Have you ever wanted to learn how R is used to handle the most common data types? The knowledge you will gain here is foundational for any aspiring R programmer. In this course, Querying and Converting Data Types in R, you will develop an understanding of all of these data types and how they are processed, converted, and filtered. First, you will explore general data analysis concepts and take a look at the data frame and its main alternatives. Next, you will learn the major data types in R: numeric, integer, factor, character, boolean, and date and time. Finally, you will discover how these data types are used in a data frame. The query and filtering methods largely depend on the data types available in that data frame. When you are finished with this course, you will have your first set of skills that will be invaluable in your further learning path. In fact, the skills taught here are so important in data science, that most of it can be used in other languages (Python, Matlab) and programs (SPSS).
Martin is a trained biostatistician, programmer, consultant and data science enthusiast. His main objective: Explaining data science in a straightforward way. You can find his latest work over at: r-tutorials.com
Course Overview (Music) Welcome to Querying and Converting Data Types in R, and this is Martin Burger for Pluralsight. In this entry-level course, you will see how R is used to handle the most common data types. The knowledge you will gain here is foundational for any aspiring R program. The main data format in R in the data frame. The beauty of that format is its ability to capture various data types like numbers, text, categories, or logical data. In this course, you will develop an understanding of all of these data types and how they are processed, converted, and filtered. We also work at the data.frame level and perform queries. Querying or filtering is a common task in analytics, therefore it is important to learn it early on in your learning path, and you will also learn about the most common alternatives to a data frame, namely the tibble and the data.table. By the end of this course, you should know the common data types and how they are handled in R, and you will also know how to perform standard tasks on a data frame. To fully benefit from this course, you do not need any prior coding or data analysis skills. I will guide you through the most important concepts of R and data analysis. We are going to start right at the beginning. In fact, the material taught here has relevance in other analytics programs as well. I really hope you use this course as your primer to data analysis in R. The Pluralsight course library will then give you plenty of opportunities to get you another step further into R.