554 first steps, new augment calendar columns

Author: usrbinr

554.R

@@ -0,0 +1,33 @@
+library(tidyverse)
+library(contoso)
+devtools::document()
+
+devtools::load_all()
+
+
+
+## create 5-5-4 calendar
+
+x <-  contoso::sales |> fpaR::mtd(order_date,revenue,calendar_type = "standard")
+
+
+con <- dbplyr::remote_con(x@datum@data)
+
+# complete calendar with all attributes
+
+new_cal <- fpaR:::seq_date_sql(start_date = "2025-01-01",end_date = "2025-12-31",time_unit = "day",con =con ) |>
+  augment_calendar(.date = date)
+
+
+## next step add in new year indicator
+new_cal |>
+  dplyr::mutate(
+    new_year_date_indicator=if_else(month_of_year==2&day_of_year==4,1,0)
+    ,.before=1
+  )
+
+## next step add in new year indicator
+
+
+
+## add in period indicator for that

R/utils-misc.R

@@ -235,6 +235,10 @@ lubridate::days
       ,week_end_date=lubridate::ceiling_date({{.date}},unit = "week")-1
       ,day_of_week=lubridate::wday({{.date}},label = FALSE)
       ,day_of_week_label=lubridate::wday({{.date}},label = TRUE)
+      ,day_of_year=lubridate::day({{.date}})
+      ,month_of_year_label_abb=lubridate::month({{.date}},label = TRUE,abbr = TRUE)
+      ,month_of_year_label_full_name=lubridate::month({{.date}},label=TRUE,abbr = FALSE)
+      ,month_of_year=lubridate::month({{.date}},label=FALSE)
       ,days_in_year=year_end_date-year_start_date
       ,days_in_quarter=quarter_end_date-quarter_start_date
       ,days_in_month=lubridate::days_in_month({{.date}})
@@ -247,6 +251,7 @@ lubridate::days
       ,days_complete_in_quarter={{.date}}-quarter_start_date
       ,days_complete_in_month={{.date}}-month_start_date
       ,days_complete_in_year={{.date}}-year_start_date
+
       ,weekend_indicator=dplyr::if_else(day_of_week_label %in% c("Saturday","Sunday"),1,0)
     ) |>
     dplyr::mutate(
@@ -324,6 +329,10 @@ augment_calendar_dbi <- function(.data,.date){
     ,week_end_date=dplyr::sql(glue::glue("date_trunc('month', {date_vec}) + INTERVAL '1' month"))
     ,day_of_week=lubridate::wday({{.date}},label = FALSE)
     ,day_of_week_label=lubridate::wday({{.date}},label = TRUE)
+    ,day_of_year=lubridate::day({{.date}})
+    ,month_of_year_label_abb=lubridate::month({{.date}},label = TRUE,abbr = TRUE)
+    ,month_of_year_label_full_name=lubridate::month({{.date}},label=TRUE,abbr = FALSE)
+    ,month_of_year=lubridate::month({{.date}},label=FALSE)
     ,days_in_year=year_end_date-year_start_date
     ,days_in_quarter=quarter_end_date-quarter_start_date
     ,days_in_month=dplyr::sql(glue::glue("last_day({date_vec})"))

README.md

@@ -29,12 +29,12 @@ Under the hood, these functions are built upon the great foundations of:
 - [duckdb](https://github.com/duckdb/duckdb-r)
 - [lubridate](https://lubridate.tidyverse.org/)
 
-`fpaR` is designed to seamlessly work with either tibbles or modern
-databases (DuckDB, Snowflake, SQLite, etc) with a unified syntax
+`fpaR` is designed to work with either tibbles or modern databases
+(DuckDB, Snowflake, SQLite, etc) with a unified syntax
 
 Even if you are working with tibbles, most functions are optimized to
-leverage [DuckDB](https://github.com/duckdb/duckdb-r) for superior speed
-and performance.[^1]
+leverage [DuckDB](https://github.com/duckdb/duckdb-r) for increased
+speed and performance[^1]
 
 By default most `fpaR` function returns a lazy DBI object which you can
 return as a tibble with `dplyr::collect()`
@@ -60,19 +60,19 @@ pak::pak("usrbinr/fpaR")
 
 ## What is in fpaR?
 
-There are 3 main categories of functions:
-
-- Time intelligence
-- Segmentation strategies
-- Factor analysis (work in progress)
-
-### Datasets
-
 > We recommend using the [Contoso](https://usrbinr.github.io/contoso/)
 > package for any practice analysis. The contoso datasets are fictional
 > business transaction of Contoso toy company which are helpful for
 > business intelligence related analysis
 
+There are 3 main categories of functions:
+
+- Time intelligence realted functions
+  (<a href="#tbl-ti-fn" class="quarto-xref">Table 1</a>)
+- Categorization strategies
+  (<a href="#tbl-abc-fn" class="quarto-xref">Table 2</a>)
+- Factor analysis (work in progress)
+
 ### Time intelligence
 
 `fpaR` provides readily available functions for most time intelligence
@@ -99,6 +99,12 @@ intelligence related analysis in a consistent, fast and transparent way
 
 Below is the full list of time intelligence functions:
 
+<div id="tbl-ti-fn">
+
+Table 1
+
+<div class="cell-output-display">
+
 | Function | Description | Shift | Aggregate | Compare |
 |----|----|----|----|----|
 | YoY | Full Year over Year |  |  | X |
@@ -123,6 +129,10 @@ Below is the full list of time intelligence functions:
 | ATD | cumlaitve total from inception to date |  | x |  |
 | DoD | Full Day over Full Day |  |  | X |
 
+</div>
+
+</div>
+
 ### **Segmentation Strategies**
 
 #### ABC Classification
@@ -147,11 +157,21 @@ product or service
 This allows you to understand retention, turnover and other attributes
 more clearly
 
+<div id="tbl-abc-fn">
+
+Table 2
+
+<div class="cell-output-display">
+
 | Function | Description | Categorizes | Time-Based | Tracks Over Time |
 |----|----|----|----|----|
 | abc() | ABC Classification groups items by relative contribution (Pareto analysis). | X |  |  |
 | cohort() | Cohort analysis groups entities by a shared start point and analyzes behavior over time. |  | X | X |
 
+</div>
+
+</div>
+
 ------------------------------------------------------------------------
 
 ### **Factor / Variation Analysis**

README.qmd

@@ -21,47 +21,33 @@ library(tidyverse)
 
 ## A Business Intelligence Toolkit for Financial Planning & Analysis (FP&A)
 
-`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, vendor or product
-segmentation, and factor/variance analysis
+`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, vendor or product segmentation, and factor/variance 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
+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](https://www.sqlbi.com/) functions
+In particular, the time intelligence functions are heavily inspired by [PowerBI DAX](https://www.sqlbi.com/) functions
 
 Under the hood, these functions are built upon the great foundations of:
 
 -   [dbplyr](https://dbplyr.tidyverse.org/)\
 -   [duckdb](https://github.com/duckdb/duckdb-r)
 -   [lubridate](https://lubridate.tidyverse.org/)
 
-`fpaR` is designed to seamlessly work with either tibbles or modern
-databases (DuckDB, Snowflake, SQLite, etc) with a unified syntax
+`fpaR` is designed to work with either tibbles or modern databases (DuckDB, Snowflake, SQLite, etc) with a unified syntax
 
-Even if you are working with tibbles, most functions are optimized to
-leverage [DuckDB](https://github.com/duckdb/duckdb-r) for superior speed
-and performance.[^1]
+Even if you are working with tibbles, most functions are optimized to leverage [DuckDB](https://github.com/duckdb/duckdb-r) for increased speed
+and performance[^1]
 
-[^1]: I plan to use [duckplyr](https://duckplyr.tidyverse.org/index.html)
-    once it expands support for lubricate functions
+[^1]: I plan to use [duckplyr](https://duckplyr.tidyverse.org/index.html) once it expands support for lubricate functions
 
-By default most `fpaR` function returns a lazy DBI object which you can
-return as a tibble with `dplyr::collect()`
+By default most `fpaR` function returns a lazy DBI object which you can return as a tibble with `dplyr::collect()`
 
 ## Key features & benefits
 
--   **Unified syntax** regardless if your data is in a tibble or a
-    database
--   **Scale** your data with
-    [duckdb](https://github.com/duckdb/duckdb-r) to optimize your
-    calculations
--   **Instant clarity** as every function summarizes its transformation
-    actions so that you can understand and validate the results
+-   **Unified syntax** regardless if your data is in a tibble or a database
+-   **Scale** your data with [duckdb](https://github.com/duckdb/duckdb-r) to optimize your calculations
+-   **Instant clarity** as every function summarizes its transformation actions so that you can understand and validate the results
 
 ## Installation
 
@@ -80,45 +66,29 @@ pak::pak("usrbinr/fpaR")
 
 ## What is in fpaR?
 
+>  We recommend using the [Contoso](https://usrbinr.github.io/contoso/) package for any practice analysis. The contoso datasets are fictional business transaction of Contoso toy company which are helpful for business intelligence related analysis
+
 There are 3 main categories of functions:
 
--   Time intelligence 
--   Segmentation strategies 
+-   Time intelligence realted functions (@tbl-ti-fn)
+-   Categorization strategies (@tbl-abc-fn)
 -   Factor analysis (work in progress)
 
 
-### Datasets
-
->  We recommend using the
-    [Contoso](https://usrbinr.github.io/contoso/) package for any practice
-    analysis. The contoso datasets are fictional business transaction of
-    Contoso toy company which are helpful for business intelligence
-    related analysis
-
-
-
 ### Time intelligence   
 
-`fpaR` provides readily available functions for most time intelligence
-analysis such as **Year-over-Year**(`yoy()`), **Month-to-Date**(`mtd()`), and
-**Current Year-to-Date over Previous Year-to-Date** (`ytdopy()`) analysis
+`fpaR` provides readily available functions for most time intelligence analysis such as **Year-over-Year**(`yoy()`), **Month-to-Date**(`mtd()`), and **Current Year-to-Date over Previous Year-to-Date** (`ytdopy()`) analysis
 
-These functions are designed to quickly answer most common time
-intelligence related analysis in a consistent, fast and transparent way
+These functions are designed to quickly answer most common time intelligence related analysis in a consistent, fast and transparent way
 
 **Key benefits:**
 
--   **Auto-fill missing dates**: Ensures no missing periods in your
-    datasets so that right period comparisons are performed
+-   **Auto-fill missing dates**: Ensures no missing periods in your datasets so that right period comparisons are performed
 
--   **Flexible calendar options**: Handle comparisons based on a
-    **standard** or **5-5-4** fiscal calendar to accommodate different
-    reporting frameworks
+-   **Flexible calendar options**: Handle comparisons based on a **standard** or **5-5-4** fiscal calendar to accommodate different reporting frameworks
+
+-   **Full transparency**: Comparing periods with imbalaced dates? the time intelligence functions will alert you to the type and number of period imbalances to ensure you are aware of misleading likewise comparisons and prints out detials of each function's actions and methods to avoid confusion
 
--   **Period imbalance indicator**: When comparing periods with dates
-    imbalance, the time intelligence functions will alert you to the
-    type and number of period imbalances to ensure you are aware of
-    misleading likewise comparisons
 
 Below is the full list of time intelligence functions:
 
@@ -127,7 +97,7 @@ Below is the full list of time intelligence functions:
 #| warning: false
 #| message: false
 #| error: false
-#| label: ti-fn
+#| label: tbl-ti-fn
 
 tibble::tribble(
    ~"Function", ~"Description", ~"Shift", ~"Aggregate", ~"Compare",
@@ -183,27 +153,28 @@ tibble::tribble(
 
 ```
 
+------------------------------------------------------------------------
 
-### **Segmentation Strategies**
+### **Classification Strategies**
 
 #### ABC Classification
 
-ABC classification is an business analysis technique that categorizes items (like products, customers, or suppliers) based on their relative contribution of a value. It facilities the Pareto Principle (the 80/20 rule), suggesting that a small percentage of items contribute to the largest percentage of the total value
+ABC classification is an business analysis technique that categorizes items (like products, customers, or suppliers) based on their relative contribution of a value. It expands upon the the Pareto Principle (the 80/20 rule), allowing the user to determine which percentage of items or group members contribute to the largest percentage of the total value
 
-The analysis will assign a letter category to each break point so that you can further focus your analysis 
+You assign the break points for the categorization and the function will label each category with a letter value
 
 #### Cohort
 
-Cohort analysis is a type of behavioral analytics that takes data from a given group of users (a cohort) and tracks their activity over time. A cohort is typically defined by a shared starting characteristic, most commonly the time period in which the entities first interacted with the product or service
+Cohort analysis is a type of behavioral analytics that takes data from a given group of users (called a cohort) and tracks their activity over time. A cohort is typically defined by a shared starting characteristic, most commonly the time period in which the entities first interacted with the product or service
 
-This allows you to understand retention, turnover and other attributes more clearly
+This allows you to understand retention, turnover and other cohort attributes more clearly
 
 ```{r}
 #| echo: false
 #| warning: false
 #| message: false
 #| error: false
-#| label: abc-fn
+#| label: tbl-abc-fn
 
 tibble::tribble(
   ~"Function", ~"Description", ~"Categorizes", ~"Time-Based", ~"Tracks Over Time",
@@ -229,17 +200,16 @@ tibble::tribble(
 ### **Factor / Variation Analysis**
 
 Breaks down revenue or cost changes into **price, volume, and mix
-effects**.
+effects**
+
+Work in progress.
 
 ------------------------------------------------------------------------
 
 ## Future capabilities
 
 Is there a future capability that you want to see here? please open up a [discussion](https://github.com/usrbinr/fpaR/discussions) on our github site
 
-
-
 ## Additional references and inspirations
 
--   [PeerChristensen's Cohort
-    Package](https://github.com/PeerChristensen/cohorts)
+-   [PeerChristensen's Cohort Package](https://github.com/PeerChristensen/cohorts)