Last updated: 2021-08-04

Checks: 7 0

Knit directory: emlr_obs_v_XXX/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20200707)

The command set.seed(20200707) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 92bb6b3

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 92bb6b3. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Unstaged changes:
    Modified:   analysis/_site.yml
    Modified:   analysis/analysis_anomalous_changes.Rmd
    Modified:   code/Workflowr_project_managment.R
    Modified:   data/auxillary/params_local.rds

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/analysis_budgets.Rmd) and HTML (docs/analysis_budgets.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html 81a46a4 jens-daniel-mueller 2021-08-03 Build site.
html b88c61b jens-daniel-mueller 2021-08-03 Build site.
html 0f0d5e5 jens-daniel-mueller 2021-08-03 Build site.
Rmd 4d7bb5a jens-daniel-mueller 2021-08-03 test rlm model fitting
html a53656d jens-daniel-mueller 2021-08-03 Build site.
html 88f7356 jens-daniel-mueller 2021-08-02 Build site.
html d759279 jens-daniel-mueller 2021-08-02 Build site.
html 127b801 jens-daniel-mueller 2021-07-24 Build site.
html 1f65ef1 jens-daniel-mueller 2021-07-23 Build site.
html 912d90e jens-daniel-mueller 2021-07-23 Build site.
html 2477316 jens-daniel-mueller 2021-07-23 rebuild: surface dcant mapping seperate
html c9ccc00 jens-daniel-mueller 2021-07-22 Build site.
Rmd 6a7f226 jens-daniel-mueller 2021-07-22 surface dcant mapping seperate
html 426b2df jens-daniel-mueller 2021-07-21 Build site.
html 971ce87 jens-daniel-mueller 2021-07-13 Build site.
Rmd 89d9fcb jens-daniel-mueller 2021-07-13 complete revision
html 910d64a jens-daniel-mueller 2021-07-02 Build site.
html 1cbf907 jens-daniel-mueller 2021-07-02 Build site.
html 955f5dc jens-daniel-mueller 2021-06-29 Build site.
html 9480f94 jens-daniel-mueller 2021-06-29 Build site.
html d054c39 jens-daniel-mueller 2021-06-24 Build site.
html 4fcd066 jens-daniel-mueller 2021-06-24 Build site.
html 3b6387f jens-daniel-mueller 2021-06-18 Build site.
html 7a8d5f8 jens-daniel-mueller 2021-06-15 Build site.
Rmd 3695731 jens-daniel-mueller 2021-06-15 included budget assesment
html e0660fe jens-daniel-mueller 2021-06-15 Build site.
Rmd 430b829 jens-daniel-mueller 2021-06-15 included budget assesment
html 3052a6c jens-daniel-mueller 2021-06-15 Build site.
html 3cb64bd jens-daniel-mueller 2021-06-15 Build site.
html c6b3da6 jens-daniel-mueller 2021-06-14 Build site.
html 48c73fc jens-daniel-mueller 2021-06-14 Build site.
html 439ee80 jens-daniel-mueller 2021-06-11 Build site.
html 33ffcab jens-daniel-mueller 2021-06-10 Build site.
html 7e1f407 jens-daniel-mueller 2021-06-10 Build site.
html 2cbe18c jens-daniel-mueller 2021-06-10 added zonal mean section control plots
html 69c79d0 jens-daniel-mueller 2021-06-08 Build site.
html 1772903 jens-daniel-mueller 2021-06-07 Build site.
html 594ed9a jens-daniel-mueller 2021-06-04 Build site.
html db7df0e jens-daniel-mueller 2021-06-04 rebuild without overlapping eras
html 2edc791 jens-daniel-mueller 2021-06-04 Build site.
html 207339d jens-daniel-mueller 2021-06-03 Build site.
html 315710b jens-daniel-mueller 2021-06-03 include anomalous changes
html be90356 jens-daniel-mueller 2021-06-02 Build site.
html d37a85d jens-daniel-mueller 2021-05-31 Build site.
html 4b7a5ee jens-daniel-mueller 2021-05-28 Build site.
html 12b455a jens-daniel-mueller 2021-05-27 Build site.
Rmd ee13efb jens-daniel-mueller 2021-05-27 optional source of local params fully implemented
html 8c736a6 jens-daniel-mueller 2021-05-27 Build site.
Rmd dc8e4e1 jens-daniel-mueller 2021-05-27 optional source of local params implemented
html 25bd183 jens-daniel-mueller 2021-05-26 Build site.
html b79cb2d jens-daniel-mueller 2021-05-20 Build site.
html 62bd574 jens-daniel-mueller 2021-05-20 Build site.
html 7c56c39 jens-daniel-mueller 2021-05-19 Build site.
html 0de759e jens-daniel-mueller 2021-05-13 Build site.
html 52e7583 jens-daniel-mueller 2021-05-12 Build site.
html 969e631 jens-daniel-mueller 2021-05-12 Build site.
html d2a83bc jens-daniel-mueller 2021-04-16 Build site.
html c0a47df jens-daniel-mueller 2021-04-16 Build site.
html 50290e8 jens-daniel-mueller 2021-04-16 overlapping eras
html a00ec94 jens-daniel-mueller 2021-04-16 Build site.
Rmd 82df560 jens-daniel-mueller 2021-04-16 corrected tref assignment
html b6fe355 jens-daniel-mueller 2021-04-16 Build site.
html 81b7c6d jens-daniel-mueller 2021-04-16 Build site.
html 0a6099c jens-daniel-mueller 2021-04-16 Build site.
html ddec5b7 jens-daniel-mueller 2021-04-15 Build site.
html 29edae5 jens-daniel-mueller 2021-04-14 Build site.
html 099d566 jens-daniel-mueller 2021-04-14 Build site.
html bb44686 jens-daniel-mueller 2021-04-14 Build site.
html bf40480 jens-daniel-mueller 2021-04-13 Build site.
html 9f31fe3 jens-daniel-mueller 2021-04-13 Build site.
html 6eecb54 jens-daniel-mueller 2021-04-13 Build site.
Rmd b9c18c8 jens-daniel-mueller 2021-04-13 included model data
html f6ed7de jens-daniel-mueller 2021-04-13 Build site.
Rmd 3efd03e jens-daniel-mueller 2021-04-13 included model data
html c98be84 jens-daniel-mueller 2021-04-13 Build site.
html 338dd3c jens-daniel-mueller 2021-04-09 Build site.
html a79ca2c jens-daniel-mueller 2021-04-09 included model data
html 7831fd3 jens-daniel-mueller 2021-04-09 Build site.
html 20b26d4 jens-daniel-mueller 2021-04-08 Build site.
html c0895f8 jens-daniel-mueller 2021-04-07 Build site.
html 156d5b7 jens-daniel-mueller 2021-04-07 Build site.
html d21a452 jens-daniel-mueller 2021-04-07 Build site.
Rmd 296db5c jens-daniel-mueller 2021-04-07 included model data

1 Version ID

The results displayed on this site correspond to the Version_ID: v_XXX

2 Data sources

dcant estimates from this sensitivity case:

  • Mean and SD per grid cell (lat, lon, depth)
  • Zonal mean and SD (basin, lat, depth)
  • Inventories (lat, lon)
# integrated per basin_AIP

dcant_budget_basin_AIP <-
  read_csv(paste(path_version_data,
                 "dcant_budget_basin_AIP.csv",
                 sep = "")) %>% 
  filter(method == "total") %>% 
  select(-method)

dcant_budget_basin_AIP_mod_truth <-
  read_csv(paste(
    path_version_data,
    "dcant_budget_basin_AIP_mod_truth.csv",
    sep = ""
  )) %>% 
  filter(method == "total") %>% 
  select(-method)

dcant_budget_basin_AIP <- bind_rows(dcant_budget_basin_AIP,
                                    dcant_budget_basin_AIP_mod_truth)

rm(dcant_budget_basin_AIP_mod_truth)

# globally integrated

dcant_budget_global <-
  read_csv(paste(path_version_data,
                 "dcant_budget_global.csv",
                 sep = "")) %>% 
  filter(method == "total") %>% 
  select(-method)

dcant_budget_global_mod_truth <-
  read_csv(paste(
    path_version_data,
    "dcant_budget_global_mod_truth.csv",
    sep = ""
  )) %>% 
  filter(method == "total") %>% 
  select(-method)

dcant_budget_global <- bind_rows(dcant_budget_global,
                                    dcant_budget_global_mod_truth)

rm(dcant_budget_global_mod_truth)

# reference year

tref <-
  read_csv(paste(path_version_data,
                 "tref.csv",
                 sep = ""))

3 Time periods

tref
# A tibble: 2 x 2
  era       median_year
  <chr>           <dbl>
1 2000-2009        2004
2 2010-2019        2014
duration <- sort(tref$median_year)[2] - sort(tref$median_year)[1]
duration
[1] 10

4 Global

4.1 Standard depth

Results integrated over the upper 3000 m

dcant_budget_global %>%
  filter(inv_depth == params_global$inventory_depth_standard) %>%
  ggplot(aes(estimate, value, fill = data_source)) +
  scale_fill_brewer(palette = "Dark2") +
  geom_col(position = position_dodge2())

Version Author Date
81a46a4 jens-daniel-mueller 2021-08-03
b88c61b jens-daniel-mueller 2021-08-03
0f0d5e5 jens-daniel-mueller 2021-08-03 
dcant_budget_global %>%
  filter(inv_depth == params_global$inventory_depth_standard) %>%
  select(-inv_depth) %>% 
  pivot_wider(names_from = estimate,
              values_from = value) %>% 
  gt(
    groupname_col = c("data_source")
  ) %>%
  summary_rows(groups = TRUE,
               fns = list(total = "sum"))
dcant dcant_pos
mod
22.888 23.512
obs
29.517 30.354
mod_truth
20.722 20.861 
dcant_budget_global_bias <- dcant_budget_global %>%
  filter(data_source %in% c("mod", "mod_truth")) %>%
  select(data_source, inv_depth, estimate, value) %>%
  pivot_wider(names_from = data_source,
              values_from = value) %>%
  mutate(dcant_bias = mod - mod_truth,
         dcant_bias_rel = dcant_bias / mod_truth * 100)

dcant_budget_global_bias %>% 
  filter(inv_depth == params_global$inventory_depth_standard) %>% 
  ggplot(aes(dcant_bias, estimate)) +
  geom_vline(xintercept = 0) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2")

Version Author Date
81a46a4 jens-daniel-mueller 2021-08-03
b88c61b jens-daniel-mueller 2021-08-03
0f0d5e5 jens-daniel-mueller 2021-08-03 
dcant_budget_global_bias %>% 
  filter(inv_depth == params_global$inventory_depth_standard) %>% 
  ggplot(aes(dcant_bias_rel, estimate)) +
  geom_vline(xintercept = 0) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2")

Version Author Date
81a46a4 jens-daniel-mueller 2021-08-03
b88c61b jens-daniel-mueller 2021-08-03
0f0d5e5 jens-daniel-mueller 2021-08-03 
dcant_budget_global_bias %>%
  filter(inv_depth == params_global$inventory_depth_standard) %>%
  ggplot() +
  geom_col(aes(estimate, mod,
               fill = "eMLR")) +
  geom_col(aes(estimate, mod_truth,
               col = "truth"),
           fill = "transparent") +
  scale_fill_manual(values = "grey60") +
  scale_color_manual(values = c("red", "red")) +
  theme(legend.title = element_blank()) +
  labs(y = "value")

Version Author Date
81a46a4 jens-daniel-mueller 2021-08-03
b88c61b jens-daniel-mueller 2021-08-03
0f0d5e5 jens-daniel-mueller 2021-08-03 
  # geom_segment(aes(
  #   x = estimate,
  #   y = mod,
  #   xend = estimate,
  #   yend = mod_truth,
  #   col = "truth"
  # )) +
  # geom_point(aes(x = estimate,
  #                y = mod_truth,
  #                col = "truth"))

4.2 Other depths

Results integrated over the upper 100, 500, 1000, 3000, 10^{4} m

dcant_budget_global <- dcant_budget_global %>%
  mutate(inv_depth = as.factor(inv_depth))

dcant_budget_global %>%
  ggplot() +
  scale_fill_viridis_d() +
  geom_col(aes(data_source, value, fill = inv_depth),
           position = position_dodge2()) +
  facet_grid(. ~ estimate, scales = "free_y")

Version Author Date
81a46a4 jens-daniel-mueller 2021-08-03
b88c61b jens-daniel-mueller 2021-08-03
0f0d5e5 jens-daniel-mueller 2021-08-03 
dcant_budget_global %>%
  pivot_wider(names_from = estimate,
              values_from = value) %>% 
  gt(
    groupname_col = c("data_source", "inv_depth"),
    row_group.sep = " | Depth: "
  ) %>%
  summary_rows(groups = TRUE,
               fns = list(total = "sum"))
dcant dcant_pos
mod | Depth: 100
3.993 3.994
mod | Depth: 500
13.581 13.597
mod | Depth: 1000
18.676 18.735
mod | Depth: 3000
22.888 23.512
mod | Depth: 10000
23.158 24.194
obs | Depth: 100
4.210 4.236
obs | Depth: 500
15.014 15.112
obs | Depth: 1000
21.351 21.583
obs | Depth: 3000
29.517 30.354
obs | Depth: 10000
32.380 33.938
mod_truth | Depth: 100
3.710 3.710
mod_truth | Depth: 500
13.589 13.596
mod_truth | Depth: 1000
18.322 18.338
mod_truth | Depth: 3000
20.722 20.861
mod_truth | Depth: 10000
21.055 21.220 
dcant_budget_global_bias %>% 
  ggplot(aes(dcant_bias, estimate)) +
  geom_vline(xintercept = 0) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ .)

Version Author Date
81a46a4 jens-daniel-mueller 2021-08-03
b88c61b jens-daniel-mueller 2021-08-03
0f0d5e5 jens-daniel-mueller 2021-08-03 
dcant_budget_global_bias %>% 
  ggplot(aes(dcant_bias_rel, estimate)) +
  geom_vline(xintercept = 0) +
  geom_col(position = "dodge") +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ .)

Version Author Date
81a46a4 jens-daniel-mueller 2021-08-03
b88c61b jens-daniel-mueller 2021-08-03
0f0d5e5 jens-daniel-mueller 2021-08-03 
dcant_budget_global_bias %>%
  ggplot() +
  geom_vline(xintercept = 0) +
  geom_col(aes(mod, estimate, fill = "eMLR"),
           position = "dodge") +
  geom_col(aes(mod_truth, estimate,
               col = "truth"),
           fill = "transparent") +
  scale_fill_manual(values = "grey60") +
  scale_color_manual(values = c("red", "red")) +
  theme(legend.title = element_blank()) +
  labs(x = "value") +
  facet_grid(inv_depth ~ .)

Version Author Date
81a46a4 jens-daniel-mueller 2021-08-03
b88c61b jens-daniel-mueller 2021-08-03
0f0d5e5 jens-daniel-mueller 2021-08-03

5 Basin AIP

5.1 Standard depth

Results integrated over the upper 3000 m

dcant_budget_basin_AIP %>%
  filter(inv_depth == params_global$inventory_depth_standard) %>%
  ggplot(aes(estimate, value, fill=basin_AIP)) +
  scale_fill_brewer(palette = "Dark2") +
  geom_col() +
  facet_grid(~data_source)

Version Author Date
81a46a4 jens-daniel-mueller 2021-08-03
b88c61b jens-daniel-mueller 2021-08-03
0f0d5e5 jens-daniel-mueller 2021-08-03 
dcant_budget_basin_AIP %>%
  filter(inv_depth == params_global$inventory_depth_standard) %>%
  pivot_wider(names_from = estimate,
              values_from = value) %>% 
  gt(
    rowname_col = "basin_AIP",
    groupname_col = c("data_source", "inv_depth"),
    row_group.sep = " | Depth: "
  ) %>%
  summary_rows(groups = TRUE,
               fns = list(total = "sum"))
dcant dcant_pos
mod | Depth: 3000
Indian 6.717 6.797
Pacific 10.467 10.959
Atlantic 5.704 5.756
total 22.89 23.51
obs | Depth: 3000
Indian 6.435 6.721
Pacific 15.443 15.723
Atlantic 7.639 7.911
total 29.52 30.36
mod_truth | Depth: 3000
Atlantic 5.108 5.126
Indian 5.604 5.621
Pacific 10.011 10.115
total 20.72 20.86 
dcant_budget_basin_AIP_bias <- dcant_budget_basin_AIP %>%
  filter(data_source %in% c("mod", "mod_truth")) %>%
  select(data_source, basin_AIP, inv_depth, estimate, value) %>%
  pivot_wider(names_from = data_source,
              values_from = value) %>%
  mutate(dcant_bias = mod - mod_truth,
         dcant_bias_rel = dcant_bias / mod_truth * 100)

dcant_budget_basin_AIP_bias %>% 
  filter(inv_depth == params_global$inventory_depth_standard) %>% 
  ggplot(aes(dcant_bias, estimate, fill=basin_AIP)) +
  geom_vline(xintercept = 0) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2")

Version Author Date
81a46a4 jens-daniel-mueller 2021-08-03
b88c61b jens-daniel-mueller 2021-08-03
0f0d5e5 jens-daniel-mueller 2021-08-03 
dcant_budget_basin_AIP_bias %>% 
  filter(inv_depth == params_global$inventory_depth_standard) %>% 
  ggplot(aes(dcant_bias_rel, estimate, fill=basin_AIP)) +
  geom_vline(xintercept = 0) +
  geom_col(position = "dodge") +
  scale_fill_brewer(palette = "Dark2")

Version Author Date
81a46a4 jens-daniel-mueller 2021-08-03
b88c61b jens-daniel-mueller 2021-08-03
0f0d5e5 jens-daniel-mueller 2021-08-03

5.2 Other depths

Results integrated over the upper 100, 500, 1000, 3000, 10^{4} m

dcant_budget_basin_AIP %>%
  ggplot(aes(data_source, value, fill=basin_AIP)) +
  scale_fill_brewer(palette = "Dark2") +
  geom_col() +
  facet_grid(inv_depth ~ estimate, scales = "free_y")

Version Author Date
81a46a4 jens-daniel-mueller 2021-08-03
b88c61b jens-daniel-mueller 2021-08-03
0f0d5e5 jens-daniel-mueller 2021-08-03 
dcant_budget_basin_AIP %>%
  pivot_wider(names_from = estimate,
              values_from = value) %>% 
  gt(
    rowname_col = "basin_AIP",
    groupname_col = c("data_source", "inv_depth"),
    row_group.sep = " | Depth: "
  ) %>%
  summary_rows(groups = TRUE,
               fns = list(total = "sum"))
dcant dcant_pos
mod | Depth: 100
Indian 0.839 0.840
Pacific 2.102 2.104
Atlantic 1.051 1.051
total 3.99 4.00
mod | Depth: 500
Indian 3.052 3.062
Pacific 7.033 7.037
Atlantic 3.497 3.497
total 13.58 13.60
mod | Depth: 1000
Indian 4.770 4.790
Pacific 9.344 9.375
Atlantic 4.562 4.569
total 18.68 18.73
mod | Depth: 3000
Indian 6.717 6.797
Pacific 10.467 10.959
Atlantic 5.704 5.756
total 22.89 23.51
mod | Depth: 10000
Indian 6.761 6.932
Pacific 10.588 11.351
Atlantic 5.808 5.910
total 23.16 24.19
obs | Depth: 100
Indian 0.902 0.910
Pacific 2.245 2.257
Atlantic 1.063 1.069
total 4.21 4.24
obs | Depth: 500
Indian 3.417 3.439
Pacific 7.712 7.760
Atlantic 3.885 3.912
total 15.01 15.11
obs | Depth: 1000
Indian 4.797 4.912
Pacific 10.754 10.834
Atlantic 5.800 5.837
total 21.35 21.58
obs | Depth: 3000
Indian 6.435 6.721
Pacific 15.443 15.723
Atlantic 7.639 7.911
total 29.52 30.36
obs | Depth: 10000
Indian 7.057 7.442
Pacific 17.419 17.915
Atlantic 7.904 8.581
total 32.38 33.94
mod_truth | Depth: 100
Atlantic 0.930 0.930
Indian 0.796 0.796
Pacific 1.983 1.983
total 3.71 3.71
mod_truth | Depth: 500
Atlantic 3.409 3.410
Indian 3.118 3.118
Pacific 7.061 7.067
total 13.59 13.60
mod_truth | Depth: 1000
Atlantic 4.523 4.530
Indian 4.652 4.652
Pacific 9.146 9.156
total 18.32 18.34
mod_truth | Depth: 3000
Atlantic 5.108 5.126
Indian 5.604 5.621
Pacific 10.011 10.115
total 20.72 20.86
mod_truth | Depth: 10000
Atlantic 5.151 5.180
Indian 5.678 5.700
Pacific 10.227 10.341
total 21.06 21.22 
dcant_budget_basin_AIP_bias %>% 
  ggplot(aes(dcant_bias, estimate, fill=basin_AIP)) +
  geom_vline(xintercept = 0) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ .)

Version Author Date
81a46a4 jens-daniel-mueller 2021-08-03
b88c61b jens-daniel-mueller 2021-08-03
0f0d5e5 jens-daniel-mueller 2021-08-03 
dcant_budget_basin_AIP_bias %>% 
  ggplot(aes(dcant_bias_rel, estimate, fill=basin_AIP)) +
  geom_vline(xintercept = 0) +
  geom_col(position = "dodge") +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ .)

Version Author Date
81a46a4 jens-daniel-mueller 2021-08-03
b88c61b jens-daniel-mueller 2021-08-03
0f0d5e5 jens-daniel-mueller 2021-08-03

6 Write files

dcant_budget_basin_AIP_bias %>%
  write_csv(paste(path_version_data,
                  "dcant_budget_basin_AIP_bias.csv", sep = ""))

dcant_budget_global_bias %>%
  write_csv(paste(path_version_data,
                  "dcant_budget_global_bias.csv", sep = ""))

sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2

Matrix products: default
BLAS:   /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/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] gt_0.2.2         kableExtra_1.3.1 marelac_2.1.10   shape_1.4.5     
 [5] scales_1.1.1     ggforce_0.3.3    metR_0.9.0       scico_1.2.0     
 [9] patchwork_1.1.1  collapse_1.5.0   forcats_0.5.0    stringr_1.4.0   
[13] dplyr_1.0.5      purrr_0.3.4      readr_1.4.0      tidyr_1.1.2     
[17] tibble_3.0.4     ggplot2_3.3.3    tidyverse_1.3.0  workflowr_1.6.2 

loaded via a namespace (and not attached):
 [1] fs_1.5.0                 lubridate_1.7.9          gsw_1.0-5               
 [4] RColorBrewer_1.1-2       webshot_0.5.2            httr_1.4.2              
 [7] rprojroot_2.0.2          tools_4.0.3              backports_1.1.10        
[10] utf8_1.1.4               R6_2.5.0                 DBI_1.1.0               
[13] colorspace_1.4-1         withr_2.3.0              tidyselect_1.1.0        
[16] compiler_4.0.3           git2r_0.27.1             cli_2.1.0               
[19] rvest_0.3.6              xml2_1.3.2               sass_0.2.0              
[22] labeling_0.4.2           checkmate_2.0.0          digest_0.6.27           
[25] rmarkdown_2.5            oce_1.2-0                pkgconfig_2.0.3         
[28] htmltools_0.5.0          dbplyr_1.4.4             rlang_0.4.10            
[31] readxl_1.3.1             rstudioapi_0.11          farver_2.0.3            
[34] generics_0.0.2           jsonlite_1.7.1           magrittr_1.5            
[37] Matrix_1.2-18            Rcpp_1.0.5               munsell_0.5.0           
[40] fansi_0.4.1              lifecycle_1.0.0          stringi_1.5.3           
[43] whisker_0.4              yaml_2.2.1               MASS_7.3-53             
[46] grid_4.0.3               blob_1.2.1               parallel_4.0.3          
[49] promises_1.1.1           crayon_1.3.4             lattice_0.20-41         
[52] haven_2.3.1              hms_0.5.3                seacarb_3.2.14          
[55] knitr_1.30               pillar_1.4.7             reprex_0.3.0            
[58] glue_1.4.2               evaluate_0.14            RcppArmadillo_0.10.1.2.0
[61] data.table_1.13.2        modelr_0.1.8             vctrs_0.3.5             
[64] tweenr_1.0.2             httpuv_1.5.4             testthat_2.3.2          
[67] cellranger_1.1.0         gtable_0.3.0             polyclip_1.10-0         
[70] assertthat_0.2.1         xfun_0.18                broom_0.7.5             
[73] RcppEigen_0.3.3.7.0      later_1.1.0.1            viridisLite_0.3.0       
[76] ellipsis_0.3.1           here_0.1