1.Introduction and Preparation

The Interbank Card Center (BKM) is established for the purpose of providing solutions to the common problems and developing the rules and standards of credit and debit cards in Turkey, within the card payment system.

For the dataset the examinations will be made through the following information:

  1. sector: Company Category
  2. credit_card_tx_nbr: Number of transactions with credit card
  3. debit_card_tx_nbr: Number of transactions with debit card
  4. credit_card_tx_amt: Credit card transaction Amount
  5. debit_card_tx_amt: Debit card transaction Amount
  6. year
  7. month 
library(tidyverse)
library(rvest)
## Loading required package: xml2
## 
## Attaching package: 'rvest'
## The following object is masked from 'package:purrr':
## 
##     pluck
## The following object is masked from 'package:readr':
## 
##     guess_encoding
library(dplyr)

raw_data_full <- ''
# Creating a list including all csv names to use in for loop
list_all <- c('201905','201904','201903','201902','201901','201812','201811','201810','201809','201808','201807','201806','201805','201804','201803','201802','201801')

for (i in 1:length(list_all)) {
  file_url<- paste('https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=',substr(list_all[i],1,4),'&filter_month=',substr(list_all[i],5,6),'&List=Listele', sep='')
  
  print(file_url)
  
  df_html <- read_html(file_url)
  df <- df_html %>% html_nodes("table") %>% .[[4]] %>% html_table(header = TRUE, fill = TRUE) %>% as.data.frame()
  
  names <- unlist(df[1,])
  names[1:5] <- c("sector", "credit_card_tx_nbr" , "debit_card_tx_nbr" , "credit_card_tx_amt" , "debit_card_tx_amt")
  colnames(df) <- names
  
  df <- df[-1,]
  
raw_data <- df %>%  mutate_if(is.numeric,funs(ifelse(is.na(.),0,.))) %>% mutate(yearmonth=list_all[i])

raw_data_full <- rbind(raw_data_full,raw_data)

}
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2019&filter_month=05&List=Listele"
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2019&filter_month=04&List=Listele"
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2019&filter_month=03&List=Listele"
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2019&filter_month=02&List=Listele"
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2019&filter_month=01&List=Listele"
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2018&filter_month=12&List=Listele"
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2018&filter_month=11&List=Listele"
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2018&filter_month=10&List=Listele"
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2018&filter_month=09&List=Listele"
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2018&filter_month=08&List=Listele"
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2018&filter_month=07&List=Listele"
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2018&filter_month=06&List=Listele"
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2018&filter_month=05&List=Listele"
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2018&filter_month=04&List=Listele"
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2018&filter_month=03&List=Listele"
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2018&filter_month=02&List=Listele"
## [1] "https://bkm.com.tr/secilen-aya-ait-sektorel-gelisim/?filter_year=2018&filter_month=01&List=Listele"
raw_data_full <- raw_data_full%>%filter(sector!="TOPLAM")

2.Analysis

2.1. The transaction amounts made with credit card per Sector in 2018 & 2019

df_totamt_by_cat <- raw_data_full %>% filter(substring(yearmonth,1,4) >= 2018)  %>% group_by(sector) %>% 
  summarise(TotalAmount=sum(as.numeric(gsub(",", ".", gsub("\\.", "", credit_card_tx_amt)) ,na.rm=T))) %>%
  arrange(desc(TotalAmount)) %>%
  mutate(rwn = row_number()) %>%
  filter(rwn <=10)

df_plot_sec  <- data.frame(Category = df_totamt_by_cat$sector, freq=df_totamt_by_cat$TotalAmount)

ggplot(df_plot_sec, aes(x=Category, y=freq, fill=Category))+
  geom_bar(stat="identity")+  theme_minimal()+
  labs(x="Sector",y="Payment Amount Million TL",title="Sectors with the most payment with credit card  ",fill="Sector")+
  theme(axis.text.x=element_blank())

2.2. Transaction Numbers Comparision of debit and credit cards in 2019

df_totNbr_cc <- raw_data_full  %>% filter(substr(yearmonth,1,4) == 2019)  %>% group_by(sector) %>% 
  summarise(TotalNbr=sum(as.numeric(gsub(",", ".", gsub("\\.", "", credit_card_tx_nbr)) ,na.rm=T)),TotalDBTNbr=sum(as.numeric(gsub(",", ".", gsub("\\.", "", debit_card_tx_nbr)) ,na.rm=T))) %>%
  arrange(desc(TotalNbr+TotalDBTNbr)) %>%
  mutate(rwn = row_number()) %>%
  filter(rwn <=5)
df_totCCNbr <- df_totNbr_cc %>% transmute(sector,cat="Credit Card",TotalNbr)


df_totNbr_dbt <- raw_data_full %>% filter(substr(yearmonth,1,4) == 2019)  %>% group_by(sector) %>% 
  summarise(TotalNbr=sum(as.numeric(gsub(",", ".", gsub("\\.", "", debit_card_tx_nbr)) ,na.rm=T)),TotalCCNbr=sum(as.numeric(gsub(",", ".", gsub("\\.", "", credit_card_tx_nbr)) ,na.rm=T))) %>%
  arrange(desc(TotalNbr+TotalCCNbr)) %>%
  mutate(rwn = row_number()) %>%
  filter(rwn <=5)

df_totDBTNbr <- df_totNbr_dbt %>% transmute(sector,cat="Debit Card",TotalNbr)


df_plot_nbr<- union(df_totCCNbr , df_totDBTNbr)

options(scipen=999)

ggplot(df_plot_nbr,aes(x=sector,y=TotalNbr, fill=cat)) + geom_bar(stat="identity",position="dodge") + 
  labs(x="Sector Name",y="# of Tx",title="Credit&Debit Card Usages",fill="Card Type") + 
  theme(axis.text.x = element_text(angle=300),legend.position = "top") + 
  scale_fill_manual(values=c("#00c0FF","#00ffff"))