In JaceInnis/RGEEtools: Ocean data functions for rgee

# start()


phil = ROI("[-98.73331425575971, 31.870991349177885],
          [-98.73331425575971, 17.469056715317958],
          [-77.55167363075971, 17.469056715317958],
          [-77.55167363075971, 31.870991349177885]")


vis = wvtolist(ROI = phil, date = "20180805", interval = 5, scale = 40000, depth = 0)

tis = ssetolist(ROI = phil, date = "20180805", interval = 5, scale = 40000)



df = geetodf(vis)

df2 = geetodf(tis)

df = inner_join(df, df2)


awr = thin(df, 1)



ggplot(df, aes(lon, lat)) +
  geom_raster(aes( fill = surface_elevation), interpolate=TRUE)+
  geom_segment(aes(x = lon,y =  lat, xend = lon + velocity_u_0/1200, yend = lat + velocity_v_0/1200),
              size = 0.4 , arrow = arrow(length = unit(0.1,"cm")), 
              data = df[awr,] )+
  scale_fill_gradientn(colours=c("blue","green","red"))+
  borders("world", 
          xlim = c(min(df$lon), max(df$lon)), 
          ylim = c(min(df$lat), max(df$lat)),
          fill = "dark green") +
  coord_fixed(xlim = c(min(df$lon), max(df$lon)),
              ylim = c(min(df$lat), max(df$lat)))+
  theme(legend.position="none")

This next code chunk is an attempt to anamate the above plot

for (i in 1:23) {

  date = lubridate::ymd("20180805")
  date = date + 14*(i-1)


vis = wvtolist(ROI = phil, date = date, interval = 14, scale = 40000, band = c('velocity_u_0','velocity_v_0'))

tis = ssetolist(ROI = phil, date = date, interval = 14, scale = 40000)



df = geetodf(vis)

df2 = geetodf(tis)

df = inner_join(df,df2)

if(!i==1){
df = left_join(template, df)
}
df$int = rep(i, nrow(df))

awr = thin(df, 2)

if(i ==1){
  fin = df
  awrf = awr
  template = df[,1:2]
}
else{
  awrf = c(awr, awr + nrow(fin))
  fin = rbind(fin,df)
}
print(i)
print(nrow(df))
}

6 is last before white

milo = min(fin$lon)
malo = max(fin$lon)
mila = min(fin$lat)
mala = max(fin$lat)

ggplot(fin, aes(lon, lat)) +
  geom_raster(aes( fill = surface_elevation), interpolate=TRUE)+
  geom_segment(aes(x = lon,y =  lat, xend = lon + velocity_u_0/1200, yend = lat + velocity_v_0/1200),
              size = 0.4 , arrow = arrow(length = unit(0.1,"cm")), 
              data = fin)+
  scale_fill_gradientn(colours=c("blue","green","red"))+
  borders("world", 
          xlim = c(milo, malo), 
          ylim = c(mila, mala),
          fill = "dark green") +
  coord_fixed(xlim = c(milo, malo),
              ylim = c(mila, mala))+
  theme(legend.position="none") +
  labs(title = 'Interval(14 day): {frame_time}', x = 'Longitude', y = 'Latitude') +
  transition_time(int) +
  ease_aes('linear')

test1 = fin %>% 
  filter(int == 1) %>% 
  select(lon, lat, velocity_u_0)


test2 = fin %>% 
  filter(int == 7)%>% 
  select(lon, lat, velocity_v_0)

test3 = full_join(test1, test2)

test3 %>% 
  filter(is.na(velocity_u_0))

fin %>% 
  filter(is.na(surface_elevation))

install.packages("gganimate")

library(gapminder)
library(gganimate)



## standard ggplot2
ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  # Here comes the gganimate specific bits
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')

library(usethis)
use_readme_rmd()

ROI = ROI("[-91.157, 28.628], [-91.157, 20.977], [-80.433, 20.977], [-80.434, 28.628]")

df = ssetolist(ROI, "20010928", 5, 30000)

df = geetodf(df)

ggplot(df, aes(lon, lat)) +
  geom_raster(aes( fill = surface_elevation))