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fixed choropleth plots

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Jeremy Kidwell 2019-01-29 16:51:19 +00:00
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mapping_draft.Rmd
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@ -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.