As discussed in Learning Block 1, files always have file extensions that indicate the read/write format. These were, for example, .docx for Word files or .pptx for PowerPoint files.
For datasets, there are global file formats that can be read by most programs, as well as program-specific file formats.
Global file formats include:
.txt (text files, where data is usually separated by a tab)
.csv (comma-separated values)
In R, there are two important file formats: .RData and .rds. Files with the extension .RData can contain multiple objects, which is useful, for example, when you want to save all objects in a single file at the end of your script. The .rds format is a compressed file format that can only store a single object. This is the most common format used in R for distributing datasets.
Unfortunately, some publicly available datasets are only provided in an SPSS-compliant file format (.sav). However, this is not a problem because with the help of a library, we can add RStudio functions that can also read this file format.
What is a library, actually? R is an open-source language where any user can write new functions and make them available to the world. During installation, only the base functions are installed, representing only a fraction of the application scope. This is one of its strengths compared to SPSS or Stata, as it allows for the inclusion of many use cases. However, it can also be overwhelming at first, as many people have encountered similar problems, resulting in more than one solution or more than one possible library that can be added. In this course, we will not provide a complete overview of various libraries for the same problem but will always introduce our preferred library. However, this should not deter you from searching for another library and finding the solution path through it.
As of March 2022, the official library website lists 18,994 different libraries that contain additional functions to perform statistical applications. Additionally, there are numerous libraries in the development stage, which can usually already be used. However, this is more suitable for experienced users - like you after completing this course.
Next, we will import different dataset file formats into R to work with them afterward.