Part 3: Network Description

# Part 3: Network Description
## #aectRTD workshop
### K. Bret Staudt Willet | Florida State University
### March 4, 2022

---

---

# <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"/></svg> Important Links

## Homebase

- **Workshop website:** https://bretsw.github.io/aect22-workshop
- **Workshop code repository:** https://github.com/bretsw/aect22-workshop
- **tidytags R package:** https://github.com/ropensci/tidytags

## Agenda

- **Part 1: Introduction to Networks**
  - Slides: [Part 1 - Networks](1-networks.html)
- **Part 2: Introduction to R**
  - Slides: [Part 2 - R](2-intro-R.html)
- **Part 3: Network Description**
  - Slides: [Part 3 - Description](3-description.html)
- **Part 4: Network Inference**
  - Slides: [Part 4 - Inference](4-inference.html)

## Help

- Ask questions in the Zoom chat!
- Or, reach out directly:
  - Email: [bret.staudtwillet@fsu.edu](mailto:bret.staudtwillet@fsu.edu)
  - Twitter: [@bretsw](https://twitter.com/bretsw)

---

# <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M635.7 167.2L556.1 31.7c-8.8-15-28.3-20.1-43.5-11.5l-69 39.1L503.3 161c2.2 3.8.9 8.5-2.9 10.7l-13.8 7.8c-3.8 2.2-8.7.9-10.9-2.9L416 75l-55.2 31.3 27.9 47.4c2.2 3.8.9 8.5-2.9 10.7l-13.8 7.8c-3.8 2.2-8.7.9-10.9-2.9L333.2 122 278 153.3 337.8 255c2.2 3.7.9 8.5-2.9 10.7l-13.8 7.8c-3.8 2.2-8.7.9-10.9-2.9l-59.7-101.7-55.2 31.3 27.9 47.4c2.2 3.8.9 8.5-2.9 10.7l-13.8 7.8c-3.8 2.2-8.7.9-10.9-2.9l-27.9-47.5-55.2 31.3 59.7 101.7c2.2 3.7.9 8.5-2.9 10.7l-13.8 7.8c-3.8 2.2-8.7.9-10.9-2.9L84.9 262.9l-69 39.1C.7 310.7-4.6 329.8 4.2 344.8l79.6 135.6c8.8 15 28.3 20.1 43.5 11.5L624.1 210c15.2-8.6 20.4-27.8 11.6-42.8z"/></svg> <br><br> **Part 3:** <br> Network Description

---

- <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M425.7 256c-16.9 0-32.8-9-41.4-23.4L320 126l-64.2 106.6c-8.7 14.5-24.6 23.5-41.5 23.5-4.5 0-9-.6-13.3-1.9L64 215v178c0 14.7 10 27.5 24.2 31l216.2 54.1c10.2 2.5 20.9 2.5 31 0L551.8 424c14.2-3.6 24.2-16.4 24.2-31V215l-137 39.1c-4.3 1.3-8.8 1.9-13.3 1.9zm212.6-112.2L586.8 41c-3.1-6.2-9.8-9.8-16.7-8.9L320 64l91.7 152.1c3.8 6.3 11.4 9.3 18.5 7.3l197.9-56.5c9.9-2.9 14.7-13.9 10.2-23.1zM53.2 41L1.7 143.8c-4.6 9.2.3 20.2 10.1 23l197.9 56.5c7.1 2 14.7-1 18.5-7.3L320 64 69.8 32.1c-6.9-.8-13.5 2.7-16.6 8.9z"/></svg> [**igraph**](https://CRAN.R-project.org/package=igraph)

- <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M425.7 256c-16.9 0-32.8-9-41.4-23.4L320 126l-64.2 106.6c-8.7 14.5-24.6 23.5-41.5 23.5-4.5 0-9-.6-13.3-1.9L64 215v178c0 14.7 10 27.5 24.2 31l216.2 54.1c10.2 2.5 20.9 2.5 31 0L551.8 424c14.2-3.6 24.2-16.4 24.2-31V215l-137 39.1c-4.3 1.3-8.8 1.9-13.3 1.9zm212.6-112.2L586.8 41c-3.1-6.2-9.8-9.8-16.7-8.9L320 64l91.7 152.1c3.8 6.3 11.4 9.3 18.5 7.3l197.9-56.5c9.9-2.9 14.7-13.9 10.2-23.1zM53.2 41L1.7 143.8c-4.6 9.2.3 20.2 10.1 23l197.9 56.5c7.1 2 14.7-1 18.5-7.3L320 64 69.8 32.1c-6.9-.8-13.5 2.7-16.6 8.9z"/></svg> [**tidygraph**](https://CRAN.R-project.org/package=tidygraph)

- <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M425.7 256c-16.9 0-32.8-9-41.4-23.4L320 126l-64.2 106.6c-8.7 14.5-24.6 23.5-41.5 23.5-4.5 0-9-.6-13.3-1.9L64 215v178c0 14.7 10 27.5 24.2 31l216.2 54.1c10.2 2.5 20.9 2.5 31 0L551.8 424c14.2-3.6 24.2-16.4 24.2-31V215l-137 39.1c-4.3 1.3-8.8 1.9-13.3 1.9zm212.6-112.2L586.8 41c-3.1-6.2-9.8-9.8-16.7-8.9L320 64l91.7 152.1c3.8 6.3 11.4 9.3 18.5 7.3l197.9-56.5c9.9-2.9 14.7-13.9 10.2-23.1zM53.2 41L1.7 143.8c-4.6 9.2.3 20.2 10.1 23l197.9 56.5c7.1 2 14.7-1 18.5-7.3L320 64 69.8 32.1c-6.9-.8-13.5 2.7-16.6 8.9z"/></svg> [**ggraph**](https://CRAN.R-project.org/package=ggraph)

---

---

What do you think this code will do?

```r
edgelist1 <- 
  read_csv("data/meteakca_edgelist.csv", 
           show_col_types = FALSE) %>%
  mutate(group = week,
         week = substr(week, 1, 1)
         ) %>% 
  group_by(from, to) %>% 
  mutate(weight = n())
glimpse(edgelist1)
```

---

Let's see!

```
## Rows: 182
## Columns: 3
## Groups: from, to [182]
## $ from   <chr> "AL", "VW", "RN", "CH", "KW", "KJ", "Kwi", "AM", "KW", "KW", "Z…
## $ to     <chr> "RN", "RN", "KW", "RN", "CH", "CH", "CH", "KW", "AM", "AL", "AL…
## $ weight <int> 1, 1, 1, 1, 2, 3, 1, 2, 2, 2, 1, 2, 1, 1, 1, 3, 1, 2, 1, 1, 1, …
```

---

```r
head(edgelist1, 15)
```

```
## # A tibble: 15 × 3
## # Groups:   from, to [15]
##    from  to    weight
##    <chr> <chr>  <int>
##  1 AL    RN         1
##  2 VW    RN         1
##  3 RN    KW         1
##  4 CH    RN         1
##  5 KW    CH         2
##  6 KJ    CH         3
##  7 Kwi   CH         1
##  8 AM    KW         2
##  9 KW    AM         2
## 10 KW    AL         2
## 11 ZT    AL         1
## 12 LW    AL         2
## 13 RS    AL         1
## 14 SP    AL         1
## 15 JK    AL         1
```

---

```r
graph1 <- tidygraph::as_tbl_graph(edgelist1)
```

- **Order:** `igraph::gorder(graph1)` = 23

- **Size:** `igraph::gsize(graph1)` = 182

- **Diameter:** `igraph::diameter(graph1)` = 4

- **Density:** `igraph::edge_density(graph1)` = 0.36

- **Node degree:** `igraph::degree(graph1, mode = 'all')` = 15

- *In-degree:* `igraph::degree(graph1, mode = 'in')` = 8

- *Out-degree:* `igraph::degree(graph1, mode = 'out')` = 8

- **Reciprocity:** `igraph::reciprocity(graph1)` = 0.663

- **Transitivity:** `igraph::transitivity(graph1)` = 0.653

---

What do you think this code will do?

```r
sociogram1 <-
  graph1 %>%
  mutate(popularity = centrality_degree(mode = 'in')) %>%
  ggraph(layout = 'kk') +
  geom_edge_arc(alpha = .2, 
                width = .5, 
                strength = .5,
                color = 'steelblue'
  ) +
  geom_node_point(alpha = .4, aes(size = popularity)) +
  scale_size(range = c(1,10)) +
  theme_wsj() + 
  scale_colour_wsj("colors6") +
  theme(axis.line=element_blank(),
        axis.text.x=element_blank(), axis.text.y=element_blank(),
        axis.ticks.x =element_blank(), axis.ticks.y =element_blank(),
        axis.title.x=element_blank(), axis.title.y=element_blank(),
        panel.background=element_blank(), panel.border=element_blank(),
        panel.grid.major=element_blank(), panel.grid.minor=element_blank())
```

---

Let's see!

---

### <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M192 160h32V32h-32c-35.35 0-64 28.65-64 64s28.65 64 64 64zM0 416c0 35.35 28.65 64 64 64h32V352H64c-35.35 0-64 28.65-64 64zm337.46-128c-34.91 0-76.16 13.12-104.73 32-24.79 16.38-44.52 32-104.73 32v128l57.53 15.97c26.21 7.28 53.01 13.12 80.31 15.05 32.69 2.31 65.6.67 97.58-6.2C472.9 481.3 512 429.22 512 384c0-64-84.18-96-174.54-96zM491.42 7.19C459.44.32 426.53-1.33 393.84.99c-27.3 1.93-54.1 7.77-80.31 15.04L256 32v128c60.2 0 79.94 15.62 104.73 32 28.57 18.88 69.82 32 104.73 32C555.82 224 640 192 640 128c0-45.22-39.1-97.3-148.58-120.81z"/></svg> **Next step:** Look at the network by group or by week

---

---

**Article:** [A social network perspective on peer supported learning in MOOCs for educators ](http://www.irrodl.org/index.php/irrodl/article/view/1852) (Kellogg, Booth, & Oliver, 2014)

---

**Data source:** [Massively Open Online Course for Educators (MOOC-Ed) network dataset](https://dataverse.harvard.edu/dataset.xhtml;jsessionid=9ad052693563b29056a88d490182?persistentId=doi%3A10.7910%2FDVN%2FZZH3UB&version=&q=&fileTypeGroupFacet=&fileAccess=&fileSortField=name&fileSortOrder=desc)]

---

---

What do you think this code will do?

```r
edgelist2 <- 
  read_csv("data/DLT1 Edgelist.csv", 
           show_col_types = FALSE) %>%
  group_by(Sender, Receiver) %>% 
  mutate(Weight = n()) %>%
  ungroup() %>%
  relocate(Sender, Receiver, Weight)
  
glimpse(edgelist2)
```

---

Let's see!

```r
edgelist2 <- 
  read_csv("data/DLT1 Edgelist.csv", 
           show_col_types = FALSE) %>%
  group_by(Sender, Receiver) %>% 
  mutate(Weight = n()) %>%
  distinct(Sender, Receiver, Weight)
glimpse(edgelist2)
```

```
## Rows: 1,978
## Columns: 3
## Groups: Sender, Receiver [1,978]
## $ Sender   <dbl> 360, 356, 344, 392, 219, 318, 4, 355, 355, 310, 248, 150, 19,…
## $ Receiver <dbl> 444, 444, 444, 444, 444, 444, 444, 356, 444, 444, 444, 444, 3…
## $ Weight   <int> 1, 2, 1, 1, 3, 2, 4, 1, 2, 1, 1, 2, 1, 1, 7, 1, 3, 1, 2, 1, 1…
```

---

```r
head(edgelist2, 15)
```

```
## # A tibble: 15 × 3
## # Groups:   Sender, Receiver [15]
##    Sender Receiver Weight
##     <dbl>    <dbl>  <int>
##  1    360      444      1
##  2    356      444      2
##  3    344      444      1
##  4    392      444      1
##  5    219      444      3
##  6    318      444      2
##  7      4      444      4
##  8    355      356      1
##  9    355      444      2
## 10    310      444      1
## 11    248      444      1
## 12    150      444      2
## 13     19      310      1
## 14    216       19      1
## 15     19      444      7
```

---

```r
graph2 <- tidygraph::as_tbl_graph(edgelist2)
```

- **Order:** `igraph::gorder(graph2)` = 442

- **Size:** `igraph::gsize(graph2)` = 1978

- **Diameter:** `igraph::diameter(graph2)` = 8

- **Density:** `igraph::edge_density(graph2)` = 0.01

- **Node degree:** `igraph::degree(graph2, mode = 'all')` = 4

- *In-degree:* `igraph::degree(graph2, mode = 'in')` = 1
  
  - *Out-degree:* `igraph::degree(graph2, mode = 'out')` = 2
  
- **Reciprocity:** `igraph::reciprocity(graph2)` = 0.219

- **Transitivity:** `igraph::transitivity(graph2)` = 0.089

---

```r
sociogram2 <-
  graph2 %>%
  mutate(popularity = centrality_degree(mode = 'in')) %>%
  ggraph(layout = 'kk') +
  geom_edge_arc(alpha = .2, 
                width = .5, 
                strength = .5,
                color = 'steelblue'
  ) +
  geom_node_point(alpha = .4, aes(size = popularity)) +
  scale_size(range = c(1,10)) +
  theme_wsj() + 
  scale_colour_wsj("colors6") +
  theme(axis.line=element_blank(),
        axis.text.x=element_blank(), axis.text.y=element_blank(),
        axis.ticks.x =element_blank(), axis.ticks.y =element_blank(),
        axis.title.x=element_blank(), axis.title.y=element_blank(),
        panel.background=element_blank(), panel.border=element_blank(),
        panel.grid.major=element_blank(), panel.grid.minor=element_blank())
```

---

---

---

**R package demo:** [Using tidytags with a conference hashtag](https://docs.ropensci.org/tidytags/articles/tidytags-with-conf-hashtags.html) (Staudt Willet & Rosenberg, 2022)

---

What do you think this code will do?

```r
google_sheet_url <- "18clYlQeJOc6W5QRuSlJ6_v3snqKJImFhU42bRkM_OX8"

google_sheet_data <- 
  google_sheet_url %>%
  tidytags::read_tags()

extra_tweet_data <- 
  tidytags::pull_tweet_data(google_sheet_data) %>%
  tidytags::process_tweets()

edgelist3 <- 
  tidytags::create_edgelist(extra_tweet_data) %>%
  group_by(sender, receiver) %>% 
  mutate(weight = n()) %>%
  distinct(sender, receiver, weight)
```

```r
write_csv(edgelist3, "data/tidytags-edgelist.csv")
```

---

Let's see!

```r
edgelist3 <-
  read_csv("data/tidytags-edgelist.csv", 
           show_col_types = FALSE) 
glimpse(edgelist3)
```

```
## Rows: 1,577
## Columns: 3
## $ sender   <chr> "lbukunAA", "bretsw", "bretsw", "bretsw", "AECT", "correia65"…
## $ receiver <chr> "lbukunAA", "bretsw", "eromerohall", "FakeBobGagne", "jmenglu…
## $ weight   <dbl> 1, 6, 3, 4, 2, 6, 6, 6, 6, 2, 7, 2, 15, 2, 2, 4, 18, 8, 2, 6,…
```

---

```r
head(edgelist3, 15)
```

```
## # A tibble: 15 × 3
##    sender         receiver        weight
##    <chr>          <chr>            <dbl>
##  1 lbukunAA       lbukunAA             1
##  2 bretsw         bretsw               6
##  3 bretsw         eromerohall          3
##  4 bretsw         FakeBobGagne         4
##  5 AECT           jmenglund03          2
##  6 correia65      AnnaRoseLeach        6
##  7 caranorth11    FredWBaker           6
##  8 PaulineMuljana tintinluo            6
##  9 PaulineMuljana AmyLomellini_ID      6
## 10 PaulineMuljana WEHSLibrary          2
## 11 PaulineMuljana robmoore3            7
## 12 PaulineMuljana soniastic            2
## 13 nicolapallitt  aectclt             15
## 14 nicolapallitt  eromerohall          2
## 15 michaelmgrant  FakeBobGagne         2
```

---

```r
graph3 <- tidygraph::as_tbl_graph(edgelist3)
```

- **Order:** `igraph::gorder(graph3)` = 511

- **Size:** `igraph::gsize(graph3)` = 1577

- **Diameter:** `igraph::diameter(graph3)` = 23

- **Density:** `igraph::edge_density(graph3)` = 0.006

- **Node degree:** `igraph::degree(graph3, mode = 'all')` = 2

- *In-degree:* `igraph::degree(graph3, mode = 'in')` = 1
  
  - *Out-degree:* `igraph::degree(graph3, mode = 'out')` = 1
  
- **Reciprocity:** `igraph::reciprocity(graph3)` = 0.263

- **Transitivity:** `igraph::transitivity(graph3)` = 0.09

---

```r
sociogram3 <-
  graph3 %>%
  mutate(popularity = centrality_degree(mode = 'in')) %>%
  ggraph(layout = 'kk') +
  geom_edge_arc(alpha = .2, 
                width = .5, 
                strength = .5,
                color = 'steelblue'
  ) +
  geom_node_point(alpha = .4, aes(size = popularity)) +
  scale_size(range = c(1,10)) +
  theme_wsj() + 
  scale_colour_wsj("colors6") +
  theme(axis.line=element_blank(),
        axis.text.x=element_blank(), axis.text.y=element_blank(),
        axis.ticks.x =element_blank(), axis.ticks.y =element_blank(),
        axis.title.x=element_blank(), axis.title.y=element_blank(),
        panel.background=element_blank(), panel.border=element_blank(),
        panel.grid.major=element_blank(), panel.grid.minor=element_blank())
```

---

---

---

---

<table class="table table-striped table-bordered" style="margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:center;"> Variable </th>
   <th style="text-align:center;"> Classroom </th>
   <th style="text-align:center;"> MOOC </th>
   <th style="text-align:center;"> Twitter </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:center;"> Order </td>
   <td style="text-align:center;"> 23.000 </td>
   <td style="text-align:center;"> 442.000 </td>
   <td style="text-align:center;"> 511.000 </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Size </td>
   <td style="text-align:center;"> 182.000 </td>
   <td style="text-align:center;"> 1978.000 </td>
   <td style="text-align:center;"> 1577.000 </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Diameter </td>
   <td style="text-align:center;"> 4.000 </td>
   <td style="text-align:center;"> 8.000 </td>
   <td style="text-align:center;"> 23.000 </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Density </td>
   <td style="text-align:center;"> 0.360 </td>
   <td style="text-align:center;"> 0.010 </td>
   <td style="text-align:center;"> 0.006 </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Median Degree </td>
   <td style="text-align:center;"> 15.000 </td>
   <td style="text-align:center;"> 4.000 </td>
   <td style="text-align:center;"> 2.000 </td>
  </tr>
  <tr>
   <td style="text-align:center;"> In-Degree </td>
   <td style="text-align:center;"> 8.000 </td>
   <td style="text-align:center;"> 1.000 </td>
   <td style="text-align:center;"> 1.000 </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Out-Degree </td>
   <td style="text-align:center;"> 8.000 </td>
   <td style="text-align:center;"> 2.000 </td>
   <td style="text-align:center;"> 1.000 </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Reciprocity </td>
   <td style="text-align:center;"> 0.663 </td>
   <td style="text-align:center;"> 0.219 </td>
   <td style="text-align:center;"> 0.263 </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Transitivity </td>
   <td style="text-align:center;"> 0.653 </td>
   <td style="text-align:center;"> 0.089 </td>
   <td style="text-align:center;"> 0.090 </td>
  </tr>
</tbody>
</table>

---

# <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M278.9 511.5l-61-17.7c-6.4-1.8-10-8.5-8.2-14.9L346.2 8.7c1.8-6.4 8.5-10 14.9-8.2l61 17.7c6.4 1.8 10 8.5 8.2 14.9L293.8 503.3c-1.9 6.4-8.5 10.1-14.9 8.2zm-114-112.2l43.5-46.4c4.6-4.9 4.3-12.7-.8-17.2L117 256l90.6-79.7c5.1-4.5 5.5-12.3.8-17.2l-43.5-46.4c-4.5-4.8-12.1-5.1-17-.5L3.8 247.2c-5.1 4.7-5.1 12.8 0 17.5l144.1 135.1c4.9 4.6 12.5 4.4 17-.5zm327.2.6l144.1-135.1c5.1-4.7 5.1-12.8 0-17.5L492.1 112.1c-4.8-4.5-12.4-4.3-17 .5L431.6 159c-4.6 4.9-4.3 12.7.8 17.2L523 256l-90.6 79.7c-5.1 4.5-5.5 12.3-.8 17.2l43.5 46.4c4.5 4.9 12.1 5.1 17 .6z"/></svg> <br><br> Try it out!

Hop over to [**Workspace 3**](workspace3.Rmd)

---

# <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M532 386.2c27.5-27.1 44-61.1 44-98.2 0-80-76.5-146.1-176.2-157.9C368.3 72.5 294.3 32 208 32 93.1 32 0 103.6 0 192c0 37 16.5 71 44 98.2-15.3 30.7-37.3 54.5-37.7 54.9-6.3 6.7-8.1 16.5-4.4 25 3.6 8.5 12 14 21.2 14 53.5 0 96.7-20.2 125.2-38.8 9.2 2.1 18.7 3.7 28.4 4.9C208.1 407.6 281.8 448 368 448c20.8 0 40.8-2.4 59.8-6.8C456.3 459.7 499.4 480 553 480c9.2 0 17.5-5.5 21.2-14 3.6-8.5 1.9-18.3-4.4-25-.4-.3-22.5-24.1-37.8-54.8zm-392.8-92.3L122.1 305c-14.1 9.1-28.5 16.3-43.1 21.4 2.7-4.7 5.4-9.7 8-14.8l15.5-31.1L77.7 256C64.2 242.6 48 220.7 48 192c0-60.7 73.3-112 160-112s160 51.3 160 112-73.3 112-160 112c-16.5 0-33-1.9-49-5.6l-19.8-4.5zM498.3 352l-24.7 24.4 15.5 31.1c2.6 5.1 5.3 10.1 8 14.8-14.6-5.1-29-12.3-43.1-21.4l-17.1-11.1-19.9 4.6c-16 3.7-32.5 5.6-49 5.6-54 0-102.2-20.1-131.3-49.7C338 339.5 416 272.9 416 192c0-3.4-.4-6.7-.7-10C479.7 196.5 528 238.8 528 288c0 28.7-16.2 50.6-29.7 64z"/></svg> <br><br> Quick Check In

**(Five minutes in groups, five minutes together)**

- What challenges did you encounter?
- What successes did you have?
- What questions remain?

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# <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M80 368H16a16 16 0 0 0-16 16v64a16 16 0 0 0 16 16h64a16 16 0 0 0 16-16v-64a16 16 0 0 0-16-16zm0-320H16A16 16 0 0 0 0 64v64a16 16 0 0 0 16 16h64a16 16 0 0 0 16-16V64a16 16 0 0 0-16-16zm0 160H16a16 16 0 0 0-16 16v64a16 16 0 0 0 16 16h64a16 16 0 0 0 16-16v-64a16 16 0 0 0-16-16zm416 176H176a16 16 0 0 0-16 16v32a16 16 0 0 0 16 16h320a16 16 0 0 0 16-16v-32a16 16 0 0 0-16-16zm0-320H176a16 16 0 0 0-16 16v32a16 16 0 0 0 16 16h320a16 16 0 0 0 16-16V80a16 16 0 0 0-16-16zm0 160H176a16 16 0 0 0-16 16v32a16 16 0 0 0 16 16h320a16 16 0 0 0 16-16v-32a16 16 0 0 0-16-16z"/></svg> <br><br> Appendix: <br> Helpful Resources <br> and Troubleshooting

---

# Resources

**Beginners:**
- [RStudio Beginners' Guide](https://education.rstudio.com/learn/beginner/)
- Book: [*Data Science in Education Using R*](https://datascienceineducation.com)
  - See [Chapter 12](https://datascienceineducation.com/c12.html) - Walkthrough 6: Exploring Relationships Using Social Network Analysis With Social Media Data
  - [Physical copy of DSIEUR](https://www.routledge.com/Data-Science-in-Education-Using-R/Estrellado-Freer-Mostipak-Rosenberg-Velasquez/p/book/9780367422257)
  - [Even more resources from DSIEUR](https://datascienceineducation.com/c18.html)

**Intermediates:**
- [RStudio Intermediates' Guide](https://education.rstudio.com/learn/intermediate/)
- [{tidytags} package notes](https://docs.ropensci.org/tidytags/index.html)
- Book: [*R for Data Science*](http://r4ds.had.co.nz/)

**Experts:**
- [RStudio Experts' Guide](https://education.rstudio.com/learn/expert/)
- Book: [*Learning Statistics with R*](https://learningstatisticswithr.com/)
- [*Data Science in Education Using R*](https://datascienceineducation.com)
  - See [Chapter 20.3 Appendix C](https://datascienceineducation.com/c20.html#c20c) - Social Network Influence and Selection Models
- SNA resources: [Dr. Ken Frank's website](https://sites.google.com/msu.edu/kenfrank/social-network-resources)

---

# Troubleshooting

- Try to find out what the specific problem is
  -  Identify what is *not* causing the problem
- "Unplug and plug it back in" - restart R; close and reopen R
- Seek out workshops and other learning opportunities
- Reach out to others! Sharing what is causing an issue can often help to clarify the problem
  - [RStudio Community forum](https://community.rstudio.com/) (highly recommended!)
  - Twitter hashtag: [#RStats](https://twitter.com/search?q=%23RStats&src=typeahead_click&f=live)
  - [Contact Bret!](http://bretsw.com)
- General strategies on learning more: [Chapter 17 of *Data Science in Education Using R*](https://datascienceineducation.com/c17.html)

---

# <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M192 160h32V32h-32c-35.35 0-64 28.65-64 64s28.65 64 64 64zM0 416c0 35.35 28.65 64 64 64h32V352H64c-35.35 0-64 28.65-64 64zm337.46-128c-34.91 0-76.16 13.12-104.73 32-24.79 16.38-44.52 32-104.73 32v128l57.53 15.97c26.21 7.28 53.01 13.12 80.31 15.05 32.69 2.31 65.6.67 97.58-6.2C472.9 481.3 512 429.22 512 384c0-64-84.18-96-174.54-96zM491.42 7.19C459.44.32 426.53-1.33 393.84.99c-27.3 1.93-54.1 7.77-80.31 15.04L256 32v128c60.2 0 79.94 15.62 104.73 32 28.57 18.88 69.82 32 104.73 32C555.82 224 640 192 640 128c0-45.22-39.1-97.3-148.58-120.81z"/></svg> <br><br> *Next up* <br> Part 4: <br> Network Inference

[`Part 4 slide deck here`](4-inference.html)