R/development_scripts/plot_survey.R
In elisekeppel/cemore: Analysis of DFO CRP CeMoRe Survey Data
# # after Eva's code
# plot_survey <- function(Save = F,
# single_survey=T,
# incidentals=F,
# hydrophone=F,
# sightings_only=F){
#
# #
# # # ----------------------------------------------------------------------
# # # ----------------- SET SURVEY DATE --------------------------------
# # # ----------------------------------------------------------------------
# # {year = 2021
# # month = "10"
# # month_abb <- month.abb[as.numeric(month)]
# # survey_title <- paste(first_up(month_abb), year)
# # folder = paste0("survey_data/raw_data/",year, "-",month)
# # surveyid = paste0("cemore_", year, tolower(month_abb))}
# # # ----------------------------------------------------------------------
#
# # ----------------------------------------------------------------------
# # ----------------- LOAD SPATIAL FILES --------------------------------
# # ----------------------------------------------------------------------
# # bc coast for plotting
# if(!Save & !exists("coast")){
# coast <- sf::st_read(dsn = "shapefiles", layer = "BC_coast_UTM9", quiet = T)
# coast_file <- coast
# }
# # higher resolution bc coast --- takes a long time to load
# if(Save & !exists("bc_shp")){
# bc_shp <- sf::st_read(dsn = "shapefiles", "BC_AK_WA_union_polygon") %>%
# sf::st_transform(crs = 4326)
# coast_file <- bc_shp
# }
# # Pacific Canada polygon for outlining Canadian border
# # canada_shp <- st_read(dsn = "shapefiles", "CanadianEEZ") %>%
# # st_transform(crs = 4326)
#
# #-----------------------------------------------------------
# # to create bathymetric layer
# #-----------------------------------------------------------
# {bathy <- getNOAA.bathy(-125.5, -122.5,48.1, 49.5,res=1, keep=TRUE) %>%
# fortify(bathy)
#
# bathy$z[which(bathy$z >= 0)] <- 0
#
# col <- rev(RColorBrewer::brewer.pal(9L, "Blues")[4:7])
# col_ramp <- colorRampPalette(col)}
#
# # ----------------------------------------------------------------------
#
# # ap_sf %<>% filter(grepl("Porpoise", Species))
# # coord2<- ggplot2::coord_sf(xlim = c(-123.4, -123.1), ylim = c(48.32, 48.5), crs = sf::st_crs(4326)) # FOR BB
#
# # if require inset to magnify area of intense sightings
# # bb <- create_bb(-123.4, -123.1, 48.32, 48.5)
# # ap_sf_cropped <- st_intersection(ap_sf, bb)
#
# # Old way of viewing tidy but not processed sightings (would need to import raw data)
# # s <- data$sightings %>%
# # dplyr::filter(!is.na(Sgt.Lat) | !is.na(Sgt.Lon), !Species == "Sea Otter") %>% #, incidential.sighting == F) %>%
# # st_as_sf(coords = c("Sgt.Lon", "Sgt.Lat"), crs = 4326) %>%
# # st_transform(crs= 3156)
#
# #---------------------------------------------------------------
# # Special case, plotting incidental sightings in Feb 2021
# #---------------------------------------------------------------
# # # at line 48 in Sighting Position Correction.R file:
# # data <- rbind(DATA[which(DATA$Event=="Survey sighting" & DATA$status=="ON"),],DATA[which(DATA$Sgt.ID=="S5a"),]) #EK edit = to include incidentals for plotting feb 2021
# # and re-run Eva's code (revert and run again after for only significant sightings in final processed data)
#
# #---------------------------------------------------------------
# # Special case, plotting large group of porpoises & incidental grey whale Apr 2021
# #---------------------------------------------------------------
# # ap_sf[which(ap_sf$Sgt_ID == "S28"),]$Species <- "H. Porpoise group 40-50"
# # # at line 48 in Sighting Position Correction.R file:
# # data <- rbind(DATA[which(DATA$Event=="Survey sighting" & DATA$status=="ON"),],DATA[which(DATA$Sgt.ID=="S34"),]) #EK edit = to include incidental grey whale sighting for plotting APR 2021
# # and re-run Eva's code (revert and run again after for only significant sightings in final processed data)
#
# #---------------------------------------------------------------
# # Special case, plotting incidental sightings in May 2021
# #---------------------------------------------------------------
# # elf <- get_track(file_name = "20210520-LineTransect_MB.gpx", folder = "2021may") # can't use get_track as it changes it to lines
# # file_name = "20210520-LineTransect_MB.gpx"
# # folder = "2021may"
# # dir <- file.path( "C:/Users/keppele/Documents/CeMoRe/Analysis/cemore_analysis/survey_data/tracklines/transects") # on oyster drive on small laptop
# # t <- plotKML::readGPX(file.path(dir, "gpx", folder, file_name))[[4]][[1]][[1]] %>%
# # mutate(time = time %>% lubridate::ymd_hms() %>% hms::as_hms(), Species = "KW - Transient", SurveyID = "cemore_2021may", month = 5, season = "Spring") %>%
# # filter(hour(time) == 23 & minute(time) == 15) %>%
# # filter(time == min(time)) %>%
# # st_as_sf(coords = c("lon", "lat"), crs = 4326) %>%
# # st_transform(crs = st_crs(bc_shp)) %>%
# # dplyr::select(geometry, Species,SurveyID,month, season)
# # ap_sf <- rbind(ap_sf, t)
# #---------------------------------------------------------------
#
# #---------------------------------------------------------------
# # Special case, incidental sightings Oct 2021
# #---------------------------------------------------------------
# # s <- bind_rows(s1,s2) %>%
# # mutate(month = month(time_index),
# # SurveyID = "cemore_2021oct",
# # month = 10,
# # year = 2021,
# # season = "Fall",
# # TransectID = "14",
# # month_abb = "Oct",
# # month_year = "Oct 2021",
# # Sgt_ID = "S99a",
# # beauf = 1,
# # PSD_nm = 0.01,
# # BestNumber = Best.Cnt) %>%
# # tidyr::separate(GPS.Pos, into = c("lat", "lon"), sep = "N") %>%
# # dplyr::mutate(lon = substr(lon, 2, nchar(lon)-3)) %>%
# # dplyr::mutate(lat = substr(lat, 1, nchar(lat)-2)) %>%
# # tidyr::separate(lon, into = c("lon.deg", "lon.min"), sep = " ") %>%
# # tidyr::separate(lat, into = c("lat.deg", "lat.min"), sep = " ") %>%
# # mutate(lon = -(as.numeric(lon.deg) + as.numeric(lon.min)/60),
# # lat = as.numeric(lat.deg) + as.numeric(lat.min)/60) %>%
# # st_as_sf(coords = c("lon", "lat"), crs = 4326) %>%
# # dplyr::select("SurveyID", "Sgt_ID", "Species","year","month","TransectID","season","month_abb","month_year","geometry", "beauf", "PSD_nm", "BestNumber")
#
# #---------------------------------------------------------------
# # Special case, incidental sightings Nov 2021
# #---------------------------------------------------------------
# # s <- s %>% # filter(Species == "Minke Whale") %>%
# # mutate(month = month(time_index),
# # SurveyID = surveyid,
# # month = 11,
# # year = 2021,
# # season = "Fall",
# # TransectID = iteration,
# # month_abb = month_abb,
# # month_year = survey_title,
# # Sgt_ID = "S99a",
# # beauf = 1,
# # PSD_nm = 0.01,
# # BestNumber = Best.Cnt)
# # %>%
# # tidyr::separate(GPS.Pos, into = c("lat", "lon"), sep = "N") %>%
# # dplyr::mutate(lon = substr(lon, 2, nchar(lon)-3)) %>%
# # dplyr::mutate(lat = substr(lat, 1, nchar(lat)-2)) %>%
# # tidyr::separate(lon, into = c("lon.deg", "lon.min"), sep = " ") %>%
# # tidyr::separate(lat, into = c("lat.deg", "lat.min"), sep = " ") %>%
# # mutate(lon = -(as.numeric(lon.deg) + as.numeric(lon.min)/60),
# # lat = as.numeric(lat.deg) + as.numeric(lat.min)/60) %>%
# # st_as_sf(coords = c("lon", "lat"), crs = 4326) %>%
# # dplyr::select("SurveyID", "Sgt_ID", "Species","year","month","TransectID","season","month_abb","month_year","geometry", "beauf", "PSD_nm", "BestNumber")
#
# # incidentals plotted at GPS position of ship
# if(incidentals){
# inc <- incid %>%
# tidyr::separate(GPS.Pos, into = c("lat", "lon"), sep = "N") %>%
# dplyr::mutate(lon = substr(lon, 2, nchar(lon)-3)) %>%
# dplyr::mutate(lat = substr(lat, 1, nchar(lat)-2)) %>%
# tidyr::separate(lon, into = c("lon.deg", "lon.min"), sep = " ") %>%
# tidyr::separate(lat, into = c("lat.deg", "lat.min"), sep = " ") %>%
# mutate(lon = -(as.numeric(lon.deg) + as.numeric(lon.min)/60),
# lat = as.numeric(lat.deg) + as.numeric(lat.min)/60,
# BestNumber = Best.Cnt, Count = Best.Cnt) %>%
# st_as_sf(coords = c("lon", "lat"), crs = 4326) %>%
# dplyr::select("Species","BestNumber","Count", geometry)
# ap_sf <- bind_rows(ap_sf,inc)
# }
#
#
# #-----------------------------------------------------------
# # to add in hydrophone deployment position(s)
# #-----------------------------------------------------------
# # (see hydrophone/hydrophone.R)
# if(hydrophone){
# source("R/hydrophone.R")
# # acoust <- y %>% filter(deployment_year == year, deployment_month == month)
# acoust <- y %>% filter(deployment_year == 2021, month(retrieval_date) == 11)
# }
# # ap_sf <- rbind(ap_sf, x)
#
# # ----------------------------------------------------------------------
# # ----------------- PLOT ON-EFFORT SIGHTINGS --------------------------------
# # ----------------------------------------------------------------------
# coord <- ggplot2::coord_sf(xlim = c(-125.3, -123), ylim = c(48.2, 49.44), crs = sf::st_crs(4326))
# # pal <- RColorBrewer::brewer.pal(12, "Paired")[c(2,4,3)]
# pal <- brewer.pal(9, "Set1")[c(1:9)] #red,blue,green,purple,orange,yellow,brown,pink,grey
#
#
# cols <- c('Humpback' = pal[1],
# 'Harbour Porpoise' = pal[3],
# 'Dalls Porpoise' = pal[4],
# 'Unknown Porpoise' = pal[9],
# 'KW - Northern Resident' = "black",
# 'KW - Southern Resident' = "black",
# 'KW - Transient' = "black",
# 'KW - Unknown ecotype' = "black",
# 'Fin Whale' = pal[5],
# 'Grey Whale' = pal[8],
# 'Minke Whale' = pal[7])
#
# shape <- c('Humpback' = 21,
# 'Harbour Porpoise' = 21,
# 'Dalls Porpoise' = 21,
# 'Unknown Porpoise' = 21,
# 'KW - Northern Resident' = 24,
# 'KW - Southern Resident' = 25,
# 'KW - Transient' = 21,
# 'KW - Unknown ecotype' = 22,
# "Fin Whale" = 21,
# "Grey Whale" = 21,
# "Minke Whale" = 21)
# # shape <- c('Humpback' = 24,
# # 'Harbour Porpoise' = 23,
# # 'Dalls Porpoise' = 23,
# # 'Unknown Porpoise' = 23,
# # 'KW - Northern Resident' = 24,
# # 'KW - Southern Resident' = 25,
# # 'KW - Transient' = 22,
# # 'KW - Unknown ecotype' = 23,
# # "Fin Whale" = 21,
# # "Grey Whale" = 21,
# # "Minke Whale" = 21,
# # "H. Porpoise group 40-50" = 8)
#
#
#
#
#
# # make bathy legend
# b_leg <- ggplot() +
# geom_raster(aes(x=x,y=y,fill = z), data = bathy) + labs(fill = "Depth (m)") +
# scale_fill_gradientn(colours = col_ramp(20)) +
# theme(legend.position = "bottom") +
# guides(fill = guide_colorbar(title.position = "left"))
#
# # leg1 <- gtable_filter(ggplot_gtable(ggplot_build(b_leg)), "guide-box")
# # leg1Grob <- grobTree(leg1)
#
# leg1 <- cowplot::get_legend(b_leg)
#
#
# #-------------------------------------------------------
# if(single_survey){
# #-------------------------------------------------------
# # to not display all potential species in legend
# ap_sf$Species %<>% droplevels()
# # s$Species %<>% droplevels()
#
# # to order legend symbols consistently
# # sp <- unique(c(levels(ap_sf$Species), levels(s$Species)))
# sp <- unique(c(levels(ap_sf$Species)))
#
# # to size symbols by count
# ap_sf <- ap_sf %>% mutate(Count =case_when(
# BestNumber == 1 ~ "1",
# BestNumber %in% c(2:5) ~ "2:5",
# BestNumber >5 ~ ">5"
# ) %>% factor(levels = c("1", "2:5", ">5")))
# # s <- s %>% mutate(Count =case_when(
# # Best.Cnt == 1 ~ "1",
# # Best.Cnt %in% c(2:5) ~ "2:5",
# # Best.Cnt >5 ~ ">5"
# # ) %>% factor(levels = c("1", "2:5", ">5")))
#
# # create object 'g' basemap with effort
# g <- ggplot() +
# geom_raster(aes(x=x,y=y,fill = z), data = bathy) + labs(fill = "Depth (m)") +
# scale_fill_gradientn(colours = col_ramp(20), guide = "none") +
# ggnewscale::new_scale("fill") +
# # + coord_map()
# # geom_contour(data = b,
# # aes(x=X, y=Y, z=PID),
# # breaks=c(-250),
# # size=c(0.6),
# # colour="grey")+
# geom_sf(data = coast_file, stroke = 0.05, fill = "light yellow", colour = "grey 60")+
# geom_sf(data = effort_lines, size = 0.25, colour = "black")
#
# g
# if(hydrophone){
# g <- g +
# geom_sf(data = acoust, aes(shape = deployed)) +
# scale_shape_manual(values = 8, name = NULL) +
# ggnewscale::new_scale("shape")
# }
#
# g <- g + geom_sf(data = ap_sf, stroke = 0, alpha = 0.8, aes(fill = Species, shape = Species, size = Count)) +#
# # geom_sf(data = s, stroke = 0.01,alpha = 0.5, aes(fill = Species, shape = Species, size = Count)) +
#
# scale_size_manual(values = c(1.5,2,3)) +
# scale_fill_manual(values = cols, name = "Sightings", breaks = sp) +
# scale_shape_manual(values = shape, name = "Sightings", breaks = sp) +
#
# guides(alpha= "none") +
# guides(shape = guide_legend(override.aes = list(size=2))) +
#
# # ggtitle(paste0("CeMoRe survey ", survey_title),
# # subtitle = "Cetacean sightings and acoustic recorder deployment locations") +
# annotation_custom(leg1, xmin=-119.1, xmax=-124.95, ymin=47.95, ymax=48.1) +
#
# coord +
# ylab("")+xlab("")
#
# }else{
# if(sightings_only){
# #--------------- SIGHTINGS ONLY ----------------------------------------
# # to not display all potential species in legend
# all_ap_sf$Species %<>% droplevels()
#
# # to order legend symbols consistently
# # sp <- unique(c(levels(all_ap_sf$Species), levels(s$Species)))
# sp <- unique(c(levels(all_ap_sf$Species)))
#
# # to size symbols by count
# all_ap_sf <- all_ap_sf %>% mutate(Count =case_when(
# BestNumber == 1 ~ "1",
# BestNumber %in% c(2:5) ~ "2:5",
# BestNumber >5 ~ ">5"
# ) %>% factor(levels = c("1", "2:5", ">5")))
#
# g <- ggplot() +
# geom_raster(aes(x=x,y=y,fill = z), data = bathy) + labs(fill = "Depth (m)") +
# scale_fill_gradientn(colours = col_ramp(20)) +
# ggnewscale::new_scale("fill") +
#
# geom_sf(data = coast_file, stroke = 0.05, fill = "light yellow", colour = "grey 60")+
#
# geom_sf(data = all_ap_sf, stroke = 0.01, alpha = 0.5, aes(fill = Species, shape = Species, size = Count)) +#
#
# scale_size_manual(values = c(1.5,2,3)) +
# scale_fill_manual(values = cols, name = "Sightings", breaks = sp) +
# scale_shape_manual(values = shape, name = "Sightings", breaks = sp) +
#
# guides(alpha= "none") +
# guides(shape = guide_legend(override.aes = list(size=2))) +
# # annotation_custom(leg1, xmin=-124.5, xmax=-124.95, ymin=47.9, ymax=48.1) +
#
# ggtitle(paste0("CeMoRe survey cetacean sightings to ", survey_title)) +
# coord +
# ylab("")+xlab("") +
# theme(axis.text.x=element_text(angle=90)) +
# # facet_grid(Species ~ season)
# facet_wrap(~ season)
# #
# # g <- cowplot::plot_grid(g, leg1,
# # align = "v", axis = "r",
# # ncol = 1,
# # rel_heights = c(1, 0.05),
# # rel_widths = c(1, 0.05)
# # if saving plot, change coast to bc_shp for higher res basemap
# if(Save){
# # ggsave(paste0("output_maps/sightings_", survey_title, "_low_res_plus_hydrophones31_78.png"))
# ggsave(paste0("../output_maps/sightings_", survey_title, "_high_res.png"), height = 15, width = 15, units = "cm")
# }
# }else{
# #------------- EFFORT LINES ONLY ------------------------------------------
#
# g <- ggplot() +
# geom_raster(aes(x=x,y=y,fill = z), data = bathy) + labs(fill = "Depth (m)") +
# scale_fill_gradientn(colours = col_ramp(20), guide = "none") +
# ggnewscale::new_scale("fill") +
# ggnewscale::new_scale_colour() +
# geom_sf(data = coast_file, stroke = 0.05, fill = "light yellow", colour = "grey 60")+
# geom_sf(data = all_effort_lines, size = 0.25) +
# scale_colour_manual(values = c("dark orange", "green4", "yellow", "dark blue"), name = "Season") +
# ggtitle("CeMoRe survey completed transects", paste0("Sept 2020 to ", survey_title)) +
# # annotation_custom(leg1Grob, xmin=-124.99, xmax=-124.95, ymin=47.89, ymax=48.15) +
# coord +
# ylab("")+xlab("") +
# theme(axis.text.x=element_text(angle=90)) +
# facet_wrap(~ season)
#
# g <- cowplot::plot_grid(g, leg1,
# align = "v", axis = "l",
# ncol = 1,
# rel_heights = c(1, 0.1),
# rel_widths = c(1, 0.1)
# )
# # if saving plot, change coast to bc_shp for higher res basemap
# if(Save){
# # ggsave(paste0("output_maps/sightings_", survey_title, "_low_res_plus_hydrophones31_78.png"))
# ggsave(paste0("../output_maps/effortlines_", survey_title, "_high_res.png"), height = 15, width = 15, units = "cm")
# }
# }
# }
# }
#
#
#
#
# # Save = F
# #
# # ggsave(paste0("../output_maps/sightings_to_", survey_title, "_low_res.png"))
#
# #
# #
# # #--------------------------------------------------------------------------
# #
# #
# #
# # # ggsave(paste0("output_maps/sightings_high_dens_porpoises_", survey_title, "_high_res.png"))
# #
# # # -------------------------------------------------------------------------------
# # # ---- substrate
# # # -------------------------------------------------------------------------------
# # # Load substrate raster and create bounding box for study area
# #
# # sub <- raster("covariates/salish_sea_20m/salish_sea_20m.tif")#crs = 3005
# # plot(sub)
# # object.size(sub) # 14464 bytes
# #
# # sub_clip <- raster("covariates/sub_arc_clip.tif")#crs = 3005
# # plot(sub_clip)
# # object.size(sub_clip) # 14464 bytes
# #
# #
# # sub_clip_df <- as.data.frame(sub_clip, xy = T)%>%
# # na.omit()
# # # sub_sf <- st_as_sf(sub_df, coords=c(lon = sub_df$x, lat = sub_df$y))
# # object.size(sub_clip_df) # 3521621736 bytes
# #
# # # bb <- create_bb(-125.5, -123., 48.1, 49.5) %>% st_transform(3005)
# # # tss_6km_2 <- st_read(
# # # "C:/Users/keppele/Documents/CeMoRe/Design/cemore_design/shapefiles/study_area",
# # # "TSS_buffer6km_2") %>%
# # # st_transform(3005)
# #
# # # plot(tss_6km_2)
# # # plot(bb, add=T)
# # # crop raster to study area
# # # sub_cropped <- crop(sub, extent(tss_6km_2))
# # # object.size(sub_cropped)/1000
# #
# # # convert to data.frame for plotting
# # sub_clip_df2 <- sub_clip %>%
# # as.bathy() %>%
# # fortify.bathy() %>%
# # na.omit()
# # object.size(sub_clip_df2) # 3521621728 bytes
# #
# # ggRasterly(data = sub_clip_df2, aes(x=x, y=y, color=z),
# # show_raster = T,
# # drop_data = T)
# #
# #
# # sub_mask <- raster("covariates/sub_extract_mask.tif") %>%
# # na.omit() #crs = 3005
# # plot(sub_mask)
# # object.size(sub_mask) # 14480 bytes bytes
# #
# # saveRDS(sub_cropped_df, "covariates/substrate_cropped_df.rds")
# # sub_cropped_df <- readRDS("covariates/substrate_cropped_df.rds")
# #
# # sub_mask_df <- sub_mask %>%
# # as.bathy() %>%
# # fortify.bathy() %>%
# # na.omit()
# # object.size(sub_clip_df2) # 3,790,000 bytes
# #
# # sub_mask_df2 <- as.data.frame(sub_mask, xy = T)%>%
# # na.omit()
# # #------------------------------------------------------------------------------
# # # try converting arc-clipped substrate layer to spdf, then st_as_sf
# # #------------------------------------------------------------------------------
# #
# #
# # sub_clip_df <- as(sub_clip,'SpatialPolygonsDataFrame')
# # object.size(sub_clip_df)
# #
# # sub_clip_sf <- st_as_sf(sub_clip_df)
# # #------------------------------------------------------------------------------
# #
# # ggplot() + #geom_sf(data = st_transform(coast_file, crs = 4326), fill = "light yellow", colour = "grey 60") +
# # # geom_sf(data = bb) +
# # geom_raster(data = sub_df, aes(x = x, y = y, fill = sub_arc_clip)) +
# # coord
# #
# # area <- st_read("C:/Users/keppele/Documents/CeMoRe/Design/cemore_design/shapefiles/study_area", "cemore_study_area_TSS_6km_can")
# # tss_6km_2 <- st_read(
# # "C:/Users/keppele/Documents/CeMoRe/Design/cemore_design/shapefiles/study_area", "TSS_buffer6km_2"
# # ) %>%
# # st_transform(3005)
# #
# #
# #
# #
# #
# # ggsave("test_w_sub.png")
# # x <- ap_sf %>% filter(Species == "H. Porpoise group 40-50")
# # ggplot() + geom_sf(data = coast_file, fill = "light yellow", colour = "grey 60") +
# # geom_sf(data = ap_sf, size = 1.5, aes(colour = Species, fill = Species, shape = Species)) +
# # # geom_raster(data = bath_df, aes(x = x, y = y, fill = salish_sea_20m)) +
# # # geom_raster(data = sub_df, aes(x = x, y = y, fill = salish_sea_20m)) +
# # # coord
# # # ggsave("test_w_bath_and_sub.png")
# #
# # #, stroke =0.05
# # scale_shape_manual(values = shape, name = NULL) +
# # scale_fill_manual(values = cols, name = NULL) +
# # scale_color_manual(values = cols, name = NULL) +
# # coord +
# # ggtitle(paste0("All CeMoRe survey cetacean on effort sightings Sept 2020 - ", survey_title)) + #gfplot:::theme_pbs() +
# # theme(panel.background= element_rect(fill = "light blue")) +
# # theme(legend.position = "right") +
# # theme(axis.text.x = element_text(angle = 90, hjust = 1)) #+
# # facet_wrap( ~ Species)
# # # # if saving plot, change coast to bc_shp for higher res basemap
# # # if(save){
# # # ggsave(paste0("output_maps/all on effort sightings.png"))
# # # }
# # # save = F
# # # #-------------------------------------------------------------------------------
# # # #---- SIGHTING PLOTS BY SPECIES, ALL SURVEYS COMBINED, COLOURED BY SURVEY
# # # #-------------------------------------------------------------------------------
# # # ap_sf$SurveyID %<>% factor(c("cemore_2020sept",
# # # "cemore_2020oct",
# # # "cemore_2020nov",
# # # "cemore_2021jan",
# # # "cemore_2021feb",
# # # "cemore_2021mar",
# # # "cemore_2021apr",
# # # "cemore_2021may"))
# # # cols <- c('cemore_2020sept' = "brown",
# # # 'cemore_2020oct'= "purple",
# # # 'cemore_2020nov' = "blue",
# # # 'cemore_2020dec' = "turquoise3",
# # # 'cemore_2021jan' = "turquoise1",
# # # 'cemore_2021feb' ="darkturquoise",
# # # 'cemore_2021mar' = "limegreen",
# # # 'cemore_2021apr' = "springgreen4",
# # # 'cemore_2021may' = "yellow",
# # # 'cemore_2021jun' = "orange",
# # # 'cemore_2021jul' = "red",
# # # 'cemore_2021aug' = "magenta")
# # # save = F
# # # coast_file <- if(save) bc_shp else coast
# # # ggplot() + geom_sf(data = coast_file, fill = "light yellow", colour = "grey 60") +
# # # # geom_sf(data = canada_shp, colour = "black", fill = NA, linetype = "solid") +
# # # # geom_sf(data = effort_lines, size = 0.25, colour = "grey 50") +
# # # # geom_sf(data = effort_lines, size = 0.25, aes(colour = Date)) +
# # #
# # # # geom_sf(data = ap_sf, size = 1.5, aes(colour = Species, fill = Species, shape = Species)) + #, stroke =0.05
# # # geom_sf(data = ap_sf, size = 1.5, aes(colour = season)) + #, stroke =0.05
# # # # scale_shape_manual(values = shape, name = NULL) +
# # # # scale_fill_manual(values = cols, name = NULL) +
# # # scale_color_manual(values = cols , name = NULL) +
# # # coord +
# # # ggtitle(paste0("CeMoRe cetacean sightings by survey")) + #gfplot:::theme_pbs() +
# # # # ggtitle(paste0("CeMoRe survey cetacean sightings - High density porpoise sightings ", survey_title)) +
# # # theme(panel.background= element_rect(fill = "light blue")) +
# # # theme(legend.position = "right") +
# # # theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
# # # facet_wrap( ~ Species)
# # # # if saving plot, change coast to bc_shp for higher res basemap
# # # if(save){
# # # ggsave(paste0("output_maps/sightings by month.png"))
# # # }
# # # save = F
# # #
# # # #-------------------------------------------------------------------------------
# # # #---- SIGHTING PLOTS BY SPECIES, ALL SURVEYS COMBINED, COLOURED BY MONTH
# # # #-------------------------------------------------------------------------------
# # # cols <- c('Sep' = "brown",
# # # 'Oct'= "purple",
# # # 'Nov' = "blue",
# # # 'Dec' = "turquoise3",
# # # 'Jan' = "turquoise1",
# # # 'Feb' ="darkturquoise",
# # # 'Mar' = "limegreen",
# # # 'Apr' = "springgreen4",
# # # 'May' = "yellow",
# # # 'Jun' = "orange",
# # # 'Jul' = "red",
# # # 'Aug' = "magenta")
# # # save = F
# # # coast_file <- if(save) bc_shp else coast
# # # ggplot() + geom_sf(data = coast_file, fill = "light yellow", colour = "grey 60") +
# # # # geom_sf(data = canada_shp, colour = "black", fill = NA, linetype = "solid") +
# # # # geom_sf(data = effort_lines, size = 0.25, colour = "grey 50") +
# # # # geom_sf(data = effort_lines, size = 0.25, aes(colour = Date)) +
# # #
# # # # geom_sf(data = ap_sf, size = 1.5, aes(colour = Species, fill = Species, shape = Species)) + #, stroke =0.05
# # # geom_sf(data = ap_sf, size = 0.75, aes(colour = factor(month_abb))) + #, stroke =0.05
# # # # scale_shape_manual(values = shape, name = NULL) +
# # # # scale_fill_manual(values = cols, name = NULL) +
# # # scale_color_manual(values = cols, name = NULL) +
# # # coord +
# # # ggtitle(paste0("CeMoRe survey cetacean sightings by month ", survey_title)) + #gfplot:::theme_pbs() +
# # # # ggtitle(paste0("CeMoRe survey cetacean sightings - High density porpoise sightings ", survey_title)) +
# # # theme(panel.background= element_rect(fill = "light blue")) +
# # # theme(legend.position = "right") +
# # # theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
# # # facet_wrap( ~ Species)
# # # if(save){
# # # ggsave(paste0("output_maps/sightings by month.png"))
# # # }
# # # save = F
# # # #-------------------------------------------------------------------------------
# # # #---- SIGHTING PLOTS BY SPECIES, ALL SURVEYS COMBINED, COLOURED BY SEASON
# # # #-------------------------------------------------------------------------------
# # cols <- c('Fall' = "orange",
# # 'Winter'= "blue",
# # 'Summer' = "red",
# # 'Spring' = "limegreen")
# # save = T
# # coast_file <- if(save) bc_shp else coast
# # ggplot() + geom_sf(data = coast_file, fill = "light yellow", colour = "grey 60") +
# # geom_sf(data = ap_sf, size = 0.8, aes(colour = factor(season))) + #, stroke =0.05
# # scale_color_manual(values = cols, name = NULL) +
# # coord +
# # ggtitle(paste0("CeMoRe survey cetacean on-effort sightings by season. Plotted ", survey_title)) + #gfplot:::theme_pbs() +
# # # ggtitle(paste0("CeMoRe survey cetacean sightings - High density porpoise sightings ", survey_title)) +
# # theme(panel.background= element_rect(fill = "light blue")) +
# # theme(legend.position = "right") +
# # theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
# # facet_wrap( ~ Species)
# #
# # # if saving plot, change coast to bc_shp for higher res basemap
# # if(save){
# # ggsave(paste0("output_maps/sightings by season ", month, year, ".png"))
# # }
# # # save = F
# # #
# # # #-------------------------------------------------------------------------------
# # # #---- SINGLE SPECIES DISTRIBUTION, ALL SURVEYS COMBINED, BY MONTH
# # # #-------------------------------------------------------------------------------
# # cols <- c('Humpback' = "blue3",
# # 'Harbour Porpoise' = "dark green",
# # 'Dalls Porpoise' = "firebrick3",
# # 'Unknown Porpoise' = "darkorange1",
# # 'KW - Northern Resident' = "green3",
# # 'KW - Southern Resident' = "violetred1",
# # 'KW - Transient' = "black",
# # 'KW - Unknown ecotype' = "black",
# # 'Sei Whale' = "grey 30",
# # 'Sperm Whale' = pal[6],
# # 'Cuvier\'s Whale' = pal[4],
# # 'Fin Whale' = pal[10],
# # 'Grey Whale' = "darkorchid4",
# # 'H. Porpoise group 40-50' = "black")
# #
# # shape <- c('Humpback' = 24,
# # 'Harbour Porpoise' = 23,
# # 'Dalls Porpoise' = 23,
# # 'Unknown Porpoise' = 23,
# # 'KW - Northern Resident' = 2,
# # 'KW - Southern Resident' = 25,
# # 'KW - Transient' = 25,
# # 'KW - Unknown ecotype' = 4,
# # 'Sei Whale' = 21,
# # 'Sperm Whale' = 21,
# # "Cuvier's Whale" = 21,
# # "Fin Whale" = 21,
# # "Grey Whale" = 21,
# # "H. Porpoise group 40-50" = 8)
# # Save = T
# # coast_file <- if(Save) bc_shp else coast
# #
# # x <- ap_sf %>% filter(Species == "Humpback")
# # x <- ap_sf %>% filter(Species == "Harbour Porpoise")
# # x <- ap_sf %>% filter(Species == "Dalls Porpoise")
# # x <- ap_sf %>% filter(Species %in% c("KW - Southern Resident", "KW - Transient"))
# # #-------------------------------------------------------------------------------
# # # for just porpoises
# # #-------------------------------------------------------------------------------
# # x <- ap_sf %>% dplyr::filter(grepl("Porpoise", Species, fixed = TRUE))
# # #-------------------------------------------------------------------------------
# #
# #
# # ggplot() + geom_sf(data = coast_file, fill = "light yellow", colour = "grey 60", size = 0.25) +
# # geom_sf(data = all_effort_lines, size = 0.1, colour = "black") +
# # geom_sf(data = x, size = 1, aes(fill = Species, shape = Species, colour = Species)) + #, stroke =0.05
# # scale_shape_manual(values = shape, name = NULL) +
# # scale_fill_manual(values = cols, name = NULL) +
# # scale_color_manual(values = cols, name = NULL) +
# # coord +
# # # ggtitle(paste0("All CeMoRe survey on effort Humpback sightings - plotted ", survey_title)) + #gfplot:::theme_pbs() +
# # ggtitle(paste0("All CeMoRe survey on effort Harbour Porpoise sightings - plotted ", survey_title)) +
# # # ggtitle(paste0("All CeMoRe survey on effort Dalls Porpoise sightings - plotted ", survey_title)) +
# # # ggtitle(paste0("All CeMoRe survey on effort Killer Whale sightings - plotted ", survey_title)) +
# # theme(panel.background= element_rect(fill = "light blue")) +
# # theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
# # theme(legend.position = "NONE") +
# # facet_wrap( ~ month_year, drop = F)
# # # if saving plot, change coast to bc_shp for higher res basemap
# #
# # if(save){
# # ggsave(paste0("output_maps/HW by month up to June 2021.png"))
# # ggsave(paste0("output_maps/HP by month up to June 2021.png"))
# # ggsave(paste0("output_maps/DP by month up to June 2021.png"))
# # ggsave(paste0("output_maps/KW by month up to June 2021.png"))
# #
# #
# # }
# # save = F
# #
# #
# # # TRY JITTERING TO SEE ALL OF THE HP SIGHTINGS
# # #display.brewer.all()
# #
# #
# #
# #
# #
# #
# #
# # # ------------------------------------------------------------------------------
# # # plot tag deployment locations
# # # ------------------------------------------------------------------------------
# # x <- read.csv("tag_data/2021-08 tag deployment locations.csv") %>%
# # st_as_sf(coords = c("lon", "lat"), crs = 4326) %>%
# # st_transform(crs = st_crs(bc_shp))
# # # st_as_sf(coords = c("lon", "lat"), crs = st_crs(bc_shp)) %>%
# # # st_transform(crs = 4326)
# #
# # Save <- T
# # if(!Save) coast_file <- coast else coast_file <- bc_shp
# #
# # ggplot() +
# # geom_sf(data = coast_file, fill = "light yellow", colour = "grey 60", size = 0.25) +
# # geom_sf(data = x, size = 1, colour = "black") +
# # coord +
# # # theme(panel.background= element_rect(fill = "light blue")) +
# # ggtitle("Humpback Whale Tagging Locations August 2021")
# #
# #
# # if(Save){
# # ggsave(paste0("tagging_maps/tagging locations 2021 August.png"))
# # }
# #
# # # ------------------------------------------------------------------------------
# # # plot hydrophone deployment locations
# # # ------------------------------------------------------------------------------
# # x <- read.csv("hydrophone_data/hydrophone_deployment_locations.csv") %>%
# # st_as_sf(coords = c("lon", "lat"), crs = 4326) %>%
# # st_transform(crs = st_crs(bc_shp))
# # # st_as_sf(coords = c("lon", "lat"), crs = st_crs(bc_shp)) %>%
# # # st_transform(crs = 4326)
# #
# # Save <- F
# # if(!Save) coast_file <- coast else coast_file <- bc_shp
# # display.brewer.all()
# #
# # bathy <- getNOAA.bathy(-125.5, -123,48.1, 49.5,res=1, keep=TRUE)
# # bathy <- as.raster(bathy)
# # plot(bathy, col = RColorBrewer::brewer.pal(20L, "Blues"))
# #
# # ggplot() + geom_sf(data = coast_file, fill = "light yellow", colour = "grey 60", size = 0.25) +
# # # geom_sf(data = x, size = 1, colour = "black") +
# # coord +
# # theme(panel.background= element_rect(fill = "light blue")) +
# # ggtitle("Hydrophone Deployment Location August 21, 2021") +
# # geom_raster(data = bathy)
# #
# # bathy %<>% as.matrix()
# # bathy %>% as.data.frame() %>%
# # rownames_to_column(var = "lon") %>%
# # gather(lat, value, -1) %>%
# # mutate_all(funs(as.numeric)) %>%
# # ggplot()+
# # geom_contour(aes(x = lon, y = lat, z = value), bins = 10, colour = "black") +
# # coord_map()
# #
# #
# # if(Save){
# # ggsave(paste0("hydrophone_data/deployment locations 2021 August.png"))
# # }
elisekeppel/cemore documentation built on Feb. 13, 2025, 11:57 a.m.
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# # after Eva's code
# plot_survey <- function(Save = F,
# single_survey=T,
# incidentals=F,
# hydrophone=F,
# sightings_only=F){
#
# #
# # # ----------------------------------------------------------------------
# # # ----------------- SET SURVEY DATE --------------------------------
# # # ----------------------------------------------------------------------
# # {year = 2021
# # month = "10"
# # month_abb <- month.abb[as.numeric(month)]
# # survey_title <- paste(first_up(month_abb), year)
# # folder = paste0("survey_data/raw_data/",year, "-",month)
# # surveyid = paste0("cemore_", year, tolower(month_abb))}
# # # ----------------------------------------------------------------------
#
# # ----------------------------------------------------------------------
# # ----------------- LOAD SPATIAL FILES --------------------------------
# # ----------------------------------------------------------------------
# # bc coast for plotting
# if(!Save & !exists("coast")){
# coast <- sf::st_read(dsn = "shapefiles", layer = "BC_coast_UTM9", quiet = T)
# coast_file <- coast
# }
# # higher resolution bc coast --- takes a long time to load
# if(Save & !exists("bc_shp")){
# bc_shp <- sf::st_read(dsn = "shapefiles", "BC_AK_WA_union_polygon") %>%
# sf::st_transform(crs = 4326)
# coast_file <- bc_shp
# }
# # Pacific Canada polygon for outlining Canadian border
# # canada_shp <- st_read(dsn = "shapefiles", "CanadianEEZ") %>%
# # st_transform(crs = 4326)
#
# #-----------------------------------------------------------
# # to create bathymetric layer
# #-----------------------------------------------------------
# {bathy <- getNOAA.bathy(-125.5, -122.5,48.1, 49.5,res=1, keep=TRUE) %>%
# fortify(bathy)
#
# bathy$z[which(bathy$z >= 0)] <- 0
#
# col <- rev(RColorBrewer::brewer.pal(9L, "Blues")[4:7])
# col_ramp <- colorRampPalette(col)}
#
# # ----------------------------------------------------------------------
#
# # ap_sf %<>% filter(grepl("Porpoise", Species))
# # coord2<- ggplot2::coord_sf(xlim = c(-123.4, -123.1), ylim = c(48.32, 48.5), crs = sf::st_crs(4326)) # FOR BB
#
# # if require inset to magnify area of intense sightings
# # bb <- create_bb(-123.4, -123.1, 48.32, 48.5)
# # ap_sf_cropped <- st_intersection(ap_sf, bb)
#
# # Old way of viewing tidy but not processed sightings (would need to import raw data)
# # s <- data$sightings %>%
# # dplyr::filter(!is.na(Sgt.Lat) | !is.na(Sgt.Lon), !Species == "Sea Otter") %>% #, incidential.sighting == F) %>%
# # st_as_sf(coords = c("Sgt.Lon", "Sgt.Lat"), crs = 4326) %>%
# # st_transform(crs= 3156)
#
# #---------------------------------------------------------------
# # Special case, plotting incidental sightings in Feb 2021
# #---------------------------------------------------------------
# # # at line 48 in Sighting Position Correction.R file:
# # data <- rbind(DATA[which(DATA$Event=="Survey sighting" & DATA$status=="ON"),],DATA[which(DATA$Sgt.ID=="S5a"),]) #EK edit = to include incidentals for plotting feb 2021
# # and re-run Eva's code (revert and run again after for only significant sightings in final processed data)
#
# #---------------------------------------------------------------
# # Special case, plotting large group of porpoises & incidental grey whale Apr 2021
# #---------------------------------------------------------------
# # ap_sf[which(ap_sf$Sgt_ID == "S28"),]$Species <- "H. Porpoise group 40-50"
# # # at line 48 in Sighting Position Correction.R file:
# # data <- rbind(DATA[which(DATA$Event=="Survey sighting" & DATA$status=="ON"),],DATA[which(DATA$Sgt.ID=="S34"),]) #EK edit = to include incidental grey whale sighting for plotting APR 2021
# # and re-run Eva's code (revert and run again after for only significant sightings in final processed data)
#
# #---------------------------------------------------------------
# # Special case, plotting incidental sightings in May 2021
# #---------------------------------------------------------------
# # elf <- get_track(file_name = "20210520-LineTransect_MB.gpx", folder = "2021may") # can't use get_track as it changes it to lines
# # file_name = "20210520-LineTransect_MB.gpx"
# # folder = "2021may"
# # dir <- file.path( "C:/Users/keppele/Documents/CeMoRe/Analysis/cemore_analysis/survey_data/tracklines/transects") # on oyster drive on small laptop
# # t <- plotKML::readGPX(file.path(dir, "gpx", folder, file_name))[[4]][[1]][[1]] %>%
# # mutate(time = time %>% lubridate::ymd_hms() %>% hms::as_hms(), Species = "KW - Transient", SurveyID = "cemore_2021may", month = 5, season = "Spring") %>%
# # filter(hour(time) == 23 & minute(time) == 15) %>%
# # filter(time == min(time)) %>%
# # st_as_sf(coords = c("lon", "lat"), crs = 4326) %>%
# # st_transform(crs = st_crs(bc_shp)) %>%
# # dplyr::select(geometry, Species,SurveyID,month, season)
# # ap_sf <- rbind(ap_sf, t)
# #---------------------------------------------------------------
#
# #---------------------------------------------------------------
# # Special case, incidental sightings Oct 2021
# #---------------------------------------------------------------
# # s <- bind_rows(s1,s2) %>%
# # mutate(month = month(time_index),
# # SurveyID = "cemore_2021oct",
# # month = 10,
# # year = 2021,
# # season = "Fall",
# # TransectID = "14",
# # month_abb = "Oct",
# # month_year = "Oct 2021",
# # Sgt_ID = "S99a",
# # beauf = 1,
# # PSD_nm = 0.01,
# # BestNumber = Best.Cnt) %>%
# # tidyr::separate(GPS.Pos, into = c("lat", "lon"), sep = "N") %>%
# # dplyr::mutate(lon = substr(lon, 2, nchar(lon)-3)) %>%
# # dplyr::mutate(lat = substr(lat, 1, nchar(lat)-2)) %>%
# # tidyr::separate(lon, into = c("lon.deg", "lon.min"), sep = " ") %>%
# # tidyr::separate(lat, into = c("lat.deg", "lat.min"), sep = " ") %>%
# # mutate(lon = -(as.numeric(lon.deg) + as.numeric(lon.min)/60),
# # lat = as.numeric(lat.deg) + as.numeric(lat.min)/60) %>%
# # st_as_sf(coords = c("lon", "lat"), crs = 4326) %>%
# # dplyr::select("SurveyID", "Sgt_ID", "Species","year","month","TransectID","season","month_abb","month_year","geometry", "beauf", "PSD_nm", "BestNumber")
#
# #---------------------------------------------------------------
# # Special case, incidental sightings Nov 2021
# #---------------------------------------------------------------
# # s <- s %>% # filter(Species == "Minke Whale") %>%
# # mutate(month = month(time_index),
# # SurveyID = surveyid,
# # month = 11,
# # year = 2021,
# # season = "Fall",
# # TransectID = iteration,
# # month_abb = month_abb,
# # month_year = survey_title,
# # Sgt_ID = "S99a",
# # beauf = 1,
# # PSD_nm = 0.01,
# # BestNumber = Best.Cnt)
# # %>%
# # tidyr::separate(GPS.Pos, into = c("lat", "lon"), sep = "N") %>%
# # dplyr::mutate(lon = substr(lon, 2, nchar(lon)-3)) %>%
# # dplyr::mutate(lat = substr(lat, 1, nchar(lat)-2)) %>%
# # tidyr::separate(lon, into = c("lon.deg", "lon.min"), sep = " ") %>%
# # tidyr::separate(lat, into = c("lat.deg", "lat.min"), sep = " ") %>%
# # mutate(lon = -(as.numeric(lon.deg) + as.numeric(lon.min)/60),
# # lat = as.numeric(lat.deg) + as.numeric(lat.min)/60) %>%
# # st_as_sf(coords = c("lon", "lat"), crs = 4326) %>%
# # dplyr::select("SurveyID", "Sgt_ID", "Species","year","month","TransectID","season","month_abb","month_year","geometry", "beauf", "PSD_nm", "BestNumber")
#
# # incidentals plotted at GPS position of ship
# if(incidentals){
# inc <- incid %>%
# tidyr::separate(GPS.Pos, into = c("lat", "lon"), sep = "N") %>%
# dplyr::mutate(lon = substr(lon, 2, nchar(lon)-3)) %>%
# dplyr::mutate(lat = substr(lat, 1, nchar(lat)-2)) %>%
# tidyr::separate(lon, into = c("lon.deg", "lon.min"), sep = " ") %>%
# tidyr::separate(lat, into = c("lat.deg", "lat.min"), sep = " ") %>%
# mutate(lon = -(as.numeric(lon.deg) + as.numeric(lon.min)/60),
# lat = as.numeric(lat.deg) + as.numeric(lat.min)/60,
# BestNumber = Best.Cnt, Count = Best.Cnt) %>%
# st_as_sf(coords = c("lon", "lat"), crs = 4326) %>%
# dplyr::select("Species","BestNumber","Count", geometry)
# ap_sf <- bind_rows(ap_sf,inc)
# }
#
#
# #-----------------------------------------------------------
# # to add in hydrophone deployment position(s)
# #-----------------------------------------------------------
# # (see hydrophone/hydrophone.R)
# if(hydrophone){
# source("R/hydrophone.R")
# # acoust <- y %>% filter(deployment_year == year, deployment_month == month)
# acoust <- y %>% filter(deployment_year == 2021, month(retrieval_date) == 11)
# }
# # ap_sf <- rbind(ap_sf, x)
#
# # ----------------------------------------------------------------------
# # ----------------- PLOT ON-EFFORT SIGHTINGS --------------------------------
# # ----------------------------------------------------------------------
# coord <- ggplot2::coord_sf(xlim = c(-125.3, -123), ylim = c(48.2, 49.44), crs = sf::st_crs(4326))
# # pal <- RColorBrewer::brewer.pal(12, "Paired")[c(2,4,3)]
# pal <- brewer.pal(9, "Set1")[c(1:9)] #red,blue,green,purple,orange,yellow,brown,pink,grey
#
#
# cols <- c('Humpback' = pal[1],
# 'Harbour Porpoise' = pal[3],
# 'Dalls Porpoise' = pal[4],
# 'Unknown Porpoise' = pal[9],
# 'KW - Northern Resident' = "black",
# 'KW - Southern Resident' = "black",
# 'KW - Transient' = "black",
# 'KW - Unknown ecotype' = "black",
# 'Fin Whale' = pal[5],
# 'Grey Whale' = pal[8],
# 'Minke Whale' = pal[7])
#
# shape <- c('Humpback' = 21,
# 'Harbour Porpoise' = 21,
# 'Dalls Porpoise' = 21,
# 'Unknown Porpoise' = 21,
# 'KW - Northern Resident' = 24,
# 'KW - Southern Resident' = 25,
# 'KW - Transient' = 21,
# 'KW - Unknown ecotype' = 22,
# "Fin Whale" = 21,
# "Grey Whale" = 21,
# "Minke Whale" = 21)
# # shape <- c('Humpback' = 24,
# # 'Harbour Porpoise' = 23,
# # 'Dalls Porpoise' = 23,
# # 'Unknown Porpoise' = 23,
# # 'KW - Northern Resident' = 24,
# # 'KW - Southern Resident' = 25,
# # 'KW - Transient' = 22,
# # 'KW - Unknown ecotype' = 23,
# # "Fin Whale" = 21,
# # "Grey Whale" = 21,
# # "Minke Whale" = 21,
# # "H. Porpoise group 40-50" = 8)
#
#
#
#
#
# # make bathy legend
# b_leg <- ggplot() +
# geom_raster(aes(x=x,y=y,fill = z), data = bathy) + labs(fill = "Depth (m)") +
# scale_fill_gradientn(colours = col_ramp(20)) +
# theme(legend.position = "bottom") +
# guides(fill = guide_colorbar(title.position = "left"))
#
# # leg1 <- gtable_filter(ggplot_gtable(ggplot_build(b_leg)), "guide-box")
# # leg1Grob <- grobTree(leg1)
#
# leg1 <- cowplot::get_legend(b_leg)
#
#
# #-------------------------------------------------------
# if(single_survey){
# #-------------------------------------------------------
# # to not display all potential species in legend
# ap_sf$Species %<>% droplevels()
# # s$Species %<>% droplevels()
#
# # to order legend symbols consistently
# # sp <- unique(c(levels(ap_sf$Species), levels(s$Species)))
# sp <- unique(c(levels(ap_sf$Species)))
#
# # to size symbols by count
# ap_sf <- ap_sf %>% mutate(Count =case_when(
# BestNumber == 1 ~ "1",
# BestNumber %in% c(2:5) ~ "2:5",
# BestNumber >5 ~ ">5"
# ) %>% factor(levels = c("1", "2:5", ">5")))
# # s <- s %>% mutate(Count =case_when(
# # Best.Cnt == 1 ~ "1",
# # Best.Cnt %in% c(2:5) ~ "2:5",
# # Best.Cnt >5 ~ ">5"
# # ) %>% factor(levels = c("1", "2:5", ">5")))
#
# # create object 'g' basemap with effort
# g <- ggplot() +
# geom_raster(aes(x=x,y=y,fill = z), data = bathy) + labs(fill = "Depth (m)") +
# scale_fill_gradientn(colours = col_ramp(20), guide = "none") +
# ggnewscale::new_scale("fill") +
# # + coord_map()
# # geom_contour(data = b,
# # aes(x=X, y=Y, z=PID),
# # breaks=c(-250),
# # size=c(0.6),
# # colour="grey")+
# geom_sf(data = coast_file, stroke = 0.05, fill = "light yellow", colour = "grey 60")+
# geom_sf(data = effort_lines, size = 0.25, colour = "black")
#
# g
# if(hydrophone){
# g <- g +
# geom_sf(data = acoust, aes(shape = deployed)) +
# scale_shape_manual(values = 8, name = NULL) +
# ggnewscale::new_scale("shape")
# }
#
# g <- g + geom_sf(data = ap_sf, stroke = 0, alpha = 0.8, aes(fill = Species, shape = Species, size = Count)) +#
# # geom_sf(data = s, stroke = 0.01,alpha = 0.5, aes(fill = Species, shape = Species, size = Count)) +
#
# scale_size_manual(values = c(1.5,2,3)) +
# scale_fill_manual(values = cols, name = "Sightings", breaks = sp) +
# scale_shape_manual(values = shape, name = "Sightings", breaks = sp) +
#
# guides(alpha= "none") +
# guides(shape = guide_legend(override.aes = list(size=2))) +
#
# # ggtitle(paste0("CeMoRe survey ", survey_title),
# # subtitle = "Cetacean sightings and acoustic recorder deployment locations") +
# annotation_custom(leg1, xmin=-119.1, xmax=-124.95, ymin=47.95, ymax=48.1) +
#
# coord +
# ylab("")+xlab("")
#
# }else{
# if(sightings_only){
# #--------------- SIGHTINGS ONLY ----------------------------------------
# # to not display all potential species in legend
# all_ap_sf$Species %<>% droplevels()
#
# # to order legend symbols consistently
# # sp <- unique(c(levels(all_ap_sf$Species), levels(s$Species)))
# sp <- unique(c(levels(all_ap_sf$Species)))
#
# # to size symbols by count
# all_ap_sf <- all_ap_sf %>% mutate(Count =case_when(
# BestNumber == 1 ~ "1",
# BestNumber %in% c(2:5) ~ "2:5",
# BestNumber >5 ~ ">5"
# ) %>% factor(levels = c("1", "2:5", ">5")))
#
# g <- ggplot() +
# geom_raster(aes(x=x,y=y,fill = z), data = bathy) + labs(fill = "Depth (m)") +
# scale_fill_gradientn(colours = col_ramp(20)) +
# ggnewscale::new_scale("fill") +
#
# geom_sf(data = coast_file, stroke = 0.05, fill = "light yellow", colour = "grey 60")+
#
# geom_sf(data = all_ap_sf, stroke = 0.01, alpha = 0.5, aes(fill = Species, shape = Species, size = Count)) +#
#
# scale_size_manual(values = c(1.5,2,3)) +
# scale_fill_manual(values = cols, name = "Sightings", breaks = sp) +
# scale_shape_manual(values = shape, name = "Sightings", breaks = sp) +
#
# guides(alpha= "none") +
# guides(shape = guide_legend(override.aes = list(size=2))) +
# # annotation_custom(leg1, xmin=-124.5, xmax=-124.95, ymin=47.9, ymax=48.1) +
#
# ggtitle(paste0("CeMoRe survey cetacean sightings to ", survey_title)) +
# coord +
# ylab("")+xlab("") +
# theme(axis.text.x=element_text(angle=90)) +
# # facet_grid(Species ~ season)
# facet_wrap(~ season)
# #
# # g <- cowplot::plot_grid(g, leg1,
# # align = "v", axis = "r",
# # ncol = 1,
# # rel_heights = c(1, 0.05),
# # rel_widths = c(1, 0.05)
# # if saving plot, change coast to bc_shp for higher res basemap
# if(Save){
# # ggsave(paste0("output_maps/sightings_", survey_title, "_low_res_plus_hydrophones31_78.png"))
# ggsave(paste0("../output_maps/sightings_", survey_title, "_high_res.png"), height = 15, width = 15, units = "cm")
# }
# }else{
# #------------- EFFORT LINES ONLY ------------------------------------------
#
# g <- ggplot() +
# geom_raster(aes(x=x,y=y,fill = z), data = bathy) + labs(fill = "Depth (m)") +
# scale_fill_gradientn(colours = col_ramp(20), guide = "none") +
# ggnewscale::new_scale("fill") +
# ggnewscale::new_scale_colour() +
# geom_sf(data = coast_file, stroke = 0.05, fill = "light yellow", colour = "grey 60")+
# geom_sf(data = all_effort_lines, size = 0.25) +
# scale_colour_manual(values = c("dark orange", "green4", "yellow", "dark blue"), name = "Season") +
# ggtitle("CeMoRe survey completed transects", paste0("Sept 2020 to ", survey_title)) +
# # annotation_custom(leg1Grob, xmin=-124.99, xmax=-124.95, ymin=47.89, ymax=48.15) +
# coord +
# ylab("")+xlab("") +
# theme(axis.text.x=element_text(angle=90)) +
# facet_wrap(~ season)
#
# g <- cowplot::plot_grid(g, leg1,
# align = "v", axis = "l",
# ncol = 1,
# rel_heights = c(1, 0.1),
# rel_widths = c(1, 0.1)
# )
# # if saving plot, change coast to bc_shp for higher res basemap
# if(Save){
# # ggsave(paste0("output_maps/sightings_", survey_title, "_low_res_plus_hydrophones31_78.png"))
# ggsave(paste0("../output_maps/effortlines_", survey_title, "_high_res.png"), height = 15, width = 15, units = "cm")
# }
# }
# }
# }
#
#
#
#
# # Save = F
# #
# # ggsave(paste0("../output_maps/sightings_to_", survey_title, "_low_res.png"))
#
# #
# #
# # #--------------------------------------------------------------------------
# #
# #
# #
# # # ggsave(paste0("output_maps/sightings_high_dens_porpoises_", survey_title, "_high_res.png"))
# #
# # # -------------------------------------------------------------------------------
# # # ---- substrate
# # # -------------------------------------------------------------------------------
# # # Load substrate raster and create bounding box for study area
# #
# # sub <- raster("covariates/salish_sea_20m/salish_sea_20m.tif")#crs = 3005
# # plot(sub)
# # object.size(sub) # 14464 bytes
# #
# # sub_clip <- raster("covariates/sub_arc_clip.tif")#crs = 3005
# # plot(sub_clip)
# # object.size(sub_clip) # 14464 bytes
# #
# #
# # sub_clip_df <- as.data.frame(sub_clip, xy = T)%>%
# # na.omit()
# # # sub_sf <- st_as_sf(sub_df, coords=c(lon = sub_df$x, lat = sub_df$y))
# # object.size(sub_clip_df) # 3521621736 bytes
# #
# # # bb <- create_bb(-125.5, -123., 48.1, 49.5) %>% st_transform(3005)
# # # tss_6km_2 <- st_read(
# # # "C:/Users/keppele/Documents/CeMoRe/Design/cemore_design/shapefiles/study_area",
# # # "TSS_buffer6km_2") %>%
# # # st_transform(3005)
# #
# # # plot(tss_6km_2)
# # # plot(bb, add=T)
# # # crop raster to study area
# # # sub_cropped <- crop(sub, extent(tss_6km_2))
# # # object.size(sub_cropped)/1000
# #
# # # convert to data.frame for plotting
# # sub_clip_df2 <- sub_clip %>%
# # as.bathy() %>%
# # fortify.bathy() %>%
# # na.omit()
# # object.size(sub_clip_df2) # 3521621728 bytes
# #
# # ggRasterly(data = sub_clip_df2, aes(x=x, y=y, color=z),
# # show_raster = T,
# # drop_data = T)
# #
# #
# # sub_mask <- raster("covariates/sub_extract_mask.tif") %>%
# # na.omit() #crs = 3005
# # plot(sub_mask)
# # object.size(sub_mask) # 14480 bytes bytes
# #
# # saveRDS(sub_cropped_df, "covariates/substrate_cropped_df.rds")
# # sub_cropped_df <- readRDS("covariates/substrate_cropped_df.rds")
# #
# # sub_mask_df <- sub_mask %>%
# # as.bathy() %>%
# # fortify.bathy() %>%
# # na.omit()
# # object.size(sub_clip_df2) # 3,790,000 bytes
# #
# # sub_mask_df2 <- as.data.frame(sub_mask, xy = T)%>%
# # na.omit()
# # #------------------------------------------------------------------------------
# # # try converting arc-clipped substrate layer to spdf, then st_as_sf
# # #------------------------------------------------------------------------------
# #
# #
# # sub_clip_df <- as(sub_clip,'SpatialPolygonsDataFrame')
# # object.size(sub_clip_df)
# #
# # sub_clip_sf <- st_as_sf(sub_clip_df)
# # #------------------------------------------------------------------------------
# #
# # ggplot() + #geom_sf(data = st_transform(coast_file, crs = 4326), fill = "light yellow", colour = "grey 60") +
# # # geom_sf(data = bb) +
# # geom_raster(data = sub_df, aes(x = x, y = y, fill = sub_arc_clip)) +
# # coord
# #
# # area <- st_read("C:/Users/keppele/Documents/CeMoRe/Design/cemore_design/shapefiles/study_area", "cemore_study_area_TSS_6km_can")
# # tss_6km_2 <- st_read(
# # "C:/Users/keppele/Documents/CeMoRe/Design/cemore_design/shapefiles/study_area", "TSS_buffer6km_2"
# # ) %>%
# # st_transform(3005)
# #
# #
# #
# #
# #
# # ggsave("test_w_sub.png")
# # x <- ap_sf %>% filter(Species == "H. Porpoise group 40-50")
# # ggplot() + geom_sf(data = coast_file, fill = "light yellow", colour = "grey 60") +
# # geom_sf(data = ap_sf, size = 1.5, aes(colour = Species, fill = Species, shape = Species)) +
# # # geom_raster(data = bath_df, aes(x = x, y = y, fill = salish_sea_20m)) +
# # # geom_raster(data = sub_df, aes(x = x, y = y, fill = salish_sea_20m)) +
# # # coord
# # # ggsave("test_w_bath_and_sub.png")
# #
# # #, stroke =0.05
# # scale_shape_manual(values = shape, name = NULL) +
# # scale_fill_manual(values = cols, name = NULL) +
# # scale_color_manual(values = cols, name = NULL) +
# # coord +
# # ggtitle(paste0("All CeMoRe survey cetacean on effort sightings Sept 2020 - ", survey_title)) + #gfplot:::theme_pbs() +
# # theme(panel.background= element_rect(fill = "light blue")) +
# # theme(legend.position = "right") +
# # theme(axis.text.x = element_text(angle = 90, hjust = 1)) #+
# # facet_wrap( ~ Species)
# # # # if saving plot, change coast to bc_shp for higher res basemap
# # # if(save){
# # # ggsave(paste0("output_maps/all on effort sightings.png"))
# # # }
# # # save = F
# # # #-------------------------------------------------------------------------------
# # # #---- SIGHTING PLOTS BY SPECIES, ALL SURVEYS COMBINED, COLOURED BY SURVEY
# # # #-------------------------------------------------------------------------------
# # # ap_sf$SurveyID %<>% factor(c("cemore_2020sept",
# # # "cemore_2020oct",
# # # "cemore_2020nov",
# # # "cemore_2021jan",
# # # "cemore_2021feb",
# # # "cemore_2021mar",
# # # "cemore_2021apr",
# # # "cemore_2021may"))
# # # cols <- c('cemore_2020sept' = "brown",
# # # 'cemore_2020oct'= "purple",
# # # 'cemore_2020nov' = "blue",
# # # 'cemore_2020dec' = "turquoise3",
# # # 'cemore_2021jan' = "turquoise1",
# # # 'cemore_2021feb' ="darkturquoise",
# # # 'cemore_2021mar' = "limegreen",
# # # 'cemore_2021apr' = "springgreen4",
# # # 'cemore_2021may' = "yellow",
# # # 'cemore_2021jun' = "orange",
# # # 'cemore_2021jul' = "red",
# # # 'cemore_2021aug' = "magenta")
# # # save = F
# # # coast_file <- if(save) bc_shp else coast
# # # ggplot() + geom_sf(data = coast_file, fill = "light yellow", colour = "grey 60") +
# # # # geom_sf(data = canada_shp, colour = "black", fill = NA, linetype = "solid") +
# # # # geom_sf(data = effort_lines, size = 0.25, colour = "grey 50") +
# # # # geom_sf(data = effort_lines, size = 0.25, aes(colour = Date)) +
# # #
# # # # geom_sf(data = ap_sf, size = 1.5, aes(colour = Species, fill = Species, shape = Species)) + #, stroke =0.05
# # # geom_sf(data = ap_sf, size = 1.5, aes(colour = season)) + #, stroke =0.05
# # # # scale_shape_manual(values = shape, name = NULL) +
# # # # scale_fill_manual(values = cols, name = NULL) +
# # # scale_color_manual(values = cols , name = NULL) +
# # # coord +
# # # ggtitle(paste0("CeMoRe cetacean sightings by survey")) + #gfplot:::theme_pbs() +
# # # # ggtitle(paste0("CeMoRe survey cetacean sightings - High density porpoise sightings ", survey_title)) +
# # # theme(panel.background= element_rect(fill = "light blue")) +
# # # theme(legend.position = "right") +
# # # theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
# # # facet_wrap( ~ Species)
# # # # if saving plot, change coast to bc_shp for higher res basemap
# # # if(save){
# # # ggsave(paste0("output_maps/sightings by month.png"))
# # # }
# # # save = F
# # #
# # # #-------------------------------------------------------------------------------
# # # #---- SIGHTING PLOTS BY SPECIES, ALL SURVEYS COMBINED, COLOURED BY MONTH
# # # #-------------------------------------------------------------------------------
# # # cols <- c('Sep' = "brown",
# # # 'Oct'= "purple",
# # # 'Nov' = "blue",
# # # 'Dec' = "turquoise3",
# # # 'Jan' = "turquoise1",
# # # 'Feb' ="darkturquoise",
# # # 'Mar' = "limegreen",
# # # 'Apr' = "springgreen4",
# # # 'May' = "yellow",
# # # 'Jun' = "orange",
# # # 'Jul' = "red",
# # # 'Aug' = "magenta")
# # # save = F
# # # coast_file <- if(save) bc_shp else coast
# # # ggplot() + geom_sf(data = coast_file, fill = "light yellow", colour = "grey 60") +
# # # # geom_sf(data = canada_shp, colour = "black", fill = NA, linetype = "solid") +
# # # # geom_sf(data = effort_lines, size = 0.25, colour = "grey 50") +
# # # # geom_sf(data = effort_lines, size = 0.25, aes(colour = Date)) +
# # #
# # # # geom_sf(data = ap_sf, size = 1.5, aes(colour = Species, fill = Species, shape = Species)) + #, stroke =0.05
# # # geom_sf(data = ap_sf, size = 0.75, aes(colour = factor(month_abb))) + #, stroke =0.05
# # # # scale_shape_manual(values = shape, name = NULL) +
# # # # scale_fill_manual(values = cols, name = NULL) +
# # # scale_color_manual(values = cols, name = NULL) +
# # # coord +
# # # ggtitle(paste0("CeMoRe survey cetacean sightings by month ", survey_title)) + #gfplot:::theme_pbs() +
# # # # ggtitle(paste0("CeMoRe survey cetacean sightings - High density porpoise sightings ", survey_title)) +
# # # theme(panel.background= element_rect(fill = "light blue")) +
# # # theme(legend.position = "right") +
# # # theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
# # # facet_wrap( ~ Species)
# # # if(save){
# # # ggsave(paste0("output_maps/sightings by month.png"))
# # # }
# # # save = F
# # # #-------------------------------------------------------------------------------
# # # #---- SIGHTING PLOTS BY SPECIES, ALL SURVEYS COMBINED, COLOURED BY SEASON
# # # #-------------------------------------------------------------------------------
# # cols <- c('Fall' = "orange",
# # 'Winter'= "blue",
# # 'Summer' = "red",
# # 'Spring' = "limegreen")
# # save = T
# # coast_file <- if(save) bc_shp else coast
# # ggplot() + geom_sf(data = coast_file, fill = "light yellow", colour = "grey 60") +
# # geom_sf(data = ap_sf, size = 0.8, aes(colour = factor(season))) + #, stroke =0.05
# # scale_color_manual(values = cols, name = NULL) +
# # coord +
# # ggtitle(paste0("CeMoRe survey cetacean on-effort sightings by season. Plotted ", survey_title)) + #gfplot:::theme_pbs() +
# # # ggtitle(paste0("CeMoRe survey cetacean sightings - High density porpoise sightings ", survey_title)) +
# # theme(panel.background= element_rect(fill = "light blue")) +
# # theme(legend.position = "right") +
# # theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
# # facet_wrap( ~ Species)
# #
# # # if saving plot, change coast to bc_shp for higher res basemap
# # if(save){
# # ggsave(paste0("output_maps/sightings by season ", month, year, ".png"))
# # }
# # # save = F
# # #
# # # #-------------------------------------------------------------------------------
# # # #---- SINGLE SPECIES DISTRIBUTION, ALL SURVEYS COMBINED, BY MONTH
# # # #-------------------------------------------------------------------------------
# # cols <- c('Humpback' = "blue3",
# # 'Harbour Porpoise' = "dark green",
# # 'Dalls Porpoise' = "firebrick3",
# # 'Unknown Porpoise' = "darkorange1",
# # 'KW - Northern Resident' = "green3",
# # 'KW - Southern Resident' = "violetred1",
# # 'KW - Transient' = "black",
# # 'KW - Unknown ecotype' = "black",
# # 'Sei Whale' = "grey 30",
# # 'Sperm Whale' = pal[6],
# # 'Cuvier\'s Whale' = pal[4],
# # 'Fin Whale' = pal[10],
# # 'Grey Whale' = "darkorchid4",
# # 'H. Porpoise group 40-50' = "black")
# #
# # shape <- c('Humpback' = 24,
# # 'Harbour Porpoise' = 23,
# # 'Dalls Porpoise' = 23,
# # 'Unknown Porpoise' = 23,
# # 'KW - Northern Resident' = 2,
# # 'KW - Southern Resident' = 25,
# # 'KW - Transient' = 25,
# # 'KW - Unknown ecotype' = 4,
# # 'Sei Whale' = 21,
# # 'Sperm Whale' = 21,
# # "Cuvier's Whale" = 21,
# # "Fin Whale" = 21,
# # "Grey Whale" = 21,
# # "H. Porpoise group 40-50" = 8)
# # Save = T
# # coast_file <- if(Save) bc_shp else coast
# #
# # x <- ap_sf %>% filter(Species == "Humpback")
# # x <- ap_sf %>% filter(Species == "Harbour Porpoise")
# # x <- ap_sf %>% filter(Species == "Dalls Porpoise")
# # x <- ap_sf %>% filter(Species %in% c("KW - Southern Resident", "KW - Transient"))
# # #-------------------------------------------------------------------------------
# # # for just porpoises
# # #-------------------------------------------------------------------------------
# # x <- ap_sf %>% dplyr::filter(grepl("Porpoise", Species, fixed = TRUE))
# # #-------------------------------------------------------------------------------
# #
# #
# # ggplot() + geom_sf(data = coast_file, fill = "light yellow", colour = "grey 60", size = 0.25) +
# # geom_sf(data = all_effort_lines, size = 0.1, colour = "black") +
# # geom_sf(data = x, size = 1, aes(fill = Species, shape = Species, colour = Species)) + #, stroke =0.05
# # scale_shape_manual(values = shape, name = NULL) +
# # scale_fill_manual(values = cols, name = NULL) +
# # scale_color_manual(values = cols, name = NULL) +
# # coord +
# # # ggtitle(paste0("All CeMoRe survey on effort Humpback sightings - plotted ", survey_title)) + #gfplot:::theme_pbs() +
# # ggtitle(paste0("All CeMoRe survey on effort Harbour Porpoise sightings - plotted ", survey_title)) +
# # # ggtitle(paste0("All CeMoRe survey on effort Dalls Porpoise sightings - plotted ", survey_title)) +
# # # ggtitle(paste0("All CeMoRe survey on effort Killer Whale sightings - plotted ", survey_title)) +
# # theme(panel.background= element_rect(fill = "light blue")) +
# # theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
# # theme(legend.position = "NONE") +
# # facet_wrap( ~ month_year, drop = F)
# # # if saving plot, change coast to bc_shp for higher res basemap
# #
# # if(save){
# # ggsave(paste0("output_maps/HW by month up to June 2021.png"))
# # ggsave(paste0("output_maps/HP by month up to June 2021.png"))
# # ggsave(paste0("output_maps/DP by month up to June 2021.png"))
# # ggsave(paste0("output_maps/KW by month up to June 2021.png"))
# #
# #
# # }
# # save = F
# #
# #
# # # TRY JITTERING TO SEE ALL OF THE HP SIGHTINGS
# # #display.brewer.all()
# #
# #
# #
# #
# #
# #
# #
# # # ------------------------------------------------------------------------------
# # # plot tag deployment locations
# # # ------------------------------------------------------------------------------
# # x <- read.csv("tag_data/2021-08 tag deployment locations.csv") %>%
# # st_as_sf(coords = c("lon", "lat"), crs = 4326) %>%
# # st_transform(crs = st_crs(bc_shp))
# # # st_as_sf(coords = c("lon", "lat"), crs = st_crs(bc_shp)) %>%
# # # st_transform(crs = 4326)
# #
# # Save <- T
# # if(!Save) coast_file <- coast else coast_file <- bc_shp
# #
# # ggplot() +
# # geom_sf(data = coast_file, fill = "light yellow", colour = "grey 60", size = 0.25) +
# # geom_sf(data = x, size = 1, colour = "black") +
# # coord +
# # # theme(panel.background= element_rect(fill = "light blue")) +
# # ggtitle("Humpback Whale Tagging Locations August 2021")
# #
# #
# # if(Save){
# # ggsave(paste0("tagging_maps/tagging locations 2021 August.png"))
# # }
# #
# # # ------------------------------------------------------------------------------
# # # plot hydrophone deployment locations
# # # ------------------------------------------------------------------------------
# # x <- read.csv("hydrophone_data/hydrophone_deployment_locations.csv") %>%
# # st_as_sf(coords = c("lon", "lat"), crs = 4326) %>%
# # st_transform(crs = st_crs(bc_shp))
# # # st_as_sf(coords = c("lon", "lat"), crs = st_crs(bc_shp)) %>%
# # # st_transform(crs = 4326)
# #
# # Save <- F
# # if(!Save) coast_file <- coast else coast_file <- bc_shp
# # display.brewer.all()
# #
# # bathy <- getNOAA.bathy(-125.5, -123,48.1, 49.5,res=1, keep=TRUE)
# # bathy <- as.raster(bathy)
# # plot(bathy, col = RColorBrewer::brewer.pal(20L, "Blues"))
# #
# # ggplot() + geom_sf(data = coast_file, fill = "light yellow", colour = "grey 60", size = 0.25) +
# # # geom_sf(data = x, size = 1, colour = "black") +
# # coord +
# # theme(panel.background= element_rect(fill = "light blue")) +
# # ggtitle("Hydrophone Deployment Location August 21, 2021") +
# # geom_raster(data = bathy)
# #
# # bathy %<>% as.matrix()
# # bathy %>% as.data.frame() %>%
# # rownames_to_column(var = "lon") %>%
# # gather(lat, value, -1) %>%
# # mutate_all(funs(as.numeric)) %>%
# # ggplot()+
# # geom_contour(aes(x = lon, y = lat, z = value), bins = 10, colour = "black") +
# # coord_map()
# #
# #
# # if(Save){
# # ggsave(paste0("hydrophone_data/deployment locations 2021 August.png"))
# # }
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