# HG changeset patch
# User ecology
# Date 1705570432 0
# Node ID 1fcd81d654678b29c467b17f484d0bf462e79f2c
planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/obisindicators commit b377ff767e3051f301c2f02cfe3e1a17b285ede4
diff -r 000000000000 -r 1fcd81d65467 analyze.r
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/analyze.r Thu Jan 18 09:33:52 2024 +0000
@@ -0,0 +1,72 @@
+#' Calculate Biodiversity Indicators, including ES50 (Hurlbert index)
+#'
+#' Calculate the expected number of marine species in a random sample of 50
+#' individuals (records)
+#'
+#' @param df data frame with unique species observations containing columns:
+#' `cell`, `species`, `records`
+#' @param esn expected number of marine species
+#'
+#' @return Data frame with the following extra columns: - `n`: number of records
+#' - `sp`: species richness - `shannon`: Shannon index - `simpson`: Simpson
+#' index - `es`: Hurlbert index (n = 50), i.e. expected species from 50
+#' samples ES(50) - `hill_1`: Hill number `exp(shannon)` - `hill_2`: Hill
+#' number `1/simpson` - `hill_inf`: Hill number `1/maxp`
+#'
+#' @details The expected number of marine species in a random sample of 50
+#' individuals (records) is an indicator on marine biodiversity richness. The
+#' ES50 is defined in OBIS as the `sum(esi)` over all species of the following
+#' per species calculation:
+#'
+#' - when `n - ni >= 50 (with n as the total number of records in the cell and
+#' ni the total number of records for the ith-species)
+#' - `esi = 1 - exp(lngamma(n-ni+1) + lngamma(n-50+1) - lngamma(n-ni-50+1) - lngamma(n+1))`
+#'
+#' - when `n >= 50` - `esi = 1`
+#'
+#' - else - `esi = NULL`
+#'
+#' Warning: ES50 assumes that individuals are randomly distributed, the sample
+#' size is sufficiently large, the samples are taxonomically similar, and that
+#' all of the samples have been taken in the same manner.
+#'
+#' @export
+#' @concept analyze
+#' @examples
+#' @importFrom gsl lngamma
+calc_indicators <- function(df, esn = 50) {
+
+ stopifnot(is.data.frame(df))
+ stopifnot(is.numeric(esn))
+ stopifnot(all(c("cell", "species", "records") %in% names(df)))
+
+ df %>%
+ dplyr::group_by(cell, species) %>%
+ dplyr::summarize(
+ ni = sum(records),
+ .groups = "drop_last") %>%
+ dplyr::mutate(n = sum(ni)) %>%
+ dplyr::group_by(cell, species) %>%
+ dplyr::mutate(
+ hi = -(ni / n * log(ni / n)),
+ si = (ni / n)^2,
+ qi = ni / n,
+ esi = dplyr::case_when(
+ n - ni >= esn ~ 1 - exp(gsl::lngamma(n - ni + 1) + gsl::lngamma(n - esn + 1) - gsl::lngamma(n - ni - esn + 1) - gsl::lngamma(n + 1)),
+ n >= esn ~ 1
+ )
+ ) %>%
+ dplyr::group_by(cell) %>%
+ dplyr::summarize(
+ n = sum(ni),
+ sp = dplyr::n(),
+ shannon = sum(hi),
+ simpson = sum(si),
+ maxp = max(qi),
+ es = sum(esi),
+ .groups = "drop") %>%
+ dplyr::mutate(
+ hill_1 = exp(shannon),
+ hill_2 = 1 / simpson,
+ hill_inf = 1 / maxp)
+}
diff -r 000000000000 -r 1fcd81d65467 data.r
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/data.r Thu Jan 18 09:33:52 2024 +0000
@@ -0,0 +1,125 @@
+#' OBIS occurrences, global sample of 1 million records
+#'
+#' Simple dataset of species and location for example purposes from 2022-04-04.
+#'
+#' @format A data frame with 1,000,000 rows and 4 variables:
+#' - `decimalLongitude`: unique x
+#' - `decimalLatitude`: unique y
+#' - `species`: unique species
+#' - `records`: number of records based on unique values of x, y, species
+#'
+#' @source \url{https://obis.org/data/access}
+#' @concept data
+"occ_1M"
+
+#' OBIS occurrences, South Atlantic full regional sample
+#'
+#' Simple dataset of species and location for example purposes from 2022-04-04.
+#'
+#' @format A data frame with 1,014,006 rows and 4 variables:
+#' - `decimalLongitude`: unique x
+#' - `decimalLatitude`: unique y
+#' - `species`: unique species
+#' - `records`: number of records based on unique values of x, y, species
+#'
+#' @source \url{https://obis.org/data/access}
+#' @concept data
+"occ_SAtlantic"
+
+#' OBIS occurrences, Florida Keys full regional sample
+#'
+#' Simple dataset of species and location for example purposes from 2022-04-04.
+#'
+#' @format A data frame with 1,014,006 rows and 4 variables:
+#' - `decimalLongitude`: unique x
+#' - `decimalLatitude`: unique y
+#' - `species`: unique species
+#' - `records`: number of records based on unique values of x, y, species
+#'
+#' @source \url{https://obis.org/data/access}
+#' @concept data
+"occ_fk"
+
+#' OBIS occurrences, temporal sample for the 1960s, limited to 1M records
+#'
+#' Simple dataset of species and location for example purposes from 2022-04-04.
+#'
+#' @format A data frame with 1,014,006 rows and 4 variables:
+#' - `decimalLongitude`: unique x
+#' - `decimalLatitude`: unique y
+#' - `species`: unique species
+#' - `records`: number of records based on unique values of x, y, species
+#'
+#' @source \url{https://obis.org/data/access}
+#' @concept data
+"occ_1960s"
+
+#' OBIS occurrences, temporal sample for the 1970s, limited to 1M records
+#'
+#' Simple dataset of species and location for example purposes from 2022-04-04.
+#'
+#' @format A data frame with 1,014,006 rows and 4 variables:
+#' - `decimalLongitude`: unique x
+#' - `decimalLatitude`: unique y
+#' - `species`: unique species
+#' - `records`: number of records based on unique values of x, y, species
+#'
+#' @source \url{https://obis.org/data/access}
+#' @concept data
+"occ_1970s"
+
+#' OBIS occurrences, temporal sample for the 1980s, limited to 1M records
+#'
+#' Simple dataset of species and location for example purposes from 2022-04-04.
+#'
+#' @format A data frame with 1,014,006 rows and 4 variables:
+#' - `decimalLongitude`: unique x
+#' - `decimalLatitude`: unique y
+#' - `species`: unique species
+#' - `records`: number of records based on unique values of x, y, species
+#'
+#' @source \url{https://obis.org/data/access}
+#' @concept data
+"occ_1980s"
+
+#' OBIS occurrences, temporal sample for the 1990s, limited to 1M records
+#'
+#' Simple dataset of species and location for example purposes from 2022-04-04.
+#'
+#' @format A data frame with 1,014,006 rows and 4 variables:
+#' - `decimalLongitude`: unique x
+#' - `decimalLatitude`: unique y
+#' - `species`: unique species
+#' - `records`: number of records based on unique values of x, y, species
+#'
+#' @source \url{https://obis.org/data/access}
+#' @concept data
+"occ_1990s"
+
+#' OBIS occurrences, temporal sample for the 2000s, limited to 1M records
+#'
+#' Simple dataset of species and location for example purposes from 2022-04-04.
+#'
+#' @format A data frame with 1,014,006 rows and 4 variables:
+#' - `decimalLongitude`: unique x
+#' - `decimalLatitude`: unique y
+#' - `species`: unique species
+#' - `records`: number of records based on unique values of x, y, species
+#'
+#' @source \url{https://obis.org/data/access}
+#' @concept data
+"occ_2000s"
+
+#' OBIS occurrences, temporal sample for the 2010s, limited to 1M records
+#'
+#' Simple dataset of species and location for example purposes from 2022-04-04.
+#'
+#' @format A data frame with 1,014,006 rows and 4 variables:
+#' - `decimalLongitude`: unique x
+#' - `decimalLatitude`: unique y
+#' - `species`: unique species
+#' - `records`: number of records based on unique values of x, y, species
+#'
+#' @source \url{https://obis.org/data/access}
+#' @concept data
+"occ_2010s"
diff -r 000000000000 -r 1fcd81d65467 macro.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/macro.xml Thu Jan 18 09:33:52 2024 +0000
@@ -0,0 +1,39 @@
+
+ 0.0.2
+
+
+ r-base
+ r-ggplot2
+
+
+
+
+
+
+
+
+
+
+ @Manual{,
+ title = {obisindicators: Develop marine biodiversity indicators from OBIS data},
+ author = {Ben Ben and Pieter Provoost and Tylar Murray},
+ year = {2022},
+ note = {https://marinebon.org/obisindicators,
+ https://github.com/marinebon/obisindicators},
+ }
+
+
+
+
+
+ DOI:10.5281/zenodo.6969395
+
+
+
+
+ topic_0610
+ topic_3050
+
+
+
+
diff -r 000000000000 -r 1fcd81d65467 obisindicators.r
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/obisindicators.r Thu Jan 18 09:33:52 2024 +0000
@@ -0,0 +1,115 @@
+#Rscript
+
+###########################################
+## Mapping alpha and beta diversity ##
+###########################################
+
+#####Packages : obisindicators
+# dplyr
+# sf
+# ggplot2
+# rnaturalearth
+# rnaturalearthdata
+# viridis
+# dggridr
+library(magrittr)
+
+## remotes::install_github("r-barnes/dggridR")
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+# url for the S2 subset
+
+if (length(args) < 4) {
+ stop("This tool needs at least 4 argument : longitude, latitude, species and number of records")
+}else {
+ raster <- args[1]
+ hr <- args[2]
+ sep <- as.character(args[3])
+ longitude <- as.numeric(args[4])
+ latitude <- as.numeric(args[5])
+ spe <- as.numeric(args[6])
+ rec <- as.numeric(args[7])
+ crs <- as.numeric(args[8])
+ reso <- as.numeric(args[9])
+ source(args[10])
+ source(args[11])
+ source(args[12])
+}
+
+if (hr == "false") {
+ hr <- FALSE
+}else {
+ hr <- TRUE
+}
+
+if (sep == "t") {
+ sep <- "\t"
+}
+
+if (crs == "0") {
+ crs <- "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs"
+}
+#####Import data
+occ <- read.table(raster, sep = sep, dec = ".", header = hr, fill = TRUE, encoding = "UTF-8") # occ_1M OR occ_SAtlantic
+occ <- na.omit(occ)
+#Get biological occurrences
+#Use the 1 million records subsampled from the full OBIS dataset
+colnames(occ)[longitude] <- c("decimalLongitude")
+colnames(occ)[latitude] <- c("decimalLatitude")
+colnames(occ)[spe] <- c("species")
+colnames(occ)[rec] <- c("records")
+
+#Create a discrete global grid
+#Create an ISEA discrete global grid of resolution 9 using the dggridR package:
+
+dggs <- dggridR::dgconstruct(projection = "ISEA", topology = "HEXAGON", res = reso)
+
+#Then assign cell numbers to the occurrence data
+occ$cell <- dggridR::dgGEO_to_SEQNUM(dggs, occ$decimalLongitude, occ$decimalLatitude)[["seqnum"]]
+
+#Calculate indicators
+#The following function calculates the number of records, species richness, Simpson index, Shannon index, Hurlbert index (n = 50), and Hill numbers for each cell.
+
+#Perform the calculation on species level data
+idx <- calc_indicators(occ)
+write.table(idx, file = "Index.csv", sep = ",", dec = ".", na = " ", col.names = TRUE, row.names = FALSE, quote = FALSE)
+
+#add cell geometries to the indicators table (idx)
+grid_idx <- sf::st_wrap_dateline(dggridR::dgcellstogrid(dggs, idx$cell))
+colnames(grid_idx) <- c("cell", "geometry")
+
+grid <- dplyr::left_join(grid_idx,
+ idx,
+ by = "cell")
+
+#Plot maps of indicators
+#Let’s look at the resulting indicators in map form.
+#Indice ES(50)
+es_50_map <- gmap_indicator(grid, "es", label = "ES(50)", crs = crs)
+es_50 <- ggplot2::ggsave("ES_50.png", es_50_map, scale = 0.38, width = 12, height = 7, units = "in", dpi = 300, limitsize = TRUE)
+
+# Shannon index
+shannon_map <- gmap_indicator(grid, "shannon", label = "Shannon index", crs = crs)
+shannon <- ggplot2::ggsave("Shannon_index.png", shannon_map, scale = 0.38, width = 12, height = 7, units = "in", dpi = 300, limitsize = TRUE)
+
+
+# Number of records, log10 scale, Geographic projection
+records_map <- gmap_indicator(grid, "n", label = "# of records", trans = "log10", crs = crs)
+records <- ggplot2::ggsave("Records.png", records_map, scale = 0.38, width = 12, height = 7, units = "in", dpi = 300, limitsize = TRUE)
+
+# Simpson index
+simpson_map <- gmap_indicator(grid, "simpson", label = "Simpson index", crs = crs)
+simpson <- ggplot2::ggsave("Simpson_index.png", simpson_map, scale = 0.38, width = 12, height = 7, units = "in", dpi = 300, limitsize = TRUE)
+
+# maxp
+maxp_map <- gmap_indicator(grid, "maxp", label = "maxp index", crs = crs)
+maxp <- ggplot2::ggsave("Maxp.png", maxp_map, scale = 0.38, width = 12, height = 7, units = "in", dpi = 300, limitsize = TRUE)
+
+#Mapping
+es_50
+shannon
+simpson
+maxp
+records
diff -r 000000000000 -r 1fcd81d65467 robis.r
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/robis.r Thu Jan 18 09:33:52 2024 +0000
@@ -0,0 +1,57 @@
+#Rscript
+
+###########################################
+## Retrieve Obis occurences data ##
+###########################################
+
+##### Packages : robis
+# https://iobis.github.io/robis/articles/getting-started.html
+# Get args
+args <- commandArgs(trailingOnly = TRUE)
+
+if (length(args) < 1) {
+ stop("This tool needs at least 1 argument : longitude, latitude, species or taxonID")
+}else {
+ sname <- args[1]
+ taxid <- args[2]
+ lat_min <- args[3]
+ lat_max <- args[4]
+ long_min <- args[5]
+ long_max <- args[6]
+}
+
+if (lat_min == "0.0" & lat_max == "0.0" & long_min == "0.0" & long_max == "0.0") {
+lat_min <- ""
+lat_max <- ""
+long_min <- ""
+long_max <- ""
+}
+
+##### Import data
+# Get biological occurrences
+if (lat_min != "" & sname != "" & taxid != "") {
+my_occs <- robis::occurrence(scientificname = sname, taxonid = taxid, geometry = paste("POLYGON ((", long_min, lat_min, ", ", long_min, lat_max, ", ", long_max, lat_min, ", ", long_max, lat_max, ", ", long_min, lat_min, "))"))
+}else if (lat_min != "" & sname != "" & taxid == "") {
+my_occs <- robis::occurrence(scientificname = sname, geometry = paste("POLYGON ((", long_min, lat_min, ", ", long_min, lat_max, ", ", long_max, lat_min, ", ", long_max, lat_max, ", ", long_min, lat_min, "))"))
+}else if (lat_min != "" & sname == "" & taxid != "") {
+my_occs <- robis::occurrence(taxonid = taxid, geometry = paste("POLYGON ((", long_min, lat_min, ", ", long_min, lat_max, ", ", long_max, lat_min, ", ", long_max, lat_max, ", ", long_min, lat_min, "))"))
+}else if (lat_min != "" & sname == "" & taxid == "") {
+my_occs <- robis::occurrence(geometry = paste("POLYGON ((", long_min, lat_min, ", ", long_min, lat_max, ", ", long_max, lat_min, ", ", long_max, lat_max, ", ", long_min, lat_min, "))"))
+}else if (lat_min == "" & sname != "" & taxid != "") {
+my_occs <- robis::occurrence(scientificname = sname, taxonid = taxid)
+}else if (lat_min == "" & sname == "" & taxid != "") {
+my_occs <- robis::occurrence(taxonid = taxid)
+}else if (lat_min == "" & sname != "" & taxid == "") {
+my_occs <- robis::occurrence(scientificname = sname)
+}
+
+
+# Dispay results
+
+# If empty
+if(length(my_occs) == 0) {
+cat("\nNo occurrences found.\nLittle tip : Check your input typo, some databases are case sensitive : Genus species.\n")
+}
+
+
+write.table(file = "output.tab", my_occs, sep = "\t", dec = ".", na = "", row.names = FALSE, col.names = TRUE, quote = FALSE)
diff -r 000000000000 -r 1fcd81d65467 robis.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/robis.xml Thu Jan 18 09:33:52 2024 +0000
@@ -0,0 +1,98 @@
+
+ retrieve data
+
+ macro.xml
+
+
+
+ r-robis
+
+
+
+
+
+
+
+
+ ^[A-Za-z ]*$
+
+
+ ^[0-9]*$
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff -r 000000000000 -r 1fcd81d65467 test-data/Index.csv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Index.csv Thu Jan 18 09:33:52 2024 +0000
@@ -0,0 +1,1122 @@
+cell,n,sp,shannon,simpson,maxp,es,hill_1,hill_2,hill_inf
+33478,13,1,0,1,1, ,1,1,1
+33890,2,1,0,1,1, ,1,1,1
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diff -r 000000000000 -r 1fcd81d65467 test-data/occ_at.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/occ_at.tabular Thu Jan 18 09:33:52 2024 +0000
@@ -0,0 +1,6930 @@
+decimalLongitude decimalLatitude species date_year records
+-66.1350021362305 -50.5466651916504 Andreella uncifera 3
+-62.7333335876465 -43.283332824707 Andreella uncifera 1
+-60.9383316040039 -45.281665802002 Andreella uncifera 1
+-60.4266662597656 -40.281665802002 Andreella uncifera 1
+-59.2483329772949 -40.033332824707 Andreella uncifera 2
+-60.9049987792969 -41.6966667175293 Andreella uncifera 1
+-61.9150009155273 -42.2849998474121 Andreella uncifera 1
+-60.5633316040039 -50.6916656494141 Andreella uncifera 1
+-61.935001373291 -48.5550003051758 Andreella uncifera 1
+-61.6033325195312 -45.1150016784668 Andreella uncifera 1
+-61.5133323669434 -42.6633338928223 Andreella uncifera 1
+-63.6083335876465 -41.4516677856445 Andreella uncifera 1
+-63.5683326721191 -50.8549995422363 Andreella uncifera 1
+-59.2449989318848 -40.3666648864746 Andreella uncifera 1
+-5.9980001449585 -49 Rhizosolenia styliformis 1992 1
+-56.632999420166 -51.9669990539551 Leitoscoloplos kerguelensis 1963 1
+-24.8080005645752 -55.8419990539551 Umbellula magniflora 1963 2
+-38.6669998168945 -53.867000579834 Serolella pagenstecheri 1986 1
+18.5 -12.5 Echinolittorina punctata 1
+18.5833339691162 -34.1333351135254 Zeus faber 1
+-57.3333015441895 -50.5833015441895 Cyamiocardium crassilabrum 1928 1
+-12.2917003631592 -37.0750007629395 Eatoniella trochiformis 1938 3
+13.0500001907349 -30.2483005523682 Pronucula benguelana 1957 1
+-34.9333000183105 -6.81669998168945 Eleotris pisonis 1
+-41.6500015258789 -2.86669993400574 Eleotris pisonis 1
+-43.86669921875 -22.8999996185303 Eleotris pisonis 1
+-52.0833015441895 -32.1666984558105 Eleotris pisonis 1
+-44.4166984558105 -2.53329992294312 Eleotris pisonis 1
+-44 -23.1667003631592 Eleotris pisonis 1
+-50.11669921875 -30 Eleotris pisonis 1
+-47.9166984558105 -25 Eleotris pisonis 1
+-44.4500007629395 -2.5 Eleotris pisonis 1
+-38.7000007629395 -12.6999998092651 Eleotris pisonis 1
+-44.4333000183105 -2.70000004768372 Sciades parkeri 1
+-46.3166999816895 -23.9832992553711 Mycteroperca acutirostris 1
+-48.5499992370605 -25.86669921875 Mycteroperca acutirostris 1
+-52.6666984558105 -33 Mycteroperca acutirostris 1
+-44.4500007629395 -2.5 Sciades parkeri 1
+-48.4000015258789 -25.5 Mycteroperca acutirostris 1
+-50.7999992370605 -0.71670001745224 Sciades parkeri 1
+-35.7832984924316 -9.60000038146973 Sciades parkeri 1
+-38.7000007629395 -12.6999998092651 Sciades parkeri 1
+-44.5 -2.56669998168945 Sciades parkeri 1
+-35.7832984924316 -9.60000038146973 Lutjanus griseus 1
+-43.86669921875 -22.8999996185303 Lutjanus griseus 1
+-48.5499992370605 -25.86669921875 Lutjanus griseus 1
+-34.8166999816895 -7.56669998168945 Sciades parkeri 1
+-37.7667007446289 -4.43330001831055 Lutjanus griseus 1
+-52.0833015441895 -32.1666984558105 Mycteroperca acutirostris 1
+-34.8166999816895 -7.56669998168945 Lutjanus griseus 1
+-48.4000015258789 -25.5 Lutjanus griseus 1
+-40.1623916625977 -20.0403251647949 Codium spongiosum 1982 1
+-40.1623916625977 -20.0403251647949 Codium spongiosum 1973 1
+-40.4356536865234 -20.641393661499 Codium spongiosum 2000 1
+-40.1335868835449 -19.967830657959 Dictyota dichotoma 1986 1
+-65.3333358764648 -55.3333320617676 Austrocidaris spinulosa 2
+-64.8833312988281 -54.9833335876465 Austrocidaris spinulosa 2
+-66.7799987792969 -55.1399993896484 Austrocidaris spinulosa 2
+-56.61669921875 -54.7167015075684 Austrocidaris spinulosa 2
+-39.0099983215332 -55.6749992370605 Antholoba achates 1
+17.4500007629395 -33.2832984924316 Diastobranchus capensis 1997 1
+-41.7999992370605 -22.7667007446289 Octopus hubbsorum 2001 1
+-66.2249984741211 -55.4383316040039 Ctenocidaris (Eurocidaris) nutrix 2
+-36.9160003662109 -54.0830001831055 Ctenocidaris (Eurocidaris) nutrix 2
+-37.25 -54 Ctenocidaris (Eurocidaris) nutrix 2
+-36.5 -54.1669998168945 Ctenocidaris (Eurocidaris) nutrix 1
+-38.9165992736816 -14.8083000183105 Amaryllis atlantica 2001 1
+-67.6500015258789 -52.3300018310547 Antholoba achates 2
+2.79999995231628 -54.5400009155273 Cribrostomoides wiesneri 1991 5
+3.37932991981506 -54.1716995239258 Cribrostomoides wiesneri 1991 1
+-39.7052993774414 -59.826301574707 Cribrostomoides wiesneri 1992 11
+-40.823299407959 -59.5428009033203 Cribrostomoides wiesneri 1992 8
+-41.9874992370605 -59.6394996643066 Cribrostomoides wiesneri 1992 7
+-66.4079971313477 -56.1080017089844 Neosmilaster steineni 1963 1
+-41.3352012634277 -59.4832000732422 Cribrostomoides wiesneri 1992 4
+-68.6669998168945 -55.1669998168945 Leptonotus blainvilleanus 4
+11.3999996185303 -4.18300008773804 Abudefduf taurus 2
+15 -35 Muraenoclinus dorsalis 2
+-38.4830017089844 -12.9670000076294 Eleotris pisonis 2
+13.4707002639771 -41.148998260498 Neogloboquadrina pachyderma 1991 111
+11.7093410491943 -6.70460748672485 Chlorophthalmus agassizi 1986 1
+12.6364002227783 -7.98898696899414 Carcharhinus limbatus 1986 1
+-7.11670017242432 -18.75 Conaea rapax 1973 30
+11.6034507751465 -16.3204536437988 Neomerinthe folgori 1986 1
+12.7852144241333 -13.0726642608643 Neomerinthe folgori 1986 1
+13.4905033111572 -11.4897603988647 Neomerinthe folgori 1986 1
+7.61232995986938 -46.7709999084473 Neogloboquadrina pachyderma 1989 39
+13.6466665267944 -11.0166664123535 Neomerinthe folgori 1986 1
+13.5691270828247 -10.8027153015137 Neomerinthe folgori 1986 1
+13.174916267395 -8.7584171295166 Neomerinthe folgori 1986 1
+12.2597818374634 -7.04792070388794 Neomerinthe folgori 1986 1
+11.8720836639404 -5.14061784744263 Neomerinthe folgori 1986 1
+11.7897167205811 -6.12797689437866 Neomerinthe folgori 1986 1
+13.4812002182007 -41.1352996826172 Neogloboquadrina pachyderma 1991 2
+-36.9500007629395 -15.6499996185303 Farranula carinata 1963 10
+-36.4669990539551 -53.5830001831055 Lissodendoryx (Lissodendoryx) flabellata 1963 1
+-3.20667004585266 -59.4467010498047 Adercotryma glomeratum 1994 8
+1.16666996479034 -55.4583015441895 Adercotryma glomeratum 1989 2
+-0.481667011976242 -56.3199996948242 Adercotryma glomeratum 1989 3
+3.59817004203796 -53.872200012207 Adercotryma glomeratum 1991 3
+-61.380500793457 0 Bitectatodinium tepikiense 1
+-61.801700592041 0 Bitectatodinium tepikiense 1
+-61.8116989135742 0 Bitectatodinium tepikiense 1
+-61.9042015075684 0 Bitectatodinium tepikiense 1
+3.37932991981506 -54.1716995239258 Eggerella bradyi 1991 3
+-40.5094985961914 -59.548698425293 Eggerella bradyi 1992 14
+-39.7052993774414 -59.826301574707 Eggerella bradyi 1992 14
+-40.823299407959 -59.5428009033203 Adercotryma glomeratum 1992 12
+-40.823299407959 -59.5428009033203 Eggerella bradyi 1992 14
+-41.3352012634277 -59.4832000732422 Eggerella bradyi 1992 7
+-35.5772018432617 -59.0587997436523 Eggerella bradyi 1992 15
+-5.99809980392456 -48.0617980957031 Neogloboquadrina pachyderma 1988 1
+-6 -48.5666999816895 Neogloboquadrina pachyderma 1988 1
+7.75670003890991 -48.3193016052246 Neogloboquadrina pachyderma 1988 1
+6.70599985122681 -49.1343002319336 Neogloboquadrina pachyderma 1988 1
+-4.99700021743774 -53.0759010314941 Neogloboquadrina pachyderma 1988 1
+-3.21180009841919 -50.7425003051758 Neogloboquadrina pachyderma 1988 1
+-2.84750008583069 -48.6940002441406 Neogloboquadrina pachyderma 1988 1
+0.0939999967813492 -41.8540000915527 Neogloboquadrina pachyderma 1988 1
+13.8283004760742 -29 Neogloboquadrina pachyderma 1988 1
+16.3551998138428 -35.6100006103516 Neogloboquadrina pachyderma 1988 1
+1.83809995651245 -44.250301361084 Neogloboquadrina pachyderma 1988 1
+1.25119996070862 -45.5028991699219 Neogloboquadrina pachyderma 1988 1
+13.0882997512817 -29.1749992370605 Neogloboquadrina pachyderma 1988 1
+0.289600014686584 -47.4023017883301 Neogloboquadrina pachyderma 1988 1
+-57.9404983520508 -45.0999984741211 Neogloboquadrina pachyderma 1988 1
+-58.8866996765137 -53.7643013000488 Neogloboquadrina pachyderma 1988 1
+-58.9039993286133 -53.7360000610352 Neogloboquadrina pachyderma 1988 1
+14.0050001144409 -27.9549999237061 Neogloboquadrina pachyderma 1988 1
+12.1549997329712 -30.4382991790771 Neogloboquadrina pachyderma 1988 1
+11.7399997711182 -31.0849990844727 Neogloboquadrina pachyderma 1988 1
+10.4809999465942 -44.4982986450195 Neogloboquadrina pachyderma 1988 1
+9.60999965667725 -45.6209983825684 Neogloboquadrina pachyderma 1988 1
+7.5939998626709 -46.7667007446289 Neogloboquadrina pachyderma 1988 1
+-7.50299978256226 -50.9519996643066 Neogloboquadrina pachyderma 1988 1
+-12.7033004760742 -49.0116996765137 Neogloboquadrina pachyderma 1988 1
+11.4517002105713 0 Protoperidinium shanghaiense 1
+-6.59170007705688 0 Protoperidinium shanghaiense 1
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+-32.1782989501953 0 Protoperidinium shanghaiense 1
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+-28.9367008209229 0 Protoperidinium shanghaiense 2
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+-48.3600006103516 0 Protoperidinium shanghaiense 1
+-43.9667015075684 0 Protoperidinium shanghaiense 1
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+-4.90000009536743 0 Protoperidinium shanghaiense 1
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+7.75829982757568 0 Protoperidinium shanghaiense 1
+1.48500001430511 0 Protoperidinium shanghaiense 1
+-2.6949999332428 0 Protoperidinium shanghaiense 1
+-57.6352996826172 -55.6155014038086 Neogloboquadrina pachyderma 1988 1
+-26.4832992553711 -4.31669998168945 Conaea rapax 1977 15
+11.6983003616333 -23.4316997528076 Eggerella bradyi 1992 17
+7.23999977111816 -24.6900005340576 Eggerella bradyi 1990 53
+-14.4899997711182 -41.2750015258789 Neogloboquadrina pachyderma 1993 91
+15.3000001907349 -29.9832992553711 Holohalaelurus regani 1988 1
+16.25 -30.1667003631592 Holohalaelurus regani 1991 1
+18.8332996368408 -35.63330078125 Holohalaelurus regani 1985 2
+15.4833002090454 -28.8999996185303 Holohalaelurus regani 1987 1
+16.9333000183105 -32.5833015441895 Holohalaelurus regani 1987 1
+14.7449998855591 -28.2332992553711 Holohalaelurus regani 1983 1
+18.9167003631592 -35.3333015441895 Holohalaelurus regani 1991 1
+17.2000007629395 -32.5833015441895 Holohalaelurus regani 1989 2
+16.2999992370605 -31.4500007629395 Holohalaelurus regani 1985 3
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+
+
diff -r 000000000000 -r 1fcd81d65467 visualize.r
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/visualize.r Thu Jan 18 09:33:52 2024 +0000
@@ -0,0 +1,56 @@
+#' Statically map indicators using ggplot
+#'
+#' @param grid spatial features, e.g. hexagons, to plot; requires a geometry
+#' spatial column
+#' @param column column name with indicator; default="shannon"
+#' @param label label to show on legend
+#' @param crs coordinate reference system; see `sf::st_crs()`
+#' @param trans For continuous scales, the name of a transformation object or
+#' the object itself. Built-in transformations include "asn", "atanh",
+#' "boxcox", "date", "exp", "hms", "identity" (default), "log", "log10", "log1p",
+#' "log2", "logit", "modulus", "probability", "probit", "pseudo_log",
+#' "reciprocal", "reverse", "sqrt" and "time". See `ggplot2::continuous_scale`
+#'
+#' @return ggplot2 plot
+#' @concept visualize
+#' @export
+#' @import rnaturalearth viridis ggplot2
+#'
+#' @examples
+gmap_indicator <- function(
+ grid, column = "shannon", label = "Shannon index", trans = "identity",
+ crs = "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs") {
+
+ world <- rnaturalearth::ne_countries(scale = "medium", returnclass = "sf")
+ bb <- sf::st_bbox(
+ sf::st_transform(grid, crs))
+
+ ggplot2::ggplot() +
+ ggplot2::geom_sf(
+ data = grid, ggplot2::aes_string(
+ fill = column, geometry = "geometry"), lwd = 0) +
+ viridis::scale_color_viridis(
+ option = "inferno", na.value = "white",
+ name = label, trans = trans) +
+ viridis::scale_fill_viridis(
+ option = "inferno", na.value = "white",
+ name = label, trans = trans) +
+ ggplot2::geom_sf(
+ data = world, fill = "#dddddd", color = NA) +
+ ggplot2::theme(
+ panel.grid.major.x = ggplot2::element_blank(),
+ panel.grid.major.y = ggplot2::element_blank(),
+ panel.grid.minor.x = ggplot2::element_blank(),
+ panel.grid.minor.y = ggplot2::element_blank(),
+ panel.background = ggplot2::element_blank(),
+ axis.text.x = ggplot2::element_blank(),
+ axis.text.y = ggplot2::element_blank(),
+ axis.ticks = ggplot2::element_blank(),
+ axis.title.x = ggplot2::element_blank(),
+ axis.title.y = ggplot2::element_blank()) +
+ ggplot2::xlab("") + ggplot2::ylab("") +
+ ggplot2::coord_sf(
+ crs = crs,
+ xlim = bb[c("xmin", "xmax")],
+ ylim = bb[c("ymin", "ymax")])
+}