wpa/.github/analyst_guide.md

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
2. R installed in your local device.

An IDE is optional, but we recommend either RStudio Desktop or VS Code.

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: This section contains the detailed installation instructions, and a general overview of how functions work.
2. Data Validation: This section introduces functions for validating Workplace Analytics data.
3. Summary Functions: This section introduces functions that calculate averages and draw comparisons across groups.
4. Distribution Functions: This section describes functions that help you explore distributions across groups.
5. Trend Functions: This section explains functions that explore time dyanmics across a wide range of metrics.
6. Network Functions: This section explores functions that help you plot and analyse networks.
7. Reports: 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.
2. A growing list of articles with detailed walkthroughs, written by multiple contributors.
3. Our Microsoft Learn module 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 section.