Marine Heatwave 2023

workflowr

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Last updated: 2024-07-10

Checks: 7 0

Knit directory: heatwave_co2_flux_2023/analysis/

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File	Version	Author	Date	Message
html	334ff26	jens-daniel-mueller	2024-07-10	manual commit
Rmd	134db94	jens-daniel-mueller	2024-06-28	manual commit
Rmd	88b4e04	jens-daniel-mueller	2024-06-26	started to apply biome mask
html	caec400	jens-daniel-mueller	2024-06-13	Build site.
html	3148fef	jens-daniel-mueller	2024-06-13	Build site.
html	a60be97	jens-daniel-mueller	2024-06-12	Build site.
html	d46002d	jens-daniel-mueller	2024-06-12	manual commit
html	03c415f	jens-daniel-mueller	2024-06-11	Build site.
html	5261667	jens-daniel-mueller	2024-06-11	manual commit
html	2b34bf8	jens-daniel-mueller	2024-06-11	manual commit
html	6954c65	jens-daniel-mueller	2024-06-06	Build site.
html	009791f	jens-daniel-mueller	2024-05-14	Build site.
html	7f9c687	jens-daniel-mueller	2024-04-23	Build site.
html	ce4e2a6	jens-daniel-mueller	2024-04-17	Build site.
html	58e3680	jens-daniel-mueller	2024-04-11	Build site.
html	dfcf790	jens-daniel-mueller	2024-04-11	Build site.
html	139bc97	jens-daniel-mueller	2024-04-11	manual deletion of files
Rmd	36a6467	jens-daniel-mueller	2024-04-11	manual commit
html	69dc18c	jens-daniel-mueller	2024-04-04	Build site.
html	c9d994c	jens-daniel-mueller	2024-04-04	Build site.
html	40cb158	jens-daniel-mueller	2024-04-03	Build site.
html	a83c8fc	jens-daniel-mueller	2024-04-03	Build site.
html	84bd8d9	jens-daniel-mueller	2024-03-29	Build site.
Rmd	01b3ddc	jens-daniel-mueller	2024-03-29	print all maps
html	6bb7ce2	jens-daniel-mueller	2024-03-25	Build site.
html	be31dbd	jens-daniel-mueller	2024-03-25	Build site.
Rmd	a3b3d64	jens-daniel-mueller	2024-03-25	header updated
html	9bfb3c6	jens-daniel-mueller	2024-03-25	Build site.
Rmd	f9d2b99	jens-daniel-mueller	2024-03-25	total cummulative intensity added
html	b49e6d3	jens-daniel-mueller	2024-03-25	Build site.
Rmd	7c3d1c8	jens-daniel-mueller	2024-03-25	total cummulative intensity added
html	63cf3cb	jens-daniel-mueller	2024-03-25	Build site.
Rmd	5eb5491	jens-daniel-mueller	2024-03-25	fig asp ratio adapted
html	95837a3	jens-daniel-mueller	2024-03-25	Build site.
Rmd	25ae269	jens-daniel-mueller	2024-03-25	Daily MHW stats added
html	4589270	jens-daniel-mueller	2024-03-24	Build site.
html	5d6554a	jens-daniel-mueller	2024-03-24	Build site.
Rmd	e537d66	jens-daniel-mueller	2024-03-24	MHW stats started
html	dd640bd	jens-daniel-mueller	2024-03-24	Build site.
Rmd	6358178	jens-daniel-mueller	2024-03-24	seperate biome mask
html	62ea4dd	jens-daniel-mueller	2024-03-24	Build site.
html	1a5167d	jens-daniel-mueller	2024-03-24	Build site.
Rmd	cf4f62f	jens-daniel-mueller	2024-03-23	MHW stats and CMEMS added

---

center <- -160
boundary <- center + 180
target_crs <- paste0("+proj=robin +over +lon_0=", center)
# target_crs <- paste0("+proj=eqearth +over +lon_0=", center)
# target_crs <- paste0("+proj=eqearth +lon_0=", center)
# target_crs <- paste0("+proj=igh_o +lon_0=", center)

worldmap <- ne_countries(scale = 'small',
                         type = 'map_units',
                         returnclass = 'sf')

worldmap <- worldmap %>% st_break_antimeridian(lon_0 = center)
worldmap_trans <- st_transform(worldmap, crs = target_crs)

# ggplot() +
#   geom_sf(data = worldmap_trans)

coastline <- ne_coastline(scale = 'small', returnclass = "sf")
coastline <- st_break_antimeridian(coastline, lon_0 = 200)
coastline_trans <- st_transform(coastline, crs = target_crs)

# ggplot() +
#   geom_sf(data = worldmap_trans, fill = "grey", col="grey") +
#   geom_sf(data = coastline_trans)


bbox <- st_bbox(c(xmin = -180, xmax = 180, ymax = 65, ymin = -78), crs = st_crs(4326))
bbox <- st_as_sfc(bbox)
bbox_trans <- st_break_antimeridian(bbox, lon_0 = center)

bbox_graticules <- st_graticule(
  x = bbox_trans,
  crs = st_crs(bbox_trans),
  datum = st_crs(bbox_trans),
  lon = c(20, 20.001),
  lat = c(-78,65),
  ndiscr = 1e3,
  margin = 0.001
)

bbox_graticules_trans <- st_transform(bbox_graticules, crs = target_crs)
rm(worldmap, coastline, bbox, bbox_trans)

# ggplot() +
#   geom_sf(data = worldmap_trans, fill = "grey", col="grey") +
#   geom_sf(data = coastline_trans) +
#   geom_sf(data = bbox_graticules_trans)

lat_lim <- ext(bbox_graticules_trans)[c(3,4)]*1.002
lon_lim <- ext(bbox_graticules_trans)[c(1,2)]*1.005

# ggplot() +
#   geom_sf(data = worldmap_trans, fill = "grey90", col = "grey90") +
#   geom_sf(data = coastline_trans) +
#   geom_sf(data = bbox_graticules_trans, linewidth = 1) +
#   coord_sf(crs = target_crs,
#            ylim = lat_lim,
#            xlim = lon_lim,
#            expand = FALSE) +
#   theme(
#     panel.border = element_blank(),
#     axis.text = element_blank(),
#     axis.ticks = element_blank()
#   )

latitude_graticules <- st_graticule(
  x = bbox_graticules,
  crs = st_crs(bbox_graticules),
  datum = st_crs(bbox_graticules),
  lon = c(20, 20.001),
  lat = c(-60,-30,0,30,60),
  ndiscr = 1e3,
  margin = 0.001
)

latitude_graticules_trans <- st_transform(latitude_graticules, crs = target_crs)

latitude_labels <- data.frame(lat_label = c("60°N","30°N","Eq.","30°S","60°S"),
                 lat = c(60,30,0,-30,-60)-4, lon = c(35)-c(0,2,4,2,0))

latitude_labels <- st_as_sf(x = latitude_labels,
               coords = c("lon", "lat"),
               crs = "+proj=longlat")

latitude_labels_trans <- st_transform(latitude_labels, crs = target_crs)

# ggplot() +
#   geom_sf(data = worldmap_trans, fill = "grey", col = "grey") +
#   geom_sf(data = coastline_trans) +
#   geom_sf(data = bbox_graticules_trans) +
#   geom_sf(data = latitude_graticules_trans,
#           col = "grey60",
#           linewidth = 0.2) +
#   geom_sf_text(data = latitude_labels_trans,
#                aes(label = lat_label),
#                size = 3,
#                col = "grey60")

Load masks

map <-
  read_rds(here::here("data/map.rds"))

key_biomes <-
  read_rds(here::here("data/key_biomes.rds"))

biome_mask <-
  read_rds(here::here("data/biome_mask.rds"))


biome_mask %>% 
  filter(!str_detect(biome, "SO-")) %>% 
  pull(lat) %>% 
  max()

[1] 79.5

Read data

MHW_cat_pixel_2023 <- read_rds(here::here("data/MHW_cat_pixel_2023.Rds"))

MHW_cat_pixel_2023 <-
  MHW_cat_pixel_2023 %>%
  rename(time = t) %>%
  mutate(lon = if_else(lon < 20, lon + 360, lon))


# MHW_cat_daily_2023 <- read_rds(here::here("/data/MHW_cat_daily_2023.Rds")

cat.clim.files <- list.files(here::here("data/input/MHW_2023"),
                             full.names = TRUE)

cat.clim <- cat.clim.files %>%
  map_dfr(read_rds)

rm(cat.clim.files)

cat.clim <-
  cat.clim %>%
  rename(time = t) %>%
  mutate(lon = if_else(lon < 20, lon + 360, lon))

cat.clim <-
  cat.clim %>%
  mutate(mday = mday(time),
         month = month(time))

inner_join(
  biome_mask %>% mutate(
    lon_min = lon - 0.5,
    lon_max = lon + 0.5,
    lat_min = lat - 0.5,
    lat_max = lat + 0.5
  ) %>%
    select(-c(lon, lat)),
  cat.clim %>% as_tibble(),
  by = join_by(between(lon, lon_min, lon_max) &
                 between(lat, lat_min, lat_max))
)

path_out <- "/net/kryo/work/datasets/gridded/ocean/2d/obs/mhw"

MHW maps

Selected dates

fillColCat <- c(
  "I Moderate" = "#ffc866",
  "II Strong" = "#ff6900",
  "III Severe" = "#9e0000",
  "IV Extreme" = "#2d0000"
)


map +
  geom_raster(data =
              cat.clim %>% filter(mday == 1,
                                  month %in% c(2, 5, 8, 11)),
            aes(lon, lat, fill = category)) +
  scale_fill_manual(values = fillColCat,
                    name = "MHW category") +
  facet_wrap( ~ time, ncol = 1)

Past versions of plot_mhw_maps-1.png

Version	Author	Date
334ff26	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
03c415f	jens-daniel-mueller	2024-06-11
0a7394b	jens-daniel-mueller	2024-06-11
009791f	jens-daniel-mueller	2024-05-14
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
b49e6d3	jens-daniel-mueller	2024-03-25
95837a3	jens-daniel-mueller	2024-03-25

map +
  geom_raster(data = cat.clim %>%
                distinct(lon, lat), aes(lon, lat))

Past versions of MHW_timeseries-1.png

Version	Author	Date
334ff26	jens-daniel-mueller	2024-07-10

area_total <-
cat.clim %>%
  distinct(lon, lat) %>% 
  mutate(area = earth_surf(lat, lon)) %>% 
  summarise(area_total = sum(area)) %>% 
  pull()

cat.clim.timeseries <-
  cat.clim %>%
  mutate(area = earth_surf(lat, lon)) %>%
  group_by(time) %>%
  summarise(area_mhw = sum(area)) %>%
  ungroup() %>%
  mutate(area_mhw_rel = area_mhw / area_total)

cat.clim.timeseries %>% 
  ggplot(aes(time, area_mhw_rel)) +
  geom_path()

Past versions of MHW_timeseries-2.png

Version	Author	Date
334ff26	jens-daniel-mueller	2024-07-10

cat.clim <-
  cat.clim %>%
  as_tibble() %>%
  select(-c(event_no, mday, month))
  
cat.clim %>% 
  write_csv(paste0(path_out,"/2023_mhw_raw.csv"))


cat.clim <-
  cat.clim %>%
  mutate(lon = if_else(lon > 180,
                       lon - 360,
                       lon))
  
# convert from tibble to stars object

cat.clim_stars <- st_as_stars(
  cat.clim,
  dims = c("lon", "lat", "time")
)

# assign coordinate system to horizontal (lon, lat) coordinates
# st_crs(cat.clim_stars) <- "+proj=longlat"
st_crs(cat.clim_stars) <- st_crs(4326)

# plot variable per depth level
ggplot() +
  geom_stars(data = cat.clim_stars %>% slice(time,1:2),
             aes(fill = category)) +
  scale_fill_manual(values = fillColCat,
                    name = "MHW category") +
  facet_wrap(~ time)


file_out <- paste0(path_out,"/2023_mhw_raw.nc")

# write stars object to .nc file
cat.clim_stars %>%
  write_mdim(file_out)

Days per category

cat.clim.stats <-
  cat.clim %>% 
  count(lon, lat, category)


map +
  geom_raster(data =
              cat.clim.stats, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       limits = c(0,365),
                       name = "Days") +
  labs(title = "MHW days per category in 2023") +
  facet_wrap(~ category, ncol = 1)

Past versions of mhw_days_category-1.png

Version	Author	Date
334ff26	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
03c415f	jens-daniel-mueller	2024-06-11
0a7394b	jens-daniel-mueller	2024-06-11
009791f	jens-daniel-mueller	2024-05-14
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
b49e6d3	jens-daniel-mueller	2024-03-25
63cf3cb	jens-daniel-mueller	2024-03-25
95837a3	jens-daniel-mueller	2024-03-25

Days exceeding category

cat.clim.stats.all <-
  cat.clim.stats %>%
  complete(lon, lat, category, n = 0)

cat.clim.stats.all <-
  cat.clim.stats.all %>%
  arrange(desc(category)) %>%
  group_by(lon, lat) %>%
  mutate(n_cum = cumsum(n)) %>%
  ungroup()

cat.clim.stats.all <-
  cat.clim.stats.all %>%
  filter(n_cum > 0) %>%
  mutate(category = paste(">=", category))


map +
  geom_raster(data =
              cat.clim.stats.all, 
              aes(lon, lat, fill = n_cum)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       limits = c(0,365),
                       name = "Days") +
  labs(title = "MHW days exceeding category in 2023") +
  facet_wrap(~ category, ncol = 1)

Past versions of mhw_days_category_limit-1.png

Version	Author	Date
334ff26	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
03c415f	jens-daniel-mueller	2024-06-11
0a7394b	jens-daniel-mueller	2024-06-11
009791f	jens-daniel-mueller	2024-05-14
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
b49e6d3	jens-daniel-mueller	2024-03-25
63cf3cb	jens-daniel-mueller	2024-03-25
95837a3	jens-daniel-mueller	2024-03-25

cat.clim.stats.all <-
  cat.clim.stats.all %>%
  select(-n) %>%
  mutate(lon = if_else(lon > 180,
                       lon - 360,
                       lon))
  
cat.clim.stats.all %>%
  write_csv(paste0(path_out, "/2023_mhw_days_cumulative_threshold.csv"))



# convert from tibble to stars object

cat.clim.stats.all_stars <- st_as_stars(
  cat.clim.stats.all %>% rename(mhw_days = n_cum),
  dims = c("lon", "lat", "category")
)

# assign coordinate system to horizontal (lon, lat) coordinates
# st_crs(cat.clim.stats.all_stars) <- "+proj=longlat"
st_crs(cat.clim.stats.all_stars) <- st_crs(4326)

# plot variable per depth level
ggplot() +
  geom_stars(data = cat.clim.stats.all_stars) +
  facet_wrap( ~ category) +
  scale_fill_viridis_c(
    option = "magma",
    direction = -1,
    limits = c(0, 365),
    name = "Days"
  )


file_out <- paste0(path_out, "/2023_mhw_days_cumulative_threshold.nc")

# write stars object to .nc file
cat.clim.stats.all_stars %>%
  write_mdim(file_out)



# reopen .nc file with all available stars functions
cat.clim.stats.all_read_ncdf <-
  read_ncdf(file_out)
cat.clim.stats.all_read_mdim <-
  read_mdim(file_out)

# plot variable in object reopened

ggplot() +
  geom_stars(data = cat.clim.stats.all_read_ncdf) +
  facet_wrap( ~ category) +
  scale_fill_viridis_c(
    option = "magma",
    direction = -1,
    limits = c(0, 365),
    name = "Days"
  )

ggplot() +
  geom_stars(data = cat.clim.stats.all_read_mdim) +
  facet_wrap( ~ category) +
  scale_fill_viridis_c(
    option = "magma",
    direction = -1,
    limits = c(0, 365),
    name = "Days"
  )

Annual mean intensity

cat.clim.intensity <-
  cat.clim %>%
  group_by(lon, lat, category) %>% 
  summarise(intensity_sum = sum(intensity),
            intensity = mean(intensity)) %>% 
  ungroup()

map +
  geom_raster(data =
              cat.clim.intensity, aes(lon, lat, fill = intensity)) +
  scale_fill_viridis_c(option = "rocket",
                       direction = -1,
                       name = "Intensity (°C)") +
  labs(title = "Annual mean intensity per category in 2023") +
  facet_wrap(~ category, ncol = 1)

Past versions of MHW_intensity_mean-1.png

Version	Author	Date
334ff26	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
03c415f	jens-daniel-mueller	2024-06-11
0a7394b	jens-daniel-mueller	2024-06-11
009791f	jens-daniel-mueller	2024-05-14
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
b49e6d3	jens-daniel-mueller	2024-03-25
63cf3cb	jens-daniel-mueller	2024-03-25
95837a3	jens-daniel-mueller	2024-03-25

Annual cummulative intensity

Per category

map +
  geom_raster(data =
              cat.clim.intensity, aes(lon, lat, fill = intensity_sum)) +
  scale_fill_viridis_c(option = "rocket",
                       direction = -1,
                       name = "Intensity sum\n(days °C)") +
  labs(title = "Annual cummulative intensity per category in 2023",
       subtitle = "Computed from daily cat.clim.2023 files") +
  facet_wrap(~ category, ncol = 1)

Past versions of MHW_intensity_cummulative_category-1.png

Version	Author	Date
334ff26	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
03c415f	jens-daniel-mueller	2024-06-11
0a7394b	jens-daniel-mueller	2024-06-11
009791f	jens-daniel-mueller	2024-05-14
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
9bfb3c6	jens-daniel-mueller	2024-03-25

All categories

map +
  geom_raster(
    data =
      cat.clim.intensity %>%
      group_by(lon, lat) %>%
      summarise(intensity_sum = sum(intensity_sum)) %>%
      ungroup(),
    aes(lon, lat, fill = intensity_sum)
  ) +
  scale_fill_viridis_c(option = "rocket",
                       direction = -1,
                       name = "Intensity sum\n(days °C)") +
  labs(title = "Annual cummulative intensity in 2023",
       subtitle = "Computed from daily cat.clim.2023 files")

Past versions of MHW_intensity_cummulative_all-1.png

Version	Author	Date
334ff26	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
03c415f	jens-daniel-mueller	2024-06-11
0a7394b	jens-daniel-mueller	2024-06-11
009791f	jens-daniel-mueller	2024-05-14
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
9bfb3c6	jens-daniel-mueller	2024-03-25

map +
  geom_raster(data =
              MHW_cat_pixel_2023 %>% ungroup(),
            aes(lon, lat, fill = intensity_sum)) +
  scale_fill_viridis_c(option = "rocket",
                       direction = -1,
                       name = "Intensity sum\n(days °C)") +
  labs(title = "Annual cummulative intensity in 2023",
       subtitle = "Computed from MHW_cat_pixel_2023")

Past versions of MHW_intensity_cummulative_all-2.png

Version	Author	Date
334ff26	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
03c415f	jens-daniel-mueller	2024-06-11
0a7394b	jens-daniel-mueller	2024-06-11
009791f	jens-daniel-mueller	2024-05-14
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
9bfb3c6	jens-daniel-mueller	2024-03-25

Session information

sessionInfo()

R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.5

Matrix products: default
BLAS:   /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] stars_0.6-0         abind_1.4-5         lubridate_1.9.0    
 [4] timechange_0.1.1    terra_1.7-65        sf_1.0-9           
 [7] rnaturalearth_0.1.0 geomtextpath_0.1.1  colorspace_2.0-3   
[10] marelac_2.1.10      shape_1.4.6         ggforce_0.4.1      
[13] metR_0.13.0         scico_1.3.1         patchwork_1.1.2    
[16] collapse_1.8.9      forcats_0.5.2       stringr_1.5.0      
[19] dplyr_1.1.3         purrr_1.0.2         readr_2.1.3        
[22] tidyr_1.3.0         tibble_3.2.1        ggplot2_3.4.4      
[25] tidyverse_1.3.2     workflowr_1.7.0    

loaded via a namespace (and not attached):
 [1] googledrive_2.0.0       ellipsis_0.3.2          class_7.3-20           
 [4] rprojroot_2.0.3         fs_1.5.2                rstudioapi_0.15.0      
 [7] proxy_0.4-27            farver_2.1.1            bit64_4.0.5            
[10] fansi_1.0.3             xml2_1.3.3              codetools_0.2-18       
[13] cachem_1.0.6            knitr_1.41              polyclip_1.10-4        
[16] jsonlite_1.8.3          gsw_1.1-1               broom_1.0.5            
[19] dbplyr_2.2.1            compiler_4.2.2          httr_1.4.4             
[22] backports_1.4.1         assertthat_0.2.1        fastmap_1.1.0          
[25] gargle_1.2.1            cli_3.6.1               later_1.3.0            
[28] tweenr_2.0.2            htmltools_0.5.3         tools_4.2.2            
[31] rnaturalearthdata_0.1.0 gtable_0.3.1            glue_1.6.2             
[34] Rcpp_1.0.11             cellranger_1.1.0        jquerylib_0.1.4        
[37] vctrs_0.6.4             lwgeom_0.2-10           xfun_0.35              
[40] ps_1.7.2                rvest_1.0.3             lifecycle_1.0.3        
[43] googlesheets4_1.0.1     oce_1.7-10              getPass_0.2-2          
[46] MASS_7.3-58.1           scales_1.2.1            vroom_1.6.0            
[49] hms_1.1.2               promises_1.2.0.1        parallel_4.2.2         
[52] yaml_2.3.6              memoise_2.0.1           sass_0.4.4             
[55] stringi_1.7.8           highr_0.9               e1071_1.7-12           
[58] checkmate_2.1.0         rlang_1.1.1             pkgconfig_2.0.3        
[61] systemfonts_1.0.4       evaluate_0.18           lattice_0.20-45        
[64] SolveSAPHE_2.1.0        labeling_0.4.2          bit_4.0.5              
[67] processx_3.8.0          tidyselect_1.2.0        here_1.0.1             
[70] seacarb_3.3.1           magrittr_2.0.3          R6_2.5.1               
[73] generics_0.1.3          DBI_1.1.3               pillar_1.9.0           
[76] haven_2.5.1             whisker_0.4             withr_2.5.0            
[79] units_0.8-0             sp_1.5-1                modelr_0.1.10          
[82] crayon_1.5.2            KernSmooth_2.23-20      utf8_1.2.2             
[85] tzdb_0.3.0              rmarkdown_2.18          grid_4.2.2             
[88] readxl_1.4.1            data.table_1.14.6       callr_3.7.3            
[91] git2r_0.30.1            reprex_2.0.2            digest_0.6.30          
[94] classInt_0.4-8          httpuv_1.6.6            textshaping_0.3.6      
[97] munsell_0.5.0           viridisLite_0.4.1       bslib_0.4.1