diff --git a/mapping_draft.Rmd b/mapping_draft.Rmd index 7faf929..5c8ac4a 100644 --- a/mapping_draft.Rmd +++ b/mapping_draft.Rmd @@ -3,12 +3,12 @@ title: "Mapping Environmental Action in Scotland" abstract: # thanks: "Replication files are available on the author's Github account (https://github.com/kidwellj/mapping_environmental_action). **Current version**: `r format(Sys.time(), '%B %d, %Y')` style: jeremy1 -author: “[Jeremy H. Kidwell](http://jeremykidwell.info)” +author: "[Jeremy H. Kidwell](http://jeremykidwell.info)" affiliation: University of Birmingham institute: University of Birmingham -e-mail: “[j.kidwell@bham.ac.uk](mailto:j.kidwell@bham.ac.uk)” -date: “`r Sys.Date()`” -bibliography: /Users/jeremy/Dropbox/bibtex/everything.bib +e-mail: "[j.kidwell@bham.ac.uk](mailto:j.kidwell@bham.ac.uk)" +date: "`r Sys.Date()`" +bibliography: ~/Dropbox/bibtex/everything.bib linkcolor: black geometry: margin=1in # fontfamily: mathpazo @@ -35,7 +35,7 @@ knitr::opts_chunk$set(fig.path='figures/', warning=FALSE, echo=FALSE, message=FA ```{r load_packages, include=FALSE} ## Default repo -setwd("/Users/jeremy/gits/mapping_environmental_action") +# setwd("/Users/jeremy/gits/mapping_environmental_action") # Set repository to be new standard, e.g. cloud server. # This will avoid a dialogue box if packages are to be installed for below on first run. @@ -57,11 +57,14 @@ require(RCurl) require(tibble) # using for grouped bar plot require(tidyr) # using for grouped bar plot require(dplyr) -library(reshape2) # using for grouped bar plot +require(plyr) +require(reshape2) # using for grouped bar plot require(pander) require(scales) require(sqldf) # using sqldf to filter while loading very large data sets require(plotly) # allows for export of plots to dynamic web pages +require(grid) # for laying out of multiple tables +require(gridBase) # Set up local workspace: if (dir.exists("data") == FALSE) { @@ -256,7 +259,7 @@ admin_lev2$permaculture_percent<- prop.table(admin_lev2$permaculture_count) admin_lev1_pop <- read.csv("./data/scotland_admin_2011pop.csv") admin_lev1 <- merge(x=admin_lev1, y=admin_lev1_pop, by.x = "code", by.y = "CODE") -admin_lev1$ecs_count_popnorm <- admin_lev1$ecs_count / admin_lev1$X2011_pop +admin_lev1$ecs_count_popnorm <- admin_lev1$ecs_count / as.numeric(admin_lev1$X2011_pop) admin_lev1$pop_percent<- prop.table(as.numeric(admin_lev1$X2011_pop)) admin_lev1$pow_count <- poly.counts(pow_pointX,admin_lev1) admin_lev1$ecs_count_pownorm <- admin_lev1$ecs_count / admin_lev1$pow_count @@ -270,33 +273,94 @@ Perhaps the first important question to ask of these groups is, where are they? (*TODO: need to implement*) Though there are too few eco-congregations and transition groups for a numerically significant representation in any of the intermediate geographies, mapping the concentration of sites by agricultural parishes allows for a more granular visual and I include this for comparison sake. Note, for the sake of a more accurate visual communication, we have also marked out areas of Scotland that are uninhabited with hash marks on the map of agricultural parishes. (*TODO: this will be done in the final draft, once I get my image masking fixed!*).[^15571030] -```{r admin_ecs_choropleth} -# TODO: -# 1. Need to augment plots to display in 2x2 plots (side-by-side comparison -# with column 1 normalised by population, i.e. admin_lev1_pop; column 2 normalised by church counts. -# Row 1 plot using polygons from admin_lev1 and row 2 plot using ploygons from admin_lev2 -# 2. Need to clip choropleth polygons to buildings shapefile +```{r plot_admin_ecs_choropleth} +# TODO: Need to clip choropleth polygons to buildings shapefile -# tidy data to select just ecs_count data -# admin_lev1_gathered <- gather(admin_lev1_sf, value="number", ecs_count) +# Prepare admin_lev1 for tidyr and reinsert dropped columns +names(admin_lev1)[names(admin_lev1) == "newcode"] <- "id" +admin_lev1@data$id <- as.integer(rownames(admin_lev1@data)) +admin_lev1@data$id <- admin_lev1@data$id-1 +admin_lev1_fortified <- tidy(admin_lev1) +admin_lev1_fortified <- join(admin_lev1_fortified,admin_lev1@data, by="id") -# plot simple choropleth map using ECS data points, as here: https://www.r-graph-gallery.com/327-chloropleth-map-from-geojson-with-ggplot2/ +# Draw initial choropleth map of ECS concentration (using sp, rather than sf data) +# Note: some ideas taken from here: https://unconj.ca/blog/choropleth-maps-with-r-and-ggplot2.html +ggplot() + + geom_polygon(aes(x = long, y = lat, group = group, + fill = cut_number(admin_lev1_fortified$ecs_count, 5)), + data = admin_lev1_fortified, + colour = 'black', + alpha = .7, + size = .3) + + viridis::scale_fill_viridis(discrete = TRUE) + + labs(x = NULL, y = NULL, fill = "Groups", + title = "Figure 1", + subtitle="Concentration of ECS groups", + caption = paste("Jeremy H. Kidwell :: jeremykidwell.info", + "Data: UK Data Service (OGL) & Jeremy H. Kidwell", + "You may redistribute this graphic under the terms of the CC-by-SA 4.0 license.", + sep = "\n")) + + theme_void() + + theme(text = element_text(family = "Arial Narrow", size = 8), + plot.title = element_text(size = 12, face = "bold"), + plot.margin = unit(c(0, 0.25, 0.0, 0.25), "in"), + panel.border = element_rect(fill = NA, colour = "#cccccc"), + legend.text = element_text(size = 8), + legend.position = c(0.9, 0.25)) -admin_lev1_fortified <- fortify(admin_lev1) -# TODO: resolve issues with dropped columns - note https://stackoverflow.com/questions/22096787/how-keep-information-from-shapefile-after-fortify -admin_lev1_fortified <- join(admin_lev1_fortified,admin_lev1@data$ecs_count, by="id") +# Save plot to PDF +ggsave("admin1_choropleth_ecs.pdf") -# draw map using sp data -ggplot() + geom_polygon(aes(x = long, y = lat, group = group), data = admin_lev1_fortified, colour = 'black', fill = admin_lev1_fortified$ecs_count, alpha = .4, size = .3) + labs(title = "Figure 1", subtitle="Concentration of ECS groups", fill = "Groups") +# Plot out first figure with normalised data: +fig2 <- ggplot() + + geom_polygon(aes(x = long, y = lat, group = group, + fill = cut_number(admin_lev1_fortified$ecs_count_pownorm, 5)), + data = admin_lev1_fortified, + colour = 'black', + alpha = .7, + size = .3) + + viridis::scale_fill_viridis(discrete = TRUE) + + labs(x = NULL, y = NULL, fill = "Groups", + title = "Figure 2", + subtitle="Concentration of ECS groups, data normalised by places of worship", + caption = paste("Jeremy H. Kidwell :: jeremykidwell.info", + "Data: UK Data Service (OGL) & Jeremy H. Kidwell", + "You may redistribute this graphic under the terms of the CC-by-SA 4.0 license.", + sep = "\n")) + + theme_void() + + theme(text = element_text(family = "Arial Narrow", size = 8), + plot.title = element_text(size = 12, face = "bold"), + plot.margin = unit(c(0, 0.25, 0.0, 0.25), "in"), + panel.border = element_rect(fill = NA, colour = "#cccccc"), + legend.text = element_text(size = 8), + legend.position = c(0.9, 0.25)) -# save plot to PDF (for testing) -ggsave("figures/admin1_choropleth_ecs.pdf") +# TODO: Need to sort out why error: "Insufficient data values to produce 5 bins." +# Plot out second figure with normalised data: +fig3 <- ggplot() + + geom_polygon(aes(x = long, y = lat, group = group, + fill = cut_number(admin_lev1_fortified$ecs_count_popnorm, 5)), + data = admin_lev1_fortified, + colour = 'black', + alpha = .7, + size = .3) + + viridis::scale_fill_viridis(discrete = TRUE) + + labs(x = NULL, y = NULL, fill = "Groups", + title = "Figure 3", + subtitle="Concentration of ECS groups, data normalised by population", + caption = paste("Jeremy H. Kidwell :: jeremykidwell.info", + "Data: UK Data Service (OGL) & Jeremy H. Kidwell", + "You may redistribute this graphic under the terms of the CC-by-SA 4.0 license.", + sep = "\n")) + + theme_void() + + theme(text = element_text(family = "Arial Narrow", size = 8), + plot.title = element_text(size = 12, face = "bold"), + plot.margin = unit(c(0, 0.25, 0.0, 0.25), "in"), + panel.border = element_rect(fill = NA, colour = "#cccccc"), + legend.text = element_text(size = 8), + legend.position = c(0.9, 0.25)) -fig2 <- ggplot() + geom_polygon(aes(x = long, y = lat, group = group), data = admin_lev1_fortified, colour = 'black', fill = 'ecs_count_pownorm', alpha = .4, size = .3) + labs(title = "Figure 2", subtitle="Concentration of ECS groups", fill = "Groups") - -fig3 <- ggplot() + geom_polygon(aes(x = long, y = lat, group = group), data = admin_lev1_fortified, colour = 'black', fill = 'ecs_count_popnorm', alpha = .4, size = .3) + labs("Figure 3", subtitle="Concentration of ECS groups", fill = "Groups") - -pdf("polishing-layout.pdf", width = 8, height = 6) +pdf("normalised_ecs.pdf", width = 8, height = 6) grid.newpage() pushViewport(viewport(layout = grid.layout(1, 2))) @@ -341,6 +405,29 @@ ggplot(admin_gathered, aes(fill=group_type, y=number, x=name)) + geom_bar(positi fig5 <- ggplot() + geom_polygon(aes(x = long, y = lat, group = group), data = admin_lev1_fortified, colour = 'black', fill = 'ecs_count', alpha = .4, size = .3) geom_point( data=ecs, aes(x=long, y=lat)) + labs(title = "Figure 5", subtitle="Concentration of ECS groups", fill = "Groups") +fig2 <- ggplot() + + geom_polygon(aes(x = long, y = lat, group = group, + fill = cut_number(admin_lev1_fortified$ecs_count_pownorm, 5)), + data = admin_lev1_fortified, + colour = 'black', + alpha = .7, + size = .3) + + viridis::scale_fill_viridis(discrete = TRUE) + + labs(x = NULL, y = NULL, fill = "Groups", + title = "Figure 2", + subtitle="Concentration of ECS groups, data normalised by places of worship", + caption = paste("Jeremy H. Kidwell :: jeremykidwell.info", + "Data: UK Data Service (OGL) & Jeremy H. Kidwell", + "You may redistribute this graphic under the terms of the CC-by-SA 4.0 license.", + sep = "\n")) + + theme_void() + + theme(text = element_text(family = "Arial Narrow", size = 8), + plot.title = element_text(size = 12, face = "bold"), + plot.margin = unit(c(0, 0.25, 0.0, 0.25), "in"), + panel.border = element_rect(fill = NA, colour = "#cccccc"), + legend.text = element_text(size = 8), + legend.position = c(0.9, 0.25)) + fig6 <- ggplot() + geom_polygon(aes(x = long, y = lat, group = group), data = admin_lev1_fortified, colour = 'black', fill = 'transition_count', alpha = .4, size = .3) geom_point( data=transition, aes(x=long, y=lat)) + labs(title = "Figure 6", subtitle="Concentration of Transition groups", fill = "Groups") fig7 <- ggplot() + geom_polygon(aes(x = long, y = lat, group = group), data = admin_lev1_fortified, colour = 'black', fill = 'dtas_count', alpha = .4, size = .3) geom_point( data=dtas, aes(x=long, y=lat)) + labs("Figure 7", subtitle="Concentration of DTAS groups", fill = "Groups") @@ -399,4 +486,4 @@ pander(as_data_frame(admin_lev1[,c(3,5,7,11,13)])) [^158261210]:From http://www.forthenvironmentlink.org, accessed 12 July 2015. [^1554162]:From the Transition map key, "Green pins are 'official' groups Blue pins are active communities who are connected to the Scottish Transition network Yellow pins show interest in this area" -[^15571030]:This was calculated by calculating a 10m wide footprint for every postcode in Scotland, areas which are not within 10m of a postcode (as of May 2014) are counted as uninhabited. +[^15571030]:This was calculated by calculating a 10m wide footprint for every postcode in Scotland, areas which are not within 10m of a postcode (as of May 2014) are counted as uninhabited. \ No newline at end of file