It is important to be aware of the memory requirements of R objects, especially when working with large datasets.
Each R object is stored in physical memory, so it is important to make sure that you have enough RAM available to store the objects that you need. If you try to read in a dataset that is larger than the amount of RAM that you have available, your computer may freeze or crash.
There are a few ways to calculate the memory requirements of an R object. One way is to use the `object.size()` function. This function will return the size of an object in bytes. For example, the following code will calculate the size of a data frame with 1,500,000 rows and 120 columns, all of which are numeric data:
object.size(df)
Another way to calculate the memory requirements of an R object is to use the `memory.profile()` function. This function will print a report of the memory usage of all of the objects in your workspace.
Once you know the memory requirements of an R object, you can use this information to decide whether or not you have enough RAM available to read in the object. If you do not have enough RAM, you may need to close some other applications or reduce the size of the dataset.
Managing memory in R:
- Close any applications that you are not using.
- Use the `object.size()` function to check the size of your objects.
- Use the `memory.profile()` function to get a report of the memory usage of all of your objects.
- Use the `save()` function to save your objects to a file. This will free up memory in your workspace.
- Use the `gc()` function to run the garbage collector. This will free up memory that is no longer being used by R.
