tidied up urbanrural data

mapping_draft.Rmd

@@ -451,10 +451,6 @@ urbanrural$permaculture_percent<- prop.table(urbanrural$permaculture_count)
 
 # Create dataframe for analysis
 urbanrural_df<-data.frame(urbanrural)
-
-# Create bar chart
-# TODO: use ggplot here to generate stacked bar chart showing representation across 8 urban/rural bands from urbanrural_df
-# TODO: generate map based on urbanrural
 ```
 
 Rather than bifurcate congregations into an urban/rural dichotomy, for this study we used the Scottish Government's eight-point remoteness scale to categorise eco-congregations along a spectrum of highly populated to remote areas. This 8-fold scale (calculated biennially) offers a more nuanced measurement that combines measurements of remoteness and population along the following lines:
@@ -473,6 +469,10 @@ The key question which this analysis seeks to answer is whether ECS, or the othe
 Of all the groups surveyed in this study, Eco-Congregation Scotland is the most heavily concentrated in large urban areas (33.53%), exceeding by almost 50% the rate for all places of worship (22.96% in large urban areas). Transition is a much more modest 20% and development trusts a bit lower at 15%. It is interesting to note that the rate of ECS concentration in these large urban areas matches the level of overall population distribution (34.5%). On the other end of the scale, Eco-Congregation Scotland is the least concentrated in remote rural areas (with 3.93% on level 7 and 5.44% on level 8 on the urban-rural scale), though again, they correlate roughly to the general population distribution (3.2% and 2.9% respectively). Places of worship outpace both the population of Scotland and the footprint of Eco-Congregation Scotland, with 14.98% in very remote rural areas, but this is exceeded by transition at 16.47% and both by Scottish community development trusts at 32.14%. So while Eco-Congregation Scotland correlates roughly with Scottish population distribution across the urban-rural scale, it has a considerably more urban profile than either of the other two groups surveyed.
 
 ```{r}
+# Create bar chart
+# TODO: use ggplot here to generate stacked bar chart showing representation across 8 urban/rural bands from urbanrural_df
+# TODO: generate map based on urbanrural
+
 var01 <- admin_lev1$ecs_count
 bins <- unique(quantile(var01, seq(0,1,length.out=30)))
 admin_lev1$binId01 <- findInterval(var01, bins)