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tidying code blocks
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@ -197,7 +197,9 @@ proj4string(dtas) <- proj4string(admin_lev1)
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permaculture <- read.csv("data/permaculture_scot-0.8.csv")
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coordinates(permaculture) <- c("X", "Y")
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proj4string(permaculture) <- proj4string(admin_lev1)
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```
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```{r process_admin_data}
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# TODO: Code below augments existing dataframes to run calculations and add columns with point counts per polygon,
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# percentages, and normalising data. I'm pretty sure this can all be done inline under create_admin_ecs_choropleth
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# code chunk without adding new columns to dataframes here, but how?!
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@ -352,7 +354,8 @@ We can compare the representation in these various regions against our compariso
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[Figure 4, normalised by PointX data; also including agricultural parishes etc. as above]
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# plot as chlorogram: https://www.r-graph-gallery.com/331-basic-cartogram/
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todo: plot as chlorogram: https://www.r-graph-gallery.com/331-basic-cartogram/
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# Appendix A
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```{r pander_admin_table}
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