Zonal sections

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Last updated: 2022-03-08

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Knit directory: emlr_obs_analysis/analysis/

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Rmd	d3903e6	jens-daniel-mueller	2022-01-10	rebuild with child docs

---

version_id_pattern <- "t"
config <- "MLR_target"

1 Read files

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", "t"))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", "t"))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", "t"))

Version_IDs <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)

print(Version_IDs)

 [1] "v_1t01" "v_1t02" "v_1t03" "v_1t04" "v_1t05" "v_1t06" "v_2t01" "v_2t02"
 [9] "v_2t03" "v_2t04" "v_2t05" "v_2t06" "v_3t01" "v_3t02" "v_3t03" "v_3t04"
[17] "v_3t05" "v_3t06"

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_target := str_c(params_local$MLR_target, collapse = "|"),
    tref1 = params_local$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_1t01"
[1] "v_1t02"
[1] "v_1t03"
[1] "v_1t04"
[1] "v_1t05"
[1] "v_1t06"
[1] "v_2t01"
[1] "v_2t02"
[1] "v_2t03"
[1] "v_2t04"
[1] "v_2t05"
[1] "v_2t06"
[1] "v_3t01"
[1] "v_3t02"
[1] "v_3t03"
[1] "v_3t04"
[1] "v_3t05"
[1] "v_3t06"

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 <- 1.5

3 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_target ~ period)
  )

[[1]]

Warning: Removed 20196 rows containing non-finite values (stat_contour_filled).

Past versions of cases_absolute-1.png

Version	Author	Date
9753eb8	jens-daniel-mueller	2022-01-26
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570e738	jens-daniel-mueller	2022-01-10

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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_target ~ 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_target ~ 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_target ~ period)
  )

[[1]]

Warning: Removed 20196 rows containing non-finite values (stat_contour_filled).

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9753eb8	jens-daniel-mueller	2022-01-26
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570e738	jens-daniel-mueller	2022-01-10

3.2.1 Density distribution

dcant_zonal_bias_all %>%
  ggplot(aes(dcant_bias, col = MLR_target)) +
  scale_color_brewer(palette = "Dark2") +
  geom_vline(xintercept = 0) +
  geom_density() +
  facet_grid(period ~.) +
  coord_cartesian(xlim = c(-10, 10))

Past versions of bias_density_distribution-1.png

Version	Author	Date
570e738	jens-daniel-mueller	2022-01-10

3.3 Bias correlation eras

dcant_zonal_bias_all_corr <- dcant_zonal_bias_all %>%
  select(lat, depth, basin_AIP, dcant_bias, MLR_target, 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_target ~ basin_AIP) +
  scale_fill_viridis_c()

Past versions of cases_bias_correlation-1.png

Version	Author	Date
7daeb3f	jens-daniel-mueller	2022-01-12
269809e	jens-daniel-mueller	2022-01-12
570e738	jens-daniel-mueller	2022-01-10

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_target ~ basin_AIP)
  )

[[1]]

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3.4.2 Basin separation

dcant_profile_all %>%
  group_split(period) %>%
  map(
    ~ ggplot(data = .x,
             aes(
               dcant, depth,
               col = MLR_target, fill = MLR_target
             )) +
      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]]

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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_target, 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]]

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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_target)) +
      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]]

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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]]

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Past versions of ensemble_mean-1.png

Version	Author	Date
9753eb8	jens-daniel-mueller	2022-01-26
7daeb3f	jens-daniel-mueller	2022-01-12
570e738	jens-daniel-mueller	2022-01-10

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7daeb3f	jens-daniel-mueller	2022-01-12
570e738	jens-daniel-mueller	2022-01-10

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_target == unique(dcant_zonal_all$MLR_target)[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]]

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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_target, col = "Individual")) +
  geom_density(data = dcant_zonal_ensemble_bias,
               aes(dcant_mean_bias, col = "Ensemble")) +
  facet_grid(period ~.) +
  coord_cartesian(xlim = c(-10, 10))

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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]]

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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()

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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)

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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()

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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`.

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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_target, 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')

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# 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_target ~ period)
  )

[[1]]

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6 Individual cases

6.1 Absoulte values

dcant_zonal_all %>%
  filter(data_source %in% c("mod", "obs"),
         period != "1994 - 2014") %>%
  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)
      ),
      col = "divergent"
    ) +
      facet_grid(MLR_target ~ period)
  )

[[1]]

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Session information

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] marelac_2.1.10  shape_1.4.6     ggforce_0.3.3   metR_0.11.0    
 [5] scico_1.3.0     patchwork_1.1.1 collapse_1.7.0  forcats_0.5.1  
 [9] stringr_1.4.0   dplyr_1.0.7     purrr_0.3.4     readr_2.1.1    
[13] tidyr_1.1.4     tibble_3.1.6    ggplot2_3.3.5   tidyverse_1.3.1
[17] 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          colorspace_2.0-2   withr_2.4.3        tidyselect_1.1.1  
[17] processx_3.5.2     bit_4.0.4          compiler_4.1.2     git2r_0.29.0      
[21] cli_3.1.1          rvest_1.0.2        xml2_1.3.3         isoband_0.2.5     
[25] labeling_0.4.2     sass_0.4.0         scales_1.1.1       checkmate_2.0.0   
[29] SolveSAPHE_2.1.0   callr_3.7.0        digest_0.6.29      rmarkdown_2.11    
[33] oce_1.5-0          pkgconfig_2.0.3    htmltools_0.5.2    highr_0.9         
[37] dbplyr_2.1.1       fastmap_1.1.0      rlang_0.4.12       readxl_1.3.1      
[41] rstudioapi_0.13    jquerylib_0.1.4    generics_0.1.1     farver_2.1.0      
[45] jsonlite_1.7.3     vroom_1.5.7        magrittr_2.0.1     Rcpp_1.0.8        
[49] munsell_0.5.0      fansi_1.0.2        lifecycle_1.0.1    stringi_1.7.6     
[53] whisker_0.4        yaml_2.2.1         MASS_7.3-55        grid_4.1.2        
[57] parallel_4.1.2     promises_1.2.0.1   crayon_1.4.2       haven_2.4.3       
[61] hms_1.1.1          seacarb_3.3.0      knitr_1.37         ps_1.6.0          
[65] pillar_1.6.4       reprex_2.0.1       glue_1.6.0         evaluate_0.14     
[69] getPass_0.2-2      data.table_1.14.2  modelr_0.1.8       vctrs_0.3.8       
[73] tzdb_0.2.0         tweenr_1.0.2       httpuv_1.6.5       cellranger_1.1.0  
[77] gtable_0.3.0       polyclip_1.10-0    assertthat_0.2.1   xfun_0.29         
[81] broom_0.7.11       later_1.3.0        viridisLite_0.4.0  ellipsis_0.3.2    
[85] here_1.0.1