updates to yaml and added notes for plots

README.md

@@ -4,7 +4,7 @@
 
 If you're new to github and reproducible research, welcome! It's nice to have you here. Github is ordinarily a place where software developers working on open source software projects deposit their code as they write software collaboratively. However, in recent years a number of scholarly researchers, especially people working on research which involves a digital component (including me!) have begun to deposit their papers in these same software repositories. The idea here is that you can download all of the source-code and data used in this paper alongside the actual text, run it yourself and ["reproduce" the results](http://kbroman.org/steps2rr/). This can serve as a useful safeguard, a layer of research transparency, and a cool teaching tool for other persons interested in doing similar work.
 
-Eschewing proprietary, expensive and unreliable software like Microsoft Word, I write in a combination of two languages: (1) [Markdown](https://en.wikipedia.org/wiki/Markdown) which is intended to be as close as possible to plain text while still allowing for things like boldfaced type, headings and footnotes; and (2) a programming language called [R](https://en.wikipedia.org/wiki/R_(programming_language)) to do all the data analysis. R is an object oriented language which was specifically designed for statistical analysis. It's also great fun to tinker with. As you look through this paper, you'll see that R code is integrated into the text of the document. This is indicated by a series of three backticks (```).
+Eschewing proprietary, expensive and unreliable software like Microsoft Word, I write in a combination of two languages: (1) [Markdown](https://en.wikipedia.org/wiki/Markdown) which is intended to be as close as possible to plain text while still allowing for things like boldfaced type, headings and footnotes; and (2) a programming language called [R](https://en.wikipedia.org/wiki/R_(programming_language)) to do all the data analysis. R is an object oriented language which was specifically designed for statistical analysis. It's also great fun to tinker with. As you look through this paper, you'll see that R code is integrated into the text of the document. This is indicated by a series of three backticks (```). There is a formal specification now at a mature stage of development, which is RMarkdown. You can read semi-official specification [for this here](https://bookdown.org/yihui/rmarkdown/pdf-document.html).
 
 To read a bit more on these things and start on your own path towards plain text reproducible research, I highly recommend:
 - Karl Broman's guide, "[Initial Steps Toward Reproducible Research](http://kbroman.org/steps2rr/)"

mapping_draft.Rmd

@@ -11,8 +11,18 @@ bibliography: /Users/jeremy/Dropbox/bibtex/everything.bib
 linkcolor: black
 fontsize: 12pt
 output:
+  html_document:
+    keep_md: true
+    toc: true
+    toc_depth: 2
+    number_sections: true
+    fig_caption: true
   pdf_document:
     keep_tex: true
+    number_sections: true
+    fig_caption: true
+    citation_package: natbib
+    latex_engine: xelatex
 ---
 
 ```{r setup, include=FALSE}
@@ -283,10 +293,15 @@ Perhaps the first important question to ask of these groups is, where are they?
 # Row 1 plot using polygons from admin_lev1 and row 2 plot using ploygons from admin_lev2
 # 3. Need to clip choropleth polygons to buildings shapefile
 
+# tidy data
 admin_lev1_gathered <- gather(admin_lev1_sf, value="number", ecs_count)
 
+# plot simple choropleth, as here: https://www.r-graph-gallery.com/327-chloropleth-map-from-geojson-with-ggplot2/
+
 myplot <- ggplot() + geom_sf(data = admin_lev1_gathered) + geom_point(data=as.data.frame(ecs), aes(x=X, y=Y))
 
+# plot as 
+
 ggsave("figures/admin_choropleth_ecs.pdf")
 ```
 
@@ -325,6 +340,8 @@ ggplot(admin.df_gathered, aes(fill=group_type, y=number, x=name)) +
   geom_bar() + 
   coord_flip()
   
+# add bubbleplot on map, as here: https://www.r-graph-gallery.com/330-bubble-map-with-ggplot2/ or here: https://www.r-graph-gallery.com/177-map-with-proportional-symbols/
+
 ```
 
 We can compare the representation in these various regions against our comparison groups to see how other community-based organisations cluster in Scottish administrative districts. Here there are some significant contrasts. Scottish Community Development trusts are most intensely concentrated in the Highlands and Argyll & Bute. But, this is consistent with all the other categories, Eco-Congregations, Places of Worship, and dtass are all over-represented in this area, varying only by the degree. Edinburgh is different, here we find that Eco-Congregations and Transition projects are over-represented, while dtass are under-represented. Finally, the highlands are another strong contrast, here we find a very strong over-representation by transition towns and dtass while the representation of Eco-Congregations is relatively close to the population share for that area. The two areas of greatest contrast for Eco-Congregations from the other groups are unsurprising, Edinburgh is the location of the ECS offices, while Stirling is the area in which ECS first began (see Appendix B for full data). 
@@ -335,6 +352,7 @@ We can compare the representation in these various regions against our compariso
 
 [Figure 4, normalised by PointX data; also including agricultural parishes etc. as above]
 
+# plot as chlorogram: https://www.r-graph-gallery.com/331-basic-cartogram/
 # Appendix A
 
 ```{r pander_admin_table}