{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# install.packages(\"igraph\")\n",
    "# install.packages(\"tidygraph\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Traversing graphs with tidygraph in R\n",
    "\n",
    "Raphael Leung \n",
    "\n",
    "The Indexing and Data Management Section (IDMS) in the House of Commons Library keeps a taxonomy/ controlled vocabulary of subject topics. Parliamentary stuff is regularly tagged against this taxonomy.\n",
    "\n",
    "We want the taxonomy to more closely adhere to Simple Knowledge Organization System (SKOS). [SKOS](https://www.w3.org/TR/2009/REC-skos-reference-20090818/) is an RDF-based model for simple knowledge structures like thesauri/ controlled vocabularies. It lets you organize your concepts into informal hierarchies and association networks, document and annotate them, link them to other concepts, group them into collections..., importantly, in a way that let's others outside your domain know what you're doing (because SKOS's a W3C recommendation). SKOS can be extended, too (i.e. [“Not take it or leave it, but take what you want, make what you need\"](http://dublincore.org/resources/training/frd_20091217/Tutorial_FRD_baker-4.pdf).) Other open linked data vocabularies you may have heard of include Dublin Core for metadata terms.\n",
    "\n",
    "If you're new to semantic web technologies, linked data vocabularies basically encourage vocabulary re-use across the Web, so you have a web of broader and narrower concepts. Core idea is to enable wider re-use and better interoperability.\n",
    "\n",
    "Anyway, there're these taxonomies that have been maintained for a long time internally but not closely analyzed. They are used for indexing. They evolve and are manually curated. They have hierarchies (e.g. broader, narrower terms). Would be nice to have a closer look...\n",
    "\n",
    "This uses `tidyraph` in R to do so. It has `igraph` under the hood combined with `dplyr` syntax.\n",
    "\n",
    "First, fetch the terms from http://lda.data.parliament.uk/terms and load in the csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>X</th><th scope=col>uri</th><th scope=col>broader</th><th scope=col>broader...uri</th><th scope=col>narrower</th><th scope=col>narrower...uri</th><th scope=col>pref.label</th><th scope=col>type</th><th scope=col>type...uri</th><th scope=col>class</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1                                          </td><td>http://data.parliament.uk/terms/347260     </td><td> 91822                                     </td><td>http://data.parliament.uk/terms/91822      </td><td>NA                                         </td><td>http://data.parliament.uk/terms/347135     </td><td>Acts                                       </td><td>                                           </td><td>http://www.w3.org/2004/02/skos/core#Concept</td><td>CTP                                        </td></tr>\n",
       "\t<tr><td>2                                          </td><td>http://data.parliament.uk/terms/347260     </td><td> 91822                                     </td><td>http://data.parliament.uk/terms/91822      </td><td>NA                                         </td><td>http://data.parliament.uk/terms/352234     </td><td>Acts                                       </td><td>Concept                                    </td><td>http://www.w3.org/2004/02/skos/core#Concept</td><td>CTP                                        </td></tr>\n",
       "\t<tr><td>3                                          </td><td>http://data.parliament.uk/terms/90131      </td><td> 90829                                     </td><td>http://data.parliament.uk/terms/90829      </td><td>NA                                         </td><td>                                           </td><td>Adjournment debates                        </td><td>                                           </td><td>http://www.w3.org/2004/02/skos/core#Concept</td><td>CTP                                        </td></tr>\n",
       "\t<tr><td>4                                          </td><td>http://data.parliament.uk/terms/347012     </td><td>347010                                     </td><td>http://data.parliament.uk/terms/347010     </td><td>NA                                         </td><td>http://data.parliament.uk/terms/347014     </td><td>Adopted legislation                        </td><td>                                           </td><td>http://www.w3.org/2004/02/skos/core#Concept</td><td>CTP                                        </td></tr>\n",
       "\t<tr><td>5                                          </td><td>http://data.parliament.uk/terms/347012     </td><td>347010                                     </td><td>http://data.parliament.uk/terms/347010     </td><td>NA                                         </td><td>http://data.parliament.uk/terms/347016     </td><td>Adopted legislation                        </td><td>Concept                                    </td><td>http://www.w3.org/2004/02/skos/core#Concept</td><td>CTP                                        </td></tr>\n",
       "\t<tr><td>6                                          </td><td>http://data.parliament.uk/terms/347012     </td><td>347010                                     </td><td>http://data.parliament.uk/terms/347010     </td><td>NA                                         </td><td>http://data.parliament.uk/terms/347018     </td><td>Adopted legislation                        </td><td>Concept                                    </td><td>http://www.w3.org/2004/02/skos/core#Concept</td><td>CTP                                        </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllll}\n",
       " X & uri & broader & broader...uri & narrower & narrower...uri & pref.label & type & type...uri & class\\\\\n",
       "\\hline\n",
       "\t 1                                             & http://data.parliament.uk/terms/347260        &  91822                                        & http://data.parliament.uk/terms/91822         & NA                                            & http://data.parliament.uk/terms/347135        & Acts                                          &                                               & http://www.w3.org/2004/02/skos/core\\#Concept & CTP                                          \\\\\n",
       "\t 2                                             & http://data.parliament.uk/terms/347260        &  91822                                        & http://data.parliament.uk/terms/91822         & NA                                            & http://data.parliament.uk/terms/352234        & Acts                                          & Concept                                       & http://www.w3.org/2004/02/skos/core\\#Concept & CTP                                          \\\\\n",
       "\t 3                                             & http://data.parliament.uk/terms/90131         &  90829                                        & http://data.parliament.uk/terms/90829         & NA                                            &                                               & Adjournment debates                           &                                               & http://www.w3.org/2004/02/skos/core\\#Concept & CTP                                          \\\\\n",
       "\t 4                                             & http://data.parliament.uk/terms/347012        & 347010                                        & http://data.parliament.uk/terms/347010        & NA                                            & http://data.parliament.uk/terms/347014        & Adopted legislation                           &                                               & http://www.w3.org/2004/02/skos/core\\#Concept & CTP                                          \\\\\n",
       "\t 5                                             & http://data.parliament.uk/terms/347012        & 347010                                        & http://data.parliament.uk/terms/347010        & NA                                            & http://data.parliament.uk/terms/347016        & Adopted legislation                           & Concept                                       & http://www.w3.org/2004/02/skos/core\\#Concept & CTP                                          \\\\\n",
       "\t 6                                             & http://data.parliament.uk/terms/347012        & 347010                                        & http://data.parliament.uk/terms/347010        & NA                                            & http://data.parliament.uk/terms/347018        & Adopted legislation                           & Concept                                       & http://www.w3.org/2004/02/skos/core\\#Concept & CTP                                          \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "X | uri | broader | broader...uri | narrower | narrower...uri | pref.label | type | type...uri | class | \n",
       "|---|---|---|---|---|---|\n",
       "| 1                                           | http://data.parliament.uk/terms/347260      |  91822                                      | http://data.parliament.uk/terms/91822       | NA                                          | http://data.parliament.uk/terms/347135      | Acts                                        |                                             | http://www.w3.org/2004/02/skos/core#Concept | CTP                                         | \n",
       "| 2                                           | http://data.parliament.uk/terms/347260      |  91822                                      | http://data.parliament.uk/terms/91822       | NA                                          | http://data.parliament.uk/terms/352234      | Acts                                        | Concept                                     | http://www.w3.org/2004/02/skos/core#Concept | CTP                                         | \n",
       "| 3                                           | http://data.parliament.uk/terms/90131       |  90829                                      | http://data.parliament.uk/terms/90829       | NA                                          |                                             | Adjournment debates                         |                                             | http://www.w3.org/2004/02/skos/core#Concept | CTP                                         | \n",
       "| 4                                           | http://data.parliament.uk/terms/347012      | 347010                                      | http://data.parliament.uk/terms/347010      | NA                                          | http://data.parliament.uk/terms/347014      | Adopted legislation                         |                                             | http://www.w3.org/2004/02/skos/core#Concept | CTP                                         | \n",
       "| 5                                           | http://data.parliament.uk/terms/347012      | 347010                                      | http://data.parliament.uk/terms/347010      | NA                                          | http://data.parliament.uk/terms/347016      | Adopted legislation                         | Concept                                     | http://www.w3.org/2004/02/skos/core#Concept | CTP                                         | \n",
       "| 6                                           | http://data.parliament.uk/terms/347012      | 347010                                      | http://data.parliament.uk/terms/347010      | NA                                          | http://data.parliament.uk/terms/347018      | Adopted legislation                         | Concept                                     | http://www.w3.org/2004/02/skos/core#Concept | CTP                                         | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  X uri                                    broader\n",
       "1 1 http://data.parliament.uk/terms/347260  91822 \n",
       "2 2 http://data.parliament.uk/terms/347260  91822 \n",
       "3 3 http://data.parliament.uk/terms/90131   90829 \n",
       "4 4 http://data.parliament.uk/terms/347012 347010 \n",
       "5 5 http://data.parliament.uk/terms/347012 347010 \n",
       "6 6 http://data.parliament.uk/terms/347012 347010 \n",
       "  broader...uri                          narrower\n",
       "1 http://data.parliament.uk/terms/91822  NA      \n",
       "2 http://data.parliament.uk/terms/91822  NA      \n",
       "3 http://data.parliament.uk/terms/90829  NA      \n",
       "4 http://data.parliament.uk/terms/347010 NA      \n",
       "5 http://data.parliament.uk/terms/347010 NA      \n",
       "6 http://data.parliament.uk/terms/347010 NA      \n",
       "  narrower...uri                         pref.label          type   \n",
       "1 http://data.parliament.uk/terms/347135 Acts                       \n",
       "2 http://data.parliament.uk/terms/352234 Acts                Concept\n",
       "3                                        Adjournment debates        \n",
       "4 http://data.parliament.uk/terms/347014 Adopted legislation        \n",
       "5 http://data.parliament.uk/terms/347016 Adopted legislation Concept\n",
       "6 http://data.parliament.uk/terms/347018 Adopted legislation Concept\n",
       "  type...uri                                  class\n",
       "1 http://www.w3.org/2004/02/skos/core#Concept CTP  \n",
       "2 http://www.w3.org/2004/02/skos/core#Concept CTP  \n",
       "3 http://www.w3.org/2004/02/skos/core#Concept CTP  \n",
       "4 http://www.w3.org/2004/02/skos/core#Concept CTP  \n",
       "5 http://www.w3.org/2004/02/skos/core#Concept CTP  \n",
       "6 http://www.w3.org/2004/02/skos/core#Concept CTP  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "terms_labels <- read.csv(\"terms_labels.csv\", header=TRUE)\n",
    "head(terms_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean up the data\n",
    "\n",
    "This uses `dplyr` (with piping syntax imported from `magrittr`) in tidyverse which mostly makes nested function calls look cleaner and more readable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in eval(substitute(list(...)), `_data`, parent.frame()):\n",
      "“NAs introduced by coercion”"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>ID</th><th scope=col>broader_ID</th><th scope=col>narrower_ID</th><th scope=col>pref.label</th><th scope=col>idmsClass</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>347260             </td><td> 91822             </td><td>347135             </td><td>Acts               </td><td>CTP                </td></tr>\n",
       "\t<tr><td>347260             </td><td> 91822             </td><td>352234             </td><td>Acts               </td><td>CTP                </td></tr>\n",
       "\t<tr><td> 90131             </td><td> 90829             </td><td>    NA             </td><td>Adjournment debates</td><td>CTP                </td></tr>\n",
       "\t<tr><td>347012             </td><td>347010             </td><td>347014             </td><td>Adopted legislation</td><td>CTP                </td></tr>\n",
       "\t<tr><td>347012             </td><td>347010             </td><td>347016             </td><td>Adopted legislation</td><td>CTP                </td></tr>\n",
       "\t<tr><td>347012             </td><td>347010             </td><td>347018             </td><td>Adopted legislation</td><td>CTP                </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " ID & broader\\_ID & narrower\\_ID & pref.label & idmsClass\\\\\n",
       "\\hline\n",
       "\t 347260              &  91822              & 347135              & Acts                & CTP                \\\\\n",
       "\t 347260              &  91822              & 352234              & Acts                & CTP                \\\\\n",
       "\t  90131              &  90829              &     NA              & Adjournment debates & CTP                \\\\\n",
       "\t 347012              & 347010              & 347014              & Adopted legislation & CTP                \\\\\n",
       "\t 347012              & 347010              & 347016              & Adopted legislation & CTP                \\\\\n",
       "\t 347012              & 347010              & 347018              & Adopted legislation & CTP                \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "ID | broader_ID | narrower_ID | pref.label | idmsClass | \n",
       "|---|---|---|---|---|---|\n",
       "| 347260              |  91822              | 347135              | Acts                | CTP                 | \n",
       "| 347260              |  91822              | 352234              | Acts                | CTP                 | \n",
       "|  90131              |  90829              |     NA              | Adjournment debates | CTP                 | \n",
       "| 347012              | 347010              | 347014              | Adopted legislation | CTP                 | \n",
       "| 347012              | 347010              | 347016              | Adopted legislation | CTP                 | \n",
       "| 347012              | 347010              | 347018              | Adopted legislation | CTP                 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  ID     broader_ID narrower_ID pref.label          idmsClass\n",
       "1 347260  91822     347135      Acts                CTP      \n",
       "2 347260  91822     352234      Acts                CTP      \n",
       "3  90131  90829         NA      Adjournment debates CTP      \n",
       "4 347012 347010     347014      Adopted legislation CTP      \n",
       "5 347012 347010     347016      Adopted legislation CTP      \n",
       "6 347012 347010     347018      Adopted legislation CTP      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(dplyr)\n",
    "\n",
    "terms_labels$uri %<>% as.character() %>% strsplit(\"/\", fixed=TRUE) %>% sapply(tail, 1)\n",
    "terms_labels$narrower...uri %<>% as.character() %>% strsplit(\"/\", fixed=TRUE) %>% sapply(tail, 1) %>% sapply(function(x) if(identical(x,character(0))) NA else x)\n",
    "terms_labels$broader...uri %<>% as.character() %>% strsplit(\"/\", fixed=TRUE) %>% sapply(tail, 1) %>% sapply(function(x) if(identical(x,character(0))) NA else x)\n",
    "terms_labels %<>% select(uri,broader...uri,narrower...uri,pref.label,class) %>% rename(ID=uri,broader_ID=broader...uri,narrower_ID=narrower...uri,idmsClass=class) %>% transform(ID = as.numeric(ID), broader_ID = as.numeric(broader_ID), narrower_ID = as.numeric(narrower_ID), pref.label = as.character(pref.label), idmsClass = as.character(idmsClass))\n",
    "head(terms_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert to edgelist \n",
    "\n",
    "I should note that, natively, the data exposed on lda.data.parliament.uk is in RDF format, a type of graph. However, there aren't many good packages that loads RDF data into R. (There's `SPARQL`, `redland`, `rrdf` R packages which are maintained to various degrees.) There is a hacky way to run SPARQL on the site but it's not an actively developed-on service and large payloads often throw up errors. The terms data was thus exported into relational/ tabular shape as a CSV, and after cleaning, looks like it does above. So it's worth noting that `tidygraph` graph objects more closely follow the property graph model (nodes and edges have properties) rather than RDF.\n",
    "\n",
    "The tabular data above isn't yet a graph data structure. Common graph data structures/ representations include edgelists, adjacency/incidence matrices, and adjacency lists. \n",
    "\n",
    "The next step converts it to an edgelist. Cocept A is broader than Concept B is represented by an edge from A to B.\n",
    "\n",
    "If you're into network analysis, the key things to note are the graph is directed/ asymmetric, unweighted, and one-mode. (\"One-mode\" because both \"from\" and \"to\" are the same type of actor, here concepts. For a less simple graph, can also treat IDMS-defined classes like CTP, TPG, ORG etc as an edge property. Since concepts are shared between classes, this would make the graph multiplex, i.e. multiple relations between the same actors, instead of uniplex. Since the taxonomies evolve, we can also bring in time to make this a dynamic/ time-dependent instead of cross-sectional. But let's start with the simplest form.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>from</th><th scope=col>to</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>95539</td><td>95476</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>95563</td><td>95477</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>95575</td><td>95477</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>95497</td><td>95478</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>95678</td><td>95478</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>95645</td><td>95479</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & from & to\\\\\n",
       "\\hline\n",
       "\t1 & 95539 & 95476\\\\\n",
       "\t3 & 95563 & 95477\\\\\n",
       "\t4 & 95575 & 95477\\\\\n",
       "\t5 & 95497 & 95478\\\\\n",
       "\t6 & 95678 & 95478\\\\\n",
       "\t7 & 95645 & 95479\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | from | to | \n",
       "|---|---|---|---|---|---|\n",
       "| 1 | 95539 | 95476 | \n",
       "| 3 | 95563 | 95477 | \n",
       "| 4 | 95575 | 95477 | \n",
       "| 5 | 95497 | 95478 | \n",
       "| 6 | 95678 | 95478 | \n",
       "| 7 | 95645 | 95479 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  from  to   \n",
       "1 95539 95476\n",
       "3 95563 95477\n",
       "4 95575 95477\n",
       "5 95497 95478\n",
       "6 95678 95478\n",
       "7 95645 95479"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "terms_labels_subclass <- terms_labels %>% filter(idmsClass==\"TPG\")\n",
    "broader <- terms_labels_subclass %>% subset(select=c(\"broader_ID\", \"ID\")) %>% na.omit() %>% unique() %>% rename(from=broader_ID,to=ID)\n",
    "narrower <- terms_labels_subclass %>% subset(select=c(\"ID\", \"narrower_ID\")) %>% na.omit() %>% unique() %>% rename(from=ID,to=narrower_ID)\n",
    "edgelist <- broader %>% rbind(narrower) %>% unique()\n",
    "head(edgelist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, I subset the data to just the smallest and most intuitive taxonomy, TPG (which stands for \"topic page\"...) If you're interested in the story, these topics can be traced to the current website at http://parliament.uk/topics/topical-issues.htm, though that has been a graveyard of weblinks for the past few years and will eventually be replaced on https://beta.parliament.uk.\n",
    "\n",
    "We'll look at all of the IDMS-defined classes at the end.\n",
    "\n",
    "### Load into `tidygraph`\n",
    "\n",
    "`tidygraph` is a fairly [new entry](https://www.data-imaginist.com/2017/introducing-tidygraph/) into the tidyverse in 2017. So it has `dplyr` verbs and `magrittr` pipes. `tidygraph` adds more graph algorithms and visualizations to its underlying `igraph`, which is an open-source network analysis package available in R, Python and C, [docs here](http://igraph.org/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "# A tbl_graph: 314 nodes and 357 edges\n",
       "#\n",
       "# A directed acyclic simple graph with 1 component\n",
       "#\n",
       "# Node Data: 314 x 1 (active)\n",
       "  name \n",
       "  <chr>\n",
       "1 95539\n",
       "2 95563\n",
       "3 95575\n",
       "4 95497\n",
       "5 95678\n",
       "6 95645\n",
       "# ... with 308 more rows\n",
       "#\n",
       "# Edge Data: 357 x 2\n",
       "   from    to\n",
       "  <int> <int>\n",
       "1     1    43\n",
       "2     2    70\n",
       "3     3    70\n",
       "# ... with 354 more rows"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"# of edges: 357 | # of nodes: 314 | # of weakly connected components: 1\"\n"
     ]
    }
   ],
   "source": [
    "library(tidygraph)\n",
    "library(igraph)\n",
    "g <- edgelist %>% as_tbl_graph(directed=TRUE)\n",
    "g\n",
    "print(paste0(\"# of edges: \",  gsize(g),\" | # of nodes: \", gorder(g), \" | # of weakly connected components: \", length(decompose(g, mode=\"weak\"))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Render the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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6zZfsqT6Vo/hi88UeBLJNMDLZw2/iuO0u1Wf5pFMsdwszib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iCeTIhgTapGGbq3yl8Cfjr4vtNN8czyJqA1DVfA2pzX+u6dY3MV7DIHVftM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D2R+rR/jyw/4EOwrWoAKKKKAPgj40fBPW/2WfE2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\n4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uwqEcYHOBxtxXp8USQRJFEixxooVUQYCgdAB2Fflr8E/DNr8O/gpa6U954YsZvH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     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "display_data"
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   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "g %>% plot(layout=layout.fruchterman.reingold, vertex.size=degree(g)*0.6, vertex.label.color=\"darkblue\", vertex.label.cex=0.6, vertex.color=\"orange\", edge.arrow.size=0.1, edge.width=0.5, edge.color=\"gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pick a graph traversal algorithm - what's BFS/DFS?\n",
    "\n",
    "`tidygraph` lets you add properties to nodes and edges with `mutate`, the `dplyr` verb to create new columns. E.g. `g %>% activate(nodes) %>% mutate(name = as.numeric(name))` activates the nodes and turn them from characters to numerics. This [blog](https://www.data-imaginist.com/2017/introducing-tidygraph/) gives a great overview of functionality of the package in addition to the [docs](https://cran.r-project.org/web/packages/tidygraph/tidygraph.pdf).\n",
    "\n",
    "Now here's the exciting part -- graph traversal! Let's make an example graph.\n",
    "\n",
    "(Less excitingly, `nbviewer` currently has trouble rendering image/svg+xml 😐 so we'll have to make do with a static image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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++++4527dpFeXl5lJOTQ1lZWZSRkUHdu3cXf/M0nsfL8LLMu+++W9QJr1y5IqYF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\nEB8AgCtAfAAALkD0f1vVUDEhR2YBAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=5, repr.plot.height=4)\n",
    "example <- create_tree(6, children=3) %>% mutate(name = letters[1:6]) \n",
    "# example %>% plot(layout=layout.reingold.tilford,vertex.label.cex=1, vertex.size=40)\n",
    "library(IRdisplay)\n",
    "display_png(file=\"igraphTraversalExample.PNG\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two main ways of traversing it: breadth-first search (BFS) and depth-first search (DFS). Both require specifying an entry point or the root node.\n",
    "* BFS: Go a level at a time, until all nodes in that level have been reached, then continue to next level. \n",
    "    * a &rarr; b &rarr; c &rarr; d &rarr; e &rarr; f\n",
    "* DFS: Go as deep as possible along each branch then backtracking upwards\n",
    "    * a &rarr; b &rarr; e &rarr; f &rarr; c &rarr; d (preorder; there are other orderings)\n",
    "\n",
    "BFS uses a queue and DFS uses a stack. A queue is horizontal, like a restaurant queue, it's first-in-first-out/ last-in-last-out (FIFO/ LILO). Main operations are enqueue and dequeue. A stack is vertical, like a stack of food trays, it's first-in-last-out/ last-in-first-out (FILO/ LIFO). Main operations are push and pop. This [IDEA assembly instructions](https://idea-instructions.com/graph-scan/) illustrates it in a fun way...\n",
    "\n",
    "You can implement queue and stack functionalities with e.g linked lists.\n",
    "\n",
    "The relative complexity of both DFS and BFS is $O(E + V)$, because you vist every edge and node in worst case. So whichever larger term dominates. If graphs's really dense, $|E|$ can be as high as $|V|^2$, so $E$ dominates and complexity is $O(E)$. For a sparse graph where $E$<$V$ then complexity is $O(V)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "# A tbl_graph: 6 nodes and 5 edges\n",
       "#\n",
       "# A rooted tree\n",
       "#\n",
       "# Node Data: 6 x 2 (active)\n",
       "  name   rank\n",
       "  <chr> <int>\n",
       "1 a         1\n",
       "2 b         2\n",
       "3 c         3\n",
       "4 d         4\n",
       "5 e         5\n",
       "6 f         6\n",
       "#\n",
       "# Edge Data: 5 x 2\n",
       "   from    to\n",
       "  <int> <int>\n",
       "1     1     2\n",
       "2     1     3\n",
       "3     1     4\n",
       "# ... with 2 more rows"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example %>% mutate(rank = map_bfs_int(node_is_root(), .f = function(rank, ...) {\n",
    "rank\n",
    "})) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "# A tbl_graph: 6 nodes and 5 edges\n",
       "#\n",
       "# A rooted tree\n",
       "#\n",
       "# Node Data: 6 x 2 (active)\n",
       "  name   rank\n",
       "  <chr> <int>\n",
       "1 a         1\n",
       "2 b         2\n",
       "3 c         5\n",
       "4 d         6\n",
       "5 e         3\n",
       "6 f         4\n",
       "#\n",
       "# Edge Data: 5 x 2\n",
       "   from    to\n",
       "  <int> <int>\n",
       "1     1     2\n",
       "2     1     3\n",
       "3     1     4\n",
       "# ... with 2 more rows"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example %>% mutate(rank = map_dfs_int(node_is_root(), .f = function(rank, ...) {\n",
    "rank\n",
    "})) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the orders in which the nodes are visited do indeed adhere to the explanation above.\n",
    "\n",
    "### Lets's get the depth for all nodes\n",
    "\n",
    "Now that we know what BFS and DFS is, let's return to the indexing taxonomy.\n",
    "\n",
    "One thing we can use the traversal algorithms for is to get the depth of each node. Depth of a node = the number of edges from the node to the tree's root node. Depth is generally useful to obtain because, e.g. here, we can then know how far down the taxonomy an indexer went to pick the subject tag. \n",
    "\n",
    "Instead of returning the depth per node, can also do calculations/ return metrics of centrality/ finding paths, etc. There're many, many applications of traversal algorithms!\n",
    "\n",
    "Here, we can add a property called \"depth\" to the nodes, calling `bfs_dist()` and passing in a `tbl_graph` object and specifying its root. `bfs_dist()` gets the number of nodes between the root and each node in a bredth first search. \n",
    "\n",
    "But, how do we find the entry point/ root of the graph?\n",
    "\n",
    "The root can be determined by passing all nodes into `neighbors()` function in igraph, to check which node has zero incoming edges, which in a one-component graph, makes it the root node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>95475:</strong> 10"
      ],
      "text/latex": [
       "\\textbf{95475:} 10"
      ],
      "text/markdown": [
       "**95475:** 10"
      ],
      "text/plain": [
       "95475 \n",
       "   10 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "which(V(g) %>% sapply(function(x) neighbors(g,x,mode=\"in\")) %>% sapply(length) == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "# A tbl_graph: 314 nodes and 357 edges\n",
       "#\n",
       "# A directed acyclic simple graph with 1 component\n",
       "#\n",
       "# Node Data: 314 x 2 (active)\n",
       "  name  depth\n",
       "  <chr> <int>\n",
       "1 95539     1\n",
       "2 95563     1\n",
       "3 95575     1\n",
       "4 95497     2\n",
       "5 95678     2\n",
       "6 95645     1\n",
       "# ... with 308 more rows\n",
       "#\n",
       "# Edge Data: 357 x 2\n",
       "   from    to\n",
       "  <int> <int>\n",
       "1     1    43\n",
       "2     2    70\n",
       "3     3    70\n",
       "# ... with 354 more rows"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g %>% mutate(depth = bfs_dist(\n",
    "    which(V(g) %>% sapply(function(x) neighbors(g,x,mode=\"in\")) %>% sapply(length) == 0)\n",
    "                          ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But, that isn't very efficient -- there are 2 `sapply`s (applies that return vectors). \n",
    "\n",
    "The following tries to do the same in a more efficient way..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "# A tbl_graph: 314 nodes and 357 edges\n",
       "#\n",
       "# A directed acyclic simple graph with 1 component\n",
       "#\n",
       "# Node Data: 314 x 3 (active)\n",
       "  name  neighborhood_in_edges depth\n",
       "  <chr>                 <dbl> <int>\n",
       "1 95475                  0        0\n",
       "2 95539                  1.00     1\n",
       "3 95563                  1.00     1\n",
       "4 95575                  1.00     1\n",
       "5 95497                  1.00     2\n",
       "6 95678                  1.00     2\n",
       "# ... with 308 more rows\n",
       "#\n",
       "# Edge Data: 357 x 2\n",
       "   from    to\n",
       "  <int> <int>\n",
       "1     2    41\n",
       "2     3   278\n",
       "3     4   278\n",
       "# ... with 354 more rows"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g %>% mutate(neighborhood_in_edges = map_local_dbl(mode=\"in\", .f = function(neighborhood, ...) {\n",
    "    gsize(neighborhood)\n",
    "    })) %>% mutate(depth = bfs_dist(which(neighborhood_in_edges==0))) %>% arrange(neighborhood_in_edges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And a quick check to see if it's right\n",
    "\n",
    "For hierarchical graphs like taxonomies, a good visual check is plotting the graph in Reingold & Tilford’s Tidier layout. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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UV7pmnT3Et3FZTWSwhERNyQh5EmEZZ28tfm2Yuoav8F7nwx4ik1jwzaf8JJo7WjRe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\ni/vJCvRkbqGXngdc/QH5o8aftMeAdK8KeG7OX4n+GtP1rTfOh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fBnhrxd4ih8L6Z4Sa/S4uNKSzLo811uyW+aG4mkiVwDJJG2XJWrfgz9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\nipOL51LlS52lFp2vbnaTlz5jLEPMldJVEu0bN9L6WTemjT87dJPiD4g0H4l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tYvr3w54V8B6ZYaDPf+MNUNvFe+Knli0y1a2ie7G+SEiR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I9D8Ua1Pe6TCbl95gtIY7INGd7l2iJcSFCFZeKzPhpqPw2s/hf4p1r+2dcg8fxaVbaD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\n0eSSIvEgVfKVHH71t5ZbHwP8DeL9G+DPxG8B6t4X1rUtB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912SVl8DT5ddXs2vO+rvdG0WnZTW7/Faf8Atq+75nua6FA5El9I+oSLzm4I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fdkAAySa+hvjWcfDHWvpD/6OSuJ8BaZN47u9Vtx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\n3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      ]
     },
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=5)\n",
    "par(mfrow = c(1, 2))\n",
    "g %>% plot(layout=layout.kamada.kawai,vertex.size=degree(g)*1.2, vertex.label.color=\"darkblue\", vertex.label.cex=0.6, vertex.color=\"orange\", edge.arrow.size=0.1, edge.width=0.5, edge.color=\"gray\")\n",
    "mtext(\"Kamada Kawai layout\") \n",
    "g %>% plot(layout=layout.reingold.tilford, vertex.size=10, vertex.label.color=\"darkblue\", vertex.label.cex=0.6, vertex.color=\"orange\", edge.arrow.size=0.2, edge.width=1, edge.color=\"darkgray\")\n",
    "mtext(\"Reingold & Tilford’s Tidier layout\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the root node is indeed 95475, which according to http://lda.data.parliament.uk/terms/95475, is \"Taxonomy\". That node leads to 20 nodes in the first layer (with distance 1), and more in the layer below. To see actual labels of the taxons, I made a d3 version too that's more human-friendly: https://bl.ocks.org/lcyraphael/421f3f14854cb49a8fd199f9e326ecaa.\n",
    "\n",
    "### Now for the rest of the graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Class</th><th scope=col>Vertices</th><th scope=col>Edges</th><th scope=col>Components</th><th scope=col>Root</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>CTP   </td><td>  147 </td><td>  151 </td><td>  1   </td><td>346696</td></tr>\n",
       "\t<tr><td>NAME  </td><td> 6985 </td><td> 6983 </td><td>  2   </td><td>NA    </td></tr>\n",
       "\t<tr><td>ID    </td><td> 7889 </td><td> 7798 </td><td>509   </td><td>NA    </td></tr>\n",
       "\t<tr><td>LEG   </td><td> 7509 </td><td> 7508 </td><td>  1   </td><td>287813</td></tr>\n",
       "\t<tr><td>SIT   </td><td> 6315 </td><td> 6949 </td><td>  1   </td><td>90102 </td></tr>\n",
       "\t<tr><td>TPG   </td><td>  314 </td><td>  357 </td><td>  1   </td><td>95475 </td></tr>\n",
       "\t<tr><td>ORG   </td><td>47096 </td><td>47694 </td><td> 17   </td><td>NA    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " Class & Vertices & Edges & Components & Root\\\\\n",
       "\\hline\n",
       "\t CTP    &   147  &   151  &   1    & 346696\\\\\n",
       "\t NAME   &  6985  &  6983  &   2    & NA    \\\\\n",
       "\t ID     &  7889  &  7798  & 509    & NA    \\\\\n",
       "\t LEG    &  7509  &  7508  &   1    & 287813\\\\\n",
       "\t SIT    &  6315  &  6949  &   1    & 90102 \\\\\n",
       "\t TPG    &   314  &   357  &   1    & 95475 \\\\\n",
       "\t ORG    & 47096  & 47694  &  17    & NA    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Class | Vertices | Edges | Components | Root | \n",
       "|---|---|---|---|---|---|---|\n",
       "| CTP    |   147  |   151  |   1    | 346696 | \n",
       "| NAME   |  6985  |  6983  |   2    | NA     | \n",
       "| ID     |  7889  |  7798  | 509    | NA     | \n",
       "| LEG    |  7509  |  7508  |   1    | 287813 | \n",
       "| SIT    |  6315  |  6949  |   1    | 90102  | \n",
       "| TPG    |   314  |   357  |   1    | 95475  | \n",
       "| ORG    | 47096  | 47694  |  17    | NA     | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  Class Vertices Edges Components Root  \n",
       "1 CTP     147      151   1        346696\n",
       "2 NAME   6985     6983   2        NA    \n",
       "3 ID     7889     7798 509        NA    \n",
       "4 LEG    7509     7508   1        287813\n",
       "5 SIT    6315     6949   1        90102 \n",
       "6 TPG     314      357   1        95475 \n",
       "7 ORG   47096    47694  17        NA    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_names = c(\"CTP\", \"NAME\", \"ID\", \"LEG\", \"SIT\", \"TPG\", \"ORG\")\n",
    "results = data.frame()\n",
    "\n",
    "for(i in class_names){\n",
    "    \n",
    "    terms_labels_subclass <- terms_labels %>% filter(idmsClass==i)\n",
    "    broader <- terms_labels_subclass %>% subset(select=c(\"broader_ID\", \"ID\")) %>% na.omit() %>% unique() %>% rename(from=broader_ID,to=ID)\n",
    "    narrower <- terms_labels_subclass %>% subset(select=c(\"ID\", \"narrower_ID\")) %>% na.omit() %>% unique() %>% rename(from=ID,to=narrower_ID)\n",
    "    edgelist <- broader %>% rbind(narrower) %>% unique()\n",
    "    g <- edgelist %>% as_tbl_graph(directed=TRUE)\n",
    "    \n",
    "    numVertex <- gorder(g)\n",
    "    numEdge <- gsize(g)\n",
    "    numComponents <- length(decompose(g, mode=\"weak\"))\n",
    "    \n",
    "    if(numComponents == 1) {\n",
    "        indegree <- V(g) %>% sapply(function(x) neighbors(g,x,mode=\"in\")) %>% sapply(length)\n",
    "        root <- names(which(indegree == 0))\n",
    "    }\n",
    "    else {\n",
    "        root = NA\n",
    "    }\n",
    "    \n",
    "    row <- data.frame(i, numVertex, numEdge, numComponents, root)\n",
    "    results <- rbind(results, row)\n",
    "}\n",
    "\n",
    "colnames(results) <- c(\"Class\", \"Vertices\", \"Edges\", \"Components\", \"Root\")\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some classes have more than 1 weakly-connected component (i.e. they have disparate sub-graphs that do not link up with each other). Note we can still run DFS/BFS on graphs with more than 1 component. With the example of `map_[bd]fs_*()` in tidygraph, set unreachable to `TRUE` so the search jumps to an unvisited node if the search is completed without visiting all nodes.\n",
    "\n",
    "In this case, we can thankfully ask the indexers more about how the taxonomies are maintained and relate to one other. E.g. we know ID class is more of a curated flat list without much of a hierarchy. So it's generally worth sanity-checking first before passing a graph into the algorithm.\n",
    "\n",
    "Happy graph traversing! 🕸️🏃"
   ]
  }
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