R/make_RZA_plots.R
In Copepoda/rrza: Maps for the Rapid Zooplankton Assesment (RZA)
#' @title Abundance Maps for Rapid Zooplankton Assesment taxa.
#' @description This function takes an RZA .xlxs dataframe and will make either
#' individual plots of each RZA taxa group or two faceted maps based on 150
#' and 500 micron mesh. The maps plot abundance per cubic meter of the
#' rza taxa for each station. The map regions are standardized for each of the
#' major cruises/grids for EcoFOCI.
#' @param rza_path The path to the directory where the .xlxs rza dataframe
#' is located along with the .xlxs file name. This is a dataframe specifically
#' formated for rza data entry.
#' @param region There are six geographic regions which produce standardized
#' maps. The six standardized maps can be set to Arctic, Northern BS, 70 meter,
#' BASIS, GOA, and MACE 2018.
#' @param facets By default facets is set to TRUE. When set to TRUE the
#' function will produce two sets of faceted maps. One of the 60 bongo rza
#' taxa and one of the 20 bongo rza taxa. When facets is set to FALSE the
#' function will produce a map for each rza taxa.
#' @return Either faceted maps or individual maps of rza taxa abundance.
#' @export make_rza_plots
#' @name rrza
make_rza_plots <- function(rza_path, region, facets = TRUE){
# reads in the first sheet in the .xlxs rza sheet------------------------------
rza <- readxl::read_excel(rza_path,sheet = 1)
# removes NA or non entererd beaker volumes to avoid function errors-----------
rza <- rza %>% dplyr::filter(!is.na(BEAKER_VOLUME))
# Latitude and longitude need to be in decimal degrees. Figures out if that is
# already the case and will convert if it is necessary.------------------------
if(class(rza$LON) == "character"){
rza <- rza %>%
dplyr::mutate(LAT = measurements::conv_unit(LAT,
from = 'deg_dec_min',
to = 'dec_deg'))%>%
dplyr::mutate(LON = measurements::conv_unit(LON,
from = 'deg_dec_min',
to = 'dec_deg'))%>%
dplyr::mutate(LAT = as.numeric(LAT))%>%
dplyr::mutate(LON = as.numeric(LON))
} else {
rza
}
# Bring in shape files from extdata within package and correct projection------
map <- sf::st_read(dsn = "inst/extdata",
layer = "Alaska_dcw_polygon_Project",
quiet = TRUE)%>%
sf::st_transform(., "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
# Bring in 200 meter isobath and corrects projection---------------------------
bath_200m <- sf::st_read(dsn = "inst/extdata",
layer = "ne_10m_bathymetry_K_200",
quiet = TRUE)%>%
sf::st_transform(., "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
# Defines the latitudinal limits for each region------------------------------
ar_xlim <- c(-170,-153)#Arctic
bs_xlim <- c(-174,-159)#BASIS Grid
ga_xlim <- c(-168,-148)#GOA
is_xlim <- c(-176,-160)#70 meter isobath
nb_xlim <- c(-172,-163)#Northern Bering Sea
mace2018_xlim <- c(-180,-170)#MACE2018 Survey
# Determines which xlim will be used based on user input-----------------------
region_xlim <- if(region == "Arctic"){
ar_xlim
} else if (region == "BASIS"){
bs_xlim
} else if (region == "70 meter"){
is_xlim
} else if (region == "Northern BS"){
nb_xlim
} else if (region == "MACE 2018"){
mace2018_xlim
} else if (region == "GOA"){
ga_xlim
} else {
stop(paste("The region",region,"is not an option. The choices are
Arctic, Northern BS, 70 meter, BASIS, GOA, and MACE 2018."))
}
# Defines the longitudinal limits for each region------------------------------
ar_ylim <- c(66,73)#Arctic
bs_ylim <- c(54,60)#BASIS Grid
ga_ylim <- c(52,60)#GOA
is_ylim <- c(53,63)#70 meter isobath
nb_ylim <- c(59,66)#Northern Bering Sea
mace2018_ylim <- c(58,62)#MACE2018 Survey
# Determines which ylim will be used based on user input-----------------------
region_ylim <- if(region == "Arctic"){
ar_ylim
} else if (region == "BASIS"){
bs_ylim
} else if (region == "70 meter"){
is_ylim
} else if (region == "Northern BS"){
nb_ylim
} else if (region == "MACE 2018"){
mace2018_ylim
} else if (region == "GOA"){
ga_xlim
}
# Makes nice names for the plots-----------------------------------------------
rza_taxa_names <- c('Euphausiids_GT15' = "Euphausiids > 15mm",
'Copepods_GT2' = "Large Copepods",
'Euphausiids_LT15' = "Euphausiids < 15mm",
'Naked_Pteropods' = "Naked Snails",
'Chaetognaths' = "Chaetognaths",
'Amphipods' = "Amphipods",
'Decapods' = "Decapod larvae",
'Other_60BON' = "Other Large",
'Copepods_LT2' = "Small Copepods",
'Shelled_Pteropods' = "Pelagic Snail",
'Other_20BON' = "Other small")
# Check for plot folder and make if it doesn't exist---------------------------
if(dir.exists(paste(rza_path,"/plots",sep = "")) == FALSE){
dir.create(paste(rza_path,"/plots",sep = ""))
}
# path to where the plots will be written--------------------------------------
remove <- length(stringr::str_count(unlist(stringr::str_split(rza_path,"/"))))
folder_path <- stringr::str_c(unlist(stringr::str_split(
rza_path,"/"))[-c(remove -1, remove)], collapse = "/")
plot_path <- paste(folder_path, "plots/", sep = "/")
# name for each of the faceted plots-------------------------------------------
name_20BON_plot <- paste(plot_path,"RZA_",unique(rza$CRUISE),
"_20BON.png", sep = "")
name_60BON_plot <- paste(plot_path,"RZA_",unique(rza$CRUISE),
"_60BON.png", sep = "")
#
if(facets == TRUE){
breaks_20 <- as.integer(range(rza %>% dplyr::filter(GEAR_NAME == "20BON")%>%
dplyr::filter(EST_NUM_PERM3 > 0)%>%
dplyr::collect %$% as.vector(log10(EST_NUM_PERM3)), na.rm = TRUE))
plot_20bon <- ggplot2::ggplot()+
ggplot2::geom_sf(color = "black", data = bath_200m[3], alpha = 0)+
ggplot2::geom_sf(fill ="#a7ad94", color = "black", data = map[1])+
ggplot2::coord_sf(xlim = region_xlim, ylim = region_ylim)+
ggplot2::geom_point(ggplot2::aes(LON,LAT, color = log10(EST_NUM_PERM3)), size = 6,
data = rza %>% dplyr::filter(GEAR_NAME == "20BON") %>%
dplyr::filter(EST_NUM_PERM3 > 0))+
ggplot2::geom_point(ggplot2::aes(LON,LAT), size = 4, shape = 4,
data = rza %>% dplyr::filter(GEAR_NAME == "20BON") %>%
dplyr::filter(EST_NUM_PERM3 == 0))+
viridis::scale_color_viridis(option = "viridis",
name = expression(paste("# ","m"^"-3")),
breaks = c(seq(from = breaks_20[1],
to = breaks_20[2], by = 1)),
labels = c(10^seq(from = breaks_20[1],
to = breaks_20[2], by = 1)))+
ggplot2::scale_x_continuous(breaks = seq(region_xlim[1],
(region_xlim[2] - 2), by = 4))+
ggplot2::scale_y_continuous(breaks = seq(region_ylim[1],
region_ylim[2], by = 2))+
ggplot2::theme_bw()+
ggplot2::xlab(label = "Longitude")+
ggplot2::ylab(label = "Latitude")+
ggplot2::theme(
axis.text.y = ggplot2::element_text(face = "bold", size = 12),
axis.text.x = ggplot2::element_text(face = "bold", size = 12),
axis.title = ggplot2::element_text(face = "bold", size = 18),
strip.text = ggplot2::element_text(face = "bold", size = 18),
legend.title = ggplot2::element_text(face = "bold",size = 16),
legend.text = ggplot2::element_text(face = "bold", size = 16),
strip.background = ggplot2::element_blank())+
ggplot2::facet_wrap(~ RZA_TAXA,
labeller = ggplot2::labeller(RZA_TAXA = rza_taxa_names),
nrow = 1)
breaks_60 <- as.integer(range(rza %>% dplyr::filter(GEAR_NAME == "60BON") %>%
dplyr::filter(EST_NUM_PERM3 > 0)%>%
dplyr::collect %$%
as.vector(log10(EST_NUM_PERM3)),
na.rm = TRUE))
plot_60bon <- ggplot2::ggplot()+
ggplot2::geom_sf(color = "black", data = bath_200m[3], alpha = 0)+
ggplot2::geom_sf(fill ="#a7ad94", color = "black", data = map[1])+
ggplot2::coord_sf(xlim = region_xlim, ylim = region_ylim)+
ggplot2::geom_point(ggplot2::aes(LON,LAT, color = log10(EST_NUM_PERM3)), size = 6,
data = rza %>% dplyr::filter(GEAR_NAME == "60BON") %>%
dplyr::filter(EST_NUM_PERM3 > 0))+
ggplot2::geom_point(ggplot2::aes(LON,LAT), size = 4, shape = 4,
data = rza %>% dplyr::filter(GEAR_NAME == "60BON") %>%
dplyr::filter(EST_NUM_PERM3 == 0))+
viridis::scale_color_viridis(option = "viridis",
name = expression(paste("# ","m"^"-3")),
breaks = c(seq(from = breaks_60[1],
to = breaks_60[2], by = 1)),
labels = c(10^seq(from = breaks_60[1],
to = breaks_60[2], by = 1)))+
ggplot2::scale_x_continuous(breaks = seq(region_xlim[1],
(region_xlim[2] - 2), by = 4))+
ggplot2::scale_y_continuous(breaks = seq(region_ylim[1],
region_ylim[2], by = 2))+
ggplot2::theme_bw()+
ggplot2::xlab(label = "Longitude")+
ggplot2::ylab(label = "Latitude")+
ggplot2::theme(
axis.text.y = ggplot2::element_text(face = "bold", size = 12),
axis.text.x = ggplot2::element_text(face = "bold", size = 12),
axis.title = ggplot2::element_text(face = "bold", size = 18),
strip.text = ggplot2::element_text(face = "bold", size = 18),
legend.title = ggplot2::element_text(face = "bold",size = 16),
legend.text = ggplot2::element_text(face = "bold", size = 16),
strip.background = ggplot2::element_blank())+
ggplot2::facet_wrap(~RZA_TAXA,
labeller = ggplot2::labeller(RZA_TAXA = rza_taxa_names),
nrow = 3)
grDevices::png(filename = name_20BON_plot, width = 1200,
height = 400, units = "px", bg = "transparent")
print(plot_20bon)
dev.off()
grDevices::png(filename = name_60BON_plot, width = 1200,
height = 1200, units = "px", bg = "transparent")
print(plot_60bon)
dev.off()
} else {
# Individual plots-------------------------------------------------------------
rza_taxa <- unique(rza$RZA_TAXA)
for(i in 1:length(rza_taxa)){
name_taxa_plot <- paste(plot_path,"RZA_",unique(rza$CRUISE), "_",
rza_taxa[i], ".png", sep = "")
breaks <- range(rza %>% dplyr::filter(RZA_TAXA == rza_taxa[i])%>%
dplyr::filter(EST_NUM_PERM3 > 0)%>%
dplyr::collect %$% as.vector(log10(EST_NUM_PERM3)),
na.rm = TRUE)
taxa_breaks <- if(sum(rza %>% dplyr::filter(RZA_TAXA == rza_taxa[i])%>%
dplyr::collect %$% as.vector(EST_NUM_PERM3),
na.rm = TRUE) == 0){
seq(from = -1,to = 1, by = 1)
}else if(abs(diff(breaks)) == 0){
breaks[1]
}else if(abs(diff(breaks)) > 0 & abs(diff(breaks)) < 0.5){
seq(from = breaks[1],to = breaks[2], by = 0.1)
}else if(abs(diff(breaks)) >= 0.5 & abs(diff(breaks)) < 1){
seq(from = breaks[1],to = breaks[2], by = 0.25)
}else if(abs(diff(breaks)) >= 1 & abs(diff(breaks)) < 2){
seq(from = breaks[1],to = breaks[2], by = 0.5)
}else if(abs(diff(breaks)) >= 2 & abs(diff(breaks)) < 3){
seq(from = breaks[1],to = breaks[2], by = 0.75)
}else if(abs(diff(breaks)) >= 3 & abs(diff(breaks)) < 4){
seq(from = breaks[1],to = breaks[2], by = 1)
}else if(abs(diff(breaks)) >= 4){
seq(from = breaks[1],to = breaks[2], by = 2)
}
rza_plot <- ggplot2::ggplot()+
ggplot2::geom_sf(color = "black", data = bath_200m[3], alpha = 0)+
ggplot2::geom_sf(fill ="#a7ad94", color = "black", data = map[1])+
ggplot2::coord_sf(xlim = region_xlim, ylim = region_ylim)+
ggplot2::geom_point(ggplot2::aes(LON,LAT), size = 4, shape = 4,
show.legend = TRUE,
data = rza %>% dplyr::filter(RZA_TAXA == rza_taxa[i])%>%
dplyr::filter(EST_NUM_PERM3 == 0))+
ggplot2::geom_point(ggplot2::aes(LON,LAT, color = log10(EST_NUM_PERM3)),
size = 6, show.legend = TRUE,
data = rza %>% dplyr::filter(RZA_TAXA == rza_taxa[i])%>%
dplyr::filter(EST_NUM_PERM3 > 0))+
ggplot2::scale_color_viridis_c(option = "viridis",
name = expression(paste("# ","m"^"-3")),
breaks = taxa_breaks,
labels = format(signif(10^taxa_breaks, digits = 1),
scientific = FALSE, trim = TRUE)
)+
ggplot2::scale_x_continuous(breaks = seq(region_xlim[1],
(region_xlim[2] - 2), by = 4))+
ggplot2::scale_y_continuous(breaks = seq(region_ylim[1],
region_ylim[2], by = 2))+
ggplot2::ggtitle(
label = rza_taxa_names[names(rza_taxa_names) == rza_taxa[i]])+
ggplot2::xlab(label = "Longitude")+
ggplot2::ylab(label = "Latitude")+
ggplot2::theme_bw()+
ggplot2::theme(
title = ggplot2::element_text(face = "bold", size = 16),
axis.text.y = ggplot2::element_text(face = "bold", size = 12),
axis.text.x = ggplot2::element_text(face = "bold", size = 12),
axis.title = ggplot2::element_text(face = "bold", size = 16),
strip.text = ggplot2::element_text(face = "bold", size = 16),
legend.title = ggplot2::element_text(face = "bold",size = 16),
legend.text = ggplot2::element_text(face = "bold", size = 16),
legend.position = "right",
strip.background = ggplot2::element_blank())
grDevices::png(filename = name_taxa_plot,
width = 400, height = 400, units = "px",bg = "transparent")
print(rza_plot)
grDevices::dev.off()
}
}
}
Copepoda/rrza documentation built on May 31, 2019, 4:52 a.m.
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#' @title Abundance Maps for Rapid Zooplankton Assesment taxa.
#' @description This function takes an RZA .xlxs dataframe and will make either
#' individual plots of each RZA taxa group or two faceted maps based on 150
#' and 500 micron mesh. The maps plot abundance per cubic meter of the
#' rza taxa for each station. The map regions are standardized for each of the
#' major cruises/grids for EcoFOCI.
#' @param rza_path The path to the directory where the .xlxs rza dataframe
#' is located along with the .xlxs file name. This is a dataframe specifically
#' formated for rza data entry.
#' @param region There are six geographic regions which produce standardized
#' maps. The six standardized maps can be set to Arctic, Northern BS, 70 meter,
#' BASIS, GOA, and MACE 2018.
#' @param facets By default facets is set to TRUE. When set to TRUE the
#' function will produce two sets of faceted maps. One of the 60 bongo rza
#' taxa and one of the 20 bongo rza taxa. When facets is set to FALSE the
#' function will produce a map for each rza taxa.
#' @return Either faceted maps or individual maps of rza taxa abundance.
#' @export make_rza_plots
#' @name rrza
make_rza_plots <- function(rza_path, region, facets = TRUE){
# reads in the first sheet in the .xlxs rza sheet------------------------------
rza <- readxl::read_excel(rza_path,sheet = 1)
# removes NA or non entererd beaker volumes to avoid function errors-----------
rza <- rza %>% dplyr::filter(!is.na(BEAKER_VOLUME))
# Latitude and longitude need to be in decimal degrees. Figures out if that is
# already the case and will convert if it is necessary.------------------------
if(class(rza$LON) == "character"){
rza <- rza %>%
dplyr::mutate(LAT = measurements::conv_unit(LAT,
from = 'deg_dec_min',
to = 'dec_deg'))%>%
dplyr::mutate(LON = measurements::conv_unit(LON,
from = 'deg_dec_min',
to = 'dec_deg'))%>%
dplyr::mutate(LAT = as.numeric(LAT))%>%
dplyr::mutate(LON = as.numeric(LON))
} else {
rza
}
# Bring in shape files from extdata within package and correct projection------
map <- sf::st_read(dsn = "inst/extdata",
layer = "Alaska_dcw_polygon_Project",
quiet = TRUE)%>%
sf::st_transform(., "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
# Bring in 200 meter isobath and corrects projection---------------------------
bath_200m <- sf::st_read(dsn = "inst/extdata",
layer = "ne_10m_bathymetry_K_200",
quiet = TRUE)%>%
sf::st_transform(., "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
# Defines the latitudinal limits for each region------------------------------
ar_xlim <- c(-170,-153)#Arctic
bs_xlim <- c(-174,-159)#BASIS Grid
ga_xlim <- c(-168,-148)#GOA
is_xlim <- c(-176,-160)#70 meter isobath
nb_xlim <- c(-172,-163)#Northern Bering Sea
mace2018_xlim <- c(-180,-170)#MACE2018 Survey
# Determines which xlim will be used based on user input-----------------------
region_xlim <- if(region == "Arctic"){
ar_xlim
} else if (region == "BASIS"){
bs_xlim
} else if (region == "70 meter"){
is_xlim
} else if (region == "Northern BS"){
nb_xlim
} else if (region == "MACE 2018"){
mace2018_xlim
} else if (region == "GOA"){
ga_xlim
} else {
stop(paste("The region",region,"is not an option. The choices are
Arctic, Northern BS, 70 meter, BASIS, GOA, and MACE 2018."))
}
# Defines the longitudinal limits for each region------------------------------
ar_ylim <- c(66,73)#Arctic
bs_ylim <- c(54,60)#BASIS Grid
ga_ylim <- c(52,60)#GOA
is_ylim <- c(53,63)#70 meter isobath
nb_ylim <- c(59,66)#Northern Bering Sea
mace2018_ylim <- c(58,62)#MACE2018 Survey
# Determines which ylim will be used based on user input-----------------------
region_ylim <- if(region == "Arctic"){
ar_ylim
} else if (region == "BASIS"){
bs_ylim
} else if (region == "70 meter"){
is_ylim
} else if (region == "Northern BS"){
nb_ylim
} else if (region == "MACE 2018"){
mace2018_ylim
} else if (region == "GOA"){
ga_xlim
}
# Makes nice names for the plots-----------------------------------------------
rza_taxa_names <- c('Euphausiids_GT15' = "Euphausiids > 15mm",
'Copepods_GT2' = "Large Copepods",
'Euphausiids_LT15' = "Euphausiids < 15mm",
'Naked_Pteropods' = "Naked Snails",
'Chaetognaths' = "Chaetognaths",
'Amphipods' = "Amphipods",
'Decapods' = "Decapod larvae",
'Other_60BON' = "Other Large",
'Copepods_LT2' = "Small Copepods",
'Shelled_Pteropods' = "Pelagic Snail",
'Other_20BON' = "Other small")
# Check for plot folder and make if it doesn't exist---------------------------
if(dir.exists(paste(rza_path,"/plots",sep = "")) == FALSE){
dir.create(paste(rza_path,"/plots",sep = ""))
}
# path to where the plots will be written--------------------------------------
remove <- length(stringr::str_count(unlist(stringr::str_split(rza_path,"/"))))
folder_path <- stringr::str_c(unlist(stringr::str_split(
rza_path,"/"))[-c(remove -1, remove)], collapse = "/")
plot_path <- paste(folder_path, "plots/", sep = "/")
# name for each of the faceted plots-------------------------------------------
name_20BON_plot <- paste(plot_path,"RZA_",unique(rza$CRUISE),
"_20BON.png", sep = "")
name_60BON_plot <- paste(plot_path,"RZA_",unique(rza$CRUISE),
"_60BON.png", sep = "")
#
if(facets == TRUE){
breaks_20 <- as.integer(range(rza %>% dplyr::filter(GEAR_NAME == "20BON")%>%
dplyr::filter(EST_NUM_PERM3 > 0)%>%
dplyr::collect %$% as.vector(log10(EST_NUM_PERM3)), na.rm = TRUE))
plot_20bon <- ggplot2::ggplot()+
ggplot2::geom_sf(color = "black", data = bath_200m[3], alpha = 0)+
ggplot2::geom_sf(fill ="#a7ad94", color = "black", data = map[1])+
ggplot2::coord_sf(xlim = region_xlim, ylim = region_ylim)+
ggplot2::geom_point(ggplot2::aes(LON,LAT, color = log10(EST_NUM_PERM3)), size = 6,
data = rza %>% dplyr::filter(GEAR_NAME == "20BON") %>%
dplyr::filter(EST_NUM_PERM3 > 0))+
ggplot2::geom_point(ggplot2::aes(LON,LAT), size = 4, shape = 4,
data = rza %>% dplyr::filter(GEAR_NAME == "20BON") %>%
dplyr::filter(EST_NUM_PERM3 == 0))+
viridis::scale_color_viridis(option = "viridis",
name = expression(paste("# ","m"^"-3")),
breaks = c(seq(from = breaks_20[1],
to = breaks_20[2], by = 1)),
labels = c(10^seq(from = breaks_20[1],
to = breaks_20[2], by = 1)))+
ggplot2::scale_x_continuous(breaks = seq(region_xlim[1],
(region_xlim[2] - 2), by = 4))+
ggplot2::scale_y_continuous(breaks = seq(region_ylim[1],
region_ylim[2], by = 2))+
ggplot2::theme_bw()+
ggplot2::xlab(label = "Longitude")+
ggplot2::ylab(label = "Latitude")+
ggplot2::theme(
axis.text.y = ggplot2::element_text(face = "bold", size = 12),
axis.text.x = ggplot2::element_text(face = "bold", size = 12),
axis.title = ggplot2::element_text(face = "bold", size = 18),
strip.text = ggplot2::element_text(face = "bold", size = 18),
legend.title = ggplot2::element_text(face = "bold",size = 16),
legend.text = ggplot2::element_text(face = "bold", size = 16),
strip.background = ggplot2::element_blank())+
ggplot2::facet_wrap(~ RZA_TAXA,
labeller = ggplot2::labeller(RZA_TAXA = rza_taxa_names),
nrow = 1)
breaks_60 <- as.integer(range(rza %>% dplyr::filter(GEAR_NAME == "60BON") %>%
dplyr::filter(EST_NUM_PERM3 > 0)%>%
dplyr::collect %$%
as.vector(log10(EST_NUM_PERM3)),
na.rm = TRUE))
plot_60bon <- ggplot2::ggplot()+
ggplot2::geom_sf(color = "black", data = bath_200m[3], alpha = 0)+
ggplot2::geom_sf(fill ="#a7ad94", color = "black", data = map[1])+
ggplot2::coord_sf(xlim = region_xlim, ylim = region_ylim)+
ggplot2::geom_point(ggplot2::aes(LON,LAT, color = log10(EST_NUM_PERM3)), size = 6,
data = rza %>% dplyr::filter(GEAR_NAME == "60BON") %>%
dplyr::filter(EST_NUM_PERM3 > 0))+
ggplot2::geom_point(ggplot2::aes(LON,LAT), size = 4, shape = 4,
data = rza %>% dplyr::filter(GEAR_NAME == "60BON") %>%
dplyr::filter(EST_NUM_PERM3 == 0))+
viridis::scale_color_viridis(option = "viridis",
name = expression(paste("# ","m"^"-3")),
breaks = c(seq(from = breaks_60[1],
to = breaks_60[2], by = 1)),
labels = c(10^seq(from = breaks_60[1],
to = breaks_60[2], by = 1)))+
ggplot2::scale_x_continuous(breaks = seq(region_xlim[1],
(region_xlim[2] - 2), by = 4))+
ggplot2::scale_y_continuous(breaks = seq(region_ylim[1],
region_ylim[2], by = 2))+
ggplot2::theme_bw()+
ggplot2::xlab(label = "Longitude")+
ggplot2::ylab(label = "Latitude")+
ggplot2::theme(
axis.text.y = ggplot2::element_text(face = "bold", size = 12),
axis.text.x = ggplot2::element_text(face = "bold", size = 12),
axis.title = ggplot2::element_text(face = "bold", size = 18),
strip.text = ggplot2::element_text(face = "bold", size = 18),
legend.title = ggplot2::element_text(face = "bold",size = 16),
legend.text = ggplot2::element_text(face = "bold", size = 16),
strip.background = ggplot2::element_blank())+
ggplot2::facet_wrap(~RZA_TAXA,
labeller = ggplot2::labeller(RZA_TAXA = rza_taxa_names),
nrow = 3)
grDevices::png(filename = name_20BON_plot, width = 1200,
height = 400, units = "px", bg = "transparent")
print(plot_20bon)
dev.off()
grDevices::png(filename = name_60BON_plot, width = 1200,
height = 1200, units = "px", bg = "transparent")
print(plot_60bon)
dev.off()
} else {
# Individual plots-------------------------------------------------------------
rza_taxa <- unique(rza$RZA_TAXA)
for(i in 1:length(rza_taxa)){
name_taxa_plot <- paste(plot_path,"RZA_",unique(rza$CRUISE), "_",
rza_taxa[i], ".png", sep = "")
breaks <- range(rza %>% dplyr::filter(RZA_TAXA == rza_taxa[i])%>%
dplyr::filter(EST_NUM_PERM3 > 0)%>%
dplyr::collect %$% as.vector(log10(EST_NUM_PERM3)),
na.rm = TRUE)
taxa_breaks <- if(sum(rza %>% dplyr::filter(RZA_TAXA == rza_taxa[i])%>%
dplyr::collect %$% as.vector(EST_NUM_PERM3),
na.rm = TRUE) == 0){
seq(from = -1,to = 1, by = 1)
}else if(abs(diff(breaks)) == 0){
breaks[1]
}else if(abs(diff(breaks)) > 0 & abs(diff(breaks)) < 0.5){
seq(from = breaks[1],to = breaks[2], by = 0.1)
}else if(abs(diff(breaks)) >= 0.5 & abs(diff(breaks)) < 1){
seq(from = breaks[1],to = breaks[2], by = 0.25)
}else if(abs(diff(breaks)) >= 1 & abs(diff(breaks)) < 2){
seq(from = breaks[1],to = breaks[2], by = 0.5)
}else if(abs(diff(breaks)) >= 2 & abs(diff(breaks)) < 3){
seq(from = breaks[1],to = breaks[2], by = 0.75)
}else if(abs(diff(breaks)) >= 3 & abs(diff(breaks)) < 4){
seq(from = breaks[1],to = breaks[2], by = 1)
}else if(abs(diff(breaks)) >= 4){
seq(from = breaks[1],to = breaks[2], by = 2)
}
rza_plot <- ggplot2::ggplot()+
ggplot2::geom_sf(color = "black", data = bath_200m[3], alpha = 0)+
ggplot2::geom_sf(fill ="#a7ad94", color = "black", data = map[1])+
ggplot2::coord_sf(xlim = region_xlim, ylim = region_ylim)+
ggplot2::geom_point(ggplot2::aes(LON,LAT), size = 4, shape = 4,
show.legend = TRUE,
data = rza %>% dplyr::filter(RZA_TAXA == rza_taxa[i])%>%
dplyr::filter(EST_NUM_PERM3 == 0))+
ggplot2::geom_point(ggplot2::aes(LON,LAT, color = log10(EST_NUM_PERM3)),
size = 6, show.legend = TRUE,
data = rza %>% dplyr::filter(RZA_TAXA == rza_taxa[i])%>%
dplyr::filter(EST_NUM_PERM3 > 0))+
ggplot2::scale_color_viridis_c(option = "viridis",
name = expression(paste("# ","m"^"-3")),
breaks = taxa_breaks,
labels = format(signif(10^taxa_breaks, digits = 1),
scientific = FALSE, trim = TRUE)
)+
ggplot2::scale_x_continuous(breaks = seq(region_xlim[1],
(region_xlim[2] - 2), by = 4))+
ggplot2::scale_y_continuous(breaks = seq(region_ylim[1],
region_ylim[2], by = 2))+
ggplot2::ggtitle(
label = rza_taxa_names[names(rza_taxa_names) == rza_taxa[i]])+
ggplot2::xlab(label = "Longitude")+
ggplot2::ylab(label = "Latitude")+
ggplot2::theme_bw()+
ggplot2::theme(
title = ggplot2::element_text(face = "bold", size = 16),
axis.text.y = ggplot2::element_text(face = "bold", size = 12),
axis.text.x = ggplot2::element_text(face = "bold", size = 12),
axis.title = ggplot2::element_text(face = "bold", size = 16),
strip.text = ggplot2::element_text(face = "bold", size = 16),
legend.title = ggplot2::element_text(face = "bold",size = 16),
legend.text = ggplot2::element_text(face = "bold", size = 16),
legend.position = "right",
strip.background = ggplot2::element_blank())
grDevices::png(filename = name_taxa_plot,
width = 400, height = 400, units = "px",bg = "transparent")
print(rza_plot)
grDevices::dev.off()
}
}
}
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