R Tutorial: References vs. Copies

Want to learn more? Take the full course at https://learn.datacamp.com/courses/scalable-data-processing-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- As mentioned earlier, big matrices have been designed to look and feel like a regular R matrix. You can retrieve subsets of a big matrix just like you would a regular R matrix. You can also set values of a big matrix like you would with a regular R matrix. However, it is sometimes important to realize that a big matrix isn't actually an R matrix. There are a couple of differences that can be important. One difference, which you already know, is that a big matrix object is normally stored on the disk, rather than RAM. This means that a big matrix can persist across R sessions and can even be shared among R sessions. Another difference is that a big matrix object is not copied like a regular matrix. When an R variable is assigned to other variables it actually gets a copy of the content. This is also true when parameters are passed to functions. Here we assign the value 42 to the variable a. Next, we create a function, foo(), that takes a single parameter, sets it to 43, and then outputs its value. When we call foo(a) The printed message tells us that a, inside the function is 43. When we print the value of a outside the function, it is still 42. This is because the function foo doesn't actually get a. It gets a copy of a. If we want to change the original a we need to do so outside of the function. There are other types of variables, like environments, that are not copied when they are passed to a function. For these types it is the actual data structure and, as a result, if you change their values inside a function, you will see those changes after the function is finished executing. Those types of objects have reference semantics. A big matrix is a reference object. This means that assignments create new variables that point to the same