The goal of geoflowr is to …
You can get the most recent version of geoflowr from Github and install with:
devtools::install_github("ropenscilabs/geoflowr")We are hopeing to plot spatial flows using ggplot2 and then using
gganimate to show changes through time. There are many ways to do this
ouside ggplot2 and there is already a geom_curve() functionthat
creates a plot similar
library(ggplot2)
library(readr)
library(purrr)
library(dplyr)
library(sf)
library(gganimate)
coords <- read_csv('data-raw/world.csv') %>%
filter(!is.na(admin)) %>%
select(admin,
x = Longitude,
y = Latitude)
#flows <- read_csv('data-raw/201802.csv') %>%
flows <- map_df(list.files(path = "data-raw", pattern = "[0-9].csv", full.names = TRUE),
read_csv, col_names = TRUE) %>%
select(year,
period,
reporter,
partner,
netweight_kg)
src <- coords %>%
rename(reporter = admin)
dst <- coords %>%
rename(partner = admin)
# join coords to src and dst
df <- flows %>%
left_join(src, by = 'reporter') %>%
left_join(dst, by = 'partner', suffix = c('_src','_dst')) %>%
filter(complete.cases(.),
partner != 'World')
world1 <- sf::st_as_sf(maps::map('world', plot = FALSE, fill = TRUE))ggplot(df) +
geom_sf(data = world1) +
geom_curve(aes(x = x_src, y = y_src, xend = x_dst, yend = y_dst,
alpha = 0.5,
size = netweight_kg / max(df$netweight_kg)),
#curvature = 0.75, angle = -45,
arrow = arrow(length = unit(0.15,"cm"))
) +
theme_void() +
theme(legend.position="none") +
transition_time(period) +
ease_aes('linear')
library(geosphere)
#greatCircle(c(df$x_src[1], df$y_src[1]), c(df$x_dst[1], df$y_dst[1]), n=360, sp=FALSE)
# finter to single month for now
mar <- df %>%
as_tibble() %>%
filter(period == 201803)
mar_pts <- lapply(1:nrow(mar), function(r){
row = mar[r,]
pts <- gcIntermediate(c(row$x_src, row$y_src), c(row$x_dst, row$y_dst), n=36, sp=FALSE, breakAtDateLine = F) %>%
as.data.frame() %>%
mutate(year = row$year,
period = row$period,
group = r,
n = 1:36,
reporter = row$reporter,
partner = row$partner,
netweight_kg = row$netweight_kg)
}) %>% plyr::ldply()ggplot() +
geom_sf(data = world1) +
geom_line(data = mar_pts, aes(x = lon, y = lat, group = group)) +
theme_void() +
transition_reveal(id = group, along = n) +
ease_aes('linear')
filter(mar_pts, lat < )# https://www.jessesadler.com/post/great-circles-sp-sf/
mar_sf <- mar %>%
rowwise() %>%
mutate(line = st_sfc(st_linestring(gcIntermediate(c(x_src, y_src), c(x_dst, y_dst), n=36, sp=FALSE, breakAtDateLine = F), dim = 'XY'))) %>%
st_as_sf(sf_column_name = 'line', crs = 4283)
mapview::mapview(mar_sf)@map
# wrld_wrap <- st_wrap_dateline(mar_sf, options = c("WRAPDATELINE=YES", "DATELINEOFFSET=180"),
# quiet = TRUE)
ggplot() +
geom_sf(data = world1) +
geom_sf(data = mar_sf) +
theme_void() +
transition_components('reporter') +
ease_aes('linear')Using these methods great circle lines and point annimations appear distorted at the poles and the borders of the map. To avoid this we propose using destination and source points to draw a complete great circle. This line can then be split where the line is intersected resulting two lines. Each line can be checked for proximity to these map regions and excluded where nesecary.
The method is outlined below, but there is an issue with using
st_split() on a windows machine.
src <- c(df$x_src[2], df$y_src[2])
dst <- c(df$x_dst[2], df$y_dst[2])
gc <- greatCircle(src, dst, n=16, sp=FALSE)
gcl = st_sfc(st_linestring(gc), crs = 4283)
mp <- st_sfc(st_multipoint(rbind(src,dst)), crs = 4283)
#mp2 = st_snap(mp, gcl, 1) # move points to intersect gcl line
# then use st_split(gcl, mp2) but requires lwgeom which is not working on windows....
ggplot() +
geom_sf(data = world1) +
geom_sf(data = gcl, size = 2.5) +
geom_sf(data = mp, colour = "red", fill = "red", size = 10) +
theme_void()