In pbs-assess/gfranges: Groundfish Ranges

library(dplyr)
library(ggplot2)
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)

predicted <- list(predicted, predicted)

do <- plot_facet_map(predicted[[1]], "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 = c("do_est", "temp"),
  ssid = c(4),
  start_time = 2008,
  end_time = 2010,
  input_cell_size = 2,
  scale_fac = 1,
  delta_t_total = 2,
  delta_t_step = 2,
  indices = c(1, 2),  
  match_logic = c(">=", "=="),
  thresholds = c(1, 1) # vector of plus/minus threshold(s) to define climate match.
)
out1$do_est.units_per_decade <- out1$do_est.units_per_decade / 5
out1$temp.units_per_decade <- out1$temp.units_per_decade / 5

gvocc1a <- plot_vocc(out1,
  vec_aes = "distance",
  vec_lwd_range = c(0.2, 0.5),
  max_vec_plotted = 50,
  NA_label = ".",
  fill_col = "do_est.units_per_decade",
  fill_label = "ml/L DO",
  high_fill = "Steel Blue 4",
  low_fill = "Red 3",
  raster_alpha = 1,
  vec_alpha = 0.25,
  axis_lables = FALSE # ,
  # viridis_option = "C",
  # viridis_dir = -1#,
  # transform_col = fourth_root_power,
  # raster_limits = c(0, 6)
)

gvocc1b <- plot_vocc(out1,
  vec_aes = "distance",
  vec_lwd_range = c(0.2, 0.5),
  max_vec_plotted = 50,
  NA_label = ".",
  fill_col = "temp.units_per_decade",
  fill_label = "°C",
  raster_alpha = 1,
  vec_alpha = 0.25,
  axis_lables = FALSE # ,
  # transform_col = fourth_root_power,
  # raster_limits = c(5, 13)
)


gvocc1 <- gridExtra::grid.arrange(gvocc1b, gvocc1a, nrow = 1, top = "VOCC vectors to maintain <1°C or 1 ml/L change between 2008 and 2010")

out2 <- make_vector_data(predicted,
  variable_names = c("do_est", "temp"),
  ssid = c(4),
  start_time = 2010,
  end_time = 2012,
  input_cell_size = 2,
  scale_fac = 1,
  delta_t_total = 2,
  delta_t_step = 2,
  indices = c(1, 2),
    match_logic = c(">=", "=="),
  thresholds = c(1, 1) # vector of plus/minus threshold(s) to define climate match.
)
out2$do_est.units_per_decade <- out2$do_est.units_per_decade / 5
out2$temp.units_per_decade <- out2$temp.units_per_decade / 5

gvocc2a <- plot_vocc(out2,
  vec_aes = "distance",
  vec_lwd_range = c(0.2, 0.5),
  max_vec_plotted = 50,
  NA_label = ".",
  fill_col = "do_est.units_per_decade",
  fill_label = "ml/L DO",
  high_fill = "Steel Blue 4",
  low_fill = "Red 3",
  raster_alpha = 1,
  vec_alpha = 0.25,
  axis_lables = FALSE # ,
  # viridis_option = "C",
  # viridis_dir = -1,
  # transform_col = fourth_root_power,
  # raster_limits = c(0, 6)
)

gvocc2b <- plot_vocc(out2,
  vec_aes = "distance",
  vec_lwd_range = c(0.2, 0.5),
  max_vec_plotted = 50,
  NA_label = ".",
  fill_col = "temp.units_per_decade",
  fill_label = "°C",
  raster_alpha = 1,
  vec_alpha = 0.25,
  axis_lables = FALSE # ,
  # transform_col = fourth_root_power,
  # raster_limits = c(5, 13)
)


gvocc2 <- gridExtra::grid.arrange(gvocc2b, gvocc2a, nrow = 1, top = "VOCC vectors to maintain <1°C or 1 ml/L change between 2010 and 2012")

out3 <- make_vector_data(predicted,
  variable_names = c("do_est", "temp"),
  ssid = c(4),
  start_time = 2012,
  end_time = 2014,
  input_cell_size = 2,
  scale_fac = 1,
  delta_t_total = 2,
  delta_t_step = 2,
  indices = c(1, 2),
    match_logic = c(">=", "=="),
  thresholds = c(1, 1) # vector of plus/minus threshold(s) to define climate match.
)
out3$do_est.units_per_decade <- out3$do_est.units_per_decade / 5
out3$temp.units_per_decade <- out3$temp.units_per_decade / 5


gvocc3a <- plot_vocc(out3,
  vec_aes = "distance",
  vec_lwd_range = c(0.2, 0.5),
  max_vec_plotted = 50,
  NA_label = ".",
  fill_col = "do_est.units_per_decade",
  fill_label = "ml/L DO",
  high_fill = "Steel Blue 4",
  low_fill = "Red 3",
  raster_alpha = 1,
  vec_alpha = 0.25,
  axis_lables = FALSE # ,
  # viridis_option = "C",
  # viridis_dir = -1,
  # transform_col = fourth_root_power,
  # raster_limits = c(0, 6)
)

gvocc3b <- plot_vocc(out3,
  vec_aes = "distance",
  vec_lwd_range = c(0.2, 0.5),
  max_vec_plotted = 50,
  NA_label = ".",
  fill_col = "temp.units_per_decade",
  fill_label = "°C",
  raster_alpha = 1,
  vec_alpha = 0.25,
  axis_lables = FALSE # ,
  # transform_col = fourth_root_power,
  # raster_limits = c(5, 13)
)


gvocc3 <- gridExtra::grid.arrange(gvocc3b, gvocc3a, nrow = 1, top = "VOCC vectors to maintain <1°C or 1 ml/L change between 2012 and 2014")

out4 <- make_vector_data(predicted,
  variable_names = c("do_est", "temp"),
  ssid = c(4),
  start_time = 2014,
  end_time = 2016,
  input_cell_size = 2,
  scale_fac = 1,
  delta_t_total = 2,
  delta_t_step = 2,
  indices = c(1, 2),
    match_logic = c(">=", "=="),
  thresholds = c(1, 1) # vector of plus/minus threshold(s) to define climate match.
)
out4$do_est.units_per_decade <- out4$do_est.units_per_decade / 5
out4$temp.units_per_decade <- out4$temp.units_per_decade / 5


gvocc4a <- plot_vocc(out4,
  vec_aes = "distance",
  vec_lwd_range = c(0.2, 0.5),
  max_vec_plotted = 50,
  NA_label = ".",
  fill_col = "do_est.units_per_decade",
  fill_label = "ml/L DO",
  high_fill = "Steel Blue 4",
  low_fill = "Red 3",
  raster_alpha = 1,
  vec_alpha = 0.25,
  axis_lables = FALSE # ,
  # viridis_option = "C",
  # viridis_dir = -1,
  # transform_col = fourth_root_power,
  # raster_limits = c(0, 6)
)

gvocc4b <- plot_vocc(out4,
  vec_aes = "distance",
  vec_lwd_range = c(0.2, 0.5),
  max_vec_plotted = 50,
  NA_label = ".",
  fill_col = "temp.units_per_decade",
  fill_label = "°C",
  raster_alpha = 1,
  vec_alpha = 0.25,
  axis_lables = FALSE # ,
  # transform_col = fourth_root_power,
  # raster_limits = c(5, 13)
)


gvocc4 <- gridExtra::grid.arrange(gvocc4b, gvocc4a, nrow = 1, top = "VOCC vectors to maintain <1°C or 1 ml/L change between 2014 and 2016")

out5 <- make_vector_data(predicted,
  variable_names = c("do_est", "temp"),
  ssid = c(4),
  start_time = 2016,
  end_time = 2018,
  input_cell_size = 2,
  scale_fac = 1,
  delta_t_total = 2,
  delta_t_step = 2,
  indices = c(1, 2),
    match_logic = c(">=", "=="),
  thresholds = c(1, 1) # vector of plus/minus threshold(s) to define climate match.
)
out5$do_est.units_per_decade <- out5$do_est.units_per_decade / 5
out5$temp.units_per_decade <- out5$temp.units_per_decade / 5


gvocc5a <- plot_vocc(out5,
  vec_aes = "distance",
  vec_lwd_range = c(0.2, 0.5),
  max_vec_plotted = 50,
  NA_label = ".",
  fill_col = "do_est.units_per_decade",
  fill_label = "ml/L DO",
  high_fill = "Steel Blue 4",
  low_fill = "Red 3",
  raster_alpha = 1,
  vec_alpha = 0.25,
  axis_lables = FALSE # ,
  # viridis_option = "C",
  # viridis_dir = -1,
  # transform_col = fourth_root_power,
  # raster_limits = c(0, 6)
)

gvocc5b <- plot_vocc(out5,
  vec_aes = "distance",
  vec_lwd_range = c(0.2, 0.5),
  max_vec_plotted = 50,
  NA_label = ".",
  fill_col = "temp.units_per_decade",
  fill_label = "°C",
  raster_alpha = 1,
  vec_alpha = 0.25,
  axis_lables = FALSE # ,
  # transform_col = fourth_root_power,
  # raster_limits = c(5, 13)
)


gvocc5 <- gridExtra::grid.arrange(gvocc5b, gvocc5a, nrow = 1, top = "VOCC vectors to maintain <1°C or 1 ml/L change between 2016 and 2018")

png(
  file = "figs/biannual_temp-do_change-4.png", # The directory you want to save the file in
  res = 600,
  units = "in",
  width = 8, # The width of the plot in inches
  height = 17
) # The height of the plot in inches

gridExtra::grid.arrange(gvocc1, gvocc2, gvocc3, gvocc4, gvocc5, nrow = 5, 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 1 ml/L change and 1 degree change

out0.5d <- make_vector_data(predicted,
  variable_names = c("do_est", "temp"),
  ssid = c(4),
  start_time = 2008,
  skip_time = 2016,
  input_cell_size = 2,
  scale_fac = 1,
  delta_t_total = 6,
  delta_t_step = 2,
  indices = c(1, 1, 1, 2, 2),
  match_logic = c(">=", "<="),
  thresholds = c(0.5, 1) # vector of plus/minus threshold(s) to define climate match.
)

gvocc <- plot_vocc(out0.5d,
  vec_aes = "distance",
  vec_lwd_range = c(0.2, 0.5),
  max_vec_plotted = 100,
  fill_col = "1_e",
  fill_label = "mean DO",
  raster_alpha = 1,
  vec_alpha = 0.25,
  axis_lables = FALSE,
  viridis_option = "C",
  viridis_dir = -1,
  transform_col = no_trans # ,
)
gvocc <- gvocc + ggtitle("VOCC <0.5 ml/L DO/>1°C (excludes 2016)")
gvocc

gtrend1 <- plot_vocc(out0.5d,
  fill_col = "temp.units_per_decade",
  fill_label = "°C ",
  # high_fill = "Steel Blue 4",
  # low_fill = "Red 3",
  raster_alpha = 1,
  vec_aes = NULL,
  viridis_option = "A",
  transform_col = no_trans
)
gtrend1 <- gtrend1 + ggtitle("Temperature trend 2008-2018")
gtrend1

gtrend <- plot_vocc(out0.5d,
  fill_col = "do_est.units_per_decade",
  fill_label = "ml/L",
  high_fill = "Steel Blue 4",
  low_fill = "Red 3",
  raster_alpha = 1,
  vec_aes = NULL,
  transform_col = no_trans
)
gtrend <- gtrend + ggtitle("Bottom DO trend 2008-2018")
gtrend

png(
  file = "figs/temp-do-2008-2018-4.png", # The directory you want to save the file in
  res = 600,
  units = "in",
  width = 10, # The width of the plot in inches
  height = 5
) # The height of the plot in inches

gridExtra::grid.arrange(gtrend1, gtrend, gvocc, nrow = 1)
dev.off()