Information

This is a project to demonstrate my analytical skills provided by ANZ Banking.Futhremore, the current project was created from the virtual intership program of the company.

Code Samples

Activating the libraries

Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
-- Attaching packages ------------------------------------------------------------------------ tidyverse 1.3.1 --
v ggplot2 3.3.5     v purrr   0.3.4
v tibble  3.1.4     v dplyr   1.0.7
v tidyr   1.1.3     v stringr 1.4.0
v readr   2.0.1     v forcats 0.5.1
-- Conflicts --------------------------------------------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

Replacing 0 and 1 with False and True and replacing all NAs with 'Not Available'

# 1 true and 0 false
# Moving to second column i need to take care of the NA values but i do not want to 
# miss 4000 observations from my dataset so i will replace NA WITH 'Not Available'
# After that i want to replace 0 and 1 with false and true

# Replacing 1 and 0 with true and false 

new <- as.logical(copied2$card_present_flag)

# Replacing NA with Not availableCopiedfile <- 
copied2 %>% replace_na(list(card_present_flag = 'Not Available'))

glimpse(Copiedfile)
Rows: 12,043
Columns: 23
$ status            <chr> "authorized", "authorized", "authorized", "authorized", "authorized", "posted", "authorized",~
$ card_present_flag <dbl> 1, 0, 1, 1, 1, NA, 1, 1, 1, NA, NA, NA, 1, NA, NA, 1, NA, NA, NA, 1, 1, 0, 1, 0, 1, NA, NA, 1~
$ bpay_biller_code  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N~
$ account           <chr> "ACC-1598451071", "ACC-1598451071", "ACC-1222300524", "ACC-1037050564", "ACC-1598451071", "AC~
$ currency          <chr> "AUD", "AUD", "AUD", "AUD", "AUD", "AUD", "AUD", "AUD", "AUD", "AUD", "AUD", "AUD", "AUD", "A~
$ long_lat          <chr> "153.41 -27.95", "153.41 -27.95", "151.23 -33.94", "153.10 -27.66", "153.41 -27.95", "151.22 ~
$ txn_description   <chr> "POS", "SALES-POS", "POS", "SALES-POS", "SALES-POS", "PAYMENT", "SALES-POS", "POS", "POS", "I~
$ merchant_id       <chr> "81c48296-73be-44a7-befa-d053f48ce7cd", "830a451c-316e-4a6a-bf25-e37caedca49e", "835c231d-8cd~
$ merchant_code     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N~
$ first_name        <chr> "Diana", "Diana", "Michael", "Rhonda", "Diana", "Robert", "Kristin", "Kristin", "Tonya", "Mic~
$ balance           <dbl> 35.39, 21.20, 5.71, 2117.22, 17.95, 1705.43, 1248.36, 1232.75, 213.16, 466.58, 4348.50, 1203.~
$ date              <dttm> 2018-08-01, 2018-08-01, 2018-08-01, 2018-08-01, 2018-08-01, 2018-08-01, 2018-08-01, 2018-08-~
$ gender            <chr> "F", "F", "M", "F", "F", "M", "F", "F", "F", "M", "M", "F", "F", "M", "M", "M", "M", "F", "F"~
$ age               <dbl> 26, 26, 38, 40, 26, 20, 43, 43, 27, 40, 19, 43, 27, 23, 43, 30, 46, 26, 47, 24, 26, 37, 25, 4~
$ merchant_suburb   <chr> "Ashmore", "Sydney", "Sydney", "Buderim", "Mermaid Beach", NA, "Kalkallo", "Melbourne", "Yoki~
$ merchant_state    <chr> "QLD", "NSW", "NSW", "QLD", "QLD", NA, "VIC", "VIC", "WA", NA, NA, NA, "WA", NA, NA, "QLD", N~
$ extraction        <chr> "2018-08-01T01:01:15.000+0000", "2018-08-01T01:13:45.000+0000", "2018-08-01T01:26:15.000+0000~
$ amount            <dbl> 16.25, 14.19, 6.42, 40.90, 3.25, 163.00, 61.06, 15.61, 19.25, 21.00, 27.00, 29.00, 6.08, 25.0~
$ transaction_id    <chr> "a623070bfead4541a6b0fff8a09e706c", "13270a2a902145da9db4c951e04b51b9", "feb79e7ecd7048a5a36e~
$ country           <chr> "Australia", "Australia", "Australia", "Australia", "Australia", "Australia", "Australia", "A~
$ customer_id       <chr> "CUS-2487424745", "CUS-2487424745", "CUS-2142601169", "CUS-1614226872", "CUS-2487424745", "CU~
$ merchant_long_lat <chr> "153.38 -27.99", "151.21 -33.87", "151.21 -33.87", "153.05 -26.68", "153.44 -28.06", NA, "144~
$ movement          <chr> "debit", "debit", "debit", "debit", "debit", "debit", "debit", "debit", "debit", "debit", "de~

First things first

Settting the working directory

setwd('C:/User/path/)

Specifing the path for the installed libraries

.libPaths('/Path')

Activating the necessary libraries

library('readr')
library('readxl')
library('tidyverse')
library('magrittr')
library('lubridate')

Languages

The project was created using R

Environement

Rstudio

Phases for the project

Data Collection

Data have been collected from ANZ Virtual Internship

Data Cleaning

1. Dropping unnecessary columns
2. Renaming variables
3. Deciding what to do with NAs values
4. Remaking the date format
5. Spreading data from columns

Exploring the Data

Installing

library('ggplot2)

i am given the chance to visualize my data and discover insights and trends and with

library('writexl')