Last updated: 2021-08-06

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: 15773a0

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 15773a0. 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/

Untracked files:
    Untracked:  .Rprofile
    Untracked:  .gitattributes
    Untracked:  .gitignore
    Untracked:  README.md
    Untracked:  _workflowr.yml
    Untracked:  analysis/_site.yml
    Untracked:  code/
    Untracked:  data/
    Untracked:  emlr_obs_v_XXX.Rproj

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 15773a0 jens-daniel-mueller 2021-08-06 included calculation of revelle factor
html da61d1a jens-daniel-mueller 2021-08-06 Build site.
html 340d731 jens-daniel-mueller 2021-08-06 Build site.
html 71546e4 jens-daniel-mueller 2021-08-06 test with stricter CANYON-B filtering
html 29444a1 jens-daniel-mueller 2021-08-05 Build site.
html 42e80c0 jens-daniel-mueller 2021-08-04 Build site.
html 48f6eed jens-daniel-mueller 2021-08-04 Build site.
html 1c597ab jens-daniel-mueller 2021-08-04 Build site.
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 4
  era       median_year start   end
  <chr>           <dbl> <dbl> <dbl>
1 2000-2009        2004  2000  2009
2 2010-2019        2014  2010  2019
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
15773a0 jens-daniel-mueller 2021-08-06
da61d1a jens-daniel-mueller 2021-08-06
340d731 jens-daniel-mueller 2021-08-06
71546e4 jens-daniel-mueller 2021-08-06
29444a1 jens-daniel-mueller 2021-08-05
42e80c0 jens-daniel-mueller 2021-08-04
48f6eed jens-daniel-mueller 2021-08-04
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
21.174 22.274
obs
31.609 32.083
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
15773a0 jens-daniel-mueller 2021-08-06
da61d1a jens-daniel-mueller 2021-08-06
340d731 jens-daniel-mueller 2021-08-06
71546e4 jens-daniel-mueller 2021-08-06
29444a1 jens-daniel-mueller 2021-08-05
42e80c0 jens-daniel-mueller 2021-08-04
48f6eed jens-daniel-mueller 2021-08-04
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
15773a0 jens-daniel-mueller 2021-08-06
da61d1a jens-daniel-mueller 2021-08-06
340d731 jens-daniel-mueller 2021-08-06
71546e4 jens-daniel-mueller 2021-08-06
29444a1 jens-daniel-mueller 2021-08-05
42e80c0 jens-daniel-mueller 2021-08-04
48f6eed jens-daniel-mueller 2021-08-04
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
15773a0 jens-daniel-mueller 2021-08-06
da61d1a jens-daniel-mueller 2021-08-06
340d731 jens-daniel-mueller 2021-08-06
71546e4 jens-daniel-mueller 2021-08-06
29444a1 jens-daniel-mueller 2021-08-05
42e80c0 jens-daniel-mueller 2021-08-04
48f6eed jens-daniel-mueller 2021-08-04
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
15773a0 jens-daniel-mueller 2021-08-06
da61d1a jens-daniel-mueller 2021-08-06
340d731 jens-daniel-mueller 2021-08-06
71546e4 jens-daniel-mueller 2021-08-06
29444a1 jens-daniel-mueller 2021-08-05
42e80c0 jens-daniel-mueller 2021-08-04
48f6eed jens-daniel-mueller 2021-08-04
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
4.098 4.098
mod | Depth: 500
13.786 13.788
mod | Depth: 1000
18.500 18.532
mod | Depth: 3000
21.174 22.274
mod | Depth: 10000
21.442 23.330
obs | Depth: 100
4.706 4.706
obs | Depth: 500
16.408 16.427
obs | Depth: 1000
23.174 23.236
obs | Depth: 3000
31.609 32.083
obs | Depth: 10000
33.024 34.311
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
15773a0 jens-daniel-mueller 2021-08-06
da61d1a jens-daniel-mueller 2021-08-06
340d731 jens-daniel-mueller 2021-08-06
71546e4 jens-daniel-mueller 2021-08-06
29444a1 jens-daniel-mueller 2021-08-05
42e80c0 jens-daniel-mueller 2021-08-04
48f6eed jens-daniel-mueller 2021-08-04
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
15773a0 jens-daniel-mueller 2021-08-06
da61d1a jens-daniel-mueller 2021-08-06
340d731 jens-daniel-mueller 2021-08-06
71546e4 jens-daniel-mueller 2021-08-06
29444a1 jens-daniel-mueller 2021-08-05
42e80c0 jens-daniel-mueller 2021-08-04
48f6eed jens-daniel-mueller 2021-08-04
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
15773a0 jens-daniel-mueller 2021-08-06
da61d1a jens-daniel-mueller 2021-08-06
340d731 jens-daniel-mueller 2021-08-06
71546e4 jens-daniel-mueller 2021-08-06
29444a1 jens-daniel-mueller 2021-08-05
42e80c0 jens-daniel-mueller 2021-08-04
48f6eed jens-daniel-mueller 2021-08-04
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
15773a0 jens-daniel-mueller 2021-08-06
da61d1a jens-daniel-mueller 2021-08-06
340d731 jens-daniel-mueller 2021-08-06
71546e4 jens-daniel-mueller 2021-08-06
29444a1 jens-daniel-mueller 2021-08-05
42e80c0 jens-daniel-mueller 2021-08-04
48f6eed jens-daniel-mueller 2021-08-04
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 5.747 5.870
Pacific 10.135 10.823
Atlantic 5.293 5.582
total 21.18 22.27
obs | Depth: 3000
Indian 7.459 7.595
Pacific 16.441 16.598
Atlantic 7.710 7.891
total 31.61 32.08
mod_truth | Depth: 3000
Indian 5.604 5.621
Pacific 10.011 10.115
Atlantic 5.108 5.126
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
15773a0 jens-daniel-mueller 2021-08-06
da61d1a jens-daniel-mueller 2021-08-06
340d731 jens-daniel-mueller 2021-08-06
71546e4 jens-daniel-mueller 2021-08-06
29444a1 jens-daniel-mueller 2021-08-05
42e80c0 jens-daniel-mueller 2021-08-04
48f6eed jens-daniel-mueller 2021-08-04
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
15773a0 jens-daniel-mueller 2021-08-06
da61d1a jens-daniel-mueller 2021-08-06
340d731 jens-daniel-mueller 2021-08-06
71546e4 jens-daniel-mueller 2021-08-06
29444a1 jens-daniel-mueller 2021-08-05
42e80c0 jens-daniel-mueller 2021-08-04
48f6eed jens-daniel-mueller 2021-08-04
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
15773a0 jens-daniel-mueller 2021-08-06
da61d1a jens-daniel-mueller 2021-08-06
340d731 jens-daniel-mueller 2021-08-06
71546e4 jens-daniel-mueller 2021-08-06
29444a1 jens-daniel-mueller 2021-08-05
42e80c0 jens-daniel-mueller 2021-08-04
48f6eed jens-daniel-mueller 2021-08-04
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 0.894 0.894
Pacific 2.157 2.157
Atlantic 1.047 1.047
total 4.10 4.10
mod | Depth: 500
Indian 3.128 3.128
Pacific 7.057 7.058
Atlantic 3.601 3.601
total 13.79 13.79
mod | Depth: 1000
Indian 4.563 4.565
Pacific 9.188 9.215
Atlantic 4.749 4.752
total 18.50 18.53
mod | Depth: 3000
Indian 5.747 5.870
Pacific 10.135 10.823
Atlantic 5.293 5.582
total 21.18 22.27
mod | Depth: 10000
Indian 5.872 6.123
Pacific 10.536 11.480
Atlantic 5.034 5.727
total 21.44 23.33
obs | Depth: 100
Indian 1.021 1.021
Pacific 2.445 2.445
Atlantic 1.239 1.239
total 4.71 4.71
obs | Depth: 500
Indian 3.619 3.624
Pacific 8.594 8.599
Atlantic 4.194 4.204
total 16.41 16.43
obs | Depth: 1000
Indian 5.297 5.310
Pacific 11.948 11.971
Atlantic 5.929 5.956
total 23.17 23.24
obs | Depth: 3000
Indian 7.459 7.595
Pacific 16.441 16.598
Atlantic 7.710 7.891
total 31.61 32.08
obs | Depth: 10000
Indian 7.549 8.050
Pacific 17.381 17.809
Atlantic 8.093 8.452
total 33.02 34.31
mod_truth | Depth: 100
Indian 0.796 0.796
Pacific 1.983 1.983
Atlantic 0.930 0.930
total 3.71 3.71
mod_truth | Depth: 500
Indian 3.118 3.118
Pacific 7.061 7.067
Atlantic 3.409 3.410
total 13.59 13.60
mod_truth | Depth: 1000
Indian 4.652 4.652
Pacific 9.146 9.156
Atlantic 4.523 4.530
total 18.32 18.34
mod_truth | Depth: 3000
Indian 5.604 5.621
Pacific 10.011 10.115
Atlantic 5.108 5.126
total 20.72 20.86
mod_truth | Depth: 10000
Indian 5.678 5.700
Pacific 10.227 10.341
Atlantic 5.151 5.180
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
15773a0 jens-daniel-mueller 2021-08-06
da61d1a jens-daniel-mueller 2021-08-06
340d731 jens-daniel-mueller 2021-08-06
71546e4 jens-daniel-mueller 2021-08-06
29444a1 jens-daniel-mueller 2021-08-05
42e80c0 jens-daniel-mueller 2021-08-04
48f6eed jens-daniel-mueller 2021-08-04
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
15773a0 jens-daniel-mueller 2021-08-06
da61d1a jens-daniel-mueller 2021-08-06
340d731 jens-daniel-mueller 2021-08-06
71546e4 jens-daniel-mueller 2021-08-06
29444a1 jens-daniel-mueller 2021-08-05
42e80c0 jens-daniel-mueller 2021-08-04
48f6eed jens-daniel-mueller 2021-08-04
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