# Introduction

Welcome to the Analyst Guide of the **wpa** R package. This document will guide you through the installation process, explain the package functionality and structure, and get you comfortable with some common functions for analysis.

## Before we begin...

Make sure you have:
1.  Analyst access to [Workplace Analytics](https://www.microsoft.com/en-us/microsoft-365/business/workplace-analytics)
2.  [R](https://www.r-project.org/) installed in your local device. 

An IDE is optional, but we recommend either [RStudio Desktop](https://rstudio.com/products/rstudio/download/#download) or [VS Code](https://code.visualstudio.com/).

## 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 this package as part of the workflow will be seamless and easy
5. **Extensibility**: 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 in this package

## Guide contents

This guide is organized in the following key sections:

1. [**Getting Started**](analyst_guide_getting_started.html): This section contains the detailed installation instructions, and a general overview of how functions work. 
2. [**Data Validation**](analyst_guide_data_validation.html): This section introduces functions for validating Workplace Analytics data. 
3. [**Summary Functions**](analyst_guide_summary.html): This section introduces functions that calculate averages and draw comparisons across groups.
4. [**Distribution Functions**](analyst_guide_distribution.html): This section describes functions that help you explore distributions across groups.
5. [**Trend Functions**](analyst_guide_trend.html): This section explains functions that explore time dyanmics across a wide range of metrics.
6. [**Network Functions**](analyst_guide_network.html): This section explores functions that help you plot and analyse networks.
7. [**Reports**](analyst_guide_reports.html): This section provides a guide to running HTML reports in the package and links to demo materials.

## Additional resources

To get the most out of **wpa**, make sure to leverage these additional resources: 

1. Our official [**wpa** cheat sheet](https://github.com/microsoft/wpa/blob/main/man/figures/wpa%20cheatsheet.pdf).
2. A growing list of [articles](https://microsoft.github.io/wpa/articles/) with detailed walkthroughs, written by multiple contributors. 
3. Our [Microsoft Learn module](https://docs.microsoft.com/en-us/learn/modules/workplace-analytics-r-package/) _Analyze Microsoft Workplace Analytics data using the wpa R package_, which takes you step-by-step through the R package and its key features. 

## Ready to go?

Let's begin with the [**Getting Started**](analyst_guide_getting_started.html) section.