deepdep package

Introduction

Package deepdep was created to acquire and visualize information on dependencies of R packages in a smart and convenient way. Most of its functionality is contained in two functions: deepdep – to get a data.frame with dependencies described and plot_dependencies – visualize this data.frame.

library(deepdep)

Use case

Suppose you’re creating an R package and you want to include graph of its dependencies to your vignette, README.md file on your git repository or article on your package. With deepdep you simply need to type one line:

plot_deepdep("YourPackageName")

But before we describe how this function works in detail, let’s see what are other functionalities of the package.

Features

deepdep package exports the following functions:

Those functions rely on each other and are ordered from the lowest to the highest level. We’ll describe what they exactly do and how on examples.

get_available_packages

This function lists, as the name indicates, available packages. The default behaviour is listing all CRAN packages.

t <- get_available_packages()
head(t, 20)
#>            A3         aaSEA      AATtools        ABACUS        abbyyR           abc 
#>          "A3"       "aaSEA"    "AATtools"      "ABACUS"      "abbyyR"         "abc" 
#>      abc.data       ABC.RAP        abcADM   ABCanalysis      abcdeFBA      ABCoptim 
#>    "abc.data"     "ABC.RAP"      "abcADM" "ABCanalysis"    "abcdeFBA"    "ABCoptim" 
#>         ABCp2         abcrf       abcrlda      abctools           abd         abdiv 
#>       "ABCp2"       "abcrf"     "abcrlda"    "abctools"         "abd"       "abdiv" 
#>           abe         abess 
#>         "abe"       "abess"

However, if you want to check if package is present in a little wider range – on CRAN or Bioconductor repositories, you simply need to set argument bioc = TRUE. In this case function is simply wrapper around BiocManager::available() and to use it you need to have BiocManager package (available via CRAN) installed.

t <- get_available_packages(bioc = TRUE)
#> 'getOption("repos")' replaces Bioconductor standard repositories, see '?repositories' for
#> details
#> 
#> replacement repositories:
#>     CRAN: https://cran.rstudio.com/
head(t, 20)
#>  [1] "A3"            "a4"            "a4Base"        "a4Classif"     "a4Core"       
#>  [6] "a4Preproc"     "a4Reporting"   "aaSEA"         "AATtools"      "ABACUS"       
#> [11] "ABAData"       "ABAEnrichment" "ABarray"       "abbyyR"        "abc"          
#> [16] "abc.data"      "ABC.RAP"       "abcADM"        "ABCanalysis"   "abcdeFBA"

Another possibility is checking what packages are installed – you do it by adding local = TRUE parameter.

t <- get_available_packages(local = TRUE)
head(t, 20)
#>  [1] "ade4"         "AmyloGraph"   "ape"          "askpass"      "backports"    "base64enc"   
#>  [7] "BBmisc"       "BH"           "BiocGenerics" "BiocManager"  "BiocVersion"  "biogram"     
#> [13] "bioseq"       "Biostrings"   "bit"          "bit64"        "bitops"       "blob"        
#> [19] "brew"         "brio"

Result of this function is cached (for more details, see Caching section of this vignette).

get_description

When you know, that given package is available, you may want to obtain DESCRIPTION of this package, at least the most essential parts of it, especially dependencies. You can do it by calling:

get_description("DALEXtra")
#> DALEXtra: Extension for 'DALEX' Package
#> Maintainer: Szymon Maksymiuk <sz.maksymiuk@gmail.com> 
#> Description: 
#>  Provides wrapper of various machine learning models.
#> In applied machine learning, there
#> is a strong belief that we need to strike a balance
#> between interpretability and accuracy.
#> However, in field of the interpretable machine learning,
#> there are more and more new ideas for explaining black-box models,
#> that are implemented in 'R'.
#> 'DALEXtra' creates 'DALEX' Biecek (2018) <arXiv:1806.08915> explainer for many type of models
#> including those created using 'python' 'scikit-learn' and 'keras' libraries, and 'java' 'h2o' library.
#> Important part of the package is Champion-Challenger analysis and innovative approach
#> to model performance across subsets of test data presented in Funnel Plot.
#> Third branch of 'DALEXtra' package is aspect importance analysis
#> that provides instance-level explanations for the groups of explanatory variables. 
#> Depends: R DALEX 
#> Imports: reticulate ggplot2 
#> LinkingTo: 
#> Suggests: auditor ingredients gbm ggrepel h2o iml lime localModel mlr mlr3 randomForest recipes rmarkdown rpart xgboost testthat tidymodels 
#> Enhances: 
#> Scrap date: 2021-05-09 09:08:34

Again, you can pass bioc = TRUE if you want to check for this package in Bioconductor repository. Notice that if package is not found there, it will be searched for on CRAN. The reason behind this type of behaviour is the fact that packages present on Bioconductor are updated more often than on CRAN and not all of them are present here. Option local = TRUE for only installed packages is also possible. If a package is not available in a given source, the function will return NULL value:

get_description("a4")
#> NULL
get_description("a4", bioc = TRUE)
#> a4: Automated Affymetrix Array Analysis Umbrella Package
#> Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu> 
#> Description: 
#>  Umbrella package is available for the entire Automated Affymetrix Array Analysis suite of package. 
#> Depends: a4Base a4Preproc a4Classif a4Core a4Reporting 
#> Imports: 
#> LinkingTo: 
#> Suggests: MLP nlcv ALL Cairo Rgraphviz GOstats 
#> Enhances: 
#> Scrap date:

Result of this function is also cached (for more details, see Caching section of this vignette).

get_downloads

This package allows you obtaining information on how many times specified package was downloaded. However, it works only with CRAN packages.

get_downloads("ggplot2")
#>                     x
#> last_day            0
#> last_week      522861
#> last_month    3006182
#> last_quarter  8471688
#> last_half    14297088
#> grand_total  66079081

Results of this function is not cached.

get_dependencies

After parsing description file, you can now create a data.frame which will describe dependencies between given package and others. You do it by using this function:

get_dependencies("ggplot2")
#>       name   version    type last_day last_week last_month last_quarter last_half grand_total
#> 1   digest      <NA> Imports        0    146499     888879      2609540   4657874    42228742
#> 2     glue      <NA> Imports        0    220543    1250176      3664383   6758641    40606261
#> 3   gtable  >= 0.1.1 Imports        0    110066     640653      1809222   3152733    25941419
#> 4  isoband      <NA> Imports        0    131742     762584      2276478   4275144    15035620
#> 5     MASS      <NA> Imports        0     21197     129978       392302    844885     9222620
#> 6     mgcv      <NA> Imports        0     19643     149225       382311    703284     6764818
#> 7    rlang >= 0.4.10 Imports        0    348917    2161098      6144583  11707882    64392027
#> 8   scales  >= 0.5.0 Imports        0    124399     728321      2092431   3707265    33059782
#> 9   tibble      <NA> Imports        0    223126    1349665      4212678   8058305    49686835
#> 10   withr  >= 2.0.0 Imports        0    166170     995159      2989090   5702601    29297142

As with two previously described functions - get_available_packages and get_description, here you can also use bioc = TRUE or local = TRUE and again, in case the package is not available, the result will be NULL. Here you have another options to set.

The first one is parameter downloads – should number of downloads of packages be included? It uses get_downloads and works only with CRAN packages.

Another, more important parameter is dependency_type. You can specify how detailed should be list of dependencies. Default value is c("Depends", "Imports"), but you can chose any combination of those and additionally "Suggests", "Enhances", "LinkingTo".

get_dependencies("ggplot2", downloads = FALSE, dependency_type = c("Imports", "Suggests", "Enhances"))
#>             name       version     type
#> 1         digest          <NA>  Imports
#> 2           glue          <NA>  Imports
#> 3         gtable      >= 0.1.1  Imports
#> 4        isoband          <NA>  Imports
#> 5           MASS          <NA>  Imports
#> 6           mgcv          <NA>  Imports
#> 7          rlang     >= 0.4.10  Imports
#> 8         scales      >= 0.5.0  Imports
#> 9         tibble          <NA>  Imports
#> 10         withr      >= 2.0.0  Imports
#> 11          covr          <NA> Suggests
#> 12          ragg          <NA> Suggests
#> 13         dplyr          <NA> Suggests
#> 14 ggplot2movies          <NA> Suggests
#> 15        hexbin          <NA> Suggests
#> 16         Hmisc          <NA> Suggests
#> 17        interp          <NA> Suggests
#> 18         knitr          <NA> Suggests
#> 19       lattice          <NA> Suggests
#> 20       mapproj          <NA> Suggests
#> 21          maps          <NA> Suggests
#> 22      maptools          <NA> Suggests
#> 23      multcomp          <NA> Suggests
#> 24       munsell          <NA> Suggests
#> 25          nlme          <NA> Suggests
#> 26       profvis          <NA> Suggests
#> 27      quantreg          <NA> Suggests
#> 28  RColorBrewer          <NA> Suggests
#> 29         rgeos          <NA> Suggests
#> 30     rmarkdown          <NA> Suggests
#> 31         rpart          <NA> Suggests
#> 32            sf      >= 0.7-3 Suggests
#> 33       svglite >= 1.2.0.9001 Suggests
#> 34      testthat      >= 2.1.0 Suggests
#> 35        vdiffr      >= 1.0.0 Suggests
#> 36          xml2          <NA> Suggests
#> 37            sp          <NA> Enhances

Result of this function is not cached (at least yet).

deepdep

The main function of the package – it is simply wrapper around get_dependencies, that allows you getting not only dependencies, but also dependencies of the dependencies iteratively! (Now you know, why we called it deepdep).

Parameters are the same as in get_dependencies, but additionally you can specify depth parameter, which describes how many iterations it function should perform. If depth equals 1, it’s simply the same as calling get_dependencies.

deepdep("ggplot2", depth = 2)
#>     origin         name   version    type origin_level dest_level
#> 1  ggplot2       digest      <NA> Imports            0          1
#> 2  ggplot2         glue      <NA> Imports            0          1
#> 3  ggplot2       gtable  >= 0.1.1 Imports            0          1
#> 4  ggplot2      isoband      <NA> Imports            0          1
#> 5  ggplot2         MASS      <NA> Imports            0          1
#> 6  ggplot2         mgcv      <NA> Imports            0          1
#> 7  ggplot2        rlang >= 0.4.10 Imports            0          1
#> 8  ggplot2       scales  >= 0.5.0 Imports            0          1
#> 9  ggplot2       tibble      <NA> Imports            0          1
#> 10 ggplot2        withr  >= 2.0.0 Imports            0          1
#> 11    mgcv         nlme >= 3.1-64 Depends            1          2
#> 12    mgcv       Matrix      <NA> Imports            1          2
#> 13  scales       farver  >= 2.0.3 Imports            1          2
#> 14  scales     labeling      <NA> Imports            1          2
#> 15  scales    lifecycle      <NA> Imports            1          2
#> 16  scales      munsell    >= 0.5 Imports            1          2
#> 17  scales           R6      <NA> Imports            1          2
#> 18  scales RColorBrewer      <NA> Imports            1          2
#> 19  scales  viridisLite      <NA> Imports            1          2
#> 20  tibble     ellipsis  >= 0.3.2 Imports            1          2
#> 21  tibble        fansi  >= 0.4.0 Imports            1          2
#> 22  tibble    lifecycle  >= 1.0.0 Imports            1          2
#> 23  tibble     magrittr      <NA> Imports            1          2
#> 24  tibble       pillar  >= 1.6.2 Imports            1          2
#> 25  tibble    pkgconfig      <NA> Imports            1          2
#> 26  tibble        rlang >=\n0.4.3 Imports            1          1
#> 27  tibble        vctrs  >= 0.3.8 Imports            1          2

plot_dependencies

As famous quote says,

A picture is worth more than a thousand words.

That’s why we have plot_dependencies function. It allows visualizing easily what are dependencies of specified package.

The function is generic, and currently supports two types of object – you can pass a deepdep object, result of the calling the deepdep function or just type name of the package. With the latter option you can also pass arguments to get_dependencies as additional parameters.

dd <- deepdep("tibble", 2)
plot_dependencies(dd)

plot of chunk plot_dependencies1

plot_dependencies("DT", depth = 2, dependency_type = c("Imports", "Depends", "Suggests"))

plot of chunk plot_dependencies1

In each of the plots you can see one package name in the centre and two circles of packages gathered around them. These are dependencies of the first and second level.

Default plot type is circular, as you can see on the examples presented above. However, you can set plot_type parameter to tree.

plot_dependencies(dd, type = "tree")

plot of chunk plot_dependencies2

Not all dependencies are plotted. To increase readability, dependencies on the same level are hidden, but you can change this behaviour

plot_dependencies(dd, same_level = TRUE)

plot of chunk plot_dependencies3

You can also make use of numbers of downloads you obtained. There is an option to add labels to only certain percentage of most downloaded packages among those that are about to be plotted. This is meant to increase readability of the plot.

plot_dependencies("tidyverse", type = "circular", label_percentage = 0.2, depth = 3)

plot of chunk plot_dependencies4

Finally, returned object is a ggplot object, so you can easily manipulate them with syntax known from ggplot2 package. We also use ggraph enhancement for plotting graphs.

plot_dependencies(dd) +
  ggplot2::scale_fill_manual(values = c("#462CF8", "#F23A90", "#AF1023")) +
  ggraph::scale_edge_color_manual(values = "black") 
#> Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the
#> existing scale.

plot of chunk plot_dependencies5

Caching

As we notoriously indicated in the text, we are using caching to make everything a little bit faster. Functions that operate on the lowest level, after scrapping information from the repositories, store them in temporary files on the local machine. As a consequence, calling function the second time with the same set of parameters, should be faster.

State is also refreshed every 20 minutes to make sure you don’t miss any major update.

To make sure that you have the most recent data, you need to call get_available_packages and get_description with parameter reset_cache = TRUE.

get_available_packages(reset_cache = TRUE)