abc doc

Author: usrbinr

vignettes/articles/abc.qmd

@@ -1,6 +1,6 @@
 ---
-title: "ABC Segmentation"
-subtitle: "How to use segmentation strategies to simplify your analysis"
+title: "ABC Classification"
+subtitle: "How to use classification strategies to focus your analysis"
 
 ---
 
@@ -15,29 +15,24 @@ quarto::write_yaml_metadata_block(
 ### Introduction
 
 In business and financial analytics, the **ABC classification** method is widely used to categorize items, customers, or other groups according to their relative contribution to a total metric.  
+The `abc()` function provides an easy way to classify a group's member by their relative contribution of either their **transaction counts** or the sum of a **numeric variable** such as sales margin or revenue.
 
-The `abc()` function provides a flexible and robust way to perform ABC segmentation, either based on **transaction counts** or the sum of a **numeric variable** such as sales margin or revenue.
-
-### Function Purpose
-
-`abc()`:
-
-- Segments a dataset into categories (A, B, C, etc.) based on cumulative contribution.
-- Allows for **custom break points** (e.g., top 10% = A, next 40% = B, etc.).
-- Works on both **grouped tibbles** and **database-backed objects**.
-- Returns a **segment object**, which is processed by `calculate()` to produce a table of results.
+- Allows for **custom break points** (e.g., top 10% = A, next 40% = B, etc.)
+- Works on both **tibbles** and **database objects**
+- Returns a **segment object**, which is processed by `calculate()` to produce a table of results
 
 ### How It Works
 
-> `fpaR::abc()` requires a grouped tibble or lazy DBI object using `dplyr::group_by()` to specify the groups that drive the contribution
+> `abc()` requires a grouped tibble or lazy DBI object using `dplyr::group_by()` to specify the group composition that drives the contribution
 
 **Value Capture**  
 
--  If `.value` is provided, the the column is aggregate per group; otherwise, it counts rows.
+-  If `.value` is provided, then that column is aggregate per group member; otherwise, it counts rows
 
 **Category Values**
 
--  Provide the break points  break points that are used to set the cumulative categories. Each break point will get a letter category starting with 'A'
+-  Provide the break points that are used to set the cumulative categories
+-  Each break point will get a letter category starting with 'A'
 -  If you want to see the stores that make up the top 40% of revenue follow by top 70% and then 90% you should put in `c(0.4,.7,.9,1)`
 
 
@@ -74,15 +69,20 @@ contoso::sales |>
       )
 ```
 
-The function returns a **segment object**. Use `calculate()` to generate the ABC classification table:
+The function returns a **segment object**
+
+- This will summarize the series of actions and meta information about your data
+- Use `calculate()` to generate the ABC classification table
+- This will return a duckdb DBI object use `dplyr::collect()` to return a tibble
 
 ```{r}
-#| label: var
+#| label: calculate
 #| echo: true
 #| eval: false
 #| warning: false
 #| message: false
 #| include: true
+
 contoso::sales |> 
    dplyr::group_by(store_key) |> 
    fpaR::abc(
@@ -122,7 +122,7 @@ This table contains grouped data with various metrics, highlighting the contribu
 
 ### Understanding the Results
 
-- Store 540 has a margin of $7,812.11 ("ABC Margin"), which accounts for about 4% ("prop_total") of the total margin across all stores
+-  Store 540 has a margin of $7,812.11 ("ABC Margin"), which accounts for about 4% ("prop_total") of the total margin across all stores
 
 -  The "cum_sum" column tracks the running total of values (e.g., revenue or count) for each store, showing the cumulative sum up to that row
 
@@ -136,7 +136,7 @@ This table contains grouped data with various metrics, highlighting the contribu
 
 -  The category_value and category_name columns define the breakpoints you provided, assigning stores to categories (e.g., 'A', 'B', 'C') based on their cumulative contribution
 
-This is summarized in @tbl-output below
+This is summarized in @tbl-output below:
 
 
 ```{r}