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When developing large packages or Shiny apps in R, it can be
difficult to track where a modification to one function might propagate
down-stream. As a simple example, suppose you’ve moved from the initial
prototype phase into a more serious development phase for a package and
realize that you would like to change parameter names in a function from
some.param to someParam in order to match
other function parameterization. This function might live in the
utils.R file and be called by functions spread out across
several other .R files. Having a map (graph) of what
functions depend on this function can be very helpful in making sure
that all usages of this function get adjusted correctly.
As this package is specifically intended for development phase, it doesn’t require the package to be built and in fact works with any directory containing R files, or even a single ’.R; file. This has the advantage of being useful outside of R package development, specifically in cases such as shiny apps which are often developed outside of the package context.
There are 2 (or 3) steps to using pkgGraphR, first
(optional) collect the function assignments with
collectFunNames, next build the graph object (a list
containing nodes and edges) with
buildPackageGraph, finally visualize the results as desired
with plotPackageGraph. The example below shows how to use
each function assuming you are in the package or app directory you want
to visualize.
library(pkgGraphR)
funclist <- collectFunNames(x = ".")
funcgraph <- buildPackageGraph(x = ".",
unique.edges = TRUE,
only.connected = FALSE)
# under default parameters, only the graph is required
plotPackageGraph(graph = funcgraph)
# alternatively, plot with grouping and/or coloring (requires fun.list)
plotPackageGraph(graph = funcgraph,
fun.list = funclist,
use.subgraphs = T,
use.colors = T)There are a few known issues which should be taken into consideration.
grViz doesn’t allow . in node names so
if you use my.function be aware that grViz
will show these as myfunction.
Very large packages (e.g. dplyr) will be difficult
to visualize. As a workaround, you can use htmlwidgets and
webshot to generate a high resolution pdf as
below.
p <- plotPackageGraph(graph = funcgraph)
htmlwidgets::saveWidget(p, "test.html")
webshot::webshot(url = "test.html", file = "test.pdf")These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.