 2 ...
## $ Age_group : chr [1:12043] "teenage" "teenage" "midage" "midage" ...ANZ_map<-cSplit(ANZ_dataset1, 5, " ", drop=F) %>% rename(longitude_cust = long_lat_1, latitude_cust=long_lat_2)
ANZ_map<-cSplit(ANZ_map, 20, " ", drop=F) %>% rename(longitude_merch = merchant_long_lat_1, latitude_merch=merchant_long_lat_2)
ANZ_map%>% select("longitude_cust","latitude_cust")ggmap::register_google(key = "AIzaSyBVRB4Dngod3xK-dXAGCTIBIhIg-Pnpl4Y")
myMap <- get_map(location = "Australia", zoom = 4)## Source : https://maps.googleapis.com/maps/api/staticmap?center=Australia&zoom=4&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx-dXAGCTIBIhIg-Pnpl4Y## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Australia&key=xxx-dXAGCTIBIhIg-Pnpl4Yggmap(myMap) + geom_point(data = ANZ_map,aes(x = longitude_merch,y = latitude_merch,col='red')) + geom_point(data = ANZ_map%>% select("longitude_cust","latitude_cust"),aes(x = longitude_cust,y = latitude_cust))## Warning: Removed 4326 rows containing missing values (geom_point).## Warning: Removed 123 rows containing missing values (geom_point).
Most of the merchants are scattered throughout the Australia and Tasmania while users are in populated cities. This is as expected.