Introduction to R Programming for Data Science

The R language supports many types of data structures that you can use to organize and store values in your code, including vectors, factors, lists, arrays, matrices, and data frames. Each data structure type serves a specific purpose and can contain specific kinds of data. So, it’s important to understand the differences between them so you can make the right choice based on your scenario. In this module, you’ll learn about the types of data you can store in each data structure and how to add, remove, or manipulate its contents.

As with most programming languages, R supports coding features that you can use to control the flow of program execution, define functions that can perform specific tasks, work with common data types, like strings and dates, and make your code more robust by intercepting likely errors and handling them before they interrupt the execution of your code. In this module, you’ll learn how to implement these fundamental programming tasks in R.

Data is everywhere! The data you need to analyze may come from a traditional database, but it may also come from a variety of different sources and systems, and it may come to you in one or more formats. For example, your data might be in text, Excel, .JSON, or .XML files. Or it may not be stored in a file at all, but instead lives on the pages of a website. How will you take all these different file formats and load them into your R working environment? This module provides you with the tools you need to read data from some common file formats and sources into data objects that you can then use and combine with other data objects in your data analysis.