Geographic visualizations

wcornwell commented 2 years ago

The idea is to show the shared ranges between Australia and other countries

for example

this is drawing to start with and then looking for the way to do this in R.

wcornwell commented 2 years ago

possibly start with Venn Diagram

notatechy99 commented 2 years ago

Sources for different styles https://geocompr.robinlovelace.net/adv-map.html https://environmentalcomputing.net/graphics/spatial-vis/simple-maps/ https://r-spatial.org/r/2018/10/25/ggplot2-sf.html

wcornwell commented 2 years ago

some data wrangling to get to here: https://github.com/traitecoevo/australian_plant_endemism/blob/master/intermediate_data/country_counts.csv

notatechy99 commented 2 years ago

country_dataset="https://raw.githubusercontent.com/traitecoevo/australian_plant_endemism/6e12021f3604d0ad20bc5c44c9c9e08c9cf1a668/intermediate_data/country_dataset.csv"
country_dataset<-read.csv(url(country_dataset))
View(country_dataset)

install.packages("cowplot",  "ggplot2", "ggrepel",
"ggspatial", "sf", "rnaturalearth", "rnaturalearthdata")

library(ggplot2)
library(rnaturalearth)
library(rnaturalearthdata)

country_count = "https://raw.githubusercontent.com/traitecoevo/australian_plant_endemism/master/intermediate_data/country_counts.csv"
country_count<-read.csv(url(country_count))
View(country_count)

country_count <- ne_countries(scale = "medium", returnclass = "sf")
class(country_count)

ggplot(data = country_count) +
    geom_sf()


ggplot(data = country_dataset) +
    geom_sf(aes(fill = country_count, length(

wcornwell commented 2 years ago

country_dataset="https://raw.githubusercontent.com/traitecoevo/australian_plant_endemism/6e12021f3604d0ad20bc5c44c9c9e08c9cf1a668/intermediate_data/country_dataset.csv"
country_dataset<-read.csv(url(country_dataset))
View(country_dataset)

library(ggplot2)
library(rnaturalearth)
library(rnaturalearthdata)
library(tidyverse)

country_count_basic = "https://raw.githubusercontent.com/traitecoevo/australian_plant_endemism/master/intermediate_data/country_counts.csv"
country_count_basic<-read_csv(url(country_count_basic))
View(country_count_basic)
country_count_basic$name<-gsub("_"," ",country_count_basic$Country)
country_count_basic$name<-str_to_title(country_count_basic$name)

country_count <- ne_countries(scale = "medium", returnclass = "sf")
class(country_count)

cc<-left_join(country_count,country_count_basic)

ggplot(data = cc) +
  geom_sf(aes(fill = `sum(presence)`),size=0.1)

wcornwell commented 2 years ago

notatechy99 commented 2 years ago

successful map but needs to be cropped to oceania

notatechy99 commented 2 years ago

##making a map with points and connections
install.packages("maps")
library(maps)
library(geosphere)

# No margin
par(mar=c(0,0,0,0))

# World map
map('world',
    col="#f2f2f2", fill=TRUE, bg="white", lwd=0.05,
    mar=rep(0,4),border=0, ylim=c(-80,80) )

# Dplyr for data wrangling and pipe function
library(dplyr)

# Cities
Sydney <- c(151, -34)
Hong_Kong <- c(114, 22)
Jakarta <- c(107, -6)
New_Guinea <- c(147, -9)

# Data frame
data <- rbind(Hong_Kong, Sydney, Jakarta, New_Guinea) %>% 
  as.data.frame()
colnames(data) <- c("long","lat")

# Show the cities on the map
map('world',
    col="#f2f2f2", fill=TRUE, bg="white", lwd=0.05,
    mar=rep(0,4),border=0, ylim=c(-80,80) 
)
points(x=data$long, y=data$lat, col="slateblue", cex=2, pch=20)

# Load geosphere
install.packages("geosphere")
library(geosphere)

# Background map
map('world',
    col="#f2f2f2", fill=TRUE, bg="white", lwd=0.05,
    mar=rep(0,4),border=0, ylim=c(-80,80) 
)

# Dot for cities
points(x=data$long, y=data$lat, col="slateblue", cex=1.5, pch=20)

# Compute the connection 
inter <- gcIntermediate(Jakarta,  Hong_Kong, n=50, addStartEnd=TRUE, breakAtDateLine=F)

# Show this connection
lines(inter, col="slateblue", lwd=2)

# conect
inter <- gcIntermediate(New_Guinea,  Sydney, n=50, addStartEnd=TRUE, breakAtDateLine=F)             
lines(inter, col="slateblue", lwd=2)

# A function to plot connections
plot_my_connection=function( dep_lon, dep_lat, arr_lon, arr_lat, ...){
    inter <- gcIntermediate(c(dep_lon, dep_lat), c(arr_lon, arr_lat), n=50, addStartEnd=TRUE, breakAtDateLine=F)             
    inter=data.frame(inter)
    diff_of_lon=abs(dep_lon) + abs(arr_lon)
    if(diff_of_lon > 180){
        lines(subset(inter, lon>=0), ...)
        lines(subset(inter, lon<0), ...)
    }else{
        lines(inter, ...)
        }
    }

# Background map
map('world',col="#f2f2f2", fill=TRUE, bg="white", lwd=0.05,mar=rep(0,4),border=0, ylim=c(-80,80) )

# Circles for cities
points(x=data$long, y=data$lat, col="slateblue", cex=1.5, pch=20)

# Connections
plot_my_connection(New_Guinea[1], New_Guinea[2], Sydney[1], Sydney[2], col="slateblue", lwd=2)
plot_my_connection(Jakarta[1], Jakarta[2], Sydney[1], Sydney[2], col="slateblue", lwd=2)
plot_my_connection(Jakarta[1], Jakarta[2], New_Guinea[1], New_Guinea[2], col="slateblue", lwd=2)
plot_my_connection(Hong_Kong[1], Hong_Kong[2], Jakarta[1], Jakarta[2], col="slateblue", lwd=2)


#creating a cropped map of Oceania
install.packages("ggplot2")
install.packages("sf")
install.packages("rnaturalearth")
library(ggplot2)
library(sf)
library(rnaturalearth)

worldmap <- ne_countries(scale = 'medium', type = 'map_units',
                         returnclass = 'sf')
# have a look at these two columns only
head(worldmap[c('name', 'continent')])

#just australia
ggplot() + geom_sf(data = worldmap) + theme_bw()
Australia <- worldmap[worldmap$name == 'Australia',]
ggplot() + geom_sf(data = Australia) + theme_bw()

#Oceania
Oceania <- worldmap[worldmap$continent == 'Oceania',]
ggplot() + geom_sf(data = Oceania) + theme_bw()

#crop. xmin is longitude and ymin/max is lat
oceania_cropped <- st_crop(worldmap, xmin = -180, xmax = 60,
                                    ymin = -54, ymax = 12)
ggplot() + geom_sf(data = oceania_cropped) + theme_bw()

# oceania cropped
oceania.c <- ggplot() + geom_sf(data = worldmap) +
    coord_sf(xlim = c(25, 180), ylim = c(-54, 50), expand = FALSE) +
    theme_bw()

#trying to add points and connections to oceania cropped
# No margin
par(mar=c(0,0,0,0))

# World map
map('world',
    col="#f2f2f2", fill=TRUE, bg="white", lwd=0.05,
    mar=rep(0,4),border=0, ylim=c(-80,80) )

# Dplyr for data wrangling and pipe function
library(dplyr)

# Cities
Sydney <- c(151, -34)
Hong_Kong <- c(114, 22)
Jakarta <- c(107, -6)
New_Guinea <- c(147, -9)

# Data frame
data <- rbind(Hong_Kong, Sydney, Jakarta, New_Guinea) %>% 
  as.data.frame()
colnames(data) <- c("long","lat")

# Show the cities on the map
map('world',
    col="#f2f2f2", fill=TRUE, bg="white", lwd=0.05,
    mar=rep(0,4),border=0, ylim=c(-80,80) 
)
points(x=data$long, y=data$lat, col="slateblue", cex=2, pch=20)

# Load geosphere

# Background map
map('world',
    col="#f2f2f2", fill=TRUE, bg="white", lwd=0.05,
    mar=rep(0,4),border=0, ylim=c(-80,80) 
)

# Dot for cities
points(x=data$long, y=data$lat, col="slateblue", cex=1.5, pch=20)

# Compute the connection 
inter <- gcIntermediate(Jakarta,  Hong_Kong, n=50, addStartEnd=TRUE, breakAtDateLine=F)

# Show this connection
lines(inter, col="slateblue", lwd=2)

# conect
inter <- gcIntermediate(New_Guinea,  Sydney, n=50, addStartEnd=TRUE, breakAtDateLine=F)             
lines(inter, col="slateblue", lwd=2)

# A function to plot connections
plot_my_connection=function( dep_lon, dep_lat, arr_lon, arr_lat, ...){
    inter <- gcIntermediate(c(dep_lon, dep_lat), c(arr_lon, arr_lat), n=50, addStartEnd=TRUE, breakAtDateLine=F)             
    inter=data.frame(inter)
    diff_of_lon=abs(dep_lon) + abs(arr_lon)
    if(diff_of_lon > 180){
        lines(subset(inter, lon>=0), ...)
        lines(subset(inter, lon<0), ...)
    }else{
        lines(inter, ...)
        }
    }

# Background map
map('world',col="#f2f2f2", fill=TRUE, bg="white", lwd=0.05,mar=rep(0,4),border=0, ylim=c(-80,80) )

# Circles for cities
points(x=data$long, y=data$lat, col="slateblue", cex=1.5, pch=20)

# Connections
plot_my_connection(New_Guinea[1], New_Guinea[2], Sydney[1], Sydney[2], col="slateblue", lwd=2)
plot_my_connection(Jakarta[1], Jakarta[2], Sydney[1], Sydney[2], col="slateblue", lwd=2)
plot_my_connection(Jakarta[1], Jakarta[2], New_Guinea[1], New_Guinea[2], col="slateblue", lwd=2)
plot_my_connection(Hong_Kong[1], Hong_Kong[2], Jakarta[1], Jakarta[2], col="slateblue", lwd=2)

wcornwell commented 2 years ago

map('world',
    col="#f2f2f2", fill=TRUE, bg="white", lwd=0.05,
    mar=rep(0,4),border=0, ylim=c(-54,12) ,xlim=c(60,180)) 
)

traitecoevo / australian_plant_endemism

Geographic visualizations #5