## NOTE: Vignettes are built within their own environment. ## Therefore, changing the library paths (libpaths) here will not change the global libpaths. # Get test library path testLibPath <- tempdir() # Get current library paths origLibPaths <- .libPaths() # Create new library paths for TESTING .libPaths(new = c(testLibPath, origLibPaths)) # Create packages within new library pkgnet:::.BuildTestLib(targetLibPath = testLibPath) knitr::opts_chunk$set( collapse = FALSE, comment = "#>", fig.align = 'center', out.width='100%' )
pkgnet
is an R package designed for the analysis of R packages! The goal of the package is to build graph representations of a package's various types of dependencies. This can inform a variety of activities, including:
Below is a brief tour of pkgnet
and its features.
pkgnet
represents aspects of R packages as graphs. The two default reporters, which we will discuss in this vignette, model their respective aspects as directed graphs: a package's dependencies on other packages, and the interdependencies of functions within a package. Before we look at the output of pkgnet
, here are few core concepts to keep in mind.
nodes <- data.frame( id = 1:4 , label = LETTERS[1:4] , x = c(1,3,2,4) , y = c(1,1,2,3) , level = c(1,1,2,3) ) edges <- data.frame( from = c(3,3,4,4) , to = c(1,2,3,2) ) g <- visNetwork::visNetwork(nodes = nodes , edges = edges , width = "100%" ) g <- visNetwork::visNodes(graph = g , shape = "circle" , font = list(size = 25 , bold = TRUE , align = 'center' ) ) g <- visNetwork::visEdges(graph = g , arrows = "to" ) g <- visNetwork::visHierarchicalLayout(graph = g , direction = "DU" , sortMethod = "directed" ) g <- visNetwork::visInteraction(graph = g , dragNodes = TRUE , dragView = TRUE , zoomView = FALSE) g
Units of the analysis are represented as nodes, and their dependency relationships are represented as edges (a.k.a. arcs or arrows). In pkgnet
, the nodes could be functions in the package you are examining, or other packages that the package depends on. The direction of edges point in the direction of dependency---the tail node depends on the head node.[^1]
[^1]: This follows the Unified Modeling Language (UML) framework, a widely used standard for software system modeling.
In the example dependency graph above:
Following the direction of the edges allows you to figure out the dependencies of a node---the other nodes that it depends on. On the flip side, tracing the edges backwards allows you to figure out the reverse dependencies (i.e., dependents) of a node---the other nodes that depend on it.
pkgnet
can analyze any R package locally installed. (Run installed.packages()
to see the full list of packages installed on your system.) For this example, let's say we are analyzing a custom built package, baseballstats
.
To analyze baseballstats
, run the following two lines of code:
library(pkgnet) report1 <- CreatePackageReport(pkg_name = "baseballstats")
THAT'S IT! You have generated a lot of valuable information with that one call for an installed package.
However, if the full source repository for the package is available on your system, you can supplement this report with other information such as code coverage from covr. To do so, specify the path to the repository in CreatePackageReport
.
library(pkgnet) report2 <- CreatePackageReport( pkg_name = "baseballstats" , pkg_path = <path to the repo> )
CreatePackageReport()
creates an HTML report with the pertinent information, and it also returns an object with the report information and more. The location of the HTML report is specified in the messages in the terminal, but it should render automatically in your browser.
These will display in the HTML report, and their content will also be attached as public bindings in the PackageReport
object returned from CreatePackageReport()
.
SummaryReporter
: This section displays general information about the package. The returned object contains basic information like the package name and path.
DependencyReporter
: This section displays information regarding the packages upon which the current package under analysis depends. This includes both base and third-party R packages. The returned object contains graph visualizations, graph measures and data tables among other methods.
FunctionReporter
: This section displays information regarding the functions within the current package under analysis and their interdependence network. The returned object contains graph visualizations, graph measures and data tables among other methods.
InheritanceReporter
: While not generated by default (as not all packages are object oriented), this reporter is very useful when investigating the parent-child structure of R6, S4 or Reference Class (a.k.a. "R5") objects. The inheritance graph is displayed in the report along with other information. The returned object contains graph visualizations, graph measures and data tables among other methods.
Aside from the Package Summary section and its returned object, each reporter is based around a graph structure. Let's look at the FunctionReporter
from baseballstats
in more detail.
Here's how the Function Network Visualization looks for baseballstats
. Note, its appearance differs based on if pkg_path
is specified in CreatePackageReport()
:
:::: {style="content: ''; display: table; clear: both;"}
::: {style="float:left;width: 48%;text-align: justify;text-justify: inter-word;margin-right: 4%;"}
Default
pkgnet:::silence_logger() funcReporter1 <- pkgnet::FunctionReporter$new() funcReporter1$set_package('baseballstats') funcReporter1$layout_type <- "layout_as_tree" g <- visNetwork::visHierarchicalLayout( graph = funcReporter1$graph_viz , direction = "UD" , sortMethod = "directed" , edgeMinimization = FALSE ) g <- visNetwork::visInteraction(graph = g , dragNodes = TRUE , dragView = TRUE , zoomView = FALSE) g
All functions and their dependencies are visible. For example, we can see that both batting_avg
and slugging_avg
functions depend upon the at_bats
function.
We also see that nothing depends on the on_base_pct
function. This might be valuable information to an R package developer.
:::
::: {style="float: right; width: 48%; text-align: justify; text-justify: inter-word;"}
With Coverage Information
pkgnet:::silence_logger() funcReporter2 <- pkgnet::FunctionReporter$new() funcReporter2$layout_type <- "layout_as_tree" funcReporter2$set_package( pkg_name = "baseballstats" , pkg_path = system.file('baseballstats',package="pkgnet") ) funcReporter2$calculate_default_measures() g <- visNetwork::visHierarchicalLayout( graph = funcReporter2$graph_viz , direction = "UD" , sortMethod = "directed" , edgeMinimization = FALSE ) g <- visNetwork::visInteraction(graph = g , dragNodes = TRUE , dragView = TRUE , zoomView = FALSE) g
Same as the default visualization except we can see coverage information as well (Pink = 0%, Green = 100%).
It appears the function with the most dependencies, at_bats
, is well covered. However, no other functions are covered by unit tests.
:::
::::
Metrics for the nodes (either packages, functions, or classes depending on the reporter) are contained in a table:
# We initialized just the reporters because we didn't want to actually generate the full html report. So we'll put funcReporter2 into a list to mock the interface for the example report2 <- list(FunctionReporter = funcReporter2)
colSubset <- c('node','type','betweenness','outDegree','inDegree','numRecursiveDeps') report2$FunctionReporter$nodes[,..colSubset]
Note, a few of these metrics provided by default are from the field of Network Theory. You can leverage the Network Graph Model Object described below to derive many more.
Network-level measures are contained in a network_measures
list.
report2$FunctionReporter$network_measures
The network model object itself is contained in the pkg_graph
attribute. The igraph formatted object itself is directly accessible via pkg_graph$igraph
.
report2$FunctionReporter$pkg_graph$node_measures(c('hubScore', 'authorityScore'))
report2$FunctionReporter$pkg_graph$igraph
With the reports and objects produced by pkgnet
by default, there is plenty to inform us on the inner workings of an R package. However, we may want to know MORE! Since the igraph objects are available, we can leverage those graphs for further analysis.
In this section, let's examine a larger R package, such as lubridate.
If you would like to follow along with the examples in this section, run these commands in your terminal to download and install lubridate
[^2].
[^2]: Examples from version 1.7.3 of Lubridate
# Create a temporary workspace mkdir -p ~/pkgnet_example && cd ~/pkgnet_example # Grab the lubridate source code git clone https://github.com/tidyverse/lubridate cd lubridate # If you want the examples to match exactly git reset --hard 9797d69abe1574dd89310c834e52d358137669b8 # Install it R CMD install .
Let's examine lubridate
's functions through the lens of each function's total number of dependents (i.e., the other functions that depend on it) and its code's unit test coverage. In our graph model for the FunctionReporter
, the subgraph of paths leading into a given node is the set of functions that directly or indirectly depend on the function that node represents.
# Run pkgnet library(pkgnet) report2 <- CreatePackageReport( pkg_name = "lubridate" , pkg_path = "~/pkgnet_example/lubridate" ) # Extract Nodes Table funcNodes <- report2$FunctionReporter$nodes # List Coverage For Most Depended-on Functions mostRef <- funcNodes[order(numRecursiveRevDeps, decreasing = TRUE), .(node, numRecursiveRevDeps, coverageRatio, totalLines) ][1:10]
#> node numRecursiveRevDeps coverageRatio totalLines #> 1: month 81 1 1 #> 2: tz 79 1 1 #> 3: reclass_date 68 1 1 #> 4: date 67 1 1 #> 5: is.Date 60 1 1 #> 6: is.POSIXt 57 1 1 #> 7: wday 56 1 1 #> 8: is.POSIXct 55 1 1 #> 9: .deprecated 55 0 10 #> 10: as_date 52 1 1
Inspecting results such as these can help an R package developer decide which function to cover with unit tests next.
In this case, check_duration
, one of the most depended-on functions (either directly or indirectly), is not covered by unit tests. However, it appears to be a simple one line function that may not be necessary to cover in unit testing. check_interval
, on the other hand, might benefit from some unit test coverage as it is a larger, uncovered function with a similar number of dependencies.
Looking at that same large package, let's say we want to explore options for consolidating functions. One approach might be to explore consolidating functions that share the same dependencies. In that case, we could use the igraph
object to highlight functions with the same out-neighborhood via Jaccard similarity.
# Get igraph object funcGraph <- report2$FunctionReporter$pkg_graph$igraph funcNames <- igraph::vertex_attr(funcGraph, name = "name") # Jaccard Similarity sim <- igraph::similarity(graph = funcGraph , mode = "out" , method = "jaccard") diag(sim) <- 0 sim[sim < 1] <- 0 simGraph <- igraph::graph_from_adjacency_matrix(adjmatrix = sim, mode = "undirected") # Find groups with same out-neighbors (similarity == 1) sameDeps <- igraph::max_cliques(graph = simGraph , min = 2 ) # Write results for (i in seq_along(sameDeps)) { cat(paste0("Group ", i, ": ")) cat(paste(funcNames[as.numeric(sameDeps[[i]])], collapse = ", ")) cat("\n") }
cat("Group 1: divisible_period, make_date Group 2: parse_date_time2, fast_strptime Group 3: .deprecated_fun, .deprecated_arg Group 4: stamp_date, stamp_time Group 5: epiweek, isoweek Group 6: ms, hm Group 7: quarter, semester Group 8: am, .roll_hms Group 9: modulo_interval_by_duration, modulo_interval_by_period Group 10: .difftime_from_pieces, .duration_from_units Group 11: divide_period_by_period, xtfrm.Period Group 12: int_diff, %--% Group 13: isoyear, epiyear Group 14: nanoseconds, microseconds, picoseconds, milliseconds Group 15: period_to_seconds, check_period, multiply_period_by_number, format.Period, divide_period_by_number, add_period_to_period Group 16: myd, dmy, yq, ymd, dym, mdy, ydm Group 17: hours, weeks, minutes, years, days, months.numeric, seconds, seconds_to_period Group 18: C_force_tz, hour.default, mday.default, c.POSIXct, .mklt, yday.default, year.default, minute.default, second.default Group 19: ehours, emilliseconds, eyears, eseconds, epicoseconds, enanoseconds, eminutes, olson_time_zones, edays, emicroseconds, eweeks Group 20: dmy_h, ydm_hms, ymd_hms, dmy_hm, ymd_h, ydm_hm, ydm_h, dmy_hms, ymd_hm, mdy_hms, mdy_hm, mdy_h" )
Now, we have identified twenty different groups of functions within lubridate that share the exact same dependencies. We could explore each group of functions for potential consolidation.
utils::remove.packages( pkgs = c('baseballstats', 'sartre', 'pkgnet') , lib = testLibPath ) # Just in case .libPaths(new = c(origLibPaths)) unlink(testLibPath)
Want to know even more about the pkgnet
package?!
Run pkgnet
on itself!
install.packages("pkgnet") pkgnetObj <- CreatePackageReport("pkgnet", c(DefaultReporters(), InheritanceReporter$new()))
Want to see pkgnet
reports for other packages?
Check out the pkgnet Gallery.
Want to ship a pkgnet
report with your R package?
Include it a vignette()
in your package. See Publishing Your pkgnet Package Report.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.