average license holdings

R/license_networks.qmd

@@ -60,7 +60,7 @@ license_types %>%
   filter(!(str_starts(license_type, "mi"))) %>%
   filter(!(str_starts(license_type, "nb"))) %>%
   filter(!(str_starts(license_type, "p")))  %>%
-  filter(!license_type %in% c("st", "sts", "csw", "csws", "mws")) %>%
+  filter(!license_type %in% c("st", "sts", "csw", "csws", "mws", "mw")) %>%
   drop_na() -> license_types # the one NA here is sut 
 
 license_types %>% 
@@ -72,7 +72,8 @@ license_types %>%
 
 ```
 
-## Licenses Issued 
+## Licenses Issued
+
 Below are two plots depicting the total number of licenses issued per year and the number of unique license types issued per year
 
 ```{r}
@@ -86,23 +87,22 @@ Below are two plots depicting the total number of licenses issued per year and t
 
 ind_lic_portfolio %>% 
   select(license_year, license_types$license_type) %>%
-  pivot_longer(cols = 2:55, names_to = "license_type", values_to = "count") %>%
+  pivot_longer(cols = 2:56, names_to = "license_type", values_to = "count") %>%
   left_join(license_types) %>%
   group_by(license_year) %>%
   summarise(total = sum(count)) -> total_licenses
 
 ggplot(data = total_licenses)+
   geom_line(aes(x = license_year, y = total)) +
   ggtitle("Total Licenses Issued") +
-  xlab("Total") + ylab("Year") +
+  ylab("Total") + xlab("Year") +
   ylim(c(10000,20000)) +
   theme_gmri(plot.title = element_text(size = 8),
              axis.text  = element_text(size = 8),
              axis.title = element_text(size = 8))
 
 ```
 
-
 ```{r}
 #| label:   unique license types each year 
 #| echo:    false
@@ -114,7 +114,7 @@ ggplot(data = total_licenses)+
 
 ind_lic_portfolio %>% 
   select(license_year, license_types$license_type) %>%
-  pivot_longer(cols = 2:55, names_to = "license_type", values_to = "count") %>%
+  pivot_longer(cols = 2:56, names_to = "license_type", values_to = "count") %>%
   filter(!count == 0) %>% 
   select(license_year, license_type) %>% 
   distinct() %>%
@@ -169,7 +169,38 @@ license_time_series %>%
 
 gridExtra::marrangeGrob(license_time_series$time_series, layout_matrix = matrix(1:12, ncol = 3, nrow = 4, byrow = TRUE), top = NULL,  left = textGrob(
   expression(bold("Total Annual")), rot = 90, gp = gpar(col = "black", fontsize = 8)))
+```
+
+## Average license holdings
+Below are the summary statisitcs of the average number of licenses held by an individual per year.
+```{r}
+#| label:   average license holdings
+#| echo:    false
+#| message: false
+#| warning: false
+#| column:  page
+#| fig-width:  7
+#| fig-height: 5
+
+ind_lic_portfolio %>% 
+  select(license_year, license_types$license_type, landings_number) %>%
+  pivot_longer(cols = 2:56, names_to = "license_type", values_to = "count") %>%
+  left_join(license_types) %>%
+  group_by(license_year, landings_number) %>%
+  summarise(total = sum(count)) %>%
+  drop_na() -> average_licenses
 
+# summary(lm(total ~ license_year, data = average_licenses))
+
+gt(broom::glance(lm(total ~ license_year, data = average_licenses)))
+ 
+average_licenses %>% 
+  ggplot() +
+  stat_smooth(aes(x = license_year, y = total), method = "lm") +
+  ggtitle("Average number of licenses held per individual") +
+  theme_gmri(plot.title = element_text(size = 8),
+             axis.text  = element_text(size = 6),
+             axis.title = element_blank())
 ```
 
 ## Annual License Network Metrics
@@ -182,6 +213,7 @@ Important nodes tend to have more connections to other nodes. Highly connected n
 ```{r}
 #| label:      annual metrics
 #| echo:       false
+#| message:    false
 #| warning:    false
 #| column:     page
 #| fig-width:  9
@@ -237,8 +269,8 @@ degree_plots <- metrics_series %>%
 gridExtra::marrangeGrob(degree_plots$time_series, layout_matrix = matrix(1:15, ncol = 3, nrow = 5, byrow= TRUE), top = NULL, left = textGrob(
   expression(bold("Degree")), rot = 90, gp = gpar(col = "black", fontsize = 8)))
 ```
+### Betweeness
 
-### Betweeness 
 "\[*Betweenness*\] ranks the nodes based on the flow of connections through the network. Importance is demonstrated through high frequency of connection with multiple other nodes. Nodes with high levels of betweenness tend to serve as a bridge for multiple sets of other important nodes." [*Source*](https://bookdown.org/jdholster1/idsr/network-analysis.html#advanced-case-study)
 
 ```{r}
@@ -267,7 +299,6 @@ gridExtra::marrangeGrob(betweeness_plots$time_series, layout_matrix = matrix(1:1
   expression(bold("Degree")), rot = 90, gp = gpar(col = "black", fontsize = 8)))
 ```
 
-
 ## Annual Network Plots
 
 Density time series and network plots are available [here](https://carlylovas.github.io/mesg-permits/R/license_network_plots.html)