In MikkoVihtakari/ggOceanMaps: Plot Data on Oceanographic Maps using 'ggplot2'

library(knitr)
knitr::opts_chunk$set(collapse = TRUE, 
                      message = FALSE, 
                      warning = FALSE, 
                      eval = FALSE,
                      comment = "#>"
)

On this website, you can find a ggOceanMaps poster presented at the Sharks International Conference 2022 in Valencia, Spain. This site also contains references in the poster and code used to make the figures and download the example datasets. Make sure to visit the front page to learn how to install ggOceanMaps, the user manual showing how to use the package and the function reference to see practical examples.

The poster

Click the picture to download a pdf of the poster with embedded links.

References

The numbered references in the poster:

Vihtakari, M. ggOceanMaps: Plot Data on Oceanographic Maps using ggplot2. R package version 1.3. (2022). https://mikkovihtakari.github.io/ggOceanMaps/
R Core Team. R: A Language and Environment for Statistical Computing. Version 4.2. R Foundation for Statistical Computing, Vienna, Austria. (2022). https://www.r-project.org/
Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag New York, 2016). https://ggplot2.tidyverse.org/
Pebesma, E. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10, 439 (2018). https://edzer.github.io/sp/
Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R News 5, 9–13 (2005). https://r-spatial.github.io/sf/
Amante, C. & Eakins, B. W. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24. https://www.doi.org/10.7289/V5C8276M
GEBCO. The GEBCO Digital Atlas published by the British Oceanographic Data Centre on behalf of IOC and IHO. (2003). https://www.gebco.net/data_and_products/gridded_bathymetry_data/
Natural Earth Data. https://www.naturalearthdata.com/
Dunnington, D. ggspatial: Spatial Data Framework for ggplot2. R package version 1.1.6. (2022). https://paleolimbot.github.io/ggspatial/

Data

Script to download data used in the poster:

# GBIF data

library(tidyverse)
library(ggOceanMaps)
library(rgbif)

## Donwload example from: https://docs.ropensci.org/rgbif

species <- c(BlueShark = "Prionace glauca", BaskingShark = "Cetorhinus maximus")

GBIF <- lapply(seq_along(species), function(i) {

  message(species[i], "...")

  key <- rgbif::name_backbone(name = species[[i]])[c("canonicalName", "speciesKey")]

  rgbif::occ_search(
    taxonKey = key$speciesKey, 
    return = "data", 
    limit = 4e5, 
    geometry = c(-20, 30, 20, 76))
})

GBIF_blue <- GBIF[[1]]$data %>% 
  rename("lat" = "decimalLatitude", "lon" = "decimalLongitude") %>% 
  dplyr::select(lon, lat) %>% 
  transform_coord(bind = TRUE) %>% 
  dist2land()

GBIF_basking <- GBIF[[2]]$data %>% 
  rename("lat" = "decimalLatitude", "lon" = "decimalLongitude") %>% 
  dplyr::select(lon, lat) %>% 
  transform_coord(bind = TRUE) %>% 
  dist2land()

# Dugnad for havet data

DugnadForHavet <- read_delim(
  "data/DFH_basking_shark.csv", # from: https://dugnadforhavet.no/dataportal
  delim = ";") %>% 
  arrange(amount) %>% 
  filter(!is.na(longitude), !is.na(latitude)) %>% 
  transform_coord(bind = TRUE) %>% 
  dist2land() %>% 
  as_tibble()

Note that the correct way to do the download would be something like shown under and on the website, but I did not have a user name when making the poster, and there were <7000 records of these shark species in the Northeast Atlantic. Hence the 100k limit was not an issue. Citing correct data sources, however, is an issue and if you are planning to use the data in more serious applications than posters, make sure to find the correct DOI using occ_download() or the web portal.

keys <- species %>% 
  name_backbone_checklist() %>% # match to backbone 
  filter(!matchType == "NONE") %>% # get matched names
  pull(usageKey)

occ_download(
  pred_in("taxonKey",key$speciesKey),
  pred("hasCoordinate", TRUE),
  pred("hasGeospatialIssue", FALSE),
  pred_within("POLYGON ((-20 30, 20 30, 20 80, -20 80, -20 30))")
)

Figures

## Background map

a1_h <- 841-1 # Poster height without margins (A1)
a1_w <- 594-1.2 # Poster width without margins (A1)

asp <- a1_h/a1_w # To get correct size because aspect ratio is fixed
lon_min <- -1.5e6 # Projected coordinates
lon_max <- 2e6 
lat_min <- -6.5e6
lat_max <- lat_min + asp*diff(c(lon_min, lon_max))

bm <- 
  basemap(
    c(lon_min, lon_max, lat_min, lat_max), 
    shapefiles = "Arctic",
    lon.interval = 10,
    bathymetry = TRUE,
    legends = FALSE) +
  theme(axis.title = element_blank()) +
  geom_spatial_point(
    data = DugnadForHavet,
    aes(x = longitude, y = latitude, size = amount),
    color = "#FF5F68", alpha = 0.8
  ) +
  geom_density_2d(
    data = DugnadForHavet, contour_var = "ndensity", size = LS(1),
    aes(x = lon.proj, y = lat.proj, color = after_stat(level))
  ) +
  geom_spatial_point(
    data = GBIF_blue %>% filter(ldist > 0), 
    aes(x = lon, y = lat), color = "#FFC95B", alpha = 0.8, size = 3
  ) +
  geom_density_2d(
    data = GBIF_blue, contour_var = "ndensity", size = LS(1),
    aes(x = lon.proj, y = lat.proj, color = after_stat(level))
  ) +
  scale_size(range = c(1,10)) +
  scale_color_distiller(palette = "Spectral", na.value = NA) +
  theme(legend.position = "none")

## Distance to land

DugnadForHavet %>% 
  mutate(species = "Basking shark") %>% 
  dplyr::select(species, ldist) %>% 
  bind_rows(
    GBIF_basking %>% mutate(species = "Basking shark") %>% 
      dplyr::select(species, ldist),
    GBIF_blue %>% 
      mutate(species = "Blue shark") %>% 
      dplyr::select(species, ldist)
  ) %>% 
  ggplot(aes(x = ldist, after_stat(ndensity), color = species)) + 
  geom_freqpoly(size = LS(5)) +
  scale_color_manual(values = c("#FF5F68", "#FFC95B")) +
  labs(x = "Standardized density", y = "Distance from land (km)", 
       color = "Species") +
  coord_cartesian(expand = FALSE, ylim = c(0,1.05), xlim = c(0,500)) +
  theme_classic() +
  theme(legend.position = c(0.7,0.9))