Book Image

R Programming By Example

By : Omar Trejo Navarro
Book Image

R Programming By Example

By: Omar Trejo Navarro

Overview of this book

R is a high-level statistical language and is widely used among statisticians and data miners to develop analytical applications. Often, data analysis people with great analytical skills lack solid programming knowledge and are unfamiliar with the correct ways to use R. Based on the version 3.4, this book will help you develop strong fundamentals when working with R by taking you through a series of full representative examples, giving you a holistic view of R. We begin with the basic installation and configuration of the R environment. As you progress through the exercises, you'll become thoroughly acquainted with R's features and its packages. With this book, you will learn about the basic concepts of R programming, work efficiently with graphs, create publication-ready and interactive 3D graphs, and gain a better understanding of the data at hand. The detailed step-by-step instructions will enable you to get a clean set of data, produce good visualizations, and create reports for the results. It also teaches you various methods to perform code profiling and performance enhancement with good programming practices, delegation, and parallelization. By the end of this book, you will know how to efficiently work with data, create quality visualizations and reports, and develop code that is modular, expressive, and maintainable.
Table of Contents (12 chapters)

Looking at dynamic data with time-series

Now we are going to focus on another very common type of graph: time-series. Our objective is to understand how our data is behaving for the last n days, and, as we have done before, we want to further disaggregate using colors, like the graph below shows:

If you have read all the chapter up until this point, you should be able to understand most of what the function is doing. The only new function is scale_x_date(). It allows us to specify date formats for the axis ticks other than the default. In this case, we want to use breaks by day (as we had done in some examples before), but we want the format of the labels to be similar to July 30, 2017, for example. To do so we make use of the date formats mentioned in a previous section in this chapter and send the desired string structure to the date_labels parameter:

graph_last_n_days &lt...