CHAPTER 5 AND 6

The final object type we'll discuss is a data frame, which is a collection of vectors with the same length. In R, data frames are used to store most datasets, and we'll get a lot of experience with them in the following chapters. Let's put the three vectors, fiveIntegers, fiveBooleans, and fiveRandoms, into a data frame with the data for now. role of the frame.We'll start a new project named Tauntauns here. This project will house all of the materials related to our Tauntaun population study.An absolute file path contains your computer’s root directory and all other subdirectories that contain a file or folder. In Windows the root directory is usually the C drive; in linux and MacOS it begins with a forward slash / or tilde ~. A relative file path locates your file relative to your working directory, which should be your Tauntaun project directory (one of the benefits of using projects is that the working directory is automatically set upon opening).The backslash key is above the Enter key on the keyboard. If we use the backslash in a file path by mistake, R may or may not recognize the following character as a special character to escape, but regardless of whether it causes an error, the intended command will not be completed.The hunter information is included in the second csv disk. We'll use the download.file function and the relative path notation datasets/hunter.csv in this example. This instructs R to navigate one level down from its current working directory to a folder called “datasets” and create a file called “hunter.csv.” We heard about the projects available in RStudio and created our Tauntaun project. We learned some new file and directory features, as well as how to read in files from Excel and use our first ‘apply' feature. All of this work lays the groundwork for a seamless transition in the following chapters.The summary function summarizes the data in an object rather than the object itself. There are a few spelling errors in the data (such as the column name individul; it was entered by volunteers, so we can't really complain), and at least one column (method) has NA values, indicating missing data. Aside from correcting typos. When saving R objects with the save feature, using the factor system saves storage space and useful memory during the R session. Additionally, certain statistical and graphic functions require the data to be stored as variables in order for the system to function properly.In this chapter, we learned some new functions and had plenty of opportunities to practice indexing. As a tauntaun biologist (as they call us in this chapter), we'll probably spend a lot of time working with results, and we've just scratched the surface.  t