README.md

In alejandrohagan/fpaR: Common Business Intelligence Functions for F&PA work

README

fpaR: A Business Intelligence Toolkit for Financial Planning & Analysis (FP&A)

Introduction

fpaR is a collection of business intelligence tools designed to simplify common financial planning and analysis (FP&A) tasks such as time intelligence calculations, customer segmentation, and factor analysis.

The package is inspired by best practices from a collection of blogs, books, industry research, and hands-on work experience, consolidating frequently performed business analyses into a fast, efficient, and reusable framework.

In particular, the time intelligence functions are heavily inspired by PowerBI DAX functions

Under the hood, these functions are built upon the great foundations of dbplyr, duckdb, and lubridate

What is in fpaR?

Time intelligence

fpaR provides readily available functions for most time intelligence anlaysis such as Year-over-Year (YoY), Month-to-Date (MTD), and Current Year-to-Date over Previous Year-to-Date (YoYTD) efficiently.

These features offer key benefits:

• Auto-fill missing dates: Ensures no missing periods in your calculations, even if your dataset doesn’t include every single day.

• Flexible calendar options: Handle comparisons based on a standard calendar or a 5-5-4 fiscal calendar to accommodate different reporting frameworks

Time intelligence can be summarized if they aggregate a value, shift a value or compare against another value.

Below is the full list of time intelligence functions

| Short Name | Description | Shift | Aggregate | Compare | |------------|----------------------------------------------------------|-------|-----------|---------| | YoY | Full Year over Year | | | X | | YTD | Year-to-Date | X | | | | PYTD | Prior Year-to-Date amount | X | X | | | YoYTD | Current Year-to-Date over Prior Year-to-Date | X | X | X | | YTDOPY | Year-to-Date over Full Previous Year | X | X | X | | QoQ | Full Quarter over Quarter | | | X | | QTD | Quarter-to-Date | X | | | | PQTD | Prior Quarter-to-Date | X | X | | | QOQTD | Quarter-over-Quarter-to-Date | X | X | X | | QTDOPQ | Quarter-to-Date over Full Previous Quarter | X | X | X | | MTD | Month-to-Date | X | | | | MoM | Full Month over Full Month | | | X | | MoMTD | Current Month-to-Date over Prior Month-to-Date | X | X | X | | PMTD | Prior Month's MTD amount | X | X | | | MTDOPM | Month-to-Date over Full Previous Month | X | X | X | | WTD | Week-to-Date | X | | | | WoW | Full Week over Full Week | | | X | | WoWTD | Current Week-to-Date over Prior Week-to-Date | X | X | X | | PWTD | Prior Week-to-Date | X | X | | | ATD | Cumulative total from inception to date | X | | | | DoD | Full Day over Full Day | | | X |

fpaR simplifies these workflows by providing ready-to-use functions that work seamlessly with both tibble data frames and all modern database clients (DuckDB, Snowflake, SQLite, etc).

Even if you are working with tibbles, most functions are optimized to leverage DuckDB for superior speed and performance.

Key Features at a Glance:

• Flexible support for both local tibbles and databases
• Optimized for speed using DuckDB
• Reusable business intelligence functions for FP&A, eliminating redundant SQL queries
• Built-in segmentation and factor analysis techniques to uncover business insights quickly
• Growing and evolving package with planned enhancements, including support for non-standard fiscal calendars

Why do we need custom time intelligence functions?

Time-based comparisons, such as Year-over-Year (YoY), Quarter-over-Quarter (QoQ), and Month-to-Date (MTD), are common for tracking business performance. However, they come with challenges:

Missing Dates

• Many datasets do not have continuous dates, especially if data is recorded only on business days or for active transactions

• Missing weekends or holidays can distort rolling period calculations

Example Issue:

| Date | Revenue | |------------|---------| | 2024-01-01 | 1,200 | | 2024-01-03 | 1,100 | | 2024-01-06 | 1,300 |

If we use dplyr::lag() to compare Day-over-Day (DoD) revenue, we would be missing 2024-01-02 and 2024-01-04 which will lead to incorrect answers or trends

To correct this, fpaR automatically fills in missing dates for each group of your data to ensure there are no missing periods when comparing periods

Practice Datasets

• Microsoft’s Contoso Dataset to help with practice with transaction data

This is an active work-in-progress, and feedback, testing, and contributions are welcome!

Core Functionalities

The package is centered around three main areas of analysis:

Time Intelligence Functions

A comprehensive set of time-based analytical functions to track trends over different periods.

Support for non-standard calendars (e.g., 5-5-4 fiscal calendar) is under development.

Future capabilities

** Segmentation Strategies **

Provides functions to segment and categorize your data into meaningful business categories.

Example Segmentation Methods:

• ABC Classification – Categorizing products/customers based on revenue contribution.
• New vs. Returning – Distinguishing first-time buyers from repeat customers.
• K-means Clustering – Grouping data points based on patterns.
• UMAP (Uniform Manifold Approximation and Projection) – Dimensionality reduction for clustering.

Factor / Variation Analysis

Breaks down revenue or cost changes into price, volume, and mix effects.

Use Cases:

• Analyzing revenue growth due to price increases vs. increased sales volume.
• Measuring the impact of product mix changes on profitability.

Installation

Install the development version of fpaR from GitHub:

# Install using pak (recommended)
pak::pak("alejandrohagan/fpaR")

alejandrohagan/fpaR documentation built on April 12, 2025, 9:51 a.m.