Last updated: 2021-08-10

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Knit directory: emlr_obs_v_XXX/

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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
0b00a2b jens-daniel-mueller 2021-08-09
755c6b1 jens-daniel-mueller 2021-08-09
cd8e0d5 jens-daniel-mueller 2021-08-06
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
23.026 23.836
obs
28.859 30.183
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
0b00a2b jens-daniel-mueller 2021-08-09
755c6b1 jens-daniel-mueller 2021-08-09
cd8e0d5 jens-daniel-mueller 2021-08-06
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
0b00a2b jens-daniel-mueller 2021-08-09
755c6b1 jens-daniel-mueller 2021-08-09
cd8e0d5 jens-daniel-mueller 2021-08-06
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
0b00a2b jens-daniel-mueller 2021-08-09
755c6b1 jens-daniel-mueller 2021-08-09
cd8e0d5 jens-daniel-mueller 2021-08-06
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
0b00a2b jens-daniel-mueller 2021-08-09
755c6b1 jens-daniel-mueller 2021-08-09
cd8e0d5 jens-daniel-mueller 2021-08-06
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
3.849 3.917
mod | Depth: 500
13.627 13.711
mod | Depth: 1000
18.488 18.606
mod | Depth: 3000
23.026 23.836
mod | Depth: 10000
23.860 25.176
obs | Depth: 100
3.958 4.092
obs | Depth: 500
14.586 14.855
obs | Depth: 1000
20.982 21.431
obs | Depth: 3000
28.859 30.183
obs | Depth: 10000
32.307 34.314
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
0b00a2b jens-daniel-mueller 2021-08-09
755c6b1 jens-daniel-mueller 2021-08-09
cd8e0d5 jens-daniel-mueller 2021-08-06
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
0b00a2b jens-daniel-mueller 2021-08-09
755c6b1 jens-daniel-mueller 2021-08-09
cd8e0d5 jens-daniel-mueller 2021-08-06
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
0b00a2b jens-daniel-mueller 2021-08-09
755c6b1 jens-daniel-mueller 2021-08-09
cd8e0d5 jens-daniel-mueller 2021-08-06
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
0b00a2b jens-daniel-mueller 2021-08-09
755c6b1 jens-daniel-mueller 2021-08-09
cd8e0d5 jens-daniel-mueller 2021-08-06
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 8.075 8.186
Pacific 9.623 10.070
Atlantic 5.329 5.580
total 23.03 23.84
obs | Depth: 3000
Indian 6.518 7.139
Pacific 14.352 14.815
Atlantic 7.989 8.228
total 28.86 30.18
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
0b00a2b jens-daniel-mueller 2021-08-09
755c6b1 jens-daniel-mueller 2021-08-09
cd8e0d5 jens-daniel-mueller 2021-08-06
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
0b00a2b jens-daniel-mueller 2021-08-09
755c6b1 jens-daniel-mueller 2021-08-09
cd8e0d5 jens-daniel-mueller 2021-08-06
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
0b00a2b jens-daniel-mueller 2021-08-09
755c6b1 jens-daniel-mueller 2021-08-09
cd8e0d5 jens-daniel-mueller 2021-08-06
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 0.857 0.858
Pacific 2.049 2.050
Atlantic 0.943 1.009
total 3.85 3.92
mod | Depth: 500
Indian 3.393 3.400
Pacific 6.913 6.915
Atlantic 3.321 3.396
total 13.63 13.71
mod | Depth: 1000
Indian 5.430 5.442
Pacific 8.749 8.775
Atlantic 4.309 4.389
total 18.49 18.61
mod | Depth: 3000
Indian 8.075 8.186
Pacific 9.623 10.070
Atlantic 5.329 5.580
total 23.03 23.84
mod | Depth: 10000
Indian 8.246 8.500
Pacific 10.175 10.792
Atlantic 5.439 5.884
total 23.86 25.18
obs | Depth: 100
Indian 0.639 0.742
Pacific 2.223 2.251
Atlantic 1.097 1.100
total 3.96 4.09
obs | Depth: 500
Indian 2.966 3.134
Pacific 7.621 7.707
Atlantic 4.000 4.014
total 14.59 14.86
obs | Depth: 1000
Indian 4.523 4.823
Pacific 10.561 10.688
Atlantic 5.898 5.920
total 20.98 21.43
obs | Depth: 3000
Indian 6.518 7.139
Pacific 14.352 14.815
Atlantic 7.989 8.228
total 28.86 30.18
obs | Depth: 10000
Indian 7.562 8.224
Pacific 16.582 17.298
Atlantic 8.163 8.792
total 32.31 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
0b00a2b jens-daniel-mueller 2021-08-09
755c6b1 jens-daniel-mueller 2021-08-09
cd8e0d5 jens-daniel-mueller 2021-08-06
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
0b00a2b jens-daniel-mueller 2021-08-09
755c6b1 jens-daniel-mueller 2021-08-09
cd8e0d5 jens-daniel-mueller 2021-08-06
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