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1 Data sources

Cant estimates from this sensitivity case:

  • Mean and SD per grid cell (lat, lon, depth)
  • Zonal mean and SD (basin, lat, depth)
cant_3d <-
  read_csv(paste(path_version_data,
                 "cant_3d.csv",
                 sep = ""))

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

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

Target variable (cstar_tref) estimates from this sensitivity case:

  • Mean and SD per grid cell (lat, lon, depth)
  • Zonal mean and SD (basin, lat, depth)
target_3d <-
  read_csv(paste(path_version_data,
                 "target_3d.csv",
                 sep = ""))

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

Cleaned synthetic model subset file as used in this sensitivity case.

GLODAP <-
  read_csv(paste(
    path_version_data,
    "GLODAPv2.2020_MLR_fitting_ready.csv",
    sep = ""
  ))

2 Calculate gamma slab maps

cant_gamma_maps <- m_cant_slab(cant_3d)

cant_gamma_maps <- cant_gamma_maps %>% 
  arrange(gamma_slab, eras)

3 Cant variability

3.1 Across models

Standard deviation across Cant from all MLR models was calculate for each grid cell (XYZ). The zonal mean of this standard deviation should reflect the uncertainty associated to the predictor selection within each slab and era.

for (i_basin_AIP in unique(cant_zonal$basin_AIP)) {
  for (i_eras in unique(cant_zonal$eras)) {
    
    print(
      p_section_zonal(
        df = cant_zonal %>%
          filter(basin_AIP == i_basin_AIP,
                 eras == i_eras),
        var = "cant_sd_mean",
        gamma = "gamma_mean",
        legend_title = "sd",
        title_text = "Zonal mean section of SD across models",
        subtitle_text =
          paste("Basin:", i_basin_AIP, "| eras:", i_eras)
      )
    )
    
  }
}

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

3.2 Across basins

Standard deviation of mean cant values was calculate across all longitudes. This standard deviation should reflect the zonal variability of cant within the basin and era.

for (i_basin_AIP in unique(cant_zonal$basin_AIP)) {
  for (i_eras in unique(cant_zonal$eras)) {
    
    print(
      p_section_zonal(
        df = cant_zonal %>%
          filter(basin_AIP == i_basin_AIP,
                 eras == i_eras),
        var = "cant_sd",
        gamma = "gamma_mean",
        legend_title = "sd",
        title_text = "Zonal mean section of Cant SD",
        subtitle_text =
          paste("Basin:", i_basin_AIP, "| eras:", i_eras)
      )
    )
    
  }
}

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
2679490 Donghe-Zhu 2021-01-21
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
2679490 Donghe-Zhu 2021-01-21
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
2679490 Donghe-Zhu 2021-01-21
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
2679490 Donghe-Zhu 2021-01-21
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
2679490 Donghe-Zhu 2021-01-21
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
2679490 Donghe-Zhu 2021-01-21
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

3.3 Correlation

3.3.1 Cant vs model SD

3.3.1.1 Era vs basin

cant_3d %>% 
  ggplot(aes(cant, cant_sd)) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 10) +
  geom_bin2d() +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10",
                       name = "log10(n)") +
  facet_grid(basin_AIP ~ eras)

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

3.3.1.2 Basin vs gamma

cant_3d %>% 
  ggplot(aes(cant, cant_sd)) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 10) +
  geom_bin2d() +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10",
                       name = "log10(n)") +
  facet_grid(gamma_slab ~ basin_AIP)

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

3.3.2 Cant vs regional SD

3.3.2.1 Era vs basin

cant_zonal %>% 
  ggplot(aes(cant_mean, cant_sd)) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 10) +
  geom_bin2d() +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10",
                       name = "log10(n)") +
  facet_grid(basin_AIP ~ eras)

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
2679490 Donghe-Zhu 2021-01-21
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

3.3.2.2 Era vs basin

cant_zonal %>% 
  ggplot(aes(cant_mean, cant_sd)) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 10) +
  geom_bin2d() +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10",
                       name = "log10(n)") +
  facet_grid(gamma_slab ~ basin_AIP)

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
2679490 Donghe-Zhu 2021-01-21
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
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022871c Donghe-Zhu 2021-01-13

4 Cant - predictor contribution

for (i_var in paste("cant",
                    c("intercept", params_local$MLR_predictors),
                    sep = "_")) {
  print(
    p_section_zonal_divergent_gamma_eras_basin(df = cant_predictor_zonal,
                                               var = i_var,
                                               gamma = "gamma")
  )
  
}

Version Author Date
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4c28e4a Donghe-Zhu 2021-01-22
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b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
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4c28e4a Donghe-Zhu 2021-01-22
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8d032c3 jens-daniel-mueller 2021-01-14
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Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
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d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13 
rm(i_var)

5 Neutral density

5.1 Slab depth

The plot below shows the depths of individual gamma slabs (color) together with the synthetic data subset available in the respective slab.

Please note that:

  • density slabs covering values >28.1 occur by definition only either in the Atlantic or Indo-Pacific basin
GLODAP_obs_coverage <- GLODAP %>% 
  count(lat, lon, gamma_slab, era)

map +
  geom_raster(data = cant_gamma_maps,
              aes(lon, lat, fill = depth_max)) +
  geom_raster(data = GLODAP_obs_coverage,
              aes(lon, lat), fill = "red") +
  facet_grid(gamma_slab ~ era) +
  scale_fill_viridis_c(direction = -1) +
  theme(axis.ticks = element_blank(),
        axis.text = element_blank(),
        legend.position = "top")

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13 
rm(GLODAP_obs_coverage)

6 Target variable

The predicted target variable (cstar_tref in this sensitivity case) is based on fitted MLRs and climatological fields of predictor variables, and calculated for each era.

6.1 Zonal mean sections

slab_breaks <- c(params_local$slabs_Atl[1:12], Inf)

for (i_basin_AIP in unique(target_zonal$basin_AIP)) {
  
  print(
  target_zonal %>%
    filter(basin_AIP == i_basin_AIP) %>%
    ggplot(aes(lat, depth,
               z = !!sym(
                 paste(params_local$MLR_target, "mean", sep = "_")
               ))) +
    geom_contour_filled(bins = 11) +
    scale_fill_viridis_d(name = params_local$MLR_target) +
    geom_contour(aes(lat, depth, z = gamma_mean),
                 breaks = slab_breaks,
                 col = "white") +
    geom_text_contour(
      aes(lat, depth, z = gamma_mean),
      breaks = slab_breaks,
      col = "white",
      skip = 1
    ) +
    scale_y_reverse() +
    coord_cartesian(expand = 0,
                    ylim = c(params_global$plotting_depth, 0)) +
    scale_x_continuous(breaks = seq(-100, 100, 20)) +
    guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
    facet_grid(era ~ eras,
               labeller = labeller(.default = label_both)) +
    labs(title = i_basin_AIP)
  )
  
}

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13

Version Author Date
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
022871c Donghe-Zhu 2021-01-13 
rm(slab_breaks)

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     metR_0.9.0       scico_1.2.0      patchwork_1.1.1 
 [9] collapse_1.5.0   forcats_0.5.0    stringr_1.4.0    dplyr_1.0.2     
[13] purrr_0.3.4      readr_1.4.0      tidyr_1.1.2      tibble_3.0.4    
[17] 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] webshot_0.5.2            httr_1.4.2               rprojroot_2.0.2         
 [7] tools_4.0.3              backports_1.1.10         R6_2.5.0                
[10] DBI_1.1.0                colorspace_2.0-0         withr_2.3.0             
[13] tidyselect_1.1.0         compiler_4.0.3           git2r_0.27.1            
[16] cli_2.2.0                rvest_0.3.6              xml2_1.3.2              
[19] isoband_0.2.3            labeling_0.4.2           checkmate_2.0.0         
[22] digest_0.6.27            rmarkdown_2.5            oce_1.2-0               
[25] pkgconfig_2.0.3          htmltools_0.5.0          dbplyr_1.4.4            
[28] rlang_0.4.10             readxl_1.3.1             rstudioapi_0.13         
[31] farver_2.0.3             generics_0.1.0           jsonlite_1.7.2          
[34] magrittr_2.0.1           Matrix_1.2-18            Rcpp_1.0.5              
[37] munsell_0.5.0            fansi_0.4.1              lifecycle_0.2.0         
[40] stringi_1.5.3            whisker_0.4              yaml_2.2.1              
[43] plyr_1.8.6               grid_4.0.3               blob_1.2.1              
[46] parallel_4.0.3           promises_1.1.1           crayon_1.3.4            
[49] lattice_0.20-41          haven_2.3.1              hms_0.5.3               
[52] seacarb_3.2.15           knitr_1.30               pillar_1.4.7            
[55] reprex_0.3.0             glue_1.4.2               evaluate_0.14           
[58] RcppArmadillo_0.10.1.2.2 data.table_1.13.6        modelr_0.1.8            
[61] vctrs_0.3.6              httpuv_1.5.4             testthat_3.0.1          
[64] cellranger_1.1.0         gtable_0.3.0             assertthat_0.2.1        
[67] xfun_0.20                broom_0.7.3              RcppEigen_0.3.3.9.1     
[70] later_1.1.0.1            viridisLite_0.3.0        memoise_1.1.0           
[73] ellipsis_0.3.1           here_1.0.1