docs: update Analyst Guide

.github/analyst_guide.md

@@ -2,6 +2,19 @@
 
 This is an analyst guide for the **wpa** package.
 
+1. [Why use R for Workplace Analytics](#why-use-r-for-workplace-analytics)
+2. [Package Structure](#-package-structure)
+
+## Why use R for Workplace Analytics?
+
+There are multiple reasons:
+
+1. **Cutting edge data science**: R is an open-source language that is known for its **active user community** and a **wide range of packages** that together enable the quick and effective implementation of data science techniques
+2. **Reproducibility**: Code-based workflows help facilitate **reproducible analysis**, which is the notion that analysis should be built in a way that is replicable by others. R as a tool promotes this good practice. 
+3. **Efficiency / scalability**: R scales relatively well in the context of large datasets. The application of functions and automated processes also help cut down routine analysis time 
+4. **Integration**: If you already use R as part of your analysis toolkit, adopting the **wpa** package as part of the workflow will be seamless and easy
+5. **Go beyond basic reporting**: One of the most appealing feature of R is the access it offers to a wide range of packages. For instance, clustering and text mining can be done very easily as part of a R workflow – which are both available from the **wpa** package
+
 ## :package: Package Structure
 
 There are four main types of functions in **wpa**: