PROJECT: Personal Finance Tracker

Overview

This portfolio aims to document the contributions made by Teoh ZhiHui to the Personal Finance Tracker.

Personal Finance Tracker is a desktop finance tracker application used for tracking personal finances such as daily expenses, recurring expenses such as monthly electricity bills, budgets and debts owed to another party. The user interacts with it using a CLI, and it has a GUI created with JavaFX. It is written in Java, and has about 30 kLoC. The product was morphed from an Address Book over a period of 8 weeks under the constraints described here.

Summary of contributions

  • Major enhancement: added the Statistics feature

    • What it does: allows the users to generate useful information from their data.

    • Justification: This feature improves the product significantly because a user can now understand their spending habits at a glance

    • Highlights: This feature allows a user to see what percentage of their expenses are spent on a particular category. The user can also quickly see how often they are spending through the expense counts.

  • Code contributed: code collated by reposense]

  • Other contributions:

    • Project management:

      • Check pull requests done by group mates

      • Create issues for tracking in GitHub Issue Tracker

      • In charge of code in logic component

      • Implement parsers for commands

    • Documentation:

      • Documented new statistics feature in User Guide (Pull Request 225)

      • Aided in the writing of Use Cases and User Stories (Pull requests #28 and #55)

    • Enhancements to existing features:

      • Update logic component in Developer’s Guide (Pull Request 108, 110)

      • Update commands to work for the Financial Tracker (Pull Request 83, 88, 96,…​)

      • Added the implementation of statistics in Developer’s Guide (Pull Request 225, 281)

      • Added test cases for Statistics class (287)

Contributions to the User Guide

Given below are sections I contributed to the User Guide. They showcase my ability to write documentation targeting end-users.

Viewing Statistics - Summary: stats

Produces statistics regarding the user’s finance.
The statistics include the frequency of entries, the total amount of money spent, and the percentage composition of the total.
The user may choose the time frame to be considered for the statistic by entering the start date and end date.

Table Example:

Category

Amount Spent ($)

Entry Count

Percentage(%)

FOOD

302

38

13.12

TRANSPORT

205

4

8.90

SHOPPING

1702

8

73.96

WORK

52

1

2.25

OTHERS

40

1

1.92

Total

2301

52

100

Format: stats [sd/START_DATE] [ed/END_DATE]

  • If START_DATE is empty but END_DATE is specified, statistics will be calculated from the one month before END_DATE to END_DATE

  • If START_DATE is specified but END_DATE is empty, statistics will be calculated from START_DATE to one month after START_DATE

  • If both START_DATE and END_DATE are empty, statistics will be calculated from one month before to the current date

  • START_DATE should be before END_DATE

  • The statistics displayed are only correct at the time of command execution. If any commands which modify the state of the Financial Tracker are added, the command has to be activated again for accurate statistics to be shown

Examples:

  • stats

  • stats sd/12-02-2018

  • stats sd/01-01-2019 ed/01-02-2019

Viewing Statistics - Trend: statstrend

Produces statistics regarding the user’s finance over a period of time to show trends.
The statistics include the total amount of money spent on different categories and the frequency of entries in those categories.
The user must choose the time frame to be considered for the statistic by entering the start date and end date.

Table Example:
Amount ($):

Period Starting (M)

01-01-2019

01-02-2019

01-03-2019 till End

FOOD

302

23

782

TRANSPORT

123

0

140

SHOPPING

324

0

$200

WORK

0

401

0

TOTAL

749

424

1122

Count:

Period Starting (M)

01-01-2019

01-02-2019

01-03-2019 till End

FOOD

20

2

30

TRANSPORT

2

0

3

SHOPPING

3

0

1

WORK

0

3

0

TOTAL

25

5

34

Format: statstrend sd/START_DATE ed/END_DATE f/FREQUENCY

  • START_DATE should be before END_DATE

  • A maximum of 10 periods will be displayed. Please enter different variables to see Statistics beyond the first 10 shown.

  • The statistics displayed are only correct at the time of command execution. If any commands which modify the state of the Financial Tracker are added, the command has to be activated again for accurate statistics to be shown

Examples:

  • statstrend sd/01-01-2018 ed/01-01-2019 f/M

  • statstrend sd/01-01-2018 ed/01-02-2018 f/W

Viewing Statistics - Compare: statscompare

Produces and displays statistics regarding the user’s finance for 2 time periods. The statistics include the total amount of money spent on different categories, the frequency of entries, and the percentage composition of the total.
The user must specify 2 time periods by specifying the starting dates and the desired period length.

Table Example:
From: 01-01-2020 To: 01-01-2021

Category

Amount Spent ($)

Entrie Count

Percentage(%)

FOOD

302

38

13.12

TRANSPORT

205

4

8.90

SHOPPING

1702

8

73.96

WORK

52

1

2.25

OTHERS

40

1

1.92

Total

2301

52

100

From: 02-02-2019 To: 02-02-2020

Category

Amount Spent ($)

Entrie Count

Percentage(%)

FOOD

508

20

27.16

TRANSPORT

722

8

30.88

SHOPPING

1003

32

42.89

WORK

85

2

3.63

OTHERS

20

1

0.85

Total

2338

63

100

Format: statscompare sd1/START_DATE_1 sd2/START_DATE_2 f/FREQUENCY

  • The statistics displayed are only correct at the time of command execution. If any commands which modify the state of the Financial Tracker are added, the command has to be activated again for accurate statistics to be shown

Examples:

  • statscompare sd1/01-01-2019 sd2/01-02-2019 f/M

  • statscompare sd1/01-01-2018 sd2/01-01-2018 f/D

Contributions to the Developer Guide

Given below are sections I contributed to the Developer Guide. They showcase my ability to write technical documentation and the technical depth of my contributions to the project.

Statistics Feature

This feature allows users to calculate various statistics for the entries in their Financial Tracker. The feature will read the expenses, debts and budgets and generate various metrics to display.

The Statistic feature consists of stats, statstrend and statscompare

This implementation is under Logic and Model components.

stats

Current implementation

Below is the UML sequence diagram and a step-by-step explanation of an example usage scenario for stats.

StatCommandSequenceDiagram

  1. User enters command stats sd/01-01-2019. The command is received by FinanceTrackerParser, which then creates a StatsCommandParser object. The created object then calls StatsCommandParser.parse("stats sd/01-01-2019") method. A StatsCommand object is then created with StatsCommand(01-01-2019, 01-02-2019) and returned to the LogicManager

  2. Logic Manager will call the execute method of the StatsCommand object which calls the calculateStatistics("stats', 01-01-2019, 01-02-2019, null) method in Model. The Model Manager will handle this command and duplicate the Expense list held in the Financial Tracker, creating a snapshot. The Model Manager will instantiate a Statistics object and call the calculateStatistics("stats', 01-01-2019, 01-02-2019, null) method.

  3. The calculateStatistics method in Statistics is a wrapper that will call the appropriate method basicStats(01-01-2019, 01-02-2019) after reading the String "stats" that was passed in.

  4. basicStats calls the method extractRelevantExpense(01-01-2019, 01-02-2019) which takes only the Expenses that lie within the time range and adds them to a new Expense list data. These relevant Expenses are then processed by the generateSummary(data) method. This method will return a table of relevant statistics.

  5. The methods trimTable(table), convertDataToString(trimmedtable) are called to process this table. htmlTableBuilder(header, table) is called to generate the HTML code required to show the Statistics in the BrowserPanel.

  6. Model Manager calls setStatistics(statistics) which sets the observable Statistics in ModelManager.

  7. The listener in BrowserPanel will detect the change and display the HTML page showing the statistics

Future Implementation

Users will be given the option to choose a Visual Representation method. This will determine the output format of the statistics. Currently, statistics can only be displayed in table format. However, in the future a feature will be implemented to show the statistics in graphical or pictorial formats. These could include pie charts, bar charts, line graphs, etc.