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04_figure_number_of_occurrences.R
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04_figure_number_of_occurrences.R
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# a script to visualize the raw and cleaned records
library(tidyverse)
library(raster)
library(viridis)
library(ggthemes)
library(speciesgeocodeR)
library(rnaturalearth)
# load data
dat <- read_csv("output/all_records.csv") %>%
filter(!is.na(decimalLongitude)) %>%
filter(!is.na(decimalLatitude))
dat_cl <- dat %>%
filter(summary)
be <- raster("input/ABROCOMIDAE_ABROCOMIDAE.tif")
# define projections
behr <- '+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +datum=WGS84 +ellps=WGS84 +units=m +no_defs'
wgs1984 <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
# Figure one - total
# convert data
pts_rw <- dat[, c("decimalLongitude", "decimalLatitude")]%>%
SpatialPoints(proj4string = CRS(wgs1984))%>%
spTransform(behr)
pts_rw <- data.frame(accepted_name_species = dat$species,
coordinates(pts_rw))
pts_cl <- dat_cl[, c("decimalLongitude", "decimalLatitude")]%>%
SpatialPoints(proj4string = CRS(wgs1984))%>%
spTransform(behr)
pts_cl <- data.frame(accepted_name_species = dat_cl$species,
coordinates(pts_cl))
# generate raster with species occurrence
abu_rw <- RichnessGrid(x = pts_rw, ras = be, type = "abu")
abu_cl <- RichnessGrid(x = pts_cl, ras = be, type = "abu")
plo <- abu_rw - abu_cl
plo <-data.frame(rasterToPoints(plo)) %>%
filter(layer != 0)
#
# plo_rw <-data.frame(rasterToPoints(abu_rw))%>%
# # filter(layer > 0 ) %>%
# mutate(dataset = "Raw")
#
# plo_cl <-data.frame(rasterToPoints(abu_cl))%>%
# # filter(layer > 0 ) %>%
# mutate(dataset = "Filtered")
#
# plo <- bind_rows(plo_rw, plo_cl)
# background map
world.inp <- suppressWarnings(rnaturalearth::ne_download(scale = 110,
type = 'land',
category = 'physical',
load = TRUE))
world.behr <- spTransform(world.inp, CRS(behr)) %>% fortify()
world.countries <- rnaturalearth::ne_countries(type = 'countries',
scale = 110)
countries.behr <- spTransform(world.countries, CRS(behr)) %>% fortify()
# Axis labels
ticks_x <- SpatialPoints(data.frame(c(-120,-100,-80, -60, -40, -20),0), proj4string = CRS(wgs1984))
ticks_x <- data.frame(x = coordinates(spTransform(ticks_x, behr))[,1],
long = c(-120,-100,-80, -60, -40, -20))
ticks_y <- SpatialPoints(data.frame(0, c(40, 20, 0, -20, -40, -60)), proj4string = CRS(wgs1984))
ticks_y <- data.frame(y = coordinates(spTransform(ticks_y, behr))[,2],
long = c(40, 20, 0, -20, -40, -60))# plot
ggplot()+
geom_polygon(data = countries.behr,
aes(x = long, y = lat, group = group),
fill = "transparent",
color = "grey20")+
geom_tile(data = plo, aes(x = x, y = y, fill = log(layer)), alpha = 0.8)+
scale_fill_viridis(name = "Absolute number\nof flagged records", direction = -1, na.value = "transparent",
breaks = c(log(1), log(10), log(100), log(1000), log(5000)),
labels = c(1,10,100,1000,5000))+
scale_x_continuous(breaks = ticks_x[,1], labels = ticks_x[,2], limits = c(-12000000, -3000000))+
scale_y_continuous(breaks = ticks_y[,1], labels = ticks_y[,2], limits = c(-6500000, 4500000))+
coord_fixed()+
theme_bw()+
theme(legend.position = "bottom",
legend.key.width = unit(1.5, "cm"),
axis.title = element_blank())#+
# facet_wrap(.~ dataset, ncol = 2)
ggsave("output/figure_number_of_records.png", height = 8, width=8)
# Figure 2
plo <- dat %>%
dplyr::select(-coordinateUncertaintyInMeters, -taxon, -countryCode, -gbifID,
-class,
-genus, -taxonRank,
-basisOfRecord,
-individualCount, -year,
-family, -.summary,
-summary) %>%
rename(decimalspecies = species) %>%
pivot_longer(cols = -contains("decimal"), names_to = "test", values_to = "test_result") %>%
filter(!test_result) %>%
mutate(test = recode(test,
.urb = "Urban areas",
coordinate_base = "Basis of record",
.dpl = "Duplicates",
record_id = "Identification level",
record_age = "Collection year",
coordinate_precision = "Coordinate precision",
.cap = "Capitals",
.inst = "Biodiversity institutions",
.cen = "Political centroids",
individual_count = "Individual count",
.sea = "Sea/land area",
.zer = "Zeros",
.equ = "Equal lat/lon")) %>%
filter(test != "Equal lat/lon")
plo <- split(plo, f = plo$test)
plo <- lapply(plo,
function(k){
pts <- k[, c("decimalLongitude", "decimalLatitude")]%>%
SpatialPoints(proj4string = CRS(wgs1984))%>%
spTransform(behr) %>%
coordinates()
pts <- data.frame(test = k$decimalspecies,
pts)
out <- pts %>%
RichnessGrid(ras = be, type = "abu") %>%
rasterToPoints() %>%
data.frame()})
plo <- bind_rows(plo, .id = "test")
plo <- plo %>%
mutate(test = factor(test, levels = c("Basis of record",
"Collection year",
"Coordinate precision",
"Identification level",
"Individual count",
"Capitals",
"Biodiversity institutions",
"Duplicates",
"Political centroids",
"Equal lat/lon",
"Sea/land area",
"Urban areas",
"Zeros")))
ggplot()+
# geom_polygon(data = world.behr,
# aes(x = long, y = lat, group = group),
# fill = "transparent",
# color = "grey20")+
geom_polygon(data = countries.behr,
aes(x = long, y = lat, group = group),
fill = "transparent",
color = "grey50")+
geom_tile(data = plo, aes(x = x, y = y, fill = log(layer)), alpha = 1)+
scale_fill_viridis(name = "Absolute number\nof flagged records", direction = -1, na.value = "transparent",
breaks = c(log(1), log(10), log(100), log(1000), log(5000)),
labels = c(1,10,100,1000,5000))+
xlim(-12000000, -3000000)+
ylim(-6500000, 4500000)+
coord_fixed()+
theme_bw()+
theme(legend.position = "bottom",
legend.key.width = unit(5, "cm"),
legend.key.height = unit(1.5, "cm"),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
text = element_text(size=25))+
facet_wrap(.~ test)
ggsave("output/figure_number_of_records_split.pdf", height = 20, width=16)