update

Affected files:
.devcontainer/devcontainer.json
.github/dependabot.yml
.obsidian/community-plugins.json
02-PROJECTS/Clients/GMH/Google Maps Embed.md
02-PROJECTS/Internal/Blog/Blog - Cross-Language Innovations for Enhancing R.md
02-PROJECTS/Internal/Blog/Blog - Ideas.md
03-AREAS/Journal/2024-08-29.md
03-AREAS/Journal/2024-09-01.md
03-AREAS/Journal/2024-09-04.md
03-AREAS/Journal/2024-09-07.md
03-AREAS/Journal/2024-09-15.md
03-AREAS/Journal/2024-09-16.md
03-AREAS/Journal/2024-09-18.md
03-AREAS/Journal/2024-09-21.md
04-RESOURCES/ClickUp/No Clocks/No Clocks - [90141611707]/Decision Log[901405221852].md
04-RESOURCES/ClickUp/No Clocks/No Clocks - [90141611707]/Operations[901405221820].md
04-RESOURCES/Code/CSS/CSS - Center Image in Viewport.md
04-RESOURCES/Code/JavaScript/JS - Show CSS Styles for HTML Elements.md
04-RESOURCES/Code/PowerShell/PowerShell - Check CPU Battery Status.md
04-RESOURCES/Code/PowerShell/PowerShell - DNSServer Module.md
04-RESOURCES/Code/PowerShell/PowerShell - Empty Recycle Bin.md
04-RESOURCES/Code/PowerShell/PowerShell - Review Explorer Recent Items.md
04-RESOURCES/Code/Python/Python - Exception Handling.md
04-RESOURCES/Code/Python/Python - OpenAI.md
04-RESOURCES/Code/Registry/Registry - Set Dark Theme.md
04-RESOURCES/Definitions/Acronyms/Browser Object Model (BOM).md
04-RESOURCES/Tools/Tool - R bslib.md

.devcontainer/devcontainer.json

@@ -0,0 +1,21 @@
+{
+  "image": "mcr.microsoft.com/devcontainers/universal:2",
+  "features": {
+    "ghcr.io/devcontainers/features/node:1": {
+      "extended": "true"
+    }
+  },
+  "postCreateCommand": "npm install",
+  "customizations": {
+    "vscode": {
+      "extensions": [
+        "davidanson.vscode-markdownlint",
+      ]
+    }
+  },
+  "portsAttributes": {
+    "8080": {
+      "label": "Build --Serve"
+    }
+  }
+}

.github/dependabot.yml

@@ -0,0 +1,6 @@
+version: 2
+updates:
+  - package-ecosystem: "npm"
+    directory: "/"
+    schedule:
+      interval: "weekly"

.obsidian/community-plugins.json

@@ -54,5 +54,7 @@
   "multi-column-markdown",
   "google-calendar",
   "obsidian-full-calendar",
-  "automatic-table-of-contents"
+  "automatic-table-of-contents",
+  "click-up-sync",
+  "vscode-editor"
 ]

02-PROJECTS/Clients/GMH/Google Maps Embed.md

@@ -0,0 +1 @@
+<iframe src="https://www.google.com/maps/d/embed?mid=19tP5Bf66khGcrNnqTBsk879W2fS-u7U&ehbc=2E312F" width="640" height="480"></iframe>

02-PROJECTS/Internal/Blog/Blog - Cross-Language Innovations for Enhancing R.md

@@ -0,0 +1,262 @@
+---
+creation_date: 2024-08-29
+modification_date: 2024-08-29
+author: Jimmy Briggs <[email protected]>
+tags:
+  - Type/Blog
+  - Topic/Dev/R
+  - Status/WIP
+aliases:
+  - Cross-Language Innovations for Enhancing R
+  - Innovative Features from Other Languages to Enhance R - A Cross Language Exploration
+publish: true
+permalink:
+description:
+image:
+cssclasses:
+---
+
+
+# Exploring Features from Other Languages to Enhance R - A Cross Language Exploration
+
+> [!INFO]
+> This blog post explores various innovative features from other languages and frameworks to enhance the R developer experience.
+
+```table-of-contents
+title: ## Contents 
+style: nestedList # TOC style (nestedList|inlineFirstLevel)
+minLevel: 1 # Include headings from the specified level
+maxLevel: 4 # Include headings up to the specified level
+includeLinks: true # Make headings clickable
+debugInConsole: false # Print debug info in Obsidian console
+```
+
+### Bash / Unix Shells
+
+1. **Pipelines and Redirection**:
+
+    - Enhance R's pipe (`|>`) functionality to work more like Unix pipelines, including features like piping into system commands or redirecting output to files, logs, or other destinations, seamlessly integrating R with shell-like environments.
+2. **Environment Modules**:
+
+    - Implement a module system in R similar to `module` command in Unix, where users can load and unload different sets of packages or configurations for different projects or environments, improving workflow modularity and isolation.
+
+### PowerShell
+
+> [!NOTE]
+> These features also include the languages C#, and .NET as PowerShell is built from the .NET Ecosystem.
+
+1. **Separate Startup Profiles**:
+	- Implement different R profile startup scripts for various use cases (e.g., interactive sessions, non-interactive scripts, IDE-specific profiles).
+2. **(Module) Manifests**:    
+    - Introduce manifest files in R packages or scripts to declare dependencies, versions, and configurations, ensuring consistency across environments. This is already implemented via the almighty `DESCRIPTION` file.
+3. **Controlled Vocabulary of Approved Verbs**:
+    - Develop a standardized set of function prefixes (verbs) in R, akin to PowerShell's approved verbs, to promote consistent naming conventions (e.g., `get_`, `set_`, `update_`, `remove_`).
+4. **Declarative Configuration Management (DSC)**:
+    - Create a declarative configuration management system in R for setting up environments, similar to PowerShell DSC, where users define desired states for their R environments.
+5. **Output Streams with Corresponding Write Statements**: 
+    - Introduce a system in R for handling different output streams (e.g., success, error, warning, verbose, debug, information, progress) with corresponding `write_*` functions. This could enhance the granularity of logging, debugging, and user feedback within R scripts and functions.
+6. **Parameter Sets**:
+    - Implement PowerShell-like parameter sets in R functions, allowing for more flexible and user-friendly function interfaces. This would enable functions to have multiple sets of arguments, each tailored for different use cases, and enforce argument combinations, making functions more robust and easier to use.
+7. **Separation of `Begin`, `Process`, and `End` Script Blocks
+  - PowerShell’s `begin`, `process`, and `end` script blocks provide a structured way to handle initialization, iterative processing, and finalization within a command or function. This feature is particularly useful for streaming data, processing large datasets, or dealing with input from the pipeline.
+
+### Conceptual Translation to R
+
+In R, this pattern can be mimicked by creating functions that explicitly handle the three stages: initialization (`begin`), processing each element (`process`), and cleanup or final actions (`end`). Although R doesn’t have a direct equivalent of PowerShell's script blocks, we can structure our R functions to achieve similar behavior.
+
+### Example R Implementation
+
+Let’s create a function in R that processes a large dataset in chunks, initializing resources in a `begin` phase, processing data in a `process` phase, and then cleaning up or summarizing in an `end` phase.
+
+```r
+#' Process Large Data in Chunks with Begin, Process, End Pattern
+#'
+#' @description This function demonstrates a pattern similar to PowerShell's
+#' begin, process, and end script blocks. It initializes resources, processes
+#' a large dataset in chunks, and then performs final cleanup or summary actions.
+#'
+#' @param data A data frame to be processed in chunks.
+#' @param chunk_size The number of rows to process at a time.
+#' @param process_function A function to apply to each chunk of data.
+#' @return A final result after processing all chunks.
+#' @export
+#' @importFrom dplyr slice
+process_large_data <- function(data, chunk_size = 1000, process_function) {
+
+  # Begin: Initialization
+  message("Initializing resources...")
+  results <- list()
+
+  # Process: Iteratively process each chunk
+  total_rows <- nrow(data)
+  num_chunks <- ceiling(total_rows / chunk_size)
+
+  for (i in seq_len(num_chunks)) {
+    start_row <- (i - 1) * chunk_size + 1
+    end_row <- min(i * chunk_size, total_rows)
+
+    chunk <- dplyr::slice(data, start_row:end_row)
+    message(glue::glue("Processing chunk {i} of {num_chunks} (rows {start_row} to {end_row})..."))
+
+    processed_chunk <- process_function(chunk)
+    results[[i]] <- processed_chunk
+  }
+
+  # End: Finalization
+  message("Finalizing and aggregating results...")
+  final_result <- do.call(rbind, results)
+
+  return(final_result)
+}
+
+# Example usage:
+# Define a simple processing function
+process_chunk <- function(chunk) {
+  chunk$processed <- TRUE  # Example processing: Add a new column
+  return(chunk)
+}
+
+# Simulate large dataset
+large_data <- data.frame(
+  id = 1:10000,
+  value = rnorm(10000)
+)
+
+# Process the large data in chunks
+final_result <- process_large_data(large_data, chunk_size = 1000, process_function = process_chunk)
+head(final_result)
+```
+
+### Explanation:
+
+1. **Begin Block (Initialization)**:
+   - In this R function, the `begin` phase is represented by the initialization of resources, such as creating an empty list to store results and printing an initialization message.
+
+2. **Process Block (Iterative Processing)**:
+   - The `process` phase iteratively processes chunks of the data. This is where the main computation happens, with each chunk being processed by a user-defined function (`process_function`).
+
+3. **End Block (Finalization)**:
+   - The `end` phase involves finalizing the results, typically aggregating them into a single data structure and performing any final actions, such as printing a summary message.
+
+### Benefits of This Pattern in R:
+
+- **Modularity**: The function is broken into distinct phases, making it easier to manage and extend.
+- **Efficiency**: Processing data in chunks reduces memory usage, which is particularly useful for large datasets.
+- **Flexibility**: Users can define custom processing functions, making the function adaptable to different use cases.
+
+By adopting this pattern, you can bring a structured, PowerShell-like approach to R, making your R code more organized and efficient when dealing with large or streaming data.
+
+### JavaScript and TypeScript
+
+1. **Enhanced Type System**:    
+    - Introduce a more robust type system in R, allowing for static type checking, type annotations, and interfaces to define expected function arguments and return types.
+2. **Interfaces and Type Aliases**:    
+    - Develop a system for defining interfaces (contracts) and type aliases in R, which could help enforce consistency and correctness in function inputs and outputs.
+3. **Asynchronous Programming**:    
+    - Implement native support for promises, async/await patterns, or reactive programming in R to handle asynchronous tasks more effectively.
+
+### Python
+
+1. **Data Classes**:    
+    - Introduce data classes in R for simple and efficient creation of classes focused on storing data, reducing boilerplate code.
+2. **Data Models and Classes (Pydantic)**:    
+    - Develop a system akin to Pydantic models in Python, where users can define schemas for validating and parsing data into R structures, ensuring data integrity.
+3. **Context Managers**:
+    - Implement context managers in R, similar to Python's `with` statement, for managing resources like file I/O, database connections, or temporary settings changes.
+
+### SQL
+
+1. **Specific Data Type Support**:
+    - Enhance R’s support for specific data types, particularly those related to time series, spatial data, and complex numbers, inspired by SQL's type system.
+2. **Window Functions**:
+    - Port SQL-style window functions into R, allowing for more advanced data manipulation directly within R, particularly in data frames or `tibble` structures.
+3. **Common Table Expressions (CTEs)**:
+    - Introduce a way to define reusable, named query parts in R data manipulation pipelines, similar to CTEs in SQL, enhancing readability and reusability of complex data transformations.
+
+### Ruby
+
+1. **Gem-Like Package Management**:
+    - Port the idea of Ruby gems into R, allowing for easier version management and dependency handling of R packages. This could include features like version locking, grouping packages by functionality, and creating bundled environments.
+1. **Blocks and Procs**: 
+    - Introduce Ruby-style blocks (anonymous functions) and Procs, allowing for more elegant handling of callback functions and iterators in R.
+2. **Active Record Pattern**:
+    - Develop an ORM (Object-Relational Mapping) system in R similar to Ruby on Rails' Active Record, enabling R to interact with databases in a more object-oriented manner.
+
+### Rust
+
+1. **Ownership and Borrowing**:    
+    - Consider a memory management system inspired by Rust's ownership and borrowing principles, ensuring safer and more efficient memory usage in R, particularly in large data operations.
+2. **Pattern Matching**:    
+    - Implement a pattern matching system in R, similar to Rust's, allowing for more expressive and readable conditional logic.
+
+### Java
+
+1. **Java Beans/POJO (Plain Old Java Objects)**:
+
+- Introduce a simple and standard way to define classes in R that are focused on holding data with getters and setters, similar to Java Beans. This could standardize how R handles simple data structures and encourage better practices for encapsulation and data management
+
+### Go
+
+1. **Goroutines**:    
+    - Port lightweight concurrent programming features, such as goroutines, to R for handling parallel processing tasks more efficiently.
+2. **Channels**:    
+    - Introduce channels in R for synchronized communication between parallel tasks, enhancing R's concurrency capabilities.
+
+### Swift
+
+1. **Optionals and Safe Unwrapping**:
+
+- Introduce a concept similar to Swift's optionals and safe unwrapping in R, allowing for safer handling of `NULL` values and missing data. This could include syntax or functions for safely unwrapping values or chaining operations that might return `NULL`.
+
+2. **Supervision Trees**:
+  - Implement a system for managing concurrent tasks in R, inspired by Erlang's supervision trees, where processes can be grouped, monitored, and restarted if they fail, improving reliability in parallel processing tasks.
+
+### Elixir / Erlang
+
+1. **Actor Model for Concurrency**:    
+    - Implement an actor model in R for managing concurrent tasks, inspired by Elixir, where each task is an independent actor that communicates via message passing.
+2. **Pattern Matching**:    
+    - Similar to Rust, Elixir's pattern matching is powerful for handling various data structures. Bringing this to R could simplify complex data processing tasks.
+
+### Haskell
+
+1. **Monads**:    
+    - Introduce monads or similar functional programming concepts to R, allowing for more sophisticated handling of side effects, state management, and asynchronous computations.
+2. **Lazy Evaluation**:    
+    - Expand on R's lazy evaluation with more control and functionality, inspired by Haskell, allowing for deferred computation and memory optimization.
+3. **Pure Functions and Immutability**:
+- Encourage the use of pure functions and immutability in R, similar to Haskell, potentially through syntactic or tooling support. This could include tools for identifying side effects or enforcing immutability in function arguments, leading to more predictable and testable code.
+
+### Python and Node.js
+
+1. **CLI-Enabled Libraries**:
+
+- Implement a system in R similar to Python's `pip/pipx` and Node's `npm/npx`, where R packages can be installed as command-line tools. This could allow users to run R-based scripts or commands directly from the terminal without needing a full R session, streamlining the use of R in scripting and automation tasks.
+
+
+
+These features, if adapted thoughtfully, could significantly enhance the R programming experience by borrowing the best practices and innovations from other languages.
+
+
+
+## Resources
+
+***
+
+## Appendix
+
+*Note created on [[2024-08-29]] and last modified on [[2024-08-29]].*
+
+### See Also
+
+- 
+
+### Backlinks
+
+```dataview
+LIST FROM [[Blog - Cross-Language Innovations for Enhancing R]] AND -"CHANGELOG" AND -"02-PROJECTS/Internal/Blog/Blog - Cross-Language Innovations for Enhancing R"
+```
+
+***
+
+(c) [No Clocks, LLC](https://github.com/noclocks) | 2024