DSBA 5122: Visual Analytics

# DSBA 5122: Visual Analytics
## Class 8: Networks and Trees
### Ryan Wesslen
### October 21, 2019

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# Networks and Trees

Read Chapter 5: Networks and Hiearchies of Mazza

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# Networks (a.k.a. graphs)

- Graphs are visual representations in which the points, called **nodes** or **vertices**, represent instances of the data.

- Nodes are correlated by connections, called **edges**, which represent relationships between the instances.

- Possible features of a network: weights, direction, labels.

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.pull-left[
<img src="../images/slides/08-class/undirected.jpeg" width="400px" style="display: block; margin: auto;" />
]
.pull-right[
<img src="../images/slides/08-class/directed.jpeg" width="400px" style="display: block; margin: auto;" />
]

[Vaidehi Joshi Medium post](https://medium.com/basecs/from-theory-to-practice-representing-graphs-cfd782c5be38)

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[BNOSAC: R NLP packages ecosystem](http://www.bnosac.be/index.php/blog/87-an-overview-of-the-nlp-ecosystem-in-r-nlproc-textasdata)

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# Layouts

See [R-Graph-Gallery Network Layouts](https://www.r-graph-gallery.com/247-network-chart-layouts.html) for more.

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# Which is the most important node?

```r
library(igraph)
library(netrankr)

g <- graph.empty(n = 11,directed = FALSE)
g <- add_edges(g,c(1,11,2,4,3,5,3,11,4,8,5,9,5,11,6,7,6,8,
                    6,10,6,11,7,9,7,10,7,11,8,9,8,10,9,10))
```

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# Centrality Measures

```r
# from igraph
cent_scores <- tibble(
   node = 1:11,
   degree = degree(g),
   betweenness = round(betweenness(g),4),
   closeness = round(closeness(g),4),
   eigenvector = round(eigen_centrality(g)$vector,4),
   subgraph = round(subgraph_centrality(g),4))

cent_scores %>%
  arrange(desc(betweenness))
```
]
.pull-right[

```
## # A tibble: 11 x 6
##     node degree betweenness closeness eigenvector subgraph
##    <int>  <dbl>       <dbl>     <dbl>       <dbl>    <dbl>
##  1     8      4       16.3     0.0556      0.839      6.67
##  2    11      5       14.7     0.0556      0.845      7.39
##  3     6      4        9.83    0.0588      0.985      7.81
##  4     4      2        9       0.04        0.242      2.42
##  5     9      4        7.33    0.0556      0.911      7.03
##  6     5      3        3.83    0.05        0.571      4.39
##  7     7      4        2.67    0.0526      1          7.94
##  8    10      4        1.33    0.0526      0.999      8.24
##  9     1      1        0       0.037       0.226      1.83
## 10     2      1        0       0.0294      0.0646     1.60
## 11     3      2        0       0.04        0.379      3.15
```

]
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# Centrality Measures using [DT DataTable](https://rstudio.github.io/DT/)

<div id="htmlwidget-5ea1126df6405d0a9b2f" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-5ea1126df6405d0a9b2f">{"x":{"filter":"none","extensions":["FixedColumns"],"data":[[1,2,3,4,5,6,7,8,9,10,11],[1,1,2,2,3,4,4,4,4,4,5],[0,0,0,9,3.8333,9.8333,2.6667,16.3333,7.3333,1.3333,14.6667],[0.037,0.0294,0.04,0.04,0.05,0.0588,0.0526,0.0556,0.0556,0.0526,0.0556],[0.226,0.0646,0.3786,0.2415,0.5709,0.9847,1,0.8386,0.9114,0.9986,0.845],[1.8251,1.5954,3.1486,2.4231,4.3871,7.8073,7.9394,6.6728,7.0327,8.2421,7.3896]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>node<\/th>\n      <th>degree<\/th>\n      <th>betweenness<\/th>\n      <th>closeness<\/th>\n      <th>eigenvector<\/th>\n      <th>subgraph<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":11,"dom":"t","scrollX":true,"scrollCollapse":true,"initComplete":"\n                        function(settings, json) {\n                          $(this.api().table().header()).css({\n                          'font-size': '12px',\n                          });\n                        }\n                    ","columnDefs":[{"className":"dt-right","targets":[0,1,2,3,4,5]}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[10,11,25,50,100],"rowCallback":"function(row, data) {\nvar value=data[0]; $(this.api().cell(row, 0).node()).css({'font-size':'12px'});\nvar value=data[1]; $(this.api().cell(row, 1).node()).css({'font-size':'12px'});\nvar value=data[2]; $(this.api().cell(row, 2).node()).css({'font-size':'12px'});\nvar value=data[3]; $(this.api().cell(row, 3).node()).css({'font-size':'12px'});\nvar value=data[4]; $(this.api().cell(row, 4).node()).css({'font-size':'12px'});\nvar value=data[5]; $(this.api().cell(row, 5).node()).css({'font-size':'12px'});\n}"}},"evals":["options.initComplete","options.rowCallback"],"jsHooks":[]}</script>

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- A **tree** (hierarchy) can be represented through a graph with a starting node called root.

- Each node has zero or more child nodes its ancestor is called the **parent** node. A node has at most one parent.

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# json files: Tweet Example

<blockquote class="twitter-tweet"><p lang="en" dir="ltr">1/ Today we’re sharing our vision for the future of the Twitter API platform!<a href="https://t.co/XweGngmxlP">https://t.co/XweGngmxlP</a></p>&mdash; Twitter Dev (@TwitterDev) <a href="https://twitter.com/TwitterDev/status/850006245121695744?ref_src=twsrc%5Etfw">April 6, 2017</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

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# json files: Tweet Example

- See [Twitter Developer JSON intro](https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/intro-to-tweet-json) for more details.

- See ['jsonlite' package](https://cran.r-project.org/web/packages/jsonlite/vignettes/json-aaquickstart.html) for handling json files in R.

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# listviewer: viewing json in R

```r
library(listviewer)

jsonedit(
  list(
    array = c(1,2,3)
    ,boolean = TRUE
    ,null = NULL
    ,number = 123
    ,object = list( a="b", c="d" )
    ,string = "Hello World. "
  )
)
```
]
.pull-right[
<div id="htmlwidget-fc287bb65faf7eb40350" style="width:302.4px;height:302.4px;" class="jsonedit html-widget"></div>
<script type="application/json" data-for="htmlwidget-fc287bb65faf7eb40350">{"x":{"data":{"array":[1,2,3],"boolean":true,"null":[],"number":123,"object":{"a":"b","c":"d"},"string":"Hello World. "},"options":{"mode":"tree","modes":["code","form","text","tree","view"]}},"evals":[],"jsHooks":[]}</script>
]

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## Katya Ognyanova's "Network visualization with R."

http://kateto.net/network-visualization

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