Correct typo in posts/international-external-debt-network

docs/index.html

@@ -221,7 +221,7 @@ <h5 class="quarto-listing-category-title">Categories</h5><div class="quarto-list
 
 <div class="quarto-listing quarto-listing-container-grid" id="listing-listing">
 <div class="list grid quarto-listing-cols-3">
-<div class="g-col-1" data-index="0" data-categories="R,Visualization" data-listing-date-sort="1732748400000" data-listing-file-modified-sort="1732801037352" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="5" data-listing-word-count-sort="931">
+<div class="g-col-1" data-index="0" data-categories="R,Visualization" data-listing-date-sort="1732748400000" data-listing-file-modified-sort="1732973818815" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="5" data-listing-word-count-sort="931">
 <a href="./posts/international-external-debt-network/index.html" class="quarto-grid-link">
 <div class="quarto-grid-item card h-100 card-left">
 <p class="card-img-top">

docs/posts/international-external-debt-network/index.html

@@ -195,7 +195,7 @@ <h2 id="toc-title">On this page</h2>
 
   <ul>
   <li><a href="#data-preparation" id="toc-data-preparation" class="nav-link active" data-scroll-target="#data-preparation">Data Preparation</a></li>
-  <li><a href="#debitor-centric-view" id="toc-debitor-centric-view" class="nav-link" data-scroll-target="#debitor-centric-view">Debitor-Centric View</a></li>
+  <li><a href="#debtor-centric-view" id="toc-debtor-centric-view" class="nav-link" data-scroll-target="#debtor-centric-view">Debtor-Centric View</a></li>
   <li><a href="#creditor-centric-view" id="toc-creditor-centric-view" class="nav-link" data-scroll-target="#creditor-centric-view">Creditor-Centric View</a></li>
   <li><a href="#concluding-remarks" id="toc-concluding-remarks" class="nav-link" data-scroll-target="#concluding-remarks">Concluding Remarks</a></li>
   </ul>
@@ -286,8 +286,8 @@ <h2 class="anchored" data-anchor-id="data-preparation">Data Preparation</h2>
 <span id="cb2-15"><a href="#cb2-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">to =</span> <span class="fu">str_squish</span>(to))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 </section>
-<section id="debitor-centric-view" class="level2">
-<h2 class="anchored" data-anchor-id="debitor-centric-view">Debitor-Centric View</h2>
+<section id="debtor-centric-view" class="level2">
+<h2 class="anchored" data-anchor-id="debtor-centric-view">Debtor-Centric View</h2>
 <p>For now, let’s narrow our focus to specific debtor countries. This step allows us to examine the relationships from a debtor’s perspective, understanding how much debt they owe and to whom. In the following, we focus on Nigeria and its neighbor Cameroon.</p>
 <div class="cell">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>selected_geographies <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"Nigeria"</span>, <span class="st">"Cameroon"</span>)</span>

docs/search.json

@@ -992,5 +992,12 @@
     "title": "Interactive Data Visualization with Python",
     "section": "Conclusion",
     "text": "Conclusion\nplotly, bokeh, and altair each cater to distinct visualization needs in Python. plotly shines with its interactive, high-quality visuals and ease of embedding in web applications, making it ideal for creating complex interactive charts and dashboards. bokeh is focused on real-time data visualizations and interactivity, particularly suited for web apps that require dynamic data streaming. Its strength lies in the seamless integration of Python code with web technologies. altair offers a declarative approach, emphasizing simplicity and efficiency in creating elegant statistical visualizations with minimal code, making it ideal for exploratory data analysis in notebooks."
+  },
+  {
+    "objectID": "posts/international-external-debt-network/index.html#debtor-centric-view",
+    "href": "posts/international-external-debt-network/index.html#debtor-centric-view",
+    "title": "Global Debt Networks",
+    "section": "Debtor-Centric View",
+    "text": "Debtor-Centric View\nFor now, let’s narrow our focus to specific debtor countries. This step allows us to examine the relationships from a debtor’s perspective, understanding how much debt they owe and to whom. In the following, we focus on Nigeria and its neighbor Cameroon.\n\nselected_geographies <- c(\"Nigeria\", \"Cameroon\")\n\nexternal_debt_sub <- external_debt |>\n  filter(from %in% selected_geographies) \n\nvisNetwork requires two data frames: nodes and edges, each with corresponding properties. To enhance the visualization, we create helper functions for formatting node titles and labels:\n\nformat_title <- function(id, value, type) {\n  prefix <- if_else(\n    type == \"debtor\", \"<br>Received: \", \"<br>Provided: \"\n  )\n  debt <- scales::label_number(\n    accuracy = 0.1, scale_cut = scales::cut_short_scale()\n  )(value)\n  title <- str_c(\n    id, prefix, debt, \" USD\"\n  )\n  title\n}\n\nformat_label <- function(id) {\n  label <- str_wrap(id, width = 20)\n  label\n}\n\nWe now construct the nodes and edges for the network. Nodes represent entities (countries or institutions), and edges represent debt relationships. The data looks like this:\n\ncreate_nodes <- function(external_debt_sub) {\n  \n  total_debt <- sum(external_debt_sub$value)\n  \n  nodes <- external_debt_sub |> \n    group_by(id = from, type = \"debtor\", color = \"Country\") |> \n    summarize(value = sum(value),\n              .groups = \"drop\") |> \n    bind_rows(\n      external_debt_sub |> \n        group_by(id = to, type = \"creditor\", color = counterpart_type) |> \n        summarize(value = sum(value),\n                  .groups = \"drop\")\n    ) |> \n    mutate(\n      title = format_title(id, value, type),\n      label = format_label(id),\n      size = value / total_debt,\n      color = case_when(\n        color == \"Other\" ~ \"#C46231\",\n        color == \"Country\" ~ \"#3193C4\",\n        color == \"Global MDBs\" ~ \"#AB31C4\",\n        color == \"Bondholders\" ~ \"#4AC431\"\n      )\n    )\n  nodes\n}\n\nnodes <- create_nodes(external_debt_sub)\nnodes\n\n# A tibble: 33 × 7\n   id                                    type  color   value title label    size\n   <chr>                                 <chr> <chr>   <dbl> <chr> <chr>   <dbl>\n 1 Cameroon                              debt… #319… 1.18e10 Came… \"Cam… 2.28e-1\n 2 Nigeria                               debt… #319… 4.00e10 Nige… \"Nig… 7.72e-1\n 3 African Dev. Bank                     cred… #C46… 4.18e 9 Afri… \"Afr… 8.07e-2\n 4 African Export-Import Bank            cred… #C46… 7.60e 7 Afri… \"Afr… 1.47e-3\n 5 Arab Bank for Economic Dev. in Afric… cred… #C46… 7.23e 7 Arab… \"Ara… 1.39e-3\n 6 Austria                               cred… #319… 8.78e 6 Aust… \"Aus… 1.69e-4\n 7 Belgium                               cred… #319… 5.69e 7 Belg… \"Bel… 1.10e-3\n 8 Bondholders                           cred… #4AC… 1.73e10 Bond… \"Bon… 3.33e-1\n 9 Central Bank of West African States … cred… #C46… 2.28e 7 Cent… \"Cen… 4.40e-4\n10 China                                 cred… #319… 8.08e 9 Chin… \"Chi… 1.56e-1\n# ℹ 23 more rows\n\n\nEdges add the connective tissue to the network, showing who owes whom. Here is how the example data looks like:\n\ncreate_edges <- function(external_debt_sub) {\n  edges <- external_debt_sub |> \n    select(from, to) |> \n    mutate(\n      shadow = TRUE, \n      color = \"grey\",\n      smooth = TRUE\n    )\n  edges\n}\n\nedges <- create_edges(external_debt_sub)\nedges\n\n# A tibble: 45 × 5\n   from     to                               shadow color smooth\n   <chr>    <chr>                            <lgl>  <chr> <lgl> \n 1 Cameroon World Bank-IDA                   TRUE   grey  TRUE  \n 2 Cameroon World Bank-IBRD                  TRUE   grey  TRUE  \n 3 Cameroon United States                    TRUE   grey  TRUE  \n 4 Cameroon United Kingdom                   TRUE   grey  TRUE  \n 5 Cameroon United Arab Emirates             TRUE   grey  TRUE  \n 6 Cameroon Turkiye                          TRUE   grey  TRUE  \n 7 Cameroon Switzerland                      TRUE   grey  TRUE  \n 8 Cameroon Spain                            TRUE   grey  TRUE  \n 9 Cameroon Saudi Arabia                     TRUE   grey  TRUE  \n10 Cameroon OPEC Fund for International Dev. TRUE   grey  TRUE  \n# ℹ 35 more rows\n\n\nThe visNetwork library brings everything together, producing an interactive network. This visualization provides a debtor-centric perspective, illustrating how selected countries distribute their debt obligations among creditors.\n\nvisualize_network <- function(external_debt_sub) {\n  nodes <- create_nodes(external_debt_sub)\n  edges <- create_edges(external_debt_sub)\n  \n  visNetwork(\n    nodes, edges, width = \"100%\", height = \"600px\"\n  ) |> \n    visNodes(shape = \"dot\")\n}\n\nvisualize_network(external_debt_sub)\n\n\n\n\n\nVisualizing the network from a debtor’s perspective sheds light on the diversity of funding sources for countries like Nigeria and Cameroon. While both nations share some creditors, the wider spread of Cameroon’s creditor network could indicate stronger diversification in funding sources—a potentially advantageous position for economic resilience."
   }
 ]

docs/sitemap.xml

@@ -6,7 +6,7 @@
   </url>
   <url>
     <loc>https://blog.tidy-intelligence.com/posts/international-external-debt-network/index.html</loc>
-    <lastmod>2024-11-28T13:37:17.352Z</lastmod>
+    <lastmod>2024-11-30T13:36:58.815Z</lastmod>
   </url>
   <url>
     <loc>https://blog.tidy-intelligence.com/posts/dplyr-vs-tidierdata/index.html</loc>

posts/international-external-debt-network/index.qmd

@@ -56,7 +56,7 @@ external_debt <- external_debt_raw |>
   mutate(to = str_squish(to))
 ```
 
-## Debitor-Centric View
+## Debtor-Centric View
 
 For now, let’s narrow our focus to specific debtor countries. This step allows us to examine the relationships from a debtor’s perspective, understanding how much debt they owe and to whom. In the following, we focus on Nigeria and its neighbor Cameroon.