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.
Why use R for Workplace Analytics?
There are multiple reasons:
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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.
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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.
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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
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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
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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:
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Getting Started: This section contains the detailed installation instructions, and a general overview of how functions work.
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Data Validation: This section introduces functions for validating Workplace Analytics data.
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Summary Functions: This section introduces functions that calculate averages and draw comparisons across groups.
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Distribution Functions: This section describes functions that help you explore distributions across groups.
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Trend Functions: This section explains functions that explore time dyanmics across a wide range of metrics.
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Network Functions: This section explores functions that help you plot and analyse networks.
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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:
- Our official wpa cheat sheet.
- A growing list of articles with detailed walkthroughs, written by multiple contributors.
- 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.