README

Introduction to fpaR

This is a collection of business intelligence tools and patterns to help with common data queries and insights. This is based on numerous blogs, books, post and work experience in what I have seen as most frequent analysis /work effort

The package functions can be split between three focus areas:

1. Segmentation strategies

   • UMAP
   • Kmeans
   • ABC
   • New vs. Returning Customers/ Vendors

2. Time intelligence functions

   • List of standard time intelligence functions such as month-to-date, year over year for standard and non-standard calendars
   • See table before complete list
   • Non-standard calendar such a 5-5-4 calendar

3. Variance analysis strategies

   • Applied statsitical techniques to underestand variance analysis including graphing techniques
     • quantile regression
     • anova
   • Like for like comparison
   • Something like a mix between marginalmeans, anova, lm and rq
     • You calculate certain components such as price, customer turnover, high margin vs. low margin segmentations
     • Then do a quantile regression on the parts to see how there is difference between high margin and low margin indicators
     • ideally we then have our factors and we can simply take the coefficients and see directly the components that is driving changes
       • so we start at customer level, store level and product level attributes and other componets that when muliplied and added get what you are calcualted -price X vol =rev -avg.price X product mix -vol =product mix X quantity

Installation

You can install the development version of fpaR from GitHub with:

# install.packages("devtools")
devtools::install_github("alejandrohagan/fpaR")

Package Components

This is correctly under development and is in early stages!

This package is heavily inspired by the DAX Patterns for the time intelligence functions

Segmentation helpers:

• make_segmentation() will use kmeans and umap to create a segmentation
• abc_segmentation()
• Turnover customers

Calculation Helpers

• count_plus() will replicate the handy dplyr::count() function but augments with portions and cumulative sum of proportions

• show_excel() will show your tibbles or data frames steps in excel

• divide will give an default NA or alternative to dividing by zero that isn’t Inf (model after DAX divide)

• Calculate() model after DAX calculate, basically a supercharged sumif that allows you to filter data with or with any filter context

Time Intelligence Functions

• Standard time intelligence functions¹

  • These are time intelligence fucntions that are consistent with a standard calendar (eg. the calendar on your computer or your phone)

• Non-standard time intelligence functions

  • These are calendars that maybe more common in the retail business (Eg. 5-4-5 quarter calcualtion)
  • These calendars aim to control for weekends as that may be larger driver of sales

Variation Analysis

• variance / factor analysis

Practice Datasets

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

# A tibble: 22 × 2
   `Short Name` Description                                                   
   <chr>        <chr>                                                         
 1 YTD          Year-to-date                                                  
 2 QTD          Quarter-to-date                                               
 3 MAT          Moving annual total                                           
 4 PY           Previous year                                                 
 5 PQ           Previous quarter                                              
 6 PM           Previous month                                                
 7 PYC          Previous year complete                                        
 8 PQC          Previous quarter complete                                     
 9 PMC          Previous month complete                                       
10 PP           Previous period; automatically selects year, quarter, or month
# ℹ 12 more rows

Footnotes

1. See table below ↩