Roaming data

library(tidyverse) # All-in-one

library(oceanexplorer) # Access World Ocean Atlas (WOA)
# See: # https://cran.r-project.org/web/packages/oceanexplorer/vignettes/oceanexplorer.html

library(sdmpredictors) # Layers used in modelling
# See: https://bio-oracle.org/code.php

library(rgbif) # Access anything on GBIF
# See: https://data-blog.gbif.org/post/gbif-api-beginners-guide/

library(pangaear) # Access anything on PANGAEA
# See: https://docs.ropensci.org/pangaear/

library(rerddap) # For accessing ERDDAP databases

# The library containing these functions
library(oceanexplorer)

# Get data
winter_temp <- get_NOAA(var = "temperature", spat_res = 1, av_period = "winter")

# Plot
plot_NOAA(WA_temp)

# The library containing these functions
library(sdmpredictors)

# Explore datasets in the package
list_datasets()

# Explore layers in a dataset
BO_layers <- list_layers(datasets = "Bio-ORACLE")

# Average surface temperature
surface_temp <- load_layers("BO22_tempmean_ss")

# The library containing these functions
library(rgbif)

# Download occurrence data
Emax <- occ_search(scientificName = "Ecklonia maxima")

Emax$data %>% 
  group_by(county) %>% 
  summarise(count = n(), .groups = "drop") %>% 
  ggplot(aes(x = county, y = count)) +
  geom_bar(aes(fill = county), stat = "identity", show.legend = F)

Emax$data %>% 
  ggplot() +
  borders() +
  geom_point(aes(x = as.numeric(Emax$data$decimalLongitude), 
                 y = as.numeric(Emax$data$decimalLatitude),
                 colour = country)) +
  coord_quickmap(xlim = c(-1, 29), ylim = c(-36, 1))

# The library containing these functions
library(pangaear)

# Search for PAR data within Kongsfjorden
  # NB: Bounding box is: minlon, minlat, maxlon, maxlat
search_res <- pg_search(query = "PAR", bbox = c(11, 78, 13, 80), count = 10)
head(search_res)

# Download
kong_PAR_dl <- pg_data(doi = search_res$doi[1])

# Prep
kong_PAR <- kong_PAR_dl[[1]]$data %>% 
  mutate(t = as.POSIXct(str_replace(`Date/Time`, "T", " ")))

# Plot
kong_PAR %>% 
  filter(t < "2012-07-31 00:00:00") %>% 
  ggplot(aes(x = t, y = `PAR [µmol/m**2/s]`)) +
  geom_line(aes(colour = as.factor(`Depth water [m]`))) +
  facet_wrap(~Comment, ncol = 1) + 
  labs(x = NULL, colour = "Depth [m]", 
       title = "PAR data from Kongsfjorden for July, 2012",
       subtitle = "Shallower values taken both above and below kelp canopy",
       caption = str_wrap(kong_PAR_dl[[1]]$citation, 100))

# First we start with the dataset name
OISST_dl <- griddap(datasetx = "ncdcOisst21Agg_LonPM180",
                    # Then we provide the site where the data are
                    url = "https://coastwatch.pfeg.noaa.gov/erddap/", 
                    # The range of dates we want
                    time = c("2020-12-25", "2020-12-31"),
                    # The depth range
                    zlev = c(0, 0),
                    # lon/lat are taken from the centre of the pixel
                    longitude = c(-179.875, 179.875),
                    latitude = c(-89.875, 89.875),
                    # Then list the data layer you want
                    # These can be found on the dataset website
                    fields = "sst")

OISST_2020 <- OISST_dl$data %>% 
    mutate(time = as.Date(stringr::str_replace(time, "T12:00:00Z", ""))) %>%
    dplyr::rename(t = time, temp = sst, lon = longitude, lat = latitude) %>%
    select(lon, lat, t, temp) %>%
    na.omit()

# Filter for quicker plotting
OISST_2020 %>% 
  filter(lon > 6, lon < 32, lat > 76, lat < 81,
         t != "2020-12-28") %>% 
  ggplot() + borders() +
  geom_raster(aes(x = lon, y = lat, fill = temp)) +
  scale_fill_viridis_c() +
  coord_quickmap(xlim = c(6, 32), ylim = c(76, 81), expand = F) +
  facet_wrap(~t) + labs(x = NULL, y = NULL)