Core-Argo Temperature Data North Atlantic
Marguerite Larriere & Jens Daniel Müller
06 April, 2024
Last updated: 2024-04-06
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Knit directory:
bgc_argo_r_argodata/analysis/
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Task
Dependencies
temp_core_va.rds - core preprocessed folder, created by temp_core_align_climatology. Not this file is written AFTER the vertical alignment stage.
Outputs
path_emlr_utilities <- "/nfs/kryo/work/jenmueller/emlr_cant/utilities/files/"
path_basin_mask <- "/nfs/kryo/work/datasets/gridded/ocean/interior/reccap2/supplementary/"
path_argo <- '/nfs/kryo/work/datasets/ungridded/3d/ocean/floats/bgc_argo'
path_argo_core <- '/nfs/kryo/work/datasets/ungridded/3d/ocean/floats/core_argo_r_argodata_2024-03-13'
path_argo_core_preprocessed <- paste0(path_argo_core, "/preprocessed_core_data")
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")Set options
# Options
# opt_profile_depth_range
# The profile must have at least one temperature reading at a depth <= opt_profile_depth_range[1, ]
# The profile must have at least one temperature reading at a depth >= opt_profile_depth_range[2, ].
# In addition if the profile depth does not exceed the min(opt_profile_depth_range[2, ]) (i.e. 600) it will be removed.
profile_range <- c(1, 2, 3)
min_depth <- c(5.0, 5.0, 5.0)
max_depth <- c(600, 1200, 1500)
opt_profile_depth_range <- data.frame(profile_range, min_depth, max_depth)
# The profile should not have a gap greater that opt_gap_limit within the range defined by opt_gap_min_depth and opt_gap_max_depth
opt_gap_limit <- c(28, 55, 110)
opt_gap_min_depth <- c(0, 400, 1000)
opt_gap_max_depth <- c(400, 1000, 1500)
# year to be refreshed are set by opt_min_year and opt_max_year
opt_min_year = 2013
opt_max_year = 2024
# opt_measure_label, opt_xlim and opt_xbreaks are associated with formatting
opt_measure_label <- "temperature anomaly (°C)"
opt_xlim <- c(-4.5, 6)
opt_xbreaks <- c(-4, -2, 0, 2, 4, 6)
# opt_exclude_shallower
# This option will exclude depths from the climatology and subsequent vertically aligned data that are shallower than opt_exclude_shallower.
# e.g. set to 4.5 to ensure that the top depth of 0.0 m is excluded
# Set to 0.0 to ensure no depths are excluded.
opt_exclude_shallower <- 4.5RECCAP biomes
#Load biome separations
region_masks_all <-
stars::read_ncdf(paste(
path_basin_mask, "RECCAP2_region_masks_all_v20221025.nc", sep = "")) %>%
as_tibble() %>%
mutate(seamask = as.factor(seamask))
#Select Atlantic
region_masks_atlantic <- region_masks_all %>%
mutate(lon = ifelse(lon > 180, lon - 360, lon)) %>% #shift longitude to be in range (-180°, 180°) for better vizualisation
pivot_longer(open_ocean:atlantic,
names_to = 'region',
values_to = 'value') %>%
mutate(value = as.factor(value))
#Base map
# map <- read_rds(paste(path_emlr_utilities, "map_landmask_WOA18.rds", sep = ""))
world_coordinates <- map_data("world")
base_map <-ggplot() +
geom_map(data = world_coordinates, map = world_coordinates,
aes(long, lat, map_id = region))Coastal regions
#Restrict base map to North Atlantic Ocean
base_map <- base_map + lims(x= c(-100, 50), y = c(0, 80))
region_masks_atlantic <- region_masks_atlantic %>%
filter(region == 'atlantic',
value != 0) %>%
mutate(coast = as.character(coast))
#Map coastal regions
base_map + geom_tile(data = region_masks_atlantic,
aes(x = lon,
y = lat,
fill = coast)) +
scale_fill_brewer(palette = 'Dark2')+
labs(title = 'Coastal Regions')
Biomes
#Map biomes of interest:
# SPSS (SubPolar Seasonally Stratified)
# STSS (SubTropical Seasonally Stratified)
# STPS (SubTropical Permanent Stratified)
biomes_names<-c("SPSS", "STSS", "STPS")
biomes_atlantic <- region_masks_atlantic %>%
filter(value %in% c(1,2,3))
base_map + geom_tile(data = biomes_atlantic,
aes(x = lon,
y = lat,
fill = value))+
scale_fill_manual(values = c("1" = "cadetblue", "2" = "azure", "3" = "lightskyblue3"),
labels = biomes_names) +
labs(title = 'RECCAP biomes')
Load Core-SST data
# Temperature profile in 2023
sst <- read_rds(file = paste0(path_argo_core_preprocessed, "/temp_core_va.rds")) %>%
filter(year==2023) %>%
mutate(lon = ifelse(lon > 180, lon - 360, lon))
# North Atlantic SST
sst_natlantic <- sst %>%
group_by(year, file_id, lat, lon, profile_range) %>%
filter(lat > 0, lon <30, lon >-100)
biomes_subset <- biomes_atlantic %>%
select(lat, lon, biome_value = value)
sst_with_biomes <- left_join(sst, biomes_subset, by = c("lat", "lon")) %>%
select(-date) %>%
filter(!is.na(biome_value))Functions
map_profiles<-function(data, by_year=FALSE, all_years=FALSE){
# Number of measurements
measurement_count <- data %>%
group_by(year, file_id, lat, lon, profile_range) %>%
summarise(count_measures = n()) %>%
ungroup()
# Number of profiles
profile_count <- measurement_count %>%
group_by(year, lat, lon, profile_range) %>%
summarise(count_profiles = n()) %>%
ungroup()
# Aggregate profile range
profile_count_agg <- profile_count %>%
group_by(year, lat, lon) %>%
summarise(count_profiles = sum(count_profiles)) %>%
mutate(profile_range = 1) %>%
ungroup()
profile_count_agg <- rbind(profile_count_agg, profile_count %>%
filter (profile_range %in% c(2, 3)) %>%
group_by(year, lat, lon) %>%
summarise(count_profiles = sum(count_profiles)) %>%
mutate(profile_range = 2) %>%
ungroup()
)
profile_count_agg <- rbind(
profile_count_agg,
profile_count %>%
filter (profile_range == 3)
)
#Type of measurement
profile_count_agg <- profile_count_agg %>%
mutate (prof_type = 'temperature')
if (by_year){
map <- profile_count_agg %>%
group_split(profile_range) %>%
map(~ base_map +
geom_tile(data = .x, aes(x = lon, y = lat, fill = count_profiles)) +
scale_fill_gradient(low = "blue", high = "red", trans = "log10") +
labs(x = 'lon', y = 'lat', fill = 'number of\nprofiles',
title = paste0('Core temperature by year and location ',
ifelse(unique(.x$profile_range) == 1, '600m',
ifelse(unique(.x$profile_range) == 2, '1200m', '1500m')),
' profiles')) +
theme(legend.position = "bottom",
axis.text = element_blank(),
axis.ticks = element_blank()) +
facet_wrap(~year, ncol = 3))
}
if (all_years){
# sum across year
profile_count_agg <- profile_count_agg %>%
group_by(profile_range, lat, lon) %>%
summarise(count_profiles = sum(count_profiles)) %>%
ungroup()
# map the location of profiles for each profile in each year
map <- profile_count_agg %>%
group_split(profile_range) %>%
map(~ base_map +
geom_tile(data = .x, aes(x = lon, y = lat, fill = count_profiles)) +
scale_fill_gradient(low = "blue", high = "red", trans = "log10") +
labs(x = 'lon', y = 'lat', fill = 'number of\nprofiles',
title = paste0('Core temperature by location ',
ifelse(unique(.x$profile_range) == 1, '600m',
ifelse(unique(.x$profile_range) == 2, '1200m', '1500m')),
'profiles')) +
theme(legend.position = "bottom",
axis.text = element_blank(),
axis.ticks = element_blank())
)
}
return(map)
}map_profiles_biomes<-function(data, by_biome=FALSE, map=FALSE, hist=FALSE){
# Number of measurements
measurement_count <- data %>%
group_by(year, file_id, lat, lon, profile_range, biome_value) %>%
summarise(count_measures = n()) %>%
ungroup()
# Number of profiles
profile_count <- measurement_count %>%
group_by(year, lat, lon, profile_range, biome_value) %>%
summarise(count_profiles = n()) %>%
ungroup()
# Aggregate profile range
profile_count_agg <- profile_count %>%
group_by(year, lat, lon, biome_value) %>%
summarise(count_profiles = sum(count_profiles)) %>%
mutate(profile_range = 1) %>%
ungroup()
profile_count_agg <- rbind(profile_count_agg, profile_count %>%
filter (profile_range %in% c(2, 3)) %>%
group_by(year, lat, lon, biome_value) %>%
summarise(count_profiles = sum(count_profiles)) %>%
mutate(profile_range = 2) %>%
ungroup()
)
profile_count_agg <- rbind(
profile_count_agg,
profile_count %>%
filter (profile_range == 3)
)
#Type of measurement
profile_count_agg <- profile_count_agg %>%
mutate (prof_type = 'temperature')
if (by_biome){
# sum across biomes
profile_count_agg <- profile_count_agg %>%
group_by(lat, lon,profile_range, biome_value) %>%
summarise(count_profiles = sum(count_profiles)) %>%
ungroup()
if(map){
# map the location of profiles for each profile in each year
map <- base_map +
geom_tile(data = profile_count_agg, aes(x = lon, y = lat, fill = count_profiles)) +
scale_fill_gradient(low = "blue", high = "red", trans = "log10") +
labs(x = 'lon', y = 'lat', fill = 'number of\nprofiles',
title = 'Core temperature by biome profiles') +
theme(legend.position = "bottom",
axis.text = element_blank(),
axis.ticks = element_blank()) +
facet_wrap(~biome_value, ncol = 3, labeller = as_labeller(c(`1` = 'SPSS', `2` = 'STSS', `3` = 'STPS')))
return(map)
}
if (hist){
hist <- ggplot(profile_count_agg, aes(x = factor(biome_value), y = count_profiles, fill = factor(profile_range))) +
geom_bar(stat = "identity") +
labs(x = "Biome", y = "Number of Profiles", fill = "Profile Range") +
scale_fill_manual(values = c("1" = "lightblue", "2" = "orange", "3" = "darkred"),
labels = c("1" = "600m", "2" = "1200m", "3" = "1500m")) +
theme_minimal() +
scale_x_discrete(labels = c("1" = "SPSS", "2" = "STSS", "3" = "STPS"))
return(hist)
}
}
}hist_profiles_months<-function(data){
# Number of measurements
measurement_count <- data %>%
group_by(month, year, file_id, lat, lon, biome_value) %>%
summarise(count_measures = n()) %>%
ungroup()
# Number of profiles
profile_count <- measurement_count %>%
group_by(month, year, lat, lon, biome_value) %>%
summarise(count_profiles = n()) %>%
ungroup()
# Aggregate profile range
profile_count_agg <- profile_count %>%
group_by(month, year, lat, lon, biome_value) %>%
summarise(count_profiles = sum(count_profiles)) %>%
ungroup()
#Type of measurement
profile_count_agg <- profile_count_agg %>%
mutate (prof_type = 'temperature')
# sum across biomes
profile_count_agg <- profile_count_agg %>%
group_by(month, lat, lon, biome_value) %>%
summarise(count_profiles = sum(count_profiles)) %>%
ungroup()
hist <- ggplot(profile_count_agg, aes(x = factor(month), y = count_profiles, fill = factor(biome_value))) +
geom_bar(stat = "identity") +
labs(title= paste0("Number of profiles per month, per biome, in 2023"), x = "Biome", y = "Number of Profiles", fill = "Profile Range") +
scale_fill_manual(values = c("1" = "lightblue", "2" = "orange", "3" = "darkred"),
labels = c("1" = "SPSS", "2" = "STSS", "3" = "STPS")) +
theme_minimal()
return(hist)
}map_profiles_months<-function(data, by_year=FALSE, all_years=FALSE){
# Number of measurements
measurement_count <- data %>%
group_by(month, year, file_id, lat, lon) %>%
summarise(count_measures = n()) %>%
ungroup()
# Number of profiles
profile_count <- measurement_count %>%
group_by(month, year, lat, lon) %>%
summarise(count_profiles = n()) %>%
ungroup()
# Aggregate profile range
profile_count_agg <- profile_count %>%
group_by(month, year, lat, lon) %>%
summarise(count_profiles = sum(count_profiles)) %>%
ungroup()
#Type of measurement
profile_count_agg <- profile_count_agg %>%
mutate (prof_type = 'temperature')
if (by_year){
map <- base_map +
geom_tile(data =profile_count_agg, aes(x = lon, y = lat, fill = count_profiles)) +
scale_fill_gradient(low = "blue", high = "red", trans = "log10") +
labs(x = 'lon', y = 'lat', fill = 'number of\nprofiles',
title = 'Core temperature profiles in 2023')+
theme(legend.position = "bottom",
axis.text = element_blank(),
axis.ticks = element_blank()) +
facet_wrap(~month, ncol = 3)
}
if (all_years){
# sum across year
profile_count_agg <- profile_count_agg %>%
group_by(lat, lon) %>%
summarise(count_profiles = sum(count_profiles)) %>%
ungroup()
# map the location of profiles for each profile in each year
map <- profile_count_agg %>%
map(~ base_map +
geom_tile(data = .x, aes(x = lon, y = lat, fill = count_profiles)) +
scale_fill_gradient(low = "blue", high = "red", trans = "log10") +
labs(x = 'lon', y = 'lat', fill = 'number of\nprofiles',
title = 'Core temperature profiles over years') +
theme(legend.position = "bottom",
axis.text = element_blank(),
axis.ticks = element_blank())
)
}
return(map)
}Profiles spatial distribution
By year
# Map the location of profiles for each profile in each year
map_profiles(sst_natlantic, by_year = TRUE)[[1]]
[[2]]
[[3]]
Over all years
# Map the location of profiles over all years
map_profiles(sst_natlantic, by_year = FALSE, all_years = TRUE)[[1]]
[[2]]
[[3]]
By biomes over all years
# Map the location of profiles over all years, per biomes
map_profiles_biomes(sst_with_biomes, by_biome=TRUE, map=TRUE, hist = FALSE)
map_profiles_biomes(sst_with_biomes, by_biome=TRUE, map=FALSE, hist = TRUE)
Histogram number of profiles per year, per months
# Map the location of profiles over all years, per biomes
hist_profiles_months(sst_with_biomes)
Map number of profiles per year, per months
# Map the location of profiles over all years, per month
map_profiles_months(sst_with_biomes, by_year = TRUE, all_years=FALSE)
Anomaly profiles for each biomes
###Load anomaly datasets
# Temperature profile
temp_anomaly_va <- read_rds(file = paste0(path_argo_core_preprocessed, "/temp_anomaly_va.rds")) %>%
filter(year==2023) %>%
mutate(lon = ifelse(lon > 180, lon - 360, lon))
# North Atlantic SST
temp_anomaly_va_natlantic <- temp_anomaly_va %>%
group_by(year, file_id, lat, lon, profile_range) %>%
filter(lat > 0, lon <30, lon >-100)
temp_anomaly_va_biomes <- left_join(temp_anomaly_va_natlantic, biomes_subset, by = c("lat", "lon")) %>%
select(-date) %>%
filter(!is.na(biome_value))plot_anomaly_profiles <- function(data, max_depth) {
anomaly_overall_mean <- data %>%
filter(depth <= max_depth) %>%
group_by(depth) %>%
summarise(temp_count = n(),
temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(anomaly, na.rm = TRUE))
ggplot(anomaly_overall_mean) +
geom_path(aes(x = temp_anomaly_mean, y = depth)) +
geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
xmin = temp_anomaly_mean - temp_anomaly_sd,
y = depth), alpha = 0.2) +
geom_vline(xintercept = 0) +
scale_y_reverse() +
coord_cartesian(xlim = opt_xlim) +
scale_x_continuous(breaks = opt_xbreaks) +
labs(title = paste0('Overall mean anomaly profiles to ', max_depth, 'm', ""),
x = opt_measure_label, y = 'depth (m)')
}# Profiles to 600m
max_depth_1 <- opt_profile_depth_range[1, "max_depth"]
plot_anomaly_profiles(temp_anomaly_va_natlantic, max_depth_1)
# Profiles to 1200m
# max_depth_2 <- opt_profile_depth_range[2, "max_depth"]
# plot_anomaly_profiles(temp_anomaly_va_natlantic, max_depth_2)
# Profiles to 1500m
# max_depth_3 <- opt_profile_depth_range[3, "max_depth"]
# plot_anomaly_profiles(temp_anomaly_va_natlantic, max_depth_3)plot_anomaly_profiles_biomes <- function(data, max_depth, group_monthly=FALSE) {
if (group_monthly) {
anomaly_overall_mean <- data %>%
filter(depth <= max_depth) %>%
group_by(depth, biome_value, month) %>%
summarise(temp_count = n(),
temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(anomaly, na.rm = TRUE))
facet_var <- ~ month
labeller <- as_labeller(setNames(month.name[1:12], as.character(1:12)))
} else {
anomaly_overall_mean <- data %>%
filter(depth <= max_depth) %>%
group_by(depth, biome_value) %>%
summarise(temp_count = n(),
temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(anomaly, na.rm = TRUE))
facet_var <- ~ factor(biome_value)
labeller <- as_labeller(c("1" = "SPSS", "2" = "STSS", "3" = "STPS"))
}
ggplot(anomaly_overall_mean) +
geom_path(aes(x = temp_anomaly_mean, y = depth, color = factor(biome_value))) +
geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
xmin = temp_anomaly_mean - temp_anomaly_sd,
y = depth, fill = factor(biome_value)), alpha = 0.2) +
geom_vline(xintercept = 0) +
scale_y_reverse() +
coord_cartesian(xlim = opt_xlim) +
scale_x_continuous(breaks = opt_xbreaks) +
labs(title = paste0('Overall mean anomaly profiles to ', max_depth, 'm', ""),
x = opt_measure_label, y = 'depth (m)', color = "Biome") +
scale_color_manual(values = c("1" = "darkblue", "2" = "darkorange", "3" = "darkred"), labels = c("SPSS", "STSS", "STPS")) +
scale_fill_manual(values = c("1" = "darkblue", "2" = "darkorange", "3" = "darkred"), name = "Biome", labels = c("SPSS", "STSS", "STPS")) +
facet_wrap(facet_var, labeller = labeller, ncol = 3) +
theme(axis.text.x = element_text(angle = 0, hjust = 1))
}# Profiles to 600m
plot_anomaly_profiles_biomes(temp_anomaly_va_biomes, max_depth_1, group_monthly=FALSE)
# Profiles to 1200m
# plot_anomaly_profiles_biomes(temp_anomaly_va_biomes, max_depth_2, group_monthly=FALSE)
# Profiles to 1500m
# plot_anomaly_profiles_biomes(temp_anomaly_va_biomes, max_depth_3, group_monthly=FALSE)
# Profile per month
plot_anomaly_profiles_biomes(temp_anomaly_va_biomes, max_depth_1, group_monthly=TRUE) #600m
# plot_anomaly_profiles_biomes(temp_anomaly_va_biomes, max_depth_2, group_monthly=TRUE) #1200m
# plot_anomaly_profiles_biomes(temp_anomaly_va_biomes, max_depth_3, group_monthly=TRUE) #1500mplot_anomaly_profiles_1month <- function(data, max_depth, month_of_interest) {
anomaly_overall_mean <- data %>%
filter(depth <= max_depth,
month == month_of_interest) %>%
group_by(depth, biome_value, month, year) %>%
summarise(temp_count = n(),
temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(anomaly, na.rm = TRUE))
plot_date <- paste("Date:", month.name[unique(anomaly_overall_mean$month)], unique(anomaly_overall_mean$year))
ggplot(anomaly_overall_mean) +
geom_path(aes(x = temp_anomaly_mean, y = depth, color = factor(biome_value))) +
geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
xmin = temp_anomaly_mean - temp_anomaly_sd,
y = depth, fill = factor(biome_value)), alpha = 0.2) +
geom_vline(xintercept = 0) +
scale_y_reverse() +
coord_cartesian(xlim = opt_xlim) +
scale_x_continuous(breaks = opt_xbreaks) +
labs(title = paste0('Overall mean anomaly profiles to ', max_depth, 'm', ""),
subtitle = plot_date,
x = opt_measure_label, y = 'depth (m)', color = "Biome") +
scale_color_manual(values = c("1" = "darkblue", "2" = "darkorange", "3" = "darkred"), labels = c("SPSS", "STSS", "STPS")) +
scale_fill_manual(values = c("1" = "darkblue", "2" = "darkorange", "3" = "darkred"), name = "Biome", labels = c("SPSS", "STSS", "STPS")) +
facet_wrap(~ month, ncol = 3)+
theme(axis.text.x = element_text(angle = 0, hjust = 1))
}# Profiles to 600m
plot_anomaly_profiles_1month(temp_anomaly_va_biomes, max_depth_1, month_of_interest=10)
# Profiles to 1200m
# plot_anomaly_profiles_1month(temp_anomaly_va_biomes, max_depth_2, month_of_interest=10)
# Profiles to 1500m
# plot_anomaly_profiles_1month(temp_anomaly_va_biomes, max_depth_3, month_of_interest=10)
#-- Focusing on specfific zone
temp_anomaly_va_biomes_zone<- temp_anomaly_va_biomes %>%
filter(lat<55, lat>40, lon > -65, lon < -35)
map_profiles_months(temp_anomaly_va_biomes_zone, by_year = TRUE)
plot_anomaly_profiles_1month(temp_anomaly_va_biomes_zone, max_depth_1, month_of_interest=9)
dev.off()
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] ggOceanMaps_1.3.4 ggspatial_1.1.7 oce_1.7-10 gsw_1.1-1
[5] lubridate_1.9.0 timechange_0.1.1 forcats_0.5.2 stringr_1.5.0
[9] dplyr_1.1.3 purrr_1.0.2 readr_2.1.3 tidyr_1.3.0
[13] tibble_3.2.1 ggplot2_3.4.4 tidyverse_1.3.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 sf_1.0-9 RColorBrewer_1.1-3
[4] httr_1.4.4 rprojroot_2.0.3 tools_4.2.2
[7] backports_1.4.1 bslib_0.4.1 utf8_1.2.2
[10] R6_2.5.1 KernSmooth_2.23-20 rgeos_0.5-9
[13] DBI_1.1.3 colorspace_2.0-3 raster_3.6-11
[16] sp_1.5-1 withr_2.5.0 tidyselect_1.2.0
[19] processx_3.8.0 compiler_4.2.2 git2r_0.30.1
[22] cli_3.6.1 rvest_1.0.3 RNetCDF_2.6-1
[25] xml2_1.3.3 labeling_0.4.2 sass_0.4.4
[28] scales_1.2.1 classInt_0.4-8 callr_3.7.3
[31] proxy_0.4-27 digest_0.6.30 rmarkdown_2.18
[34] pkgconfig_2.0.3 htmltools_0.5.3 highr_0.9
[37] maps_3.4.1 dbplyr_2.2.1 fastmap_1.1.0
[40] rlang_1.1.1 readxl_1.4.1 rstudioapi_0.15.0
[43] farver_2.1.1 jquerylib_0.1.4 generics_0.1.3
[46] jsonlite_1.8.3 googlesheets4_1.0.1 magrittr_2.0.3
[49] ncmeta_0.3.5 Rcpp_1.0.10 munsell_0.5.0
[52] fansi_1.0.3 abind_1.4-5 lifecycle_1.0.3
[55] terra_1.7-65 stringi_1.7.8 whisker_0.4
[58] yaml_2.3.6 grid_4.2.2 parallel_4.2.2
[61] promises_1.2.0.1 crayon_1.5.2 lattice_0.20-45
[64] stars_0.6-0 haven_2.5.1 hms_1.1.2
[67] knitr_1.41 ps_1.7.2 pillar_1.9.0
[70] codetools_0.2-18 reprex_2.0.2 glue_1.6.2
[73] evaluate_0.18 getPass_0.2-2 modelr_0.1.10
[76] vctrs_0.6.4 tzdb_0.3.0 httpuv_1.6.6
[79] cellranger_1.1.0 gtable_0.3.1 assertthat_0.2.1
[82] cachem_1.0.6 xfun_0.35 lwgeom_0.2-10
[85] broom_1.0.5 e1071_1.7-12 later_1.3.0
[88] class_7.3-20 googledrive_2.0.0 gargle_1.2.1
[91] units_0.8-0 ellipsis_0.3.2