Introduction

In this assignment, I will implement window functions in dplyr to calculate the year-to-date average and six-day moving average for each item. I will use temperature time-series data for New York City and Los Angeles to demonstrate how window functions can be applied to time-series data.

A window function performs a calculation across a set of related rows while keeping every original row in the dataset. Window functions allow you to calculate:

• Running totals — such as the sum of values from the beginning up to the current row.

• Moving averages — such as the average of the current row and the previous five rows.

• Rankings — such as ranking values within a group.

Similar to an aggregate function using GROUP BY a window function performs calculations across multiple rows. However, unlike an aggregate function, a window function does not combine multiple rows into a single row. Instead, it keeps the original rows and adds the calculated result to each row.

Approach

I will use window functions in dplyr to calculate the year-to-date temperature average for New York City and Los Angeles. I will also calculate the six-day moving average for both cities and compare the results.

First, I will import my 2025 temperature time-series data into RStudio. Then, I will use dplyr window functions to calculate the year-to-date temperature averages and six-day moving averages for New York City and Los Angeles. Finally, I will compare the averages between the two cities to examine how their temperatures change over time.

Anticipated Challenges

One anticipated challenge is making sure the temperature data is correctly ordered by date before applying the window functions. If the dates are not in the correct order, the year-to-date and six-day moving averages may be calculated incorrectly.

Another challenge is handling missing temperature values. Missing data could affect the accuracy of the averages, especially for the six-day moving average