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Fig_03_ggridges_19_clusters.R
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Fig_03_ggridges_19_clusters.R
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# Spider graphs from univariate hotspots and multivariate clusters ---------------------------------
require(tidyverse)
require(ggplot2)
library(here)
require(raster)
#remotes::install_github("AllanCameron/geomtextpath")
setwd(here())
setwd('results/')
dfg <- read.csv("gstar.csv")
#18.24306 to 52.33333 and longitude from 75.98951 to 134.28917
dfradar <- dfg
dfg$tile <- row.names(dfg)
library(reshape2)
dfm <- melt(dfg[,c(todo,"tile", 'x', 'y')],
id.vars= c("tile", 'x', 'y'))
# Define selected tiles
dfmplot <- dfm
# Selection or regions for display ---------------------------------------------------------------------------
require(rnaturalearth)
worldmap <- ne_countries(scale = 'medium', returnclass = 'sf')
asia <- worldmap[worldmap$continent == 'Asia',]
target <- ne_countries(type = "countries", country = c('Bangladesh',
'Bhutan',
'Brunei',
'Cambodia',
'China',
'India',
'Indonesia',
'Laos',
'Malaysia',
'Myanmar',
'Nepal',
'Philippines',
'Singapore',
'Sri Lanka',
'Thailand',
'Timor-Leste',
'Vietnam'))
hosts_muylaert <- raster::raster('D:/OneDrive - Massey University/bat_non_bat/data/region/hosts_muylaert.tif')
bbox(hosts_muylaert)
ras_dom<-raster::raster(xmn=68.25, xmx= 141.0, ymn=-10.25, ymx=53.5,
crs="+proj=longlat +datum=WGS84 +no_defs ",
resolution=res(hosts_muylaert), vals=NA)
tofocus <- colnames( dfradar %>% dplyr::select(!c( 'ID', contains("95"))) )
dfgsub <- dfradar[tofocus]
str(dfgsub)
colnames(dfgsub[3:ncol(dfgsub)])
coordinates(dfgsub) <- ~ x + y
crs(dfgsub) <- "+proj=longlat +datum=WGS84 +no_defs "
colnames(dfgsub@data)
# G star values rasterized
pigg <- rasterize(dfgsub, ras_dom, field = c("pig_gilbert"), update = TRUE)
cattleg <- rasterize(dfgsub, ras_dom, field = c('cattle_gilbert'), update = TRUE)
hostsg <- rasterize(dfgsub, ras_dom, field = c('lincomb_hosts'), update = TRUE)
mammalg <- rasterize(dfgsub, ras_dom, field = c("mmb"), update = TRUE)
forest_transitiong <- rasterize(dfgsub, ras_dom, field = c('hewson_forest_transition_potential'), update = TRUE)
forest_qualityg <- rasterize(dfgsub, ras_dom, field = c('forest_integrity_grantham'), update = TRUE)
builtupg <- rasterize(dfgsub, ras_dom, field = c('builtup'), update = TRUE)
transg <- rasterize(dfgsub, ras_dom, field = c('trans'), update = TRUE)
pollutiong <- rasterize(dfgsub, ras_dom, field = c('pollution'), update = TRUE)
energyg <- rasterize(dfgsub, ras_dom, field = c('energy'), update = TRUE)
agriharvg <- rasterize(dfgsub, ras_dom, field = c('agriharv'), update = TRUE)
popg <- rasterize(dfgsub, ras_dom, field = c('pop_2020_worldpop'), update = TRUE)
traveltimeg <- rasterize(dfgsub, ras_dom, field = c('motor_travel_time_weiss'), update = TRUE)
namesg <- c('pigg', 'cattleg', 'hostsg', 'mammalg',
'forest_transitiong', 'forest_qualityg',
'builtupg','transg', 'pollutiong','energyg','agriharvg',
'popg','traveltimeg' )
stackg <- stack(pigg, cattleg, hostsg, mammalg,
forest_transitiong, forest_qualityg,
builtupg,transg, pollutiong, energyg, agriharvg,
popg, traveltimeg )
names(stackg) <- namesg
##############################################################################################################
# Zonal countries
testando <- raster::extract( stackg, target, fun=mean, na.rm=TRUE, df=TRUE, weights = FALSE, sp=TRUE)
head(testando)
require(sf)
test1 <- sf::st_as_sf(testando)
sf::st_crs(crs(test1))
tomeltg <- as.data.frame(test1)
# Removing travel time, pollution and transportation
namesg <- namesg[!namesg %in% c('transg','pollutiong','traveltimeg')]
mgg <- melt(tomeltg[,c(namesg, 'name')],
id.vars= c('name'))
mgg
# Radar plots
circlefull <- length(unique(mgg$variable)) + 1
unique(mgg$variable)
mggp <- mgg %>% mutate(variablelab = fct_recode(variable,
"Bat \n hosts" = "hostsg",
'Cattle' ="cattleg" ,
'Forest \n loss risk' = 'forest_transitiong',
'Forest \n quality' = 'forest_qualityg',
'Population' = 'popg',
'Access \n to healthcare' = 'traveltimeg',
'Mammals' = "mammalg",
'Pigs \n'="pigg",
'Energy \n'="energyg",
'Builtup \n'="builtupg",
'Pollution \n'="pollutiong",
'Transportation \n'="transg",
'Agriculture \n and harvest'="agriharvg" ))
max(mggp$value)
mggp %>% filter(name == 'Bangladesh')
# Straight
basez <- mggp %>% #filter(name != 'Bangladesh') %>%
ggplot(aes(x = variablelab, y = value, group = name)) +
facet_wrap(~name)+ #ncol
#geom_polygon(size = 1, alpha= 1, colour = 'gray15', fill = NA, lty = 3) +
geom_point(size = 2, shape = 18) + # colour='gray15'
ylim(-16.0, 20.0) + ggtitle("") +
annotate("rect", alpha = 0.4, ymin = -1.645, ymax = 1.645, xmin = 0, xmax = circlefull,
fill = "khaki")+
annotate("rect", alpha = 0.4, ymin = -Inf, ymax = -1.645, xmin = 0, xmax = circlefull,
fill = "royalblue3")+
annotate("rect", alpha = 0.4, ymin = 1.645, ymax = 20, xmin = 0, xmax = circlefull,
fill = "violetred4",
colour = NA)+
#geomtextpath::coord_curvedpolar()+ ####coord_polar() +
theme_light()+
labs(x = 'Components', y = 'G*i', fill = 'Region', colour = 'Region' ) +
theme(strip.background = element_rect(fill = "grey32"),
legend.position = "right",
text = element_text(size = 14),
axis.text.x = element_text(angle = 45, hjust = 0.99, size=9))
basez
# Hotspot variation plot (could be radar plot or flat)
baseradar <- mggp %>% #filter(name != 'Bangladesh') %>%
ggplot(aes(x = variablelab, y = value, group = name))+ #, colour = tile, fill = tile)) +
facet_wrap(~name)+ #ncol
geom_polygon(size = 1, alpha= 1, colour = 'gray15', fill = NA, lty = 3) +
geom_point(size = 2, shape = 18) + # colour='gray15'
ylim(-16.0, 20.0) + ggtitle("") +
annotate("rect", alpha = 0.4, ymin = -1.645, ymax = 1.645, xmin = 0, xmax = circlefull,
fill = "khaki")+
annotate("rect", alpha = 0.4, ymin = -Inf, ymax = -1.645, xmin = 0, xmax = circlefull,
fill = "royalblue3")+
annotate("rect", alpha = 0.4, ymin = 1.645, ymax = 20, xmin = 0, xmax = circlefull,
fill = "violetred4",
colour = NA)+
coord_polar() + #geomtextpath::coord_curvedpolar()+ # For curving labels
theme_light()+
labs(x = 'Components', y = 'G*i', fill = 'Region', colour = 'Region' ) +
theme(strip.background = element_rect(fill = "grey32"),
legend.position = "right",
text = element_text(size = 14), axis.text.x = element_text(angle = 0, hjust = 0.99, size=9))
baseradar
# Exporting
ggsave('hotspots_coord_polar.png',
plot = baseradar,
dpi = 400,
width = 13,
height = 14,
limitsize = TRUE)
#-------------------------------------------------------------------------
# Final plot
setwd('skater_optimal_cluster_size_19/')
clusters <- read.csv("clusters_rgeoda_c19.csv")
clusters$bovliv <- NULL
clusters$mammals_iucn_mode <- NULL
setwd(here())
setwd('results/')
# -------------------------------------------------------------------------------
# Zonal with multivariate clusters from skater and univariate hotspot rasters
# Making raster from cluster
dcc <- clusters %>% dplyr::select(c('x', 'y', 'cluster'))
coordinates(dcc) <- ~ x + y
crs(dcc) <- "+proj=longlat +datum=WGS84 +no_defs "
dcc
craster <- rasterize(dcc, ras_dom, field = c('cluster'), update = TRUE)
plot(craster)
zcluster <- as.data.frame(raster::zonal(stackg, craster, 'mean'))
head(zcluster)
zclusterm <- melt(zcluster , id.vars= c('zone'))
head(zclusterm)
zclustermlab <- zclusterm %>% mutate(variablelab = fct_recode(variable,
"Bat hosts" = "hostsg", #\n
'Cattle' ="cattleg" ,
'Forest loss risk' = 'forest_transitiong', #\n
'Forest quality' = 'forest_qualityg', #\n
'Population' = 'popg',
'Access to healthcare' = 'traveltimeg', #\n
'Mammals' = "mammalg",
'Pigs'="pigg",
'Energy'="energyg",
'Builtup'="builtupg",
'Pollution'="pollutiong",
'Transportation'="transg",
'Agriculture and harvest'="agriharvg" )) #\n
pal <- wesanderson::wes_palette("Moonrise3",
length(unique(zclustermlab$zone)),
type = "continuous")
# Removing trans, pollution and travel time
zclustermlab <- zclustermlab[!zclustermlab$variable %in% c("transg", "pollutiong", "traveltimeg"),]
baseradarz <- zclustermlab %>%
ggplot(aes(x = variablelab, y = value, group = zone))+ #, colour = tile, fill = tile)) +
facet_wrap(~zone, nrow = 5, ncol=4)+
#geom_polygon(size = 1, alpha= 1, colour = 'gray15', fill = NA, lty = 3) +
geom_point(size = 2 ) + # colour='gray15' #shape = 18
ylim(-20.0, 20.0) + ggtitle("") +
annotate("rect", alpha = 0.4, ymin = -1.645, ymax = 1.645, xmin = 0, xmax = circlefull,
fill = "khaki")+
annotate("rect", alpha = 0.4, ymin = -20 , ymax = -1.645, xmin = 0, xmax = circlefull,#-Inf
fill = "royalblue3")+
annotate("rect", alpha = 0.4, ymin = 1.645, ymax = 20, xmin = 0, xmax = circlefull,
fill = "violetred4", colour = NA)+
theme_light()+
coord_flip()+
labs(x = 'Clusters', y = 'G*i', fill = 'Region', colour = 'Region' ) +
theme(strip.background = element_rect(fill ="grey32"),
strip.text.x = element_text(size=16),
legend.position = "right",
text = element_text(size = 14), #panel.spacing = unit(0.5, "lines"),
axis.text.x = element_text(angle = 45, hjust = 1.0, size=13))
baseradarz
##------------------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------------------
library(ggridges)
joyc <- melt(clusters , id.vars= c('x','y','cluster'))
joyc <- joyc[!joyc$variable %in% c("motor_travel_time_weiss",
"pollution", "trans"),]
unique(joyc$variable )
myorder <- rev(c('lincomb_hosts',
'mmb',
'pig_gilbert',
'cattle_gilbert',
'hewson_forest_transition_potential',
'forest_integrity_grantham',
'energy',
"builtup",
"agriharv",
'pop_2020_worldpop'))
joyc$variable <- factor(joyc$variable,
levels = myorder)
pal <- wesanderson::wes_palette("Moonrise3",
length(unique(zclustermlab$zone)),
type = "continuous")
cridges <- joyc %>%
mutate(variablelab = fct_recode(variable,
"Bat hosts" = "lincomb_hosts", #\n
'Cattle' ="cattle_gilbert" ,
'Forest loss risk' = 'hewson_forest_transition_potential', #\n
'Forest quality' = 'forest_integrity_grantham', #\n
'Population' = 'pop_2020_worldpop',
'Mammals' = "mmb",
'Pigs'="pig_gilbert",
'Energy'="energy",
'Builtup'="builtup",
'Agriculture and harvest'="agriharv" )) %>%
filter(variable != 'hosts_sanchez' & variable != 'hosts_muylaert') %>%
ggplot(aes(x =value , y = variablelab)) +
facet_wrap(~cluster)+
geom_density_ridges(scale=2, quantile_lines = TRUE, quantiles = 2) +
annotate("rect", alpha = 0.4, xmin = -1.645, xmax = 1.645, ymin = 0, ymax = circlefull,
fill = "khaki")+
annotate("rect", alpha = 0.4, xmin = -20 , xmax = -1.645, ymin = 0, ymax = circlefull,
fill = "royalblue3")+
annotate("rect", alpha = 0.4, xmin = 1.645, xmax = 20, ymin = 0, ymax = circlefull,
fill = "violetred4", colour = NA)+
coord_cartesian(xlim = c(-7, 20))+
ylab('')+ xlab('G*i')+
theme(strip.background = element_rect(fill = "snow3"),
legend.position = "right",
text = element_text(size = 14),
axis.text.x = element_text(angle = 45, hjust = 0.99, size=9))
cridges
gr <- ggplot_gtable(ggplot_build(cridges))
# Understanding grob slots - this must adapt according to the optimal number of clusters from maxp
gr$grobs[[121]]$grobs[[1]]$children[[1]]$gp$fill
gr$grobs[[102]]$grobs[[1]]$children[[1]]$gp$fill
102+19
strip_bothr <- c(118,119,120,121,
113,114,115,116,117,
108, 109, 110 ,111 ,112,
102,103,104,105,106,107)
strip_bothr <- c(117, 118,119,120,121,
112, 113,114,115,116,
107, 108, 109, 110 ,111 ,
102,103,104,105,106)
fillsr <- unique(pal)
k <- 1
for (i in strip_bothr) {
j <- which(grepl('rect', gr$grobs[[i]]$grobs[[1]]$childrenOrder))
gr$grobs[[i]]$grobs[[1]]$children[[j]]$gp$fill <- fillsr[k]
k <- k+1
}
# SLOW
grid::grid.draw(gr)
setwd(here())
setwd('results/skater_optimal_cluster_size_19/')
png("facet_clusters_ridges.png",
width = 28,
height = 25, unit='cm', res=600)
print(grid::grid.draw(gr))
dev.off()
#---------------------------------------------------------------------------------------------