space-saving | This is a C implementation | Learning library

The git solutions seem to be a lot more hassle than a simple package manager.

This is not about hassle.

This is about two different ways to build a project:

1. through binary dependencies, with a package manager (Nexus, or Conan for C++: you declare your dependencies, and the package manager fetches them and uses them during the compilation.
   That is what a pom.xml or a npm-package.json: just one more file within your unique codebase, which will instruct the compiler to download the relevant dependencies
2. through source dependencies, with Git submodules or subtrees, where you store references to other source code, import them, and recompile everything.
   For instance, if your system is composed of a front-end GUI sources and a backend sources, you could reference both repositories within one parent project repositories, combining their sources into one code base.

The first is good when building a system, where each part has its own release lifecycle, and you want to depend to pre-built dependencies.

The second is used when the dependencies are more tightly linked to the main program.

Or when there are no binary dependencies (which is the case, for instance, with Go and its modules).

The following is an extension to an answer I wrote for a previous question about utilising the space from empty facet panels, but I think it's sufficiently different to warrant its own space.

Essentially, I wrote a function that takes a ggplot/grob object converted by ggplotGrob(), converts it to grob if it isn't one, and digs into the underlying grobs to move the legend grob into the cells that correspond to the empty space.

Function:

Yes, you need to copy also shared libraries (for example mentioned libR.so), because they are required by dynamically linked R binaries.

But this image size optimization isn't worth it, unless you have specific use case. Price of saved disk space is probably much more lower than value of the time, which you will spend on this optimization. I will use some ready R image from rocker (rocker/r-ver) in your case - proved R images for general R use.

According to Microsoft:

Changing a column from sparse to nonsparse or nonsparse to sparse requires changing the storage format of the column. The SQL Server Database Engine uses the following procedure to accomplish this change:

1) Adds a new column to the table in the new storage size and format.

2) For each row in the table, updates and copies the value stored in the old column to the new column.

3) Removes the old column from the table schema.

4) Rebuilds the table (if there is no clustered index) or rebuilds the clustered index to reclaim space used by the old column.

Note

Step 2 can fail when the size of the data in the row exceeds the maximum allowable row size. This size includes the size of the data stored in the old column and the updated data stored in the new column. This limit is 8060 bytes for tables that do not contain any sparse columns or 8018 bytes for tables that contain sparse columns. This error can occur even if all eligible columns have been pushed off-row.

https://docs.microsoft.com/en-us/sql/relational-databases/tables/use-sparse-columns

Since you don't care about the order of the subset nor do you care about restoring each element to its position in the original array, this is simply a case of producing a random subset of an array, which is similar to drawing cards from a deck.

To take unique elements from an array, you can simply shuffle the source array and then take a number of elements at the first X indices:

The condition is added into the Where clause. In order to specify the collection name, we also need to join Collection views, here I joined v_FullCollectionMembership and v_Collection. Change the collection name COL.Name = 'Win7' to your own Collection name here.