**Thinking inside the box**, and kindly contributed to R-bloggers)

`Depends`

.
Far from it, and I immediately replied listing lattice and
Matrix as packages with way more other packages depending upon them.
But as the question seemed deserving of a bit more analysis, I spent a few minutes on this and prepared three charts listing package in order of reverse Depends, reverse Imports and reverse LinkingTo.

First off, the reverse `Depends:`

. This is the standard means of
declaring a dependence of one package upon another.

Unsurprisingly, the MASS package from the
classic Venables and Ripley
book comes first, with Deepayan Sarkar's powerful
lattice package (also
covered in a
book)
coming second. These are both *recommended* packages which are commonly distributed with R itself.
Next are mvtnorm
and
survival. Our
Rcpp is up there in
the top-ten, but not a frontrunner.

With the advent of namespaces a few R releases ago, it became possible to
import functions from other packages. So the `Imports:`

statement
now provides an alternative to the (older) `Depends:`

. The next
chart displays the same relationship for `Imports:`

:

Now lattice still leads, but Hadleys's plyr package grabbed the second spot just before MASS and Matrix.

It is interesting to see that the sheer number of `Imports:`

is
still not where the `Depends:`

are. On the other hand, we see a
number of more recent packages popping up in the second chart. This may
reflect more recent coding practices. It will be interesting to see how this
stacks up over time when we revisit this chart.

Lastly, we can also look at `LinkingTo:`

, a declaration used to
provide a `C/C++`

-level dependency at the source code level. We use
this in the
Rcpp family to
provide automatic resolution of the header files needed to compile against
our packages. And unsurprisingly, because packages using
Rcpp
actually use its API (rather than R functions), the package is a little ahead
of others. In the package we find three more packages of the
Rcpp
family, but only a limited number of other packages as
`C/C++`

-level dependencies are still somewhat rare in the R universe.
There are also fewer packages overall making use of this mechanism.

One could of course take this one level further and sum up dependencies in a
recursive manner, or visualize the relationship differently. But these
`dotchart`

graphs provide a first visual description of the
magnitude of `Depends`

, `Imports`

and
`LinkingTo`

among CRAN packages for R.

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**Thinking inside the box**.

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