draft figure--any thoughts?
wcornwell opened this issue · 5 comments
wcornwell commented
I went with the 6 biggest families in Australia--sort of interesting which families have that crazy WA diversification, and which don'....
@dfalster @rubysaltbush ?
wcornwell commented
wcornwell commented
wcornwell commented
keeping code here for now:
library(ausflora)
library(tidyverse)
library(ozmaps)
library(sf)
resources<-load_taxonomic_resources()
ss <- create_species_state_origin_matrix(resources = resources)
apcfam<-APCalign:::get_apc_genus_family_lookup()
ss$genus<-word(ss$species,1,1)
ss2<-left_join(ss,apcfam)
plot_taxa_heat_map <- function(fam, s2=s2) {
ss2 %>%
pivot_longer(2:19, names_to = "State") %>%
filter(family==fam) %>%
filter(value != "not present") %>%
filter(
value %in% c(
"native",
"presumed extinct",
"naturalised",
"formerly naturalised",
"doubtfully naturalised"
)
) %>%
filter(State %in% c("WA", "Qld", "NT", "NSW", "Vic", "Tas", "SA", "ACT")) %>%
group_by(State, value) %>%
summarise (`number of species` = n()) %>%
mutate(family=fam)
}
plot_list<-list()
for (i in c("Fabaceae","Myrtaceae","Orchidaceae","Asteraceae","Poaceae","Proteaceae")){
plot_list[[i]]<-plot_taxa_heat_map(i)
}
all_data<-bind_rows(plot_list)
ggplot(all_data,aes(x = State, y = value, fill = `number of species`)) +
geom_tile(color = "black") +
scale_fill_gradient2(
low = "#075AFF",
mid = "#FFFFCC",
high = "#FF0000"
) +
coord_fixed() + facet_wrap(~family,ncol = 2)+ylab("")+xlab("State/Territory")
aus <- ozmap_data(data = "states")
all_data$NAME<-case_when(all_data$State=="NSW" ~ "New South Wales",
all_data$State=="ACT" ~ "Australian Capital Territory",
all_data$State=="NT" ~ "Northern Territory",
all_data$State=="Tas" ~ "Tasmania",
all_data$State=="WA" ~ "Western Australia",
all_data$State=="Qld" ~ "Queensland",
all_data$State=="Vic" ~ "Victoria",
all_data$State=="SA" ~ "South Australia"
)
merged_data<-left_join(aus,all_data)
native<-filter(merged_data,value=="native")
ggplot(data = native) +
geom_sf(aes(fill = `number of species`))+
scale_fill_gradient2(low = "#075AFF", mid = "#FFFFCC", high = "#FF0000")+
facet_wrap(~family, ncol = 2) +
labs(title = "Native Species Richness in Australian States and Territories")+theme_void()
naturalised<-filter(merged_data,value=="naturalised")
ggplot(data = naturalised) +
geom_sf(aes(fill = `number of species`))+
scale_fill_gradient2(low = "#075AFF", mid = "#FFFFCC", high = "#FF0000")+
facet_wrap(~family, ncol = 2) +
labs(title = "Naturalised Species Richness in Australian States and Territories")+theme_void()
rubysaltbush commented
These look amazing! I think the heatmaps are probably best for this paper, as they make the different categories beyond just "native" or "introduced" explicit. The maps are more interpretable for distribution etc but maybe belong more in a paper looking at said distributions?
wcornwell commented