ASB-4012 Codding for Business Application Assignment – Project in R | Bangor University

Assignment : Project in R

The due date for the assignment is 3^rd December 2025 at 23:55.

• This assignment contributes to 50% of the overall module grade.
• Assignments are to be submitted through Turnitin in Blackboard. You can submit your assignment multiple times up until the deadline. Once the submission date has passed, the system will not allow re-submission.
• Students may request an extension to the stipulated due date. To request an extension, you will need to log into My Bangor https://my.bangor.ac.uk/en/ and locate the request centre from the drop-down list of the Online Services tab.
• Where an assignment is late without an agreed extension, the following rules will apply without prejudice:
  o Submitted up to 7 days after the original submission date – Assignment will be capped at the lowest possible passing grade, which at 50 marks.
  o Submitted after 7 days without a valid extension – Assignment will be zero graded.

Before submitting your assignment, please ensure that it is properly referenced to guard against accusations of malpractice. Guidance on referencing can be found at Teaching & Learning Support Resources Gateway and Referencing for Bangor Business School.

• You are reminded not to copy material from any sources without properly referencing it, as this constitutes plagiarism. Plagiarism is defined as using without acknowledgement another person’s words or ideas and submitting them for assessment as though it were one’s own work. This includes copying materials from the internet, unfair use of generative artificial intelligence software such as Chat GPT, rewriting published material without acknowledging the source and the translation of materials using unauthorised methods or essay mills. Cases of plagiarism will be referred to the Business School Academic Integrity Officer for investigation and may be subject to a deduction of marks, which can result in an overall mark of 0%.

You are reminded that the use of such services to generate work in substitute for your original contributions contravenes Bangor’s Academic Integrity policy. Any detected attempt to use such tools will also result in a referral to the Business School Academic Integrity Officer.  Academic Integrity Procedure: https://www.bangor.ac.uk/regulations/procs/proc05.php.en.       

COURSEWORK DETAILS

Please direct all your enquiries on the project to Dr Heather He. You must complete ALL the tasks listed in this brief. This assignment is about data analysis using R. Therefore, all data import/output, manipulation, exploration, analysis, and presentation must be conducted using R. You will lose credit if, for example, you use Python to perform any of the tasks.

You should use good coding style throughout your program to explain what you are doing. 

• Include a comment at the top of your program to document when the program was written, your student ID, the module code and semester, and the purpose of the program.
• Good coding style includes, but is not limited to appropriate indentation, comments, meaningful names, sensible use of whitespace, consistent formatting, and logical and efficient code

Task Background

A local coffee shop, Bean & Brew, collects detailed records of all customer orders. These records are stored in a single RDS file named raw_orders_coffee.rds. The shop management has asked you to help them develop programs that will support the maintenance and analysis of their data system.

An RDS file contains R objects and can be loaded into R using the readRDS function. The file provided to you includes data covering the period from May 2025 to August 2025. It contains the following fields:

Business Rules for Bean & Brew Orders

• Orders can be placed as either Dine In or Deliveroo. Dine In has no extra charge. Deliveroo orders incur an additional £1.99 delivery fee.
• Discount codes o On the first day of every month, all loyalty programme members

(Customer_Membership_Status== 1) receive a 10% discount on their entire order. This discount applies to both Dine-In and Deliveroo orders. This should be reported automatically by the program as MEMBER10 in the Discount Code field (i.e., the user won’t enter MEMBER10, the program needs to do that).
o Customers may use the following code: OFF5 → 5% off the total price
o If a member places an order on Membership Day and also uses a discount code, only the larger discount applies.
o The applied discount code (e.g., OFF5 or MEMBER10) must be recorded in the Discount Code field.

• A 12% VAT is applied to the sub-total after discounts have been applied, but before delivery charges, if any.

Task 1 [30%] : Data Preparation

Clean the data in the raw_orders_coffee.rds file and split it into three data frames so that it is ready to be put into a database. Specifically, the tables are:

• customers: Extract the unique customer names, IDs, Date of Birth (DoB) and membership status and place them into a data frame containing customers’ information. Break each customer’s name into first and last name columns. Additionally, use the DoB column to create an age column that reports the customer’s age in years (whole number rounded down) as of the 31st of August 2025.
• items_in_orders: Extract the product names, quantities and unit prices and place them into a data frame containing the items in each order (i.e. one row for every item in an order). Each row in the data frame should be associated with an order, identifiable by the order ID column.
• orders: Create an orders data frame containing summary information on about each order. Specifically, the data frame should contain the order ID, customer ID of the customer purchasing the order, order type, discount code and the date the order was placed. Additionally, the orders data frame should contain fields to record the Deliveroo fee (if any), sub-total before discount, value of the discount (if any), sub-total after discount, VAT and final total of each order. Pound figures should be rounded to two decimal places
• Save all three data frames as separate CSV files.
• Note: To show you what you are trying to achieve, sample CSV files for the “customers”,

“orders” and “items_in_orders” data frames using the first 50 rows in the raw_orders_coffee.rds file. It is a good idea to check that your final data frames match these samples.   

Task 2 [20%]: Customer Reporting Function

The coffee shop is planning to develop a web application. To support this process, build an R function that will be used to pass information on to the application. This function will be used to generate a report about a given customer. It will take in a customer ID, start date and end date (both dates will be in YYYY-MM-DD format) and then print out information on about the customer’s orders between (inclusive) the start and end dates. Specifically, it will print out the customer’s full name, age, number of orders, mean/median order totals, percentage breakdown of the order types and percentage breakdown of products. You can assume that the “customers”, “items in orders” and “orders” data frames are loaded globally (i.e., you can refer to them in your function without passing them in as parameters). If the function finds no orders for a given customer ID and date range, it should tell the user whether or not the customer exists in the data.

Task 3 [50%]: Customer Reporting Function

Generate two visualisations/charts based on information on from the dataset. Choose visualisations to best showcase interesting insights uncovered from your data analysis. At a minimum, each chart

• Should be complemented with an interpretation (maximum 150 words) and
• Be labelled appropriately: title, axes, legend, and so on.

You may add other attributes to your charts. You may also add notes to assist the interpretation on of

the charts. Carefully consider you visualisations on choices; the owner of the business is your audience for these visualisations.

Instructions

Submit to Blackboard: 

• An R Markdown file in .RMD format containing your codes. The markdown file should be submitted to the “R Markdown Submission – Project in R” tab on Blackboard. Failure to submit this file will result in reduction to your marks.
• A report in .PDF format produced from knitr. Your report should contain (i) the text of your R codes; and (ii) the outputs of your responses to all task. You must submit the report to the Turnitin “Report Submission – Project in R” tab on Blackboard. The report must be produced from knitr; failure to deliver this will result in reduction to your marks. There are two approaches you could take to produce the PDF report:
  o You could either knit your R Markdown file into a HTML file and then save it to a PDF file, or
  o You could directly knit your R Markdown file into a PDF file.

• Please name your files using your student ID number, e.g. 500123456.RMB. Do not include your name in your file names.
• You are allowed to test submit your report before the due date. You can use Turnitin to check your assignment for plagiarism before you submit your final version. The last submission prior to the deadline will be treated as the final submission and will be the copy that is assessed by the marker.

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