pCO2 product synopsis
Jens Daniel Müller
20 March, 2024
Last updated: 2024-03-20
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Knit directory:
heatwave_co2_flux_2023/analysis/
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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")Read data
files <- list.files("../data",
pattern = "_anomaly_map_annual.csv",
full.names = TRUE)
pco2_product_coarse_annual_regression <-
read_csv(files,
id = "product")
pco2_product_coarse_annual_regression <-
pco2_product_coarse_annual_regression %>%
mutate(product = str_extract(product, "OceanSODA|SOM_FFN"))files <- list.files("../data",
pattern = "_anomaly_map_monthly.csv",
full.names = TRUE)
pco2_product_coarse_monthly_regression <-
read_csv(files,
id = "product")
pco2_product_coarse_monthly_regression <-
pco2_product_coarse_monthly_regression %>%
mutate(product = str_extract(product, "OceanSODA|SOM_FFN"))files <- list.files("../data",
pattern = "_anomaly_hovmoeller_monthly.csv",
full.names = TRUE)
pco2_product_hovmoeller_monthly_regression <-
read_csv(files,
id = "product")
pco2_product_hovmoeller_monthly_regression <-
pco2_product_hovmoeller_monthly_regression %>%
mutate(product = str_extract(product, "OceanSODA|SOM_FFN"))files <- list.files("../data",
pattern = "_biome_annual_detrended.csv",
full.names = TRUE)
pco2_product_annual_detrended <-
read_csv(files,
id = "product")
pco2_product_annual_detrended <-
pco2_product_annual_detrended %>%
mutate(product = str_extract(product, "OceanSODA|SOM_FFN"))map <-
read_rds("../data/map.rds")
key_biomes <-
read_rds("../data/key_biomes.rds")Define labels and breaks
name_core <- c("fgco2", "sol", "spco2", "kw", "fgco2_int", "fgco2_hov")labels_breaks <- function(i_name) {
if (i_name == "dco2") {
i_legend_title <- "ΔpCO<sub>2</sub><br>(µatm)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "dfco2") {
i_legend_title <- "ΔfCO<sub>2</sub><br>(µatm)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "atm_co2") {
i_legend_title <- "pCO<sub>2,atm</sub><br>(µatm)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "sol") {
i_legend_title <- "CO<sub>2</sub> solubility<br>(mol m<sup>-3</sup> µatm<sup>-1</sup>)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "kw") {
i_legend_title <- "K<sub>w</sub><br>(m yr<sup>-1</sup>)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "spco2") {
i_legend_title <- "pCO<sub>2,ocean</sub><br>(µatm)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "sfco2") {
i_legend_title <- "fCO<sub>2,ocean</sub><br>(µatm)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "fgco2") {
i_legend_title <- "FCO<sub>2</sub><br>(mol m<sup>-2</sup> yr<sup>-1</sup>)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "fgco2_hov") {
i_legend_title <- "FCO<sub>2</sub><br>(PgC deg<sup>-1</sup> yr<sup>-1</sup>)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "fgco2_int") {
i_legend_title <- "FCO<sub>2</sub><br>(PgC yr<sup>-1</sup>)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "temperature") {
i_legend_title <- "SST<br>(°C)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "salinity") {
i_legend_title <- "SSS"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "chl") {
i_legend_title <- "Chl-a<br>(mg m<sup>-3</sup>)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "mld") {
i_legend_title <- "MLD<br>(m)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "press") {
i_legend_title <- "pressure<sub>atm</sub><br>(unit?)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
all_labels_breaks <- lst(i_legend_title,
# i_breaks,
# i_contour_level,
# i_contour_level_abs
)
return(all_labels_breaks)
}
# labels_breaks("fgco2")
x_axis_labels <-
c(
"dco2" = labels_breaks("dco2")$i_legend_title,
"dfco2" = labels_breaks("dfco2")$i_legend_title,
"atm_co2" = labels_breaks("atm_co2")$i_legend_title,
"sol" = labels_breaks("sol")$i_legend_title,
"kw" = labels_breaks("kw")$i_legend_title,
"spco2" = labels_breaks("spco2")$i_legend_title,
"sfco2" = labels_breaks("sfco2")$i_legend_title,
"fgco2_hov" = labels_breaks("fgco2_hov")$i_legend_title,
"fgco2_int" = labels_breaks("fgco2_int")$i_legend_title,
"temperature" = labels_breaks("temperature")$i_legend_title,
"salinity" = labels_breaks("salinity")$i_legend_title,
"chl" = labels_breaks("chl")$i_legend_title,
"mld" = labels_breaks("mld")$i_legend_title,
"press" = labels_breaks("press")$i_legend_title
)Maps
The following maps show the absolute state of each variable in 2023 as provided through the pCO2 product, the change in that variable from 1990 to 2023, as well es the anomalies in 2023. Changes and anomalies are determined based on the predicted value of a linear regression model fit to the data from 1990 to 2022.
Maps are first presented as annual means, and than as monthly means. Note that the 2023 predictions for the monthly maps are done individually for each month, such the mean seasonal anomaly from the annual mean is removed.
Note: The increase the computational speed, I regridded all maps to 5X5° grid.
Annual means
2023 anomaly
pco2_product_coarse_annual_regression %>%
filter(name %in% name_core) %>%
group_split(name) %>%
# head(4) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = resid)) +
labs(title = "2023 anomaly") +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
facet_wrap( ~ product, ncol = 1)
)[[1]]
[[2]]
[[3]]
[[4]]
Monthly means
2023 anomaly
pco2_product_coarse_monthly_regression %>%
filter(name %in% name_core) %>%
group_split(name) %>%
head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = resid)) +
labs(title = "2023 anomaly") +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
facet_grid(month ~ product)
)[[1]]
Hovmoeller plots
The following Hovmoeller plots show the value of each variable as provided through the pCO2 product, as well as the anomalies from the prediction of a linear/quadratic fit to the data from 1990 to 2022.
Hovmoeller plots are first presented as annual means, and than as monthly means. Note that the predictions for the monthly Hovmoeller plots are done individually for each month, such the mean seasonal anomaly from the annual mean is removed.
Monthly means
Anomalies
pco2_product_hovmoeller_monthly_regression %>%
filter(name %in% name_core) %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(decimal, lat, fill = resid)) +
geom_raster() +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
coord_cartesian(expand = 0) +
labs(title = "Monthly mean anomalies",
y = "Latitude") +
theme(axis.title.x = element_blank()) +
facet_wrap( ~ product, ncol = 1)
)[[1]]
[[2]]
[[3]]
[[4]]
Regional means and integrals
The following plots show biome- or global- averaged/integrated values of each variable as provided through the pCO2 product, as well as the anomalies from the prediction of a linear/quadratic fit to the data from 1990 to 2022.
Anomalies are first presented relative to the predicted annual mean of each year, hence preserving the seasonality. Furthermore, anomalies are presented relative to the predicted monthly mean values, such that the mean seasonality is removed.
Anomalies
Annual mean trends
pco2_product_annual_detrended %>%
filter(biome %in% "Global",
name %in% name_core) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(year < 2022),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(year >= 2022),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted annual mean | Global") +
facet_grid(name ~ product,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
pco2_product_annual_detrended %>%
filter(biome %in% key_biomes,
name %in% name_core) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(year < 2022),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(year >= 2022),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Anomalies from predicted annual mean |", .x$biome)) +
facet_grid(
name ~ product,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
Flux anomaly correlation
The following plots aim to unravel the correlation between biome- or globally- integrated monthly flux anomalies and the corresponding anomalies of the means/integrals of each other variable.
Anomalies are first presented are first presented in absolute units. Due to the different flux magnitudes, we need to plot the globally and biome-integrated fluxes separately. Secondly, we normalize the anomalies to the monthly spread (expressed as standard deviation) of the anomalies from 1990 to 2022.
Monthly anomalies
Absolute
pco2_product_monthly_detrended_anomaly <-
pco2_product_monthly_detrended %>%
select(year, month, biome, name, resid) %>%
pivot_wider(names_from = name,
values_from = resid)
pco2_product_monthly_detrended_anomaly %>%
filter(biome == "Global") %>%
pivot_longer(-c(year, month, biome, fgco2_int)) %>%
ggplot(aes(value, fgco2_int)) +
geom_hline(yintercept = 0) +
geom_point(data = . %>% filter(year <= 2022),
aes(fill = year),
shape = 21) +
scale_fill_grayC(name = "") +
new_scale_fill() +
geom_path(data = . %>% filter(year > 2022),
aes(col = as.factor(month), group = 1)) +
geom_point(data = . %>% filter(year > 2022),
aes(fill = as.factor(month)),
shape = 21,
size = 3) +
scale_color_scico_d(palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = "Month\nof 2023") +
scale_fill_scico_d(palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = "Month\nof 2023") +
labs(title = "Globally integrated fluxes",
y = labels_breaks("fgco2_int")$i_legend_title) +
facet_wrap(
~ name,
scales = "free_x",
labeller = labeller(name = x_axis_labels),
strip.position = "bottom",
ncol = 2
) +
theme(
strip.text.x.bottom = element_markdown(),
strip.placement = "outside",
strip.background.x = element_blank(),
axis.title.y = element_markdown(),
axis.title.x = element_blank()
)pco2_product_monthly_detrended_anomaly %>%
filter(biome != "Global") %>%
pivot_longer(-c(year, month, biome, fgco2_int)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(value, fgco2_int)) +
geom_hline(yintercept = 0) +
geom_point(
data = . %>% filter(year <= 2022),
aes(fill = year),
shape = 21
) +
scale_fill_grayC(name = "") +
new_scale_fill() +
geom_path(data = . %>% filter(year > 2022),
aes(col = as.factor(month), group = 1)) +
geom_point(
data = . %>% filter(year > 2022),
aes(fill = as.factor(month)),
shape = 21,
size = 3
) +
scale_color_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = "Month\nof 2023"
) +
scale_fill_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = "Month\nof 2023"
) +
facet_wrap( ~ biome, ncol = 3, scales = "free_x") +
labs(
title = "Biome integrated fluxes",
y = labels_breaks("fgco2_int")$i_legend_title,
x = labels_breaks(.x %>% distinct(name))$i_legend_title
) +
theme(axis.title.x = element_markdown(),
axis.title.y = element_markdown())
)Relative to spread
pco2_product_monthly_detrended_anomaly_spread <-
pco2_product_monthly_detrended_anomaly %>%
pivot_longer(-c(month, biome, year)) %>%
filter(year < 2023) %>%
group_by(month, biome, name) %>%
summarise(spread = sd(value)) %>%
ungroup()
pco2_product_monthly_detrended_anomaly_relative <-
full_join(
pco2_product_monthly_detrended_anomaly_spread,
pco2_product_monthly_detrended_anomaly %>%
pivot_longer(-c(month, biome, year))
)
pco2_product_monthly_detrended_anomaly_relative <-
pco2_product_monthly_detrended_anomaly_relative %>%
mutate(value = value / spread) %>%
select(-spread) %>%
pivot_wider() %>%
pivot_longer(-c(month, biome, year, fgco2_int))
pco2_product_monthly_detrended_anomaly_relative %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(value, fgco2_int)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 0) +
geom_point(
data = . %>% filter(year <= 2022),
aes(fill = year),
shape = 21
) +
scale_fill_grayC() +
new_scale_fill() +
geom_path(data = . %>% filter(year > 2022),
aes(col = as.factor(month), group = 1)) +
geom_point(
data = . %>% filter(year > 2022),
aes(fill = as.factor(month)),
shape = 21,
size = 3
) +
scale_color_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = "Month\nof 2023"
) +
scale_fill_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = "Month\nof 2023"
) +
facet_wrap( ~ biome, ncol = 3) +
coord_fixed() +
labs(
title = "Biome integrated fluxes normalized to spread",
y = str_split_i(labels_breaks("fgco2_int")$i_legend_title, "<br>", i = 1),
x = str_split_i(labels_breaks(.x %>% distinct(name))$i_legend_title, "<br>", i = 1)
) +
theme(axis.title.x = element_markdown(),
axis.title.y = element_markdown())
)
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] ggtext_0.1.2 khroma_1.9.0 ggnewscale_0.4.8
[4] terra_1.7-65 sf_1.0-9 rnaturalearth_0.1.0
[7] geomtextpath_0.1.1 colorspace_2.0-3 marelac_2.1.10
[10] shape_1.4.6 ggforce_0.4.1 metR_0.13.0
[13] scico_1.3.1 patchwork_1.1.2 collapse_1.8.9
[16] forcats_0.5.2 stringr_1.5.0 dplyr_1.1.3
[19] purrr_1.0.2 readr_2.1.3 tidyr_1.3.0
[22] tibble_3.2.1 ggplot2_3.4.4 tidyverse_1.3.2
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 markdown_1.4 fs_1.5.2
[7] gridtext_0.1.5 rstudioapi_0.15.0 proxy_0.4-27
[10] farver_2.1.1 bit64_4.0.5 fansi_1.0.3
[13] lubridate_1.9.0 xml2_1.3.3 codetools_0.2-18
[16] cachem_1.0.6 knitr_1.41 polyclip_1.10-4
[19] jsonlite_1.8.3 workflowr_1.7.0 gsw_1.1-1
[22] broom_1.0.5 dbplyr_2.2.1 compiler_4.2.2
[25] httr_1.4.4 backports_1.4.1 assertthat_0.2.1
[28] fastmap_1.1.0 gargle_1.2.1 cli_3.6.1
[31] later_1.3.0 tweenr_2.0.2 htmltools_0.5.3
[34] tools_4.2.2 gtable_0.3.1 glue_1.6.2
[37] rnaturalearthdata_0.1.0 Rcpp_1.0.11 cellranger_1.1.0
[40] jquerylib_0.1.4 vctrs_0.6.4 xfun_0.35
[43] rvest_1.0.3 timechange_0.1.1 lifecycle_1.0.3
[46] googlesheets4_1.0.1 oce_1.7-10 MASS_7.3-58.1
[49] scales_1.2.1 vroom_1.6.0 hms_1.1.2
[52] promises_1.2.0.1 parallel_4.2.2 yaml_2.3.6
[55] memoise_2.0.1 sass_0.4.4 stringi_1.7.8
[58] highr_0.9 e1071_1.7-12 checkmate_2.1.0
[61] commonmark_1.8.1 rlang_1.1.1 pkgconfig_2.0.3
[64] systemfonts_1.0.4 evaluate_0.18 lattice_0.20-45
[67] SolveSAPHE_2.1.0 labeling_0.4.2 bit_4.0.5
[70] tidyselect_1.2.0 seacarb_3.3.1 magrittr_2.0.3
[73] R6_2.5.1 generics_0.1.3 DBI_1.1.3
[76] pillar_1.9.0 haven_2.5.1 whisker_0.4
[79] withr_2.5.0 units_0.8-0 sp_1.5-1
[82] modelr_0.1.10 crayon_1.5.2 KernSmooth_2.23-20
[85] utf8_1.2.2 tzdb_0.3.0 rmarkdown_2.18
[88] grid_4.2.2 readxl_1.4.1 data.table_1.14.6
[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 bslib_0.4.1