R is considered the predominant language for data scientist and statisticians. This course is designed to teach you how to use R for your own data science projects. It assumes no other prior experience in R or other programming language, but a programming background will help in learning it.
You’ll learn about the open source user interface called RStudio, exploring the many numerous features it offers. You will get a chance to try it yourself by creating and running some R code and then executing a couple of exercises.
Next, take a tour of the R Workspace, which is an environment that enables you to create code, save code, and maintain code more efficiently. We’ll also take a look at some best practices and conventions in R versus other languages.
Return to RStudio to create some basic types, see the differences, and use the class function to determine the type. You’ll spend time working with variables and then can explore how R alerts us to missing data with the term NA.
You’ll review vectors, matrices, arrays, lists and factors, arithmetic and relational operators, and logical and assignment operators.
Lastly, learn to work with data frames. Data frames are native to R and were designed to allow developers to create, manage, and explore tabular data. You’ll learn about the differences in the data frame versus the data table and also how they are interchangeable.