The `miniCRAN`

package exposes two functions that provide
information about dependencies:

The function

`pkgDep()`

returns a character vector with the names of dependencies. Internally,`pkgDep()`

is a wrapper around`tools::package_dependencies()`

, a base R function that, well, tells you about package dependencies. My`pkgDep()`

function is in one way a convenience, but more importantly it sets different defaults (more about this later).The function

`makeDepGraph()`

creates a graph representation of the dependencies.

The package `chron`

neatly illustrates the different roles
of Imports, Suggests and Enhances:

`chron`

**Imports**the base packages graphics and stats. This means that`chron`

internally makes use of graphics and stats and will always load these packages.`chron`

**Suggests**the packages scales and ggplot2. This means that`chron`

uses some functions from these packages in examples or in its vignettes. However, these functions are not necessary to use`chron`

`chron`

**Enhances**the package`zoo`

, meaning that it adds something to`zoo`

packages. These enhancements are made available to you if you have`zoo`

installed.

The function `pkgDep()`

exposes not only these
dependencies, but also all recursive dependencies. In other words, it
answers the question which packages need to be installed to satisfy all
dependencies of dependencies.

This means that the algorithm is as follows:

- First retrieve a list of
`Suggests`

and`Enhances`

, using a non-recursive dependency search - Next, perform a recursive search for all
`Imports`

,`Depends`

and`LinkingTo`

The resulting list of packages should then contain the complete list necessary to satisfy all dependencies. In code:

`library("miniCRAN")`

```
<- "chron"
tags pkgDep(tags, availPkgs = cranJuly2014)
```

```
## [1] "chron" "RColorBrewer" "dichromat" "munsell" "plyr"
## [6] "labeling" "colorspace" "Rcpp" "digest" "gtable"
## [11] "reshape2" "scales" "proto" "MASS" "stringr"
## [16] "ggplot2"
```

To create an igraph plot of the dependencies, use the function
`makeDepGraph()`

and plot the results:

```
<- makeDepGraph(tags, enhances = TRUE, availPkgs = cranJuly2014)
dg set.seed(1)
plot(dg, legendPosition = c(-1, 1), vertex.size = 20)
```

Note how the dependencies expand to `zoo`

(enhanced),
`scales`

and `ggplot`

(suggested) and then
recursively from there to get all the `Imports`

and
`LinkingTo`

dependencies.

As a final example, create a dependency graph of seven very popular R packages:

```
<- c("ggplot2", "data.table", "plyr", "knitr", "shiny", "xts", "lattice")
tags pkgDep(tags, suggests = TRUE, enhances = FALSE, availPkgs = cranJuly2014)
```

```
## [1] "ggplot2" "data.table" "plyr" "knitr" "shiny"
## [6] "xts" "lattice" "digest" "gtable" "reshape2"
## [11] "scales" "proto" "MASS" "Rcpp" "stringr"
## [16] "RColorBrewer" "dichromat" "munsell" "labeling" "colorspace"
## [21] "evaluate" "formatR" "highr" "markdown" "mime"
## [26] "httpuv" "caTools" "RJSONIO" "xtable" "htmltools"
## [31] "bitops" "zoo" "SparseM" "survival" "Formula"
## [36] "latticeExtra" "cluster" "maps" "sp" "foreign"
## [41] "mvtnorm" "TH.data" "sandwich" "nlme" "Matrix"
## [46] "bit" "codetools" "iterators" "timeDate" "quadprog"
## [51] "Hmisc" "BH" "quantreg" "mapproj" "hexbin"
## [56] "maptools" "multcomp" "testthat" "mgcv" "chron"
## [61] "reshape" "fastmatch" "bit64" "abind" "foreach"
## [66] "doMC" "itertools" "testit" "rgl" "XML"
## [71] "RCurl" "Cairo" "timeSeries" "tseries" "its"
## [76] "fts" "tis" "KernSmooth"
```

```
<- makeDepGraph(tags, enhances = TRUE, availPkgs = cranJuly2014)
dg set.seed(1)
plot(dg, legendPosition = c(-1, -1), vertex.size = 10, cex = 0.7)
```