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Script#8_OVERSEE_v8.R
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Script#8_OVERSEE_v8.R
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##### 06/06/2018: R Script to identify and remove the duplicate observations in the v7 datasets
### Aims to
# - load the v7 dataset, identify whether you have both OBIS & GBIF (unique(source) > 1), and separate dataset if you need to
# - identify duplicate observations uqing and is based on coords, depth, date and species name
# - remove the duplicates within each dataset
# - remove the duplicates between the 2 data sources (OBIS & GBIF) when needed
# - rbind and save in v8 dir
module load R/3.4.3/ # To load latest R version on kryo
### Latest update: 25/07/2018
library("dplyr")
library("tidyr")
library("stringr")
library("reshape2")
### ----------------------------------------------------------------------------------------------------------------------------
##### Script to identify and remive duplicates observations between GIBF and OBIS
# https://stackoverflow.com/questions/3171426/compare-two-data-frames-to-find-the-rows-in-data-frame-1-that-are-not-present-in
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v7-5.1v3.2/")
dir()
# Load
data <- get(load("Copepoda_05_06_18.Rdata"))
head(data)
str(data)
data$x <- as.numeric(as.character(data$x))
data$y <- as.numeric(as.character(data$y))
data$depth <- as.numeric(as.character(data$depth))
data$dist2coast <- as.numeric(as.character(data$dist2coast))
data$salinityWOA13 <- as.numeric(as.character(data$salinityWOA13))
data$date <- as.Date(data$date)
data$day <- as.numeric(as.character(data$day))
data$month <- as.numeric(as.character(data$month))
data$year <- as.numeric(as.character(data$year))
summary(data)
### Round the coordinates to the 1/4° degree
data$xbin <- as.factor(round(data$x, 3))
data$ybin <- as.factor(round(data$y, 3))
head(data)
tail(data)
### Separate OBIS from GBIF again
obis <- data[data$source == "OBIS",]
gbif <- data[data$source == "GBIF",]
dim(obis) ; dim(gbif)
summary(obis)
summary(gbif)
### 125 observations have NA value for day column in GBIF...check them and remove them
gbif[is.na(gbif$day),] #
unique(gbif[is.na(gbif$day),"species"]) # common copepods...
gbif$day[is.na(gbif$day)] <- 99
obis$day[is.na(obis$day)] <- 99
summary(obis)
summary(gbif)
### Create an ID based on: xbin, ybin, day, month, year, species name
obis$id <- paste(obis$xbin, obis$ybin, obis$depth, obis$day, obis$month, obis$year, obis$species, sep = "_")
gbif$id <- paste(gbif$xbin, gbif$ybin, gbif$depth, gbif$day, gbif$month, gbif$year, gbif$species, sep = "_")
length(obis$id) ; length(unique(obis$id)) # 793409 / 1342103 --> 40% are ducplicates
obis$id[1:1000]
length(gbif$id) ; length(unique(gbif$id)) # 521829/ 598290 , 13% are duplicates
gbif$id[1:1000]
### Remove ducplicates from both obis and gbif
gbif2 <- gbif[!duplicated(gbif$id),]
dim(gbif) ; dim(gbif2)
obis2 <- obis[!duplicated(obis$id),]
dim(obis) ; dim(obis2)
### Now, compare the ids between OBIS & GBIF and remove those in GBIF
# Basically, setdiff(bigFrame, smallFrame) gets you the extra records in the first table.
diff <- setdiff(gbif2$id, obis2$id)
# class(diff) # character
length(diff)
(length(diff) / nrow(gbif2))*100 # 17.68% are not duplicates
### Use 'diff' to identify the obs of gbif2 that are to be added to obis2
tokeep <- gbif2[gbif2$id %in% diff,]
data2 <- rbind(obis2, tokeep)
dim(data2)
# And check if there duplicates in data2$id
length(unique(data2$id)) # ok
### Great, use the code above to identify and remove duplicates across datasets that combine OBIS & GBIF
### ----------------------------------------------------------------------------------------------------------------------------
##### 1°) v7-5.1v3.1
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v7-5.1v3.1/")
dir()
# For testing:
# f <- dir()[2]
for(f in dir()) {
# Useless message()
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v7-5.1v3.1/")
message(paste("Doing ", f, sep = ""))
data <- get(load(f))
# Converting some stuff if needed
data$x <- as.numeric(as.character(data$x))
data$y <- as.numeric(as.character(data$y))
data$depth <- as.numeric(as.character(data$depth))
if( f == "Thecosomata_MAREDAT_05_06_18.Rdata" ) {
colnames(data)[7] <- "date"
} else {
data$date <- as.Date(data$date)
} # eo if else loop
data$day <- as.numeric(as.character(data$day))
data$month <- as.numeric(as.character(data$month))
data$year <- as.numeric(as.character(data$year))
# Round the coordinates to the 1/4° degree
data$xbin <- as.factor(round(data$x, 3))
data$ybin <- as.factor(round(data$y, 3))
# head(data)
# tail(data)
# If length(unique(data$source)) > 1 -> you have OBIS, GBIF
if( length(unique(data$source)) > 1 ) {
# Separate OBIS from GBIF again
obis <- data[data$source == "OBIS",]
gbif <- data[data$source == "GBIF",]
# Convert NA days to 999
gbif$day[is.na(gbif$day)] <- 999
obis$day[is.na(obis$day)] <- 999
# Create an ID based on: xbin, ybin, depth, day, month, year, species name
obis$id <- paste(obis$xbin, obis$ybin, obis$depth, obis$day, obis$month, obis$year, obis$species, sep = "_")
gbif$id <- paste(gbif$xbin, gbif$ybin, gbif$depth, gbif$day, gbif$month, gbif$year, gbif$species, sep = "_")
# Remove ducplicates from both obis and gbif
gbif2 <- gbif[!duplicated(gbif$id),]
obis2 <- obis[!duplicated(obis$id),]
# nrow(gbif2) ; nrow(gbif)
# nrow(obis2) ; nrow(obis)
# Now, compare the ids between OBIS & GBIF and remove those in GBIF
diff <- setdiff(gbif2$id, obis2$id)
# length(diff)
# (length(diff) / nrow(gbif2))*100 # % of non duplicates
# Use 'diff' to identify the obs of gbif2 that are to be added to obis2
tokeep <- gbif2[gbif2$id %in% diff,]
# rbind
data2 <- rbind(obis2, tokeep)
# nrow(data2) / nrow(data)
# length(unique(data2$id)) # ok
# Got to proper dir and save
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v8-5.1v3.1/")
save(data2, file = str_replace(f, "05_06_18", "06_06_18") )
# Clean
rm(data2, data, tokeep, obis2, gbif2, obis, gbif) ; gc()
} else {
# Simply apply the same steps as above but on 'data'
data$day[is.na(data$day)] <- 999
# Create an ID based on: xbin, ybin, day, month, year, species name
data$id <- paste(data$xbin, data$ybin, data$depth, data$day, data$month, data$year, data$species, sep = "_")
# Remove ducplicates from both obis and gbif
data2 <- data[!duplicated(data$id),]
# dim(data2) ; dim(data)
# Got to proper dir and save
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v8-5.1v3.1/")
save(data2, file = str_replace(f, "05_06_18", "06_06_18"))
# Clean
rm(data2, data) ; gc()
} # eo if else loop
} # eo for loop
### Check results
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v8-5.1v3.1/")
# dir()
for(f in dir()) {
# Useless message
message(paste(str_replace(f, "_06_06_18.Rdata", ""), sep = ""))
d <- get(load(f))
message(paste(nrow(d), sep = ""))
message(paste(length(unique(d$species)), sep = ""))
message(paste("", sep = ""))
rm(d)
} # eo for loop
### ----------------------------------------------------------------------------------------------------------------------------
##### 2°) v7-5.1v3.2
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v7-5.1v3.2/")
dir()
# For testing:
# f <- dir()[2]
for(f in dir()) {
# Useless message()
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v7-5.1v3.2/")
message(paste("Doing ", f, sep = ""))
data <- get(load(f))
# Converting some stuff if needed
data$x <- as.numeric(as.character(data$x))
data$y <- as.numeric(as.character(data$y))
data$depth <- as.numeric(as.character(data$depth))
if( f == "Thecosomata_MAREDAT_05_06_18.Rdata" ) {
colnames(data)[7] <- "date"
} else {
data$date <- as.Date(data$date)
} # eo if else loop
data$day <- as.numeric(as.character(data$day))
data$month <- as.numeric(as.character(data$month))
data$year <- as.numeric(as.character(data$year))
# Round the coordinates to the 1/4° degree
data$xbin <- as.factor(round(data$x, 3))
data$ybin <- as.factor(round(data$y, 3))
# head(data)
# tail(data)
# If length(unique(data$source)) > 1 -> you have OBIS, GBIF
if( length(unique(data$source)) > 1 ) {
# Separate OBIS from GBIF again
obis <- data[data$source == "OBIS",]
gbif <- data[data$source == "GBIF",]
# Convert NA days to 999
gbif$day[is.na(gbif$day)] <- 999
obis$day[is.na(obis$day)] <- 999
# Create an ID based on: xbin, ybin, depth, day, month, year, species name
obis$id <- paste(obis$xbin, obis$ybin, obis$depth, obis$day, obis$month, obis$year, obis$species, sep = "_")
gbif$id <- paste(gbif$xbin, gbif$ybin, gbif$depth, gbif$day, gbif$month, gbif$year, gbif$species, sep = "_")
# Remove ducplicates from both obis and gbif
gbif2 <- gbif[!duplicated(gbif$id),]
obis2 <- obis[!duplicated(obis$id),]
# nrow(gbif2) ; nrow(gbif)
# nrow(obis2) ; nrow(obis)
# Now, compare the ids between OBIS & GBIF and remove those in GBIF
diff <- setdiff(gbif2$id, obis2$id)
# length(diff)
# (length(diff) / nrow(gbif2))*100 # % of non duplicates
# Use 'diff' to identify the obs of gbif2 that are to be added to obis2
tokeep <- gbif2[gbif2$id %in% diff,]
# rbind
data2 <- rbind(obis2, tokeep)
# nrow(data2) / nrow(data)
# length(unique(data2$id)) # ok
# Got to proper dir and save
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v8-5.1v3.2/")
save(data2, file = str_replace(f, "05_06_18", "06_06_18") )
# Clean
rm(data2, data, tokeep, obis2, gbif2, obis, gbif) ; gc()
} else {
# Simply apply the same steps as above but on 'data'
data$day[is.na(data$day)] <- 999
# Create an ID based on: xbin, ybin, day, month, year, species name
data$id <- paste(data$xbin, data$ybin, data$depth, data$day, data$month, data$year, data$species, sep = "_")
# Remove ducplicates from both obis and gbif
data2 <- data[!duplicated(data$id),]
# dim(data2) ; dim(data)
# Got to proper dir and save
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v8-5.1v3.2/")
save(data2, file = str_replace(f, "05_06_18", "06_06_18"))
# Clean
rm(data2, data) ; gc()
} # eo if else loop
} # eo for loop
### Check results
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v8-5.1v3.2/")
# dir()
for(f in dir()) {
# Useless message
message(paste(str_replace(f, "_06_06_18.Rdata", ""), sep = ""))
d <- get(load(f))
message(paste(nrow(d), sep = ""))
message(paste(length(unique(d$species)), sep = ""))
message(paste("", sep = ""))
rm(d)
} # eo for loop
### ----------------------------------------------------------------------------------------------------------------------------
##### 3°) v7-5.2v3.1
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v7-5.2v3.1/")
dir()
# For testing:
# f <- dir()[2]
for(f in dir()) {
# Useless message()
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v7-5.2v3.1/")
message(paste("Doing ", f, sep = ""))
data <- get(load(f))
# Converting some stuff if needed
data$x <- as.numeric(as.character(data$x))
data$y <- as.numeric(as.character(data$y))
data$depth <- as.numeric(as.character(data$depth))
if( f == "Thecosomata_MAREDAT_05_06_18.Rdata" ) {
colnames(data)[7] <- "date"
} else {
data$date <- as.Date(data$date)
} # eo if else loop
data$day <- as.numeric(as.character(data$day))
data$month <- as.numeric(as.character(data$month))
data$year <- as.numeric(as.character(data$year))
# Round the coordinates to the 1/4° degree
data$xbin <- as.factor(round(data$x, 3))
data$ybin <- as.factor(round(data$y, 3))
# head(data)
# tail(data)
# If length(unique(data$source)) > 1 -> you have OBIS, GBIF
if( length(unique(data$source)) > 1 ) {
# Separate OBIS from GBIF again
obis <- data[data$source == "OBIS",]
gbif <- data[data$source == "GBIF",]
# Convert NA days to 999
gbif$day[is.na(gbif$day)] <- 999
obis$day[is.na(obis$day)] <- 999
# Create an ID based on: xbin, ybin, depth, day, month, year, species name
obis$id <- paste(obis$xbin, obis$ybin, obis$depth, obis$day, obis$month, obis$year, obis$species, sep = "_")
gbif$id <- paste(gbif$xbin, gbif$ybin, gbif$depth, gbif$day, gbif$month, gbif$year, gbif$species, sep = "_")
# Remove ducplicates from both obis and gbif
gbif2 <- gbif[!duplicated(gbif$id),]
obis2 <- obis[!duplicated(obis$id),]
# nrow(gbif2) ; nrow(gbif)
# nrow(obis2) ; nrow(obis)
# Now, compare the ids between OBIS & GBIF and remove those in GBIF
diff <- setdiff(gbif2$id, obis2$id)
# length(diff)
# (length(diff) / nrow(gbif2))*100 # % of non duplicates
# Use 'diff' to identify the obs of gbif2 that are to be added to obis2
tokeep <- gbif2[gbif2$id %in% diff,]
# rbind
data2 <- rbind(obis2, tokeep)
# nrow(data2) / nrow(data)
# length(unique(data2$id)) # ok
# Got to proper dir and save
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v8-5.2v3.1/")
save(data2, file = str_replace(f, "05_06_18", "06_06_18") )
# Clean
rm(data2, data, tokeep, obis2, gbif2, obis, gbif) ; gc()
} else {
# Simply apply the same steps as above but on 'data'
data$day[is.na(data$day)] <- 999
# Create an ID based on: xbin, ybin, day, month, year, species name
data$id <- paste(data$xbin, data$ybin, data$depth, data$day, data$month, data$year, data$species, sep = "_")
# Remove ducplicates from both obis and gbif
data2 <- data[!duplicated(data$id),]
# dim(data2) ; dim(data)
# Got to proper dir and save
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v8-5.2v3.1/")
save(data2, file = str_replace(f, "05_06_18", "06_06_18"))
# Clean
rm(data2, data) ; gc()
} # eo if else loop
} # eo for loop
### Check results
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v8-5.2v3.1/")
# dir()
for(f in dir()) {
# Useless message
message(paste(str_replace(f, "_06_06_18.Rdata", ""), sep = ""))
d <- get(load(f))
message(paste(nrow(d), sep = ""))
message(paste(length(unique(d$species)), sep = ""))
message(paste("", sep = ""))
rm(d)
} # eo for loop
### ----------------------------------------------------------------------------------------------------------------------------
##### 4°) v7-5.2v3.2
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v7-5.2v3.2/")
dir()
# For testing:
# f <- dir()[2]
for(f in dir()) {
# Useless message()
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v7-5.2v3.2/")
message(paste("Doing ", f, sep = ""))
data <- get(load(f))
# Converting some stuff if needed
data$x <- as.numeric(as.character(data$x))
data$y <- as.numeric(as.character(data$y))
data$depth <- as.numeric(as.character(data$depth))
if( f == "Thecosomata_MAREDAT_05_06_18.Rdata" ) {
colnames(data)[7] <- "date"
} else {
data$date <- as.Date(data$date)
} # eo if else loop
data$day <- as.numeric(as.character(data$day))
data$month <- as.numeric(as.character(data$month))
data$year <- as.numeric(as.character(data$year))
# Round the coordinates to the 1/4° degree
data$xbin <- as.factor(round(data$x, 3))
data$ybin <- as.factor(round(data$y, 3))
# head(data)
# tail(data)
# If length(unique(data$source)) > 1 -> you have OBIS, GBIF
if( length(unique(data$source)) > 1 ) {
# Separate OBIS from GBIF again
obis <- data[data$source == "OBIS",]
gbif <- data[data$source == "GBIF",]
# Convert NA days to 999
gbif$day[is.na(gbif$day)] <- 999
obis$day[is.na(obis$day)] <- 999
# Create an ID based on: xbin, ybin, depth, day, month, year, species name
obis$id <- paste(obis$xbin, obis$ybin, obis$depth, obis$day, obis$month, obis$year, obis$species, sep = "_")
gbif$id <- paste(gbif$xbin, gbif$ybin, gbif$depth, gbif$day, gbif$month, gbif$year, gbif$species, sep = "_")
# Remove ducplicates from both obis and gbif
gbif2 <- gbif[!duplicated(gbif$id),]
obis2 <- obis[!duplicated(obis$id),]
# nrow(gbif2) ; nrow(gbif)
# nrow(obis2) ; nrow(obis)
# Now, compare the ids between OBIS & GBIF and remove those in GBIF
diff <- setdiff(gbif2$id, obis2$id)
# length(diff)
# (length(diff) / nrow(gbif2))*100 # % of non duplicates
# Use 'diff' to identify the obs of gbif2 that are to be added to obis2
tokeep <- gbif2[gbif2$id %in% diff,]
# rbind
data2 <- rbind(obis2, tokeep)
# nrow(data2) / nrow(data)
# length(unique(data2$id)) # ok
# Got to proper dir and save
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v8-5.2v3.2/")
save(data2, file = str_replace(f, "05_06_18", "06_06_18") )
# Clean
rm(data2, data, tokeep, obis2, gbif2, obis, gbif) ; gc()
} else {
# Simply apply the same steps as above but on 'data'
data$day[is.na(data$day)] <- 999
# Create an ID based on: xbin, ybin, day, month, year, species name
data$id <- paste(data$xbin, data$ybin, data$depth, data$day, data$month, data$year, data$species, sep = "_")
# Remove ducplicates from both obis and gbif
data2 <- data[!duplicated(data$id),]
# dim(data2) ; dim(data)
# Got to proper dir and save
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v8-5.2v3.2/")
save(data2, file = str_replace(f, "05_06_18", "06_06_18"))
# Clean
rm(data2, data) ; gc()
} # eo if else loop
} # eo for loop
### Check results
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v8-5.2v3.2/")
# dir()
for(f in dir()) {
# Useless message
message(paste(str_replace(f, "_06_06_18.Rdata", ""), sep = ""))
d <- get(load(f))
message(paste(nrow(d), sep = ""))
message(paste(length(unique(d$species)), sep = ""))
message(paste("", sep = ""))
rm(d)
} # eo for loop
### ----------------------------------------------------------------------------------------------------------------------------
##### 06/06/18: Combine all datasets and map sampling effort
library("rgeos")
library("raster")
library("maptools")
library("rgdal")
library("dplyr")
library("tidyr")
library("stringr")
library("reshape2")
library("geosphere")
library("ncdf4")
library("ggplot2")
library("RColorBrewer")
### Load coastline for mapping afterwards
cl <- read.csv("world_coast.csv", h = T)
coast <- list(
# the coast polygon itself, a bit lighter than usual to avoid taking too much attention out of the data itself
geom_polygon(aes(x = lon, y = lat), data = cl, fill = "grey50"),
geom_path(aes(x = lon, y = lat), data = cl, colour = "black", linetype = 1, size = 0.5),
# appropriate projection
coord_quickmap(),
# remove extra space around the coast
scale_x_continuous(name = "Longitude",
breaks = c(-180,-150,-120,-90,-60,-30,0,30,60,90,120,150,180),
labels = c("-180°E","-150°E","-120°E","-90°E","-60°E","-30°E","0°","30°E","60°E","90°E","120°E","150°E","180°E"),
expand = c(0,0)),
scale_y_continuous(name = "Latitude",
breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°","30°N","60°N","90°N"),
expand = c(0,0)),
# dark gray background for the panel and legend
theme(
panel.background = element_rect(fill = "white"), # background
legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "white")
)
)
### Load and plot
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v8-5.1v3.2/")
files <- dir()
res <- lapply(files, function(f) {
message(paste(f, sep = ""))
data <- get(load(f))
return(data[,c("id","x","y","species","family","order")])
}
) # eo lapply
table <- do.call(rbind, res)
dim(table)
str(table)
length(unique(table$species))
### Counts per sp/ family/ orders
counts <- data.frame(table %>% count(species) )
head(counts)
counts[order(counts$n, decreasing = T),]
# Assess richness of sampling effort thresholds
dim(counts[counts$n >= 1000,]) # 135 ; 168 ; 99 ; 133
dim(counts[counts$n >= 100,]) # 517 ; 608 ; 314 ; 408
dim(counts[counts$n >= 50,]) # 689 ; 786 ; 447 ; 529
dim(counts[counts$n >= 30,]) # 926
# counts <- data.frame(table %>% count(order) )
# counts[order(counts$n, decreasing = T),]
### Bin x and y in 1/4°, use dplyr() to compute sampling effort and then map with 'coast'
colnames(table)
table$xbin <- round(table$x, digits = 0.1)
table$ybin <- round(table$y, digits = 0.1)
summary(table)
table[1:1000,c("x","y","xbin","ybin")]
table$id2 <- paste(table$xbin, table$ybin, sep = "_")
### dplyr
require("dplyr")
require("viridis")
table2 <- data.frame(table %>%
group_by(id2) %>%
summarise(x = unique(xbin), y = unique(ybin), n = n(), richness = length(unique(species)))
) # eo ddf
dim(table2)
head(table2)
map_sampling <- ggplot() + geom_tile(aes(x = x, y = y, fill = log(n)), data = table2) +
scale_fill_distiller(name = "Sampling effort\nlog(n occurrences)", palette = "YlGnBu") + coast + coord_quickmap()
map_rich <- ggplot() + geom_tile(aes(x = x, y = y, fill = richness), data = table2) +
scale_fill_distiller(name = "Species richness", palette = "YlGnBu") + coast + coord_quickmap()
# Save
ggsave(plot = map_sampling, filename = "map_effort_v8v5.2v3.2.pdf", dpi = 300, width = 12, height = 9)
ggsave(plot = map_rich, filename = "map_richness_v8v5.2v3.2.pdf", dpi = 300, width = 12, height = 9)
# OKAY
### ----------------------------------------------------------------------------------------------------------------------------
##### 25/07/18: Map sampling effort of each major zooplankton group, to help choose main background selection groups
library("rgeos")
library("raster")
library("maptools")
library("rgdal")
library("dplyr")
library("tidyr")
library("stringr")
library("reshape2")
library("geosphere")
library("ncdf4")
library("ggplot2")
library("RColorBrewer")
### Load coastline for mapping afterwards
cl <- read.csv("world_coast.csv", h = T)
coast <- list(
# the coast polygon itself, a bit lighter than usual to avoid taking too much attention out of the data itself
geom_polygon(aes(x = lon, y = lat), data = cl, fill = "grey50"),
geom_path(aes(x = lon, y = lat), data = cl, colour = "black", linetype = 1, size = 0.5),
# appropriate projection
coord_quickmap(),
# remove extra space around the coast
scale_x_continuous(name = "Longitude",
breaks = c(-180,-150,-120,-90,-60,-30,0,30,60,90,120,150,180),
labels = c("-180°E","-150°E","-120°E","-90°E","-60°E","-30°E","0°","30°E","60°E","90°E","120°E","150°E","180°E"),
expand = c(0,0)),
scale_y_continuous(name = "Latitude",
breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°","30°N","60°N","90°N"),
expand = c(0,0)),
# dark gray background for the panel and legend
theme(
panel.background = element_rect(fill = "white"), # background
legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "white")
)
)
### Load and plot
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v8-5.1v3.2/")
files <- dir()
res <- lapply(files, function(f) {
message(paste(f, sep = ""))
data <- get(load(f))
return(data[,c("id","x","y","species","family","order","class","phylum")])
}
) # eo lapply
table <- do.call(rbind, res)
dim(table) # 766'033 6
str(table)
colnames(table)
unique(table$order)
unique(table$class)
unique(table$phylum)
### Map sampling effort for unique phylum !
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/")
for(p in unique(table$phylum)) {
message(paste("Mapping ", p, sep = ""))
data <- table[table$phylum == p,]
# provide 1° bins & ID
data$xbin <- round(data$x, digits = 0.1)
data$ybin <- round(data$y, digits = 0.1)
data$id2 <- paste(data$xbin, data$ybin, sep = "_")
# compute sampling effort
table2 <- data.frame(data %>%
group_by(id2) %>%
summarise(x = unique(xbin), y = unique(ybin), n = n(), richness = length(unique(species)))
) # eo ddf
# Make map
map_sampling <- ggplot() + geom_tile(aes(x = x, y = y, fill = log(n)), data = table2) +
scale_fill_distiller(name = "Sampling effort\nlog(n occurrences)", palette = "YlGnBu") +
xlab("Longitude") + ylab("Latitude") + coast + coord_quickmap() + ggtitle(p)
# Save
ggsave(plot = map_sampling, filename = paste("map_sampling_effort_", p, "_v8-5.1v3.2.pdf", sep = ""), dpi = 300, width = 10, height = 8)
# Make space
rm(data, table2, map_sampling)
gc()
} # eo for loop mapping