Zonal sections
Jens Daniel Müller
12 April, 2022
Last updated: 2022-04-12
Checks: 7 0
Knit directory: emlr_obs_analysis/analysis/
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version_id_pattern <- "d"
config <- "MLR_basins"1 Read files
# identify required version IDs
Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = paste0("v_1", "d"))
Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = paste0("v_2", "d"))
Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = paste0("v_3", "d"))
Version_IDs <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)
print(Version_IDs) [1] "v_1d01" "v_1d02" "v_1d03" "v_1d04" "v_1d05" "v_1d06" "v_2d01" "v_2d02"
[9] "v_2d03" "v_2d04" "v_2d05" "v_2d06" "v_3d01" "v_3d02" "v_3d03" "v_3d04"
[17] "v_3d05" "v_3d06"for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
print(i_Version_IDs)
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_zonal <-
read_csv(paste(path_version_data,
"dcant_zonal.csv",
sep = ""))
dcant_zonal_mod_truth <-
read_csv(paste(path_version_data,
"dcant_zonal_mod_truth.csv",
sep = ""))
dcant_zonal <- bind_rows(dcant_zonal,
dcant_zonal_mod_truth)
dcant_profile <-
read_csv(paste(path_version_data,
"dcant_profile.csv",
sep = ""))
dcant_profile_mod_truth <-
read_csv(paste(path_version_data,
"dcant_profile_mod_truth.csv",
sep = ""))
dcant_profile <- bind_rows(dcant_profile,
dcant_profile_mod_truth)
dcant_budget_basin_AIP_layer <-
read_csv(paste(path_version_data,
"dcant_budget_basin_AIP_layer.csv",
sep = ""))
dcant_zonal_bias <-
read_csv(paste(path_version_data,
"dcant_zonal_bias.csv",
sep = ""))
dcant_zonal <- dcant_zonal %>%
mutate(Version_ID = i_Version_IDs)
dcant_profile <- dcant_profile %>%
mutate(Version_ID = i_Version_IDs)
dcant_budget_basin_AIP_layer <- dcant_budget_basin_AIP_layer %>%
mutate(Version_ID = i_Version_IDs)
dcant_zonal_bias <- dcant_zonal_bias %>%
mutate(Version_ID = i_Version_IDs)
params_local <-
read_rds(paste(path_version_data,
"params_local.rds",
sep = ""))
params_local <- bind_cols(
Version_ID = i_Version_IDs,
MLR_basins := str_c(params_local$MLR_basins, collapse = "|"),
tref1 = params_local$tref1,
tref2 = params_local$tref2)
tref <- read_csv(paste(path_version_data,
"tref.csv",
sep = ""))
params_local <- params_local %>%
mutate(median_year_1 = sort(tref$median_year)[1],
median_year_2 = sort(tref$median_year)[2],
duration = median_year_2 - median_year_1,
period = paste(median_year_1, "-", median_year_2))
if (exists("dcant_zonal_all")) {
dcant_zonal_all <- bind_rows(dcant_zonal_all, dcant_zonal)
}
if (!exists("dcant_zonal_all")) {
dcant_zonal_all <- dcant_zonal
}
if (exists("dcant_profile_all")) {
dcant_profile_all <- bind_rows(dcant_profile_all, dcant_profile)
}
if (!exists("dcant_profile_all")) {
dcant_profile_all <- dcant_profile
}
if (exists("dcant_budget_basin_AIP_layer_all")) {
dcant_budget_basin_AIP_layer_all <-
bind_rows(dcant_budget_basin_AIP_layer_all,
dcant_budget_basin_AIP_layer)
}
if (!exists("dcant_budget_basin_AIP_layer_all")) {
dcant_budget_basin_AIP_layer_all <- dcant_budget_basin_AIP_layer
}
if (exists("dcant_zonal_bias_all")) {
dcant_zonal_bias_all <- bind_rows(dcant_zonal_bias_all, dcant_zonal_bias)
}
if (!exists("dcant_zonal_bias_all")) {
dcant_zonal_bias_all <- dcant_zonal_bias
}
if (exists("params_local_all")) {
params_local_all <- bind_rows(params_local_all, params_local)
}
if (!exists("params_local_all")) {
params_local_all <- params_local
}
}[1] "v_1d01"
[1] "v_1d02"
[1] "v_1d03"
[1] "v_1d04"
[1] "v_1d05"
[1] "v_1d06"
[1] "v_2d01"
[1] "v_2d02"
[1] "v_2d03"
[1] "v_2d04"
[1] "v_2d05"
[1] "v_2d06"
[1] "v_3d01"
[1] "v_3d02"
[1] "v_3d03"
[1] "v_3d04"
[1] "v_3d05"
[1] "v_3d06"rm(dcant_zonal, dcant_zonal_bias, dcant_zonal_mod_truth,
dcant_budget_basin_AIP_layer,
tref)all_predictors <- c("saltempaouoxygenphosphatenitratesilicate")
params_local_all <- params_local_all %>%
mutate(MLR_predictors = str_remove_all(all_predictors,
MLR_predictors))2 Uncertainty limit
sd_uncertainty_limit <- 1.53 Individual cases
3.1 Absoulte values
dcant_zonal_all %>%
filter(data_source %in% c("mod", "obs")) %>%
group_by(basin_AIP, data_source) %>%
group_split() %>%
# head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "dcant",
plot_slabs = "n",
subtitle_text = paste(
"data_source: ",
unique(.x$data_source),
"| basin:",
unique(.x$basin_AIP)
)
) +
facet_grid(MLR_basins ~ period)
)[[1]]Warning: Removed 20190 rows containing non-finite values (stat_contour_filled).
[[2]]Warning: Removed 9522 rows containing non-finite values (stat_contour_filled).
[[3]]Warning: Removed 13338 rows containing non-finite values (stat_contour_filled).
[[4]]Warning: Removed 6354 rows containing non-finite values (stat_contour_filled).
[[5]]Warning: Removed 22212 rows containing non-finite values (stat_contour_filled).
[[6]]Warning: Removed 10782 rows containing non-finite values (stat_contour_filled).
p_dcant_Indian_1994_2004 <-
dcant_zonal_all %>%
filter(data_source %in% c("obs"),
period == "1994 - 2004",
basin_AIP == "Indian") %>%
p_section_zonal_continous_depth(var = "dcant",
plot_slabs = "n",
subtitle_text = "Indian Ocean") +
facet_grid(MLR_basins ~ period)
# ggsave(plot = p_dcant_Indian_1994_2004,
# path = "output/other",
# filename = "zonal_indian_1994_2004.png",
# height = 8,
# width = 5)
p_dcant_Indian_2004_2014 <-
dcant_zonal_all %>%
filter(data_source %in% c("obs"),
period == "2004 - 2014",
basin_AIP == "Pacific") %>%
p_section_zonal_continous_depth(var = "dcant",
plot_slabs = "n",
subtitle_text = "Pacific Ocean") +
facet_grid(MLR_basins ~ period)
# ggsave(plot = p_dcant_Indian_2004_2014,
# path = "output/other",
# filename = "zonal_Pacific_2004_2014.png",
# height = 8,
# width = 5)3.2 Biases
dcant_zonal_bias_all %>%
group_by(basin_AIP) %>%
group_split() %>%
# head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "dcant_bias",
col = "divergent",
plot_slabs = "n",
subtitle_text = paste("basin:",
unique(.x$basin_AIP)
)
) +
facet_grid(MLR_basins ~ period)
)[[1]]Warning: Removed 20283 rows containing non-finite values (stat_contour_filled).
[[2]]Warning: Removed 13338 rows containing non-finite values (stat_contour_filled).
[[3]]Warning: Removed 22230 rows containing non-finite values (stat_contour_filled).
3.2.1 Density distribution
dcant_zonal_bias_all %>%
ggplot(aes(dcant_bias, col = MLR_basins)) +
scale_color_brewer(palette = "Dark2") +
geom_vline(xintercept = 0) +
geom_density() +
facet_grid(period ~.) +
coord_cartesian(xlim = c(-10, 10))Warning: Removed 111 rows containing non-finite values (stat_density).
3.3 Bias correlation eras
dcant_zonal_bias_all_corr <- dcant_zonal_bias_all %>%
select(lat, depth, basin_AIP, dcant_bias, MLR_basins, period) %>%
pivot_wider(names_from = period,
values_from = dcant_bias,
names_prefix = "dcant_bias ")
dcant_zonal_bias_all_corr %>%
ggplot(aes(`dcant_bias 1994 - 2004`, `dcant_bias 2004 - 2014`)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 0) +
geom_bin2d() +
coord_fixed() +
facet_grid(MLR_basins ~ basin_AIP) +
scale_fill_viridis_c()Warning: Removed 37 rows containing non-finite values (stat_bin2d).
3.4 Concentration profiles
3.4.1 Data source
dcant_profile_all %>%
group_split(period) %>%
map(
~ ggplot(data = .x,
aes(
dcant, depth,
col = data_source, fill = data_source
)) +
geom_hline(yintercept = params_global$inventory_depth_standard) +
geom_vline(xintercept = 0) +
geom_ribbon(
aes(xmin = dcant - dcant_sd,
xmax = dcant + dcant_sd),
alpha = 0.2,
col = "transparent"
) +
geom_path() +
scale_y_reverse() +
labs(title = paste("period", unique(.x$period))) +
facet_grid(MLR_basins ~ basin_AIP)
)[[1]]
[[2]]
[[3]]
3.4.2 Basin separation
dcant_profile_all %>%
group_split(period) %>%
map(
~ ggplot(data = .x,
aes(
dcant, depth,
col = MLR_basins, fill = MLR_basins
)) +
geom_hline(yintercept = params_global$inventory_depth_standard) +
geom_vline(xintercept = 0) +
geom_path() +
scale_y_reverse() +
labs(title = paste("period", unique(.x$period))) +
facet_grid(data_source ~ basin_AIP)
)[[1]]
[[2]]
[[3]]
3.4.3 Era
dcant_profile_all %>%
arrange(depth) %>%
filter(period != "1994 - 2014") %>%
group_split(data_source) %>%
map(
~ ggplot(
data = .x,
aes(
dcant,
depth,
col = period,
group = interaction(MLR_basins, period)
)
) +
geom_hline(yintercept = params_global$inventory_depth_standard) +
geom_vline(xintercept = 0) +
geom_path() +
scale_y_reverse() +
labs(title = paste("data_source", unique(.x$data_source))) +
facet_grid(. ~ basin_AIP)
)[[1]]
[[2]]
[[3]]
3.5 Layer budgets
dcant_budget_basin_AIP_layer_all %>%
filter(estimate == "dcant") %>%
mutate(dcant = value,
inv_depth = fct_inorder(as.factor(inv_depth))) %>%
group_split(period) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(dcant, inv_depth,
fill = MLR_basins)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_y_discrete(limits = rev) +
scale_fill_brewer(palette = "Dark2") +
labs(title = paste("period", unique(.x$period))) +
facet_grid(data_source ~ basin_AIP)
)[[1]]
[[2]]
[[3]]
4 Ensemble
dcant_zonal_ensemble <- dcant_zonal_all %>%
filter(data_source %in% c("mod", "obs")) %>%
group_by(lat, depth, basin_AIP, data_source, period) %>%
summarise(
dcant_ensemble_mean = mean(dcant),
dcant_sd = sd(dcant),
dcant_range = max(dcant) - min(dcant)
) %>%
ungroup()`summarise()` has grouped output by 'lat', 'depth', 'basin_AIP', 'data_source'. You can override using the `.groups` argument.dcant_budget_basin_AIP_layer_ensemble <-
dcant_budget_basin_AIP_layer_all %>%
mutate(inv_depth = fct_inorder(as.factor(inv_depth))) %>%
filter(data_source %in% c("mod", "obs"),
estimate == "dcant") %>%
rename(dcant = value) %>%
group_by(inv_depth, data_source, period, basin_AIP) %>%
summarise(
dcant_mean = mean(dcant),
dcant_sd = sd(dcant),
dcant_max = max(dcant),
dcant_min = min(dcant)
) %>%
ungroup()`summarise()` has grouped output by 'inv_depth', 'data_source', 'period'. You can override using the `.groups` argument.4.1 Mean
dcant_zonal_ensemble %>%
group_by(basin_AIP) %>%
group_split() %>%
# head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "dcant_ensemble_mean",
plot_slabs = "n",
subtitle_text = paste("basin:",
unique(.x$basin_AIP))
) +
facet_grid(data_source ~ period)
)[[1]]Warning: Removed 4953 rows containing non-finite values (stat_contour_filled).
[[2]]Warning: Removed 3282 rows containing non-finite values (stat_contour_filled).
[[3]]Warning: Removed 5499 rows containing non-finite values (stat_contour_filled).
4.2 Mean bias
dcant_zonal_ensemble_bias <- full_join(
dcant_zonal_ensemble %>%
filter(data_source == "mod") %>%
select(lat, depth, basin_AIP, period, dcant_ensemble_mean, dcant_sd),
dcant_zonal_all %>%
filter(data_source == "mod_truth",
MLR_basins == unique(dcant_zonal_all$MLR_basins)[1]) %>%
select(lat, depth, basin_AIP, period, dcant_mod_truth = dcant)
)Joining, by = c("lat", "depth", "basin_AIP", "period")dcant_zonal_ensemble_bias <- dcant_zonal_ensemble_bias %>%
mutate(dcant_mean_bias = dcant_ensemble_mean - dcant_mod_truth)
dcant_zonal_ensemble_bias %>%
group_by(basin_AIP) %>%
group_split() %>%
# head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "dcant_mean_bias",
col = "divergent",
plot_slabs = "n",
subtitle_text = paste("basin:",
unique(.x$basin_AIP)
)
) +
facet_grid(. ~ period)
)[[1]]Warning: Removed 3366 rows containing non-finite values (stat_contour_filled).
[[2]]Warning: Removed 2223 rows containing non-finite values (stat_contour_filled).
[[3]]Warning: Removed 3702 rows containing non-finite values (stat_contour_filled).
4.2.1 Density distribution
dcant_zonal_bias_all %>%
ggplot() +
scale_color_manual(values = c("red", "grey")) +
geom_vline(xintercept = 0) +
geom_density(aes(dcant_bias, group = MLR_basins, col = "Individual")) +
geom_density(data = dcant_zonal_ensemble_bias,
aes(dcant_mean_bias, col = "Ensemble")) +
facet_grid(period ~.) +
coord_cartesian(xlim = c(-10, 10))Warning: Removed 111 rows containing non-finite values (stat_density).
4.3 Mean depth layer budgets
dcant_lat_grid_ensemble %>%
ggplot(aes(lat_grid, dcant_mean)) +
geom_hline(yintercept = 0) +
geom_col(position = "dodge",
fill = "grey80",
col = "grey20") +
geom_errorbar(aes(
ymin = dcant_min,
ymax = dcant_max
),
col = "grey20",
width = 0) +
scale_color_brewer(palette = "Set1") +
coord_flip() +
scale_fill_brewer(palette = "Dark2") +
facet_grid(data_source ~ period)4.4 Standard deviation
dcant_zonal_ensemble %>%
group_by(basin_AIP) %>%
group_split() %>%
# head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "dcant_sd",
breaks = c(seq(0,4,0.4), Inf),
plot_slabs = "n",
subtitle_text = paste("basin:",
unique(.x$basin_AIP))
) +
facet_grid(data_source ~ period)
)[[1]]Warning: Removed 4953 rows containing non-finite values (stat_contour_filled).
[[2]]Warning: Removed 3282 rows containing non-finite values (stat_contour_filled).
[[3]]Warning: Removed 5499 rows containing non-finite values (stat_contour_filled).
4.5 SD as uncertainty
uncertainty_grid <- dcant_zonal_ensemble %>%
filter(dcant_sd > sd_uncertainty_limit) %>%
distinct(depth, lat, data_source, period, basin_AIP)
uncertainty_grid <- uncertainty_grid %>%
mutate(
lat_grid = cut(lat, seq(-90, 90, 5), seq(-87.5, 87.5, 5)),
lat_grid = as.numeric(as.character(lat_grid)),
depth_grid = cut(depth, seq(0, 1e4, 500), seq(250, 1e4, 500)),
depth_grid = as.numeric(as.character(depth_grid))
) %>%
distinct(depth_grid, lat_grid, data_source, period, basin_AIP)
uncertainty_grid %>%
filter(data_source == "obs") %>%
ggplot() +
geom_point(aes(lat_grid, depth_grid),
shape = 3) +
facet_grid(basin_AIP ~ period) +
scale_y_reverse()
4.6 SD vs bias
dcant_zonal_ensemble_bias %>%
ggplot(aes(dcant_mean_bias, dcant_sd)) +
geom_bin2d() +
scale_fill_viridis_c() +
facet_grid(basin_AIP ~ period)
dcant_zonal_ensemble_bias %>%
select(dcant_ensemble_mean, dcant_mean_bias, period) %>%
pivot_longer(dcant_ensemble_mean:dcant_mean_bias,
names_to = "estimate",
values_to = "value") %>%
ggplot(aes(value, col=estimate, linetype = period)) +
scale_color_brewer(palette = "Set1") +
geom_density()
dcant_zonal_ensemble %>%
ggplot(aes(dcant_sd)) +
geom_histogram() +
facet_grid(data_source ~ period) +
coord_cartesian(ylim = c(0,50))`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
4.7 Composed figure
uncertainty_grid <- uncertainty_grid %>%
filter(data_source == "obs",
period != "1994 - 2014")
p_zonal_ensemble <- dcant_zonal_ensemble %>%
filter(data_source == "obs",
period != "1994 - 2014") %>%
p_section_zonal_continous_depth(var = "dcant_ensemble_mean",
plot_slabs = "n",
title_text = NULL) +
geom_point(data = uncertainty_grid,
aes(lat_grid, depth_grid),
shape = 3,
col = "white") +
facet_grid(basin_AIP ~ period,
switch = "y") +
theme(legend.position = "left",
strip.background.y = element_blank(),
strip.text.y = element_blank())
p_profiles <-
dcant_profile_all %>%
arrange(depth) %>%
filter(period != "1994 - 2014",
data_source == "obs") %>%
ggplot(aes(
dcant,
depth,
col = period,
fill = "grey80",
group = interaction(MLR_basins, period)
)) +
geom_hline(yintercept = params_global$inventory_depth_standard) +
geom_vline(xintercept = 0) +
geom_path() +
scale_y_reverse(name = "Depth (m)",
limits = c(5000,0)) +
scale_x_continuous(name = expression(Delta * C[ant] ~ (µmol~kg^{-1}))) +
coord_cartesian(expand = 0) +
scale_color_brewer(palette = "Set1") +
facet_grid(basin_AIP ~.) +
theme(legend.position = "top",
legend.direction = "vertical",
legend.title = element_blank(),
strip.background = element_blank(),
strip.text = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank())
p_layer_budget <- dcant_budget_basin_AIP_layer_ensemble %>%
filter(data_source == "obs",
period != "1994 - 2014") %>%
mutate(depth =
as.numeric(str_split(inv_depth, " - ", simplify = TRUE)[, 1]) + 250) %>%
filter(depth < 5000) %>%
ggplot(aes(dcant_mean, inv_depth, col = period)) +
geom_col(position = "dodge",
orientation = "y",
fill = "grey80") +
geom_errorbar(
aes(xmin = dcant_min,
xmax = dcant_max),
width = 0,
position = position_dodge(width = 0.9)
) +
scale_color_brewer(palette = "Set1", guide = "none") +
scale_x_continuous(
limits = c(0, NA),
expand = c(0, 0),
name = expression(Delta * C[ant] ~ (PgC))
) +
scale_y_discrete(name = "Depth intervals (m)",
limits = rev) +
facet_grid(basin_AIP ~ .) +
theme(legend.position = "top",
legend.title = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank())
p_zonal_ensemble + p_profiles + p_layer_budget +
plot_layout(widths = c(5,1,1)) +
plot_annotation(tag_levels = 'a')Warning: Removed 2962 rows containing non-finite values (stat_contour_filled).Warning: Removed 38 rows containing missing values (geom_point).Warning: Removed 36 row(s) containing missing values (geom_path).Warning: Removed 1 rows containing missing values (geom_col).
# ggsave("output/publication/Fig_zonal_mean.png",
# width=15.25,
# height=9.27)5 Cases vs ensemble
5.1 Offset from mean
dcant_zonal_all <- full_join(dcant_zonal_all %>% select(-dcant_sd),
dcant_zonal_ensemble)Joining, by = c("data_source", "lat", "depth", "basin_AIP", "period")dcant_zonal_all <- dcant_zonal_all %>%
mutate(dcant_offset = dcant - dcant_ensemble_mean)
legend_title <- expression(atop(Delta * C[ant, offset],
(mu * mol ~ kg ^ {
-1
})))
dcant_zonal_all %>%
filter(data_source %in% c("mod", "obs")) %>%
group_by(basin_AIP, data_source) %>%
group_split() %>%
# head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "dcant_offset",
col = "divergent",
plot_slabs = "n",
subtitle_text = paste("basin:",
unique(.x$basin_AIP),
"| data_source",
unique(.x$data_source))
) +
facet_grid(MLR_basins ~ period)
)[[1]]Warning: Removed 20190 rows containing non-finite values (stat_contour_filled).
[[2]]Warning: Removed 9522 rows containing non-finite values (stat_contour_filled).
[[3]]Warning: Removed 13338 rows containing non-finite values (stat_contour_filled).
[[4]]Warning: Removed 6354 rows containing non-finite values (stat_contour_filled).
[[5]]Warning: Removed 22212 rows containing non-finite values (stat_contour_filled).
[[6]]Warning: Removed 10782 rows containing non-finite values (stat_contour_filled).
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3
Matrix products: default
BLAS: /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/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] colorspace_2.0-2 marelac_2.1.10 shape_1.4.6 ggforce_0.3.3
[5] metR_0.11.0 scico_1.3.0 patchwork_1.1.1 collapse_1.7.0
[9] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[13] readr_2.1.1 tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5
[17] tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 bit64_4.0.5 gsw_1.0-6 lubridate_1.8.0
[5] RColorBrewer_1.1-2 httr_1.4.2 rprojroot_2.0.2 tools_4.1.2
[9] backports_1.4.1 bslib_0.3.1 utf8_1.2.2 R6_2.5.1
[13] DBI_1.1.2 withr_2.4.3 tidyselect_1.1.1 processx_3.5.2
[17] bit_4.0.4 compiler_4.1.2 git2r_0.29.0 cli_3.1.1
[21] rvest_1.0.2 xml2_1.3.3 isoband_0.2.5 labeling_0.4.2
[25] sass_0.4.0 scales_1.1.1 checkmate_2.0.0 SolveSAPHE_2.1.0
[29] callr_3.7.0 digest_0.6.29 rmarkdown_2.11 oce_1.5-0
[33] pkgconfig_2.0.3 htmltools_0.5.2 highr_0.9 dbplyr_2.1.1
[37] fastmap_1.1.0 rlang_0.4.12 readxl_1.3.1 rstudioapi_0.13
[41] jquerylib_0.1.4 generics_0.1.1 farver_2.1.0 jsonlite_1.7.3
[45] vroom_1.5.7 magrittr_2.0.1 Rcpp_1.0.8 munsell_0.5.0
[49] fansi_1.0.2 lifecycle_1.0.1 stringi_1.7.6 whisker_0.4
[53] yaml_2.2.1 MASS_7.3-55 grid_4.1.2 parallel_4.1.2
[57] promises_1.2.0.1 crayon_1.4.2 haven_2.4.3 hms_1.1.1
[61] seacarb_3.3.0 knitr_1.37 ps_1.6.0 pillar_1.6.4
[65] reprex_2.0.1 glue_1.6.0 evaluate_0.14 getPass_0.2-2
[69] data.table_1.14.2 modelr_0.1.8 vctrs_0.3.8 tzdb_0.2.0
[73] tweenr_1.0.2 httpuv_1.6.5 cellranger_1.1.0 gtable_0.3.0
[77] polyclip_1.10-0 assertthat_0.2.1 xfun_0.29 broom_0.7.11
[81] later_1.3.0 viridisLite_0.4.0 ellipsis_0.3.2 here_1.0.1