Course title: Econ115a: Econometrics
Instructor: Christopher Llones
Assignment: Coffee Sales Dataset Analysis in R
Due Date: 15 October 2025

Objective

This assignment will help you apply R programming skills to analyze real-world sales data from a coffee shop. You’ll use dplyr and other relevant packages to explore customer behavior, beverage preferences, and sales performance across time and payment methods.

Instructions

  • Use R and the dplyr package to answer each question.

  • Submit your R script file (.R) with your code and outputs.

  • Use the pipe operator (%>%) for all data manipulations.

  • You may use additional packages like lubridate, ggplot2, or stringr if needed.

  • Ensure your code is clean, commented, and reproducible.

Dataset and files

  1. Access the dataset and R script template from the econ115a-assignment1 folder.

  2. Submit your completed R script file (.R) by the due date and upload using this link: Submission Link.

Questions

Part 1: Data exploration

  1. How many rows and columns are in the dataset?

  2. List all unique coffee types sold.

  3. What are the earliest and latest transaction dates?

Part 2: sales behavior

  1. What is the total revenue generated across all transactions?

  2. Which beverage type generated the highest total sales?

  3. What is the average amount spent per transaction?

Part 3: time-based analysis

  1. Which time of day (Morning, Afternoon, Evening) had the highest average spending?

  2. Which weekday had the most transactions?

  3. What is the most popular beverage during the evening?

Part 4: payment method insights

  1. Compare total revenue between cash and card payments.

  2. What percentage of transactions were made using card?

  3. Is there a noticeable spending difference between payment methods?

Bonus Challenge

  1. Create a monthly summary of total sales. Which month had the highest revenue?

  2. Identify any beverage that consistently appears in high-spending transactions (above ₱35).

Grading rubrics

Criteria Excellent (5pts) Good (4pts) Fair (2-3 pts) Needs improvement (0-1 pt)
Code accuracy All answers are correct and match expected outputs. Most answers are correct with minor errors. Several answers are incorrect or incomplete. Many answers are missing or incorrect.
Use of dplyr Functions Consistently uses appropriate dplyr verbs (filter, mutate, summarise, etc.). Uses dplyr functions correctly in most cases. Uses some dplyr functions but inconsistently or incorrectly. Rarely uses dplyr or misuses functions.
Pipe Operator Usage (%>%) Pipe operator is used fluently and correctly throughout. Mostly correct usage with occasional syntax issues. Used sporadically or with frequent errors. Not used or used incorrectly.
Data Manipulation & Filtering Demonstrates strong understanding of filtering, grouping, and summarizing. Shows good grasp with minor gaps. Basic filtering and grouping attempted but lacks depth. Little to no meaningful data manipulation.
Insight & Interpretation Provides thoughtful insights or observations where applicable. Some interpretation is present. Minimal interpretation or unclear reasoning. No interpretation or irrelevant commentary.
Bonus Challenge (Q13–Q14) Completed with correct logic and creative approach. Attempted with mostly correct logic. Attempted but contains errors or lacks clarity. Not attempted or incorrect.
Reproducibility Code runs without errors and produces expected results. Minor issues but generally reproducible. Some errors prevent full reproducibility. Code fails to run or produces major errors.