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.
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.
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.
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
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
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
This guide is organized in the following key sections:
Getting Started: This section contains the detailed installation instructions, and a general overview of how functions work.
Data Validation: This section introduces functions for validating Workplace Analytics data.
Summary Functions: This section introduces functions that calculate averages and draw comparisons across groups.