minor changes

Author: gulinan

intro_networks.Rmd

@@ -124,7 +124,7 @@ knitr::include_graphics('images/example.png')
 - "Traditional modeling approaches in this realm have treated **workers as narrowly linked to specific jobs**. 
 - "In the real world, however, **jobs and sectors are linked**. <span style="color:red">Displaced workers can often transition to another job or sector requiring similar skills.</span>"
 - "In this way, **job markets are much like ecosystems, where organisms are linked in a complex web of relationships**.
-- "The authors modeled the relationships between jobs in cities across the United States. They predicted that **cities with jobs connected by overlapping skills** and geography would fare better in the face of economic shock than those without such networks."
+- "The authors modeled the relationships between jobs in cities across the United States. They predicted that **cities with jobs connected by overlapping skills and geography** would fare better in the face of economic shock than those without such networks."
 - "They found that while cities of similar sizes would be affected similarly in the beginning phases of automation shocks, those with **well-connected job networks** would provide **better opportunities** for displaced workers to find other jobs." 
 - "This provides a buffer against widespread unemployment, and in some cases even leads to **more jobs** being created in the aftermath of the initial automation shock."
 - A city like Burlington, Vermont, where job connectivity is high, would fare much better than Bloomington, Indiana, a similar-sized city where job connectivity is low.

intro_networks.html

@@ -115,7 +115,7 @@
 - "Traditional modeling approaches in this realm have treated **workers as narrowly linked to specific jobs**. 
 - "In the real world, however, **jobs and sectors are linked**. <span style="color:red">Displaced workers can often transition to another job or sector requiring similar skills.</span>"
 - "In this way, **job markets are much like ecosystems, where organisms are linked in a complex web of relationships**.
-- "The authors modeled the relationships between jobs in cities across the United States. They predicted that **cities with jobs connected by overlapping skills** and geography would fare better in the face of economic shock than those without such networks."
+- "The authors modeled the relationships between jobs in cities across the United States. They predicted that **cities with jobs connected by overlapping skills and geography** would fare better in the face of economic shock than those without such networks."
 - "They found that while cities of similar sizes would be affected similarly in the beginning phases of automation shocks, those with **well-connected job networks** would provide **better opportunities** for displaced workers to find other jobs." 
 - "This provides a buffer against widespread unemployment, and in some cases even leads to **more jobs** being created in the aftermath of the initial automation shock."
 - A city like Burlington, Vermont, where job connectivity is high, would fare much better than Bloomington, Indiana, a similar-sized city where job connectivity is low.

intro_tidygraph_ggraph.Rmd

@@ -499,9 +499,8 @@ trader_1 <- bitcoin_tbl_graph %>%
 
 ```{r}
 trader_1 %>% 
-  activate(nodes) %>% 
    as_tibble() %>%
-  View() 
+    View() 
 ```
 
 ---

intro_tidygraph_ggraph.html

@@ -614,8 +614,8 @@
 #sort the nodes by their degree (in descending form)
 node_degree <- bitcoin_tbl_graph %>% 
                 activate(nodes) %>% 
-                mutate(degree = centrality_degree()) %>% 
-                as_tibble() %>% 
+                mutate(degree = centrality_degree()) %>%  #returns degree column in an active node table
+                as_tibble() %>% #get node table
                 arrange(-degree)
 
 #View(node_degree) 
@@ -632,22 +632,49 @@
   activate(edges) %>% 
   filter(weight == 1) %>% #find who gets 1
   as_tibble() %>%
-  count(to) %>%  #count how many 1's received that person
-  View()
+  count(to) #%>%  #count how many 1's received that person
+  #View()
 ```
 
 ---
--  If we want to filter just edges from ID 34, we can use the `filter()` function from `dplyr` and create a new graph object with `as_tbl_graph()`:
+-  If we want to filter just edges from ID 1, we can use the `filter()` function from `dplyr` and create a new graph object with `as_tbl_graph()`:
 
 
 ```r
-trader_34 <- bitcoin_tbl_graph %>% 
+trader_1 <- bitcoin_tbl_graph %>% 
              activate(edges) %>% 
-             filter(from == 34) %>% 
+             filter(from == 1) %>% 
              as_tbl_graph() 
 #View(trader_34)
 ```
 
+
+```r
+trader_1 %>% 
+   as_tibble() #%>%
+```
+
+```
+#> # A tibble: 195 × 3
+#>     from    to weight
+#>    <int> <int>  <dbl>
+#>  1     1    41    0.1
+#>  2     1    12    0.1
+#>  3     1     6    0.9
+#>  4     1    11    0.2
+#>  5     1     2    0.3
+#>  6     1    26    0.2
+#>  7     1    31    0.1
+#>  8     1    29    0.2
+#>  9     1    45    0.1
+#> 10     1    20    0.6
+#> # … with 185 more rows
+```
+
+```r
+    #View() 
+```
+
 ---
 - Calculate the centrality of betwenness in the bitcoin network.
 
@@ -734,7 +761,7 @@
 ]
 
 .panel[.panel-name[Output]
-![](intro_tidygraph_ggraph_files/figure-html/unnamed-chunk-39-1.png)<!-- -->
+![](intro_tidygraph_ggraph_files/figure-html/unnamed-chunk-40-1.png)<!-- -->
 ]
 ]
 
@@ -769,7 +796,7 @@
 ]
 
 .panel[.panel-name[Output]
-![](intro_tidygraph_ggraph_files/figure-html/unnamed-chunk-41-1.png)<!-- -->
+![](intro_tidygraph_ggraph_files/figure-html/unnamed-chunk-42-1.png)<!-- -->
 ]
 ]
 
@@ -796,7 +823,7 @@
 ]
 
 .panel[.panel-name[Output]
-![](intro_tidygraph_ggraph_files/figure-html/unnamed-chunk-43-1.png)<!-- -->
+![](intro_tidygraph_ggraph_files/figure-html/unnamed-chunk-44-1.png)<!-- -->
 
 ]
 ]