Introduction

R for Analysts is designed to help intermediate R users hone their R skills to process and analyze data. This course takes a use-case-based approach by walking through a knowledge discovery and data mining example using R. We do not shy away from using third-party packages when doing simplifies our work.

Pre-requisites

This course has no prerequisites. While we do not cover Microsoft R Server (MRS) during this course, a secondary goal of the course is to prepare users for MRS and its set of tools and capabilities for scalable big data-processing and analytics. This course covers all the requirements to prepare users for MRS training, although we recommend spacing out this course and the Introduction to MRS for Analysts course to give participants time to absorb the material.

The course uses third-party packages for specific functionalities they provide: namely, GIS packages, ggplot2 for plotting, and dplyr for data processing. However, only dplyr is relevant to the course and explored in-depth. Data visualization and GIS packages are out of scope and not covered in any in-depth sort of way, although a basic explanation is provided. Moreover, all the code will be provided for users who want to delve more in-depth on their own time.

Learning objective

After completing this course, participants will be able to use R to perform a thorough data analysis task that starts with ingesting a raw flat file and after cleaning and preparing the data for exploratory data analysis, with lots of summaries and pretty plots to boot. The user will gain an appreciation for packages such as dplyr in helping us set up robust and easy-to-modify data pipelines, ggplot2 and its straightforward notation, and will learn to think better like an R programmer.

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Created by a Microsoft Employee.