In pbs-assess/gfranges: Groundfish Ranges
library(dplyr)
library(ggplot2)
library(sdmTMB)
library(gfranges)
Load predicted values
pred_temp_do <- readRDS(here::here("analysis/tmb-sensor-explore/models/predicted-DO.rds"))
Define subset of the spatial range
predicted <- pred_temp_do %>%
filter(ssid!=4) %>%
filter(ssid!=16)
#glimpse(predicted)
do <-plot_facet_map(predicted, "do_est", transform_col = no_trans) +
labs(fill="ml/L") +
ggtitle("Bottom DO")
print(do)
Biannual changes starting 2005 and ending 2017
out1 <- make_vector_data(predicted,
variable_names = "do_est",
ssid = c(1,3),
start_time = 2009,
end_time = 2011,
input_cell_size = 2,
scale_fac = 1,
delta_t_total = 2,
delta_t_step = 2,
indices = c(1, 2),
thresholds = c(1) # vector of plus/minus threshold(s) to define climate match.
)
gvocc1 <- plot_vocc(out1,
low_col = "white",
mid_col = "white",
high_col = "grey87",
vec_aes = "distance",
vec_lwd_range = c(0.2, 0.5),
max_vec_plotted = 50,
NA_label = ".",
fill_col = "var_1_e",
fill_label = "2011",
raster_alpha = 1,
vec_alpha = 0.25,
axis_lables = FALSE,
viridis_option = "C",
viridis_dir = -1,
transform_col = no_trans#,
#raster_limits = c(5, 13)
)
#gvocc1 <- gvocc1 + ggtitle("VOCC vectors for <1°C change in bottom DO")
gvocc1
out2 <- make_vector_data(predicted,
variable_names = "do_est",
ssid = c(1,3),
start_time = 2011,
end_time = 2013,
input_cell_size = 2,
scale_fac = 1,
delta_t_total = 2,
delta_t_step = 2,
indices = c(1, 2),
thresholds = c(1) # vector of plus/minus threshold(s) to define climate match.
)
gvocc2 <- plot_vocc(out2,
low_col = "white",
mid_col = "white",
high_col = "grey87",
vec_aes = "distance",
vec_lwd_range = c(0.2, 0.5),
max_vec_plotted = 50,
NA_label = ".",
fill_col = "var_1_e",
fill_label = "2013",
raster_alpha = 1,
vec_alpha = 0.25,
axis_lables = FALSE,
viridis_option = "C",
viridis_dir = -1,
transform_col = no_trans#,
#raster_limits = c(5, 13)
)
#gvocc2 <- gvocc2 + ggtitle(" ")
gvocc2
out3 <- make_vector_data(predicted,
variable_names = "do_est",
ssid = c(1,3),
start_time = 2013,
end_time = 2015,
input_cell_size = 2,
scale_fac = 1,
delta_t_total = 2,
delta_t_step = 2,
indices = c(1, 2),
thresholds = c(1) # vector of plus/minus threshold(s) to define climate match.
)
gvocc3 <- plot_vocc(out3,
low_col = "white",
mid_col = "white",
high_col = "grey87",
vec_aes = "distance",
vec_lwd_range = c(0.2, 0.5),
max_vec_plotted = 50,
NA_label = ".",
fill_col = "var_1_e",
fill_label = "2015",
raster_alpha = 1,
vec_alpha = 0.25,
axis_lables = FALSE,
viridis_option = "C",
viridis_dir = -1,
transform_col = no_trans#,
#raster_limits = c(5, 13)
)
#gvocc3 <- gvocc3 + ggtitle(" ")
gvocc3
out4 <- make_vector_data(predicted,
variable_names = "do_est",
ssid = c(1,3),
start_time = 2015,
end_time = 2017,
input_cell_size = 2,
scale_fac = 1,
delta_t_total = 2,
delta_t_step = 2,
indices = c(1, 2),
thresholds = c(1) # vector of plus/minus threshold(s) to define climate match.
)
gvocc4 <- plot_vocc(out4,
low_col = "white",
mid_col = "white",
high_col = "grey87",
vec_aes = "distance",
vec_lwd_range = c(0.2, 0.5),
max_vec_plotted = 50,
NA_label = ".",
fill_col = "var_1_e",
fill_label = "2017",
raster_alpha = 1,
vec_alpha = 0.25,
axis_lables = FALSE,
viridis_option = "C",
viridis_dir = -1,
transform_col = no_trans#,
#raster_limits = c(5, 13)
)
#gvocc4 <- gvocc4 + ggtitle(" ")
gvocc4
SPARE for now
out5 <- make_vector_data(predicted,
variable_names = "do_est",
ssid = c(1,3),
start_time = 2013,
end_time = 2015,
input_cell_size = 2,
scale_fac = 1,
delta_t_total = 2,
delta_t_step = 2,
indices = c(1, 2),
thresholds = c(1) # vector of plus/minus threshold(s) to define climate match.
)
gvocc5 <- plot_vocc(out5,
low_col = "white",
mid_col = "white",
high_col = "grey87",
vec_aes = "distance",
vec_lwd_range = c(0.2, 0.5),
max_vec_plotted = 50,
NA_label = ".",
fill_col = "var_1_e",
fill_label = "2015",
raster_alpha = 1,
vec_alpha = 0.25,
axis_lables = FALSE,
viridis_option = "C",
viridis_dir = -1,
transform_col = no_trans#,
#raster_limits = c(5, 13)
)
#gvocc5 <- gvocc5 + ggtitle(" ")
gvocc5
out6 <- make_vector_data(predicted,
variable_names = "do_est",
ssid = c(1,3),
start_time = 2015,
end_time = 2017,
input_cell_size = 2,
scale_fac = 1,
delta_t_total = 2,
delta_t_step = 2,
indices = c(1, 2),
thresholds = c(1) # vector of plus/minus threshold(s) to define climate match.
)
gvocc6 <- plot_vocc(out6,
low_col = "white",
mid_col = "white",
high_col = "grey87",
vec_aes = "distance",
vec_lwd_range = c(0.2, 0.5),
max_vec_plotted = 50,
NA_label = ".",
fill_col = "var_1_e",
fill_label = "2017",
raster_alpha = 1,
vec_alpha = 0.25,
axis_lables = FALSE,
viridis_option = "C",
viridis_dir = -1,
transform_col = no_trans#,
#raster_limits = c(5, 13)
)
#gvocc6 <- gvocc6 + ggtitle(" ")
gvocc6
png(file = "biannual_do_change.png", # The directory you want to save the file in
res = 600,
units = 'in',
width = 8, # The width of the plot in inches
height = 10) # The height of the plot in inches
gridExtra::grid.arrange(gvocc1, gvocc2, gvocc3, gvocc4, nrow = 2, top = grid::textGrob("VOCC vectors for <1 ml/L biannual changes in bottom DO"))#,gp=gpar(fontsize=20,font=3))
dev.off()
Cumulative change between last two decades
VOCC for 0.5 ml/L change and 1 degree change
out0.5d <- make_vector_data(predicted,
variable_names = "do_est",
ssid = c(1,3),
start_time = 2009,
#skip_time = 2011,
input_cell_size = 2,
scale_fac = 1,
delta_t_total = 5,
delta_t_step = 2,
indices = c(1, 1, 1, 2, 2),
thresholds = c(0.5) # vector of plus/minus threshold(s) to define climate match.
)
gvocc <- plot_vocc(out0.5d,
low_col = "white",
mid_col = "white",
high_col = "grey87",
vec_aes = "distance",
vec_lwd_range = c(0.2, 0.5),
max_vec_plotted = 100,
fill_col = "var_1_e",
fill_label = "2015-2017",
raster_alpha = 1,
vec_alpha = 0.25,
axis_lables = FALSE,
viridis_option = "C",
viridis_dir = -1,
transform_col = no_trans#,
)
gvocc <- gvocc + ggtitle("VOCC vectors for <0.5 ml/L change")
gvocc
out1d <- make_vector_data(predicted,
variable_names = "do_est",
ssid = c(1,3),
start_time = 2009,
#skip_time = 2011,
input_cell_size = 2,
scale_fac = 1,
delta_t_total = 5,
delta_t_step = 2,
indices = c(1, 1, 1, 2, 2),
thresholds = c(1) # vector of plus/minus threshold(s) to define climate match.
)
gvocc1d <- plot_vocc(out1d,
low_col = "white",
mid_col = "white",
high_col = "grey87",
vec_aes = "distance",
vec_lwd_range = c(0.2, 0.5),
max_vec_plotted = 100,
fill_col = "var_1_e",
fill_label = "2015-2017",
raster_alpha = 1,
vec_alpha = 0.25,
axis_lables = FALSE,
viridis_option = "C",
viridis_dir = -1,
transform_col = no_trans
)
gvocc1d <- gvocc1d + ggtitle("VOCC vectors for <1 ml/L change")
gvocc1d
gtrend <- plot_vocc(out1d,
fill_col = "units_per_decade",
fill_label = "ml/L per decade",
raster_alpha = 1,
vec_aes = NULL,
transform_col = no_trans
)
gtrend <- gtrend + ggtitle("Bottom DO trend 2009-2017")
gtrend
png(file = "do-2009-2017-1n3.png", # The directory you want to save the file in
res = 600,
units = 'in',
width = 10, # The width of the plot in inches
height = 7) # The height of the plot in inches
gridExtra::grid.arrange(gtrend, gvocc, gvocc1d, nrow = 1)
dev.off()
pbs-assess/gfranges documentation built on Dec. 13, 2021, 4:50 p.m.
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library(dplyr) library(ggplot2) library(sdmTMB) library(gfranges)
Load predicted values
pred_temp_do <- readRDS(here::here("analysis/tmb-sensor-explore/models/predicted-DO.rds"))
Define subset of the spatial range
predicted <- pred_temp_do %>% filter(ssid!=4) %>% filter(ssid!=16) #glimpse(predicted) do <-plot_facet_map(predicted, "do_est", transform_col = no_trans) + labs(fill="ml/L") + ggtitle("Bottom DO") print(do)
Biannual changes starting 2005 and ending 2017
out1 <- make_vector_data(predicted, variable_names = "do_est", ssid = c(1,3), start_time = 2009, end_time = 2011, input_cell_size = 2, scale_fac = 1, delta_t_total = 2, delta_t_step = 2, indices = c(1, 2), thresholds = c(1) # vector of plus/minus threshold(s) to define climate match. ) gvocc1 <- plot_vocc(out1, low_col = "white", mid_col = "white", high_col = "grey87", vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = ".", fill_col = "var_1_e", fill_label = "2011", raster_alpha = 1, vec_alpha = 0.25, axis_lables = FALSE, viridis_option = "C", viridis_dir = -1, transform_col = no_trans#, #raster_limits = c(5, 13) ) #gvocc1 <- gvocc1 + ggtitle("VOCC vectors for <1°C change in bottom DO") gvocc1
out2 <- make_vector_data(predicted, variable_names = "do_est", ssid = c(1,3), start_time = 2011, end_time = 2013, input_cell_size = 2, scale_fac = 1, delta_t_total = 2, delta_t_step = 2, indices = c(1, 2), thresholds = c(1) # vector of plus/minus threshold(s) to define climate match. ) gvocc2 <- plot_vocc(out2, low_col = "white", mid_col = "white", high_col = "grey87", vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = ".", fill_col = "var_1_e", fill_label = "2013", raster_alpha = 1, vec_alpha = 0.25, axis_lables = FALSE, viridis_option = "C", viridis_dir = -1, transform_col = no_trans#, #raster_limits = c(5, 13) ) #gvocc2 <- gvocc2 + ggtitle(" ") gvocc2
out3 <- make_vector_data(predicted, variable_names = "do_est", ssid = c(1,3), start_time = 2013, end_time = 2015, input_cell_size = 2, scale_fac = 1, delta_t_total = 2, delta_t_step = 2, indices = c(1, 2), thresholds = c(1) # vector of plus/minus threshold(s) to define climate match. ) gvocc3 <- plot_vocc(out3, low_col = "white", mid_col = "white", high_col = "grey87", vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = ".", fill_col = "var_1_e", fill_label = "2015", raster_alpha = 1, vec_alpha = 0.25, axis_lables = FALSE, viridis_option = "C", viridis_dir = -1, transform_col = no_trans#, #raster_limits = c(5, 13) ) #gvocc3 <- gvocc3 + ggtitle(" ") gvocc3
out4 <- make_vector_data(predicted, variable_names = "do_est", ssid = c(1,3), start_time = 2015, end_time = 2017, input_cell_size = 2, scale_fac = 1, delta_t_total = 2, delta_t_step = 2, indices = c(1, 2), thresholds = c(1) # vector of plus/minus threshold(s) to define climate match. ) gvocc4 <- plot_vocc(out4, low_col = "white", mid_col = "white", high_col = "grey87", vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = ".", fill_col = "var_1_e", fill_label = "2017", raster_alpha = 1, vec_alpha = 0.25, axis_lables = FALSE, viridis_option = "C", viridis_dir = -1, transform_col = no_trans#, #raster_limits = c(5, 13) ) #gvocc4 <- gvocc4 + ggtitle(" ") gvocc4
SPARE for now
out5 <- make_vector_data(predicted, variable_names = "do_est", ssid = c(1,3), start_time = 2013, end_time = 2015, input_cell_size = 2, scale_fac = 1, delta_t_total = 2, delta_t_step = 2, indices = c(1, 2), thresholds = c(1) # vector of plus/minus threshold(s) to define climate match. ) gvocc5 <- plot_vocc(out5, low_col = "white", mid_col = "white", high_col = "grey87", vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = ".", fill_col = "var_1_e", fill_label = "2015", raster_alpha = 1, vec_alpha = 0.25, axis_lables = FALSE, viridis_option = "C", viridis_dir = -1, transform_col = no_trans#, #raster_limits = c(5, 13) ) #gvocc5 <- gvocc5 + ggtitle(" ") gvocc5
out6 <- make_vector_data(predicted, variable_names = "do_est", ssid = c(1,3), start_time = 2015, end_time = 2017, input_cell_size = 2, scale_fac = 1, delta_t_total = 2, delta_t_step = 2, indices = c(1, 2), thresholds = c(1) # vector of plus/minus threshold(s) to define climate match. ) gvocc6 <- plot_vocc(out6, low_col = "white", mid_col = "white", high_col = "grey87", vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = ".", fill_col = "var_1_e", fill_label = "2017", raster_alpha = 1, vec_alpha = 0.25, axis_lables = FALSE, viridis_option = "C", viridis_dir = -1, transform_col = no_trans#, #raster_limits = c(5, 13) ) #gvocc6 <- gvocc6 + ggtitle(" ") gvocc6
png(file = "biannual_do_change.png", # The directory you want to save the file in res = 600, units = 'in', width = 8, # The width of the plot in inches height = 10) # The height of the plot in inches gridExtra::grid.arrange(gvocc1, gvocc2, gvocc3, gvocc4, nrow = 2, top = grid::textGrob("VOCC vectors for <1 ml/L biannual changes in bottom DO"))#,gp=gpar(fontsize=20,font=3)) dev.off()
Cumulative change between last two decades VOCC for 0.5 ml/L change and 1 degree change
out0.5d <- make_vector_data(predicted, variable_names = "do_est", ssid = c(1,3), start_time = 2009, #skip_time = 2011, input_cell_size = 2, scale_fac = 1, delta_t_total = 5, delta_t_step = 2, indices = c(1, 1, 1, 2, 2), thresholds = c(0.5) # vector of plus/minus threshold(s) to define climate match. )
gvocc <- plot_vocc(out0.5d, low_col = "white", mid_col = "white", high_col = "grey87", vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 100, fill_col = "var_1_e", fill_label = "2015-2017", raster_alpha = 1, vec_alpha = 0.25, axis_lables = FALSE, viridis_option = "C", viridis_dir = -1, transform_col = no_trans#, ) gvocc <- gvocc + ggtitle("VOCC vectors for <0.5 ml/L change") gvocc
out1d <- make_vector_data(predicted, variable_names = "do_est", ssid = c(1,3), start_time = 2009, #skip_time = 2011, input_cell_size = 2, scale_fac = 1, delta_t_total = 5, delta_t_step = 2, indices = c(1, 1, 1, 2, 2), thresholds = c(1) # vector of plus/minus threshold(s) to define climate match. )
gvocc1d <- plot_vocc(out1d, low_col = "white", mid_col = "white", high_col = "grey87", vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 100, fill_col = "var_1_e", fill_label = "2015-2017", raster_alpha = 1, vec_alpha = 0.25, axis_lables = FALSE, viridis_option = "C", viridis_dir = -1, transform_col = no_trans ) gvocc1d <- gvocc1d + ggtitle("VOCC vectors for <1 ml/L change") gvocc1d
gtrend <- plot_vocc(out1d, fill_col = "units_per_decade", fill_label = "ml/L per decade", raster_alpha = 1, vec_aes = NULL, transform_col = no_trans ) gtrend <- gtrend + ggtitle("Bottom DO trend 2009-2017") gtrend
png(file = "do-2009-2017-1n3.png", # The directory you want to save the file in res = 600, units = 'in', width = 10, # The width of the plot in inches height = 7) # The height of the plot in inches gridExtra::grid.arrange(gtrend, gvocc, gvocc1d, nrow = 1) dev.off()
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