Customizing R Environments

In this course, learn how R is different from other data tools. We’ll discover R's framework and import packages to make an impact on projects. Through this course we’ll use R, a popular statistical computing language. Some knowledge of R will help.
Course info
Level
Intermediate
Updated
May 29, 2020
Duration
26m
Table of contents
Description
Course info
Level
Intermediate
Updated
May 29, 2020
Duration
26m
Description

Understanding the differences in data analysis tools can be confusing. In this course, Customizing R Environments, you will gain the ability to understand the R framework, learn how to customize an environment, and finally discover the best data tool to suit your goals. First, you will learn how to use a function to identify environments. Next, you will discover how library packages are accessible to R. Finally, you will explore a powerful package within R. When you are finished with this course, you will have the skills and knowledge to see how R is different from other data analysis tools.

About the author
About the author

Emilee has an M.S. in Business Statistics from Mercer University and currently works as a Data Scientist. She has worked with data for 5+ years, spending the majority of her time in financial industry. She created marketing solutions at an investment management firm, consulted with lenders on risk models at a credit bureau, and currently works in consumer banking. Through her love of data, she has always had a passion for teaching. Prior to grad school, she worked as a teacher.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hello everyone. My name is Emilee McWilliams, and welcome to my course, Customizing R Environments. I'm a data scientist in the financial industry, and I've seen a variety of data analysis projects, and many times in data there are several tools available for use, including Excel and SAS, but it's not always clear which one is the best to use. In this course, we are going to understand our environments, learn the framework of R, and create and customize an R environment. Some of the major topics that we will cover include learning how R environments work, create a new environment, find how packages are imported, discover how packages can provide powerful functions and values for data analysis. By the end of this course, you'll know what differentiates R from other statistical and data tools. Some prior knowledge of functions will be helpful. I hope you'll join me on this journey to learn R with the Customizing R Environments course, at Pluralsight.