Last updated: 2021-11-21

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1 Read files

# identify required version IDs

Version_IDs <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = "v_2c")
# 
# Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
#                             pattern = "v_20")
# 
# Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
#                             pattern = "v_30")
# 
# Version_IDs <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)

1.1 Global

for (i_Version_IDs in Version_IDs) {
  # i_Version_IDs <- Version_IDs[1]
  
  print(i_Version_IDs)
  
  path_version_data     <-
    paste(path_observations,
          i_Version_IDs,
          "/data/",
          sep = "")
  
  # load and join data files
  
  dcant_budget_global <-
    read_csv(paste(path_version_data,
                   "dcant_budget_global.csv",
                   sep = ""))
  
  dcant_budget_global_mod_truth <-
    read_csv(paste(
      path_version_data,
      "dcant_budget_global_mod_truth.csv",
      sep = ""
    ))
  
  dcant_budget_global_bias <-
    read_csv(paste(path_version_data,
                   "dcant_budget_global_bias.csv",
                   sep = ""))
  
  dcant_budget_global <- bind_rows(dcant_budget_global,
                                      dcant_budget_global_mod_truth)
  
  dcant_budget_global <- dcant_budget_global %>%
    mutate(Version_ID = i_Version_IDs)
  
  dcant_budget_global_bias <- dcant_budget_global_bias %>%
    mutate(Version_ID = i_Version_IDs)

  params_local <-
    read_rds(paste(path_version_data,
                   "params_local.rds",
                   sep = ""))
  
  params_local <- bind_cols(
    Version_ID = i_Version_IDs,
    MLR_basins = params_local$MLR_basins,
    tref1 = params_local$tref1,
    tref2 = params_local$tref2,
    gap_filling = params_local$gap_filling,
    cstar_deep_sd = params_local$cstar_deep_sd,
    rarefication = params_local$rarefication,
    rarefication_threshold = params_local$rarefication_threshold,
    MLR_predictors = str_c(params_local$MLR_predictors, collapse = "+"),
    vif_max = params_local$vif_max
  )
  
  tref <- read_csv(paste(path_version_data,
                         "tref.csv",
                         sep = ""))
  
  params_local <- params_local %>%
    mutate(
      median_year_1 = sort(tref$median_year)[1],
      median_year_2 = sort(tref$median_year)[2],
      duration = median_year_2 - median_year_1,
      period = paste(median_year_1, "-", median_year_2)
    )
  
  if (exists("dcant_budget_global_all")) {
    dcant_budget_global_all <-
      bind_rows(dcant_budget_global_all, dcant_budget_global)
  }
  
  if (!exists("dcant_budget_global_all")) {
    dcant_budget_global_all <- dcant_budget_global
  }
  
  if (exists("dcant_budget_global_bias_all")) {
    dcant_budget_global_bias_all <-
      bind_rows(dcant_budget_global_bias_all,
                dcant_budget_global_bias)
  }

  if (!exists("dcant_budget_global_bias_all")) {
    dcant_budget_global_bias_all <- dcant_budget_global_bias
  }
  
  if (exists("params_local_all")) {
    params_local_all <- bind_rows(params_local_all, params_local)
  }
  
  if (!exists("params_local_all")) {
    params_local_all <- params_local
  }
  
  
}
[1] "v_2c01"
[1] "v_2c02"
[1] "v_2c03"
[1] "v_2c04"
[1] "v_2c05"
[1] "v_2c06"
[1] "v_2c07"
params_local_all <-
  params_local_all %>%
  mutate(cstar_deep_sd = as.factor(cstar_deep_sd))
  # mutate(period = factor(period, c("1994 - 2004", "2004 - 2014", "1994 - 2014")))

rm(
  dcant_budget_global,
  dcant_budget_global_bias,
  dcant_budget_global_mod_truth,
  params_local,
  tref
)

dcant_budget_global_all <- full_join(dcant_budget_global_all,
                                     params_local_all)

dcant_budget_global_bias_all <-
  full_join(dcant_budget_global_bias_all,
            params_local_all)
dcant_budget_global_all <- dcant_budget_global_all %>%
  filter(estimate == "dcant", 
         method == "total") %>% 
  select(-c(estimate, method)) %>% 
  rename(dcant = value)

dcant_budget_global_all_depth <- dcant_budget_global_all

dcant_budget_global_all <- dcant_budget_global_all %>%
  filter(inv_depth == params_global$inventory_depth_standard)

dcant_budget_global_bias_all <- dcant_budget_global_bias_all %>%
  filter(estimate == "dcant") %>%
  select(-c(estimate))

dcant_budget_global_bias_all_depth <- dcant_budget_global_bias_all

dcant_budget_global_bias_all <- dcant_budget_global_bias_all %>%
  filter(inv_depth == params_global$inventory_depth_standard)

1.2 Regional

# Version_IDs <- Version_IDs[1:length(Version_IDs)-1]

for (i_Version_IDs in Version_IDs) {
  # i_Version_IDs <- Version_IDs[1]
  
  print(i_Version_IDs)
  
  path_version_data     <-
    paste(path_observations,
          i_Version_IDs,
          "/data/",
          sep = "")
  
  # load and join data files
  
  dcant_budget_basin_AIP <-
    read_csv(paste(path_version_data,
                   "dcant_budget_basin_AIP.csv",
                   sep = ""))
  
  dcant_budget_basin_AIP_mod_truth <-
    read_csv(paste(
      path_version_data,
      "dcant_budget_basin_AIP_mod_truth.csv",
      sep = ""
    ))
  
    
  dcant_budget_basin_AIP <- bind_rows(dcant_budget_basin_AIP,
                                      dcant_budget_basin_AIP_mod_truth)
  
  dcant_budget_basin_AIP_bias <-
    read_csv(paste(path_version_data,
                   "dcant_budget_basin_AIP_bias.csv",
                   sep = ""))
  
  dcant_slab_budget_bias <-
    read_csv(paste0(path_version_data,
                    "dcant_slab_budget_bias.csv"))

  dcant_slab_budget <-
    read_csv(paste0(path_version_data,
                    "dcant_slab_budget.csv"))

  dcant_budget_basin_AIP <- dcant_budget_basin_AIP %>%
    mutate(Version_ID = i_Version_IDs)
  
  dcant_budget_basin_AIP_bias <- dcant_budget_basin_AIP_bias %>%
    mutate(Version_ID = i_Version_IDs)
  
  dcant_slab_budget <- dcant_slab_budget %>%
    mutate(Version_ID = i_Version_IDs)
  
  dcant_slab_budget_bias <- dcant_slab_budget_bias %>%
    mutate(Version_ID = i_Version_IDs)
  
  if (exists("dcant_budget_basin_AIP_all")) {
    dcant_budget_basin_AIP_all <-
      bind_rows(dcant_budget_basin_AIP_all, dcant_budget_basin_AIP)
  }
  
  if (!exists("dcant_budget_basin_AIP_all")) {
    dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP
  }
  
  if (exists("dcant_budget_basin_AIP_bias_all")) {
    dcant_budget_basin_AIP_bias_all <-
      bind_rows(dcant_budget_basin_AIP_bias_all,
                dcant_budget_basin_AIP_bias)
  }
  
  if (!exists("dcant_budget_basin_AIP_bias_all")) {
    dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias
  }
  
  if (exists("dcant_slab_budget_all")) {
    dcant_slab_budget_all <-
      bind_rows(dcant_slab_budget_all, dcant_slab_budget)
  }
  
  if (!exists("dcant_slab_budget_all")) {
    dcant_slab_budget_all <- dcant_slab_budget
  }
  
  if (exists("dcant_slab_budget_bias_all")) {
    dcant_slab_budget_bias_all <-
      bind_rows(dcant_slab_budget_bias_all,
                dcant_slab_budget_bias)
  }
  
  if (!exists("dcant_slab_budget_bias_all")) {
    dcant_slab_budget_bias_all <- dcant_slab_budget_bias
  }
  
}
[1] "v_2c01"
[1] "v_2c02"
[1] "v_2c03"
[1] "v_2c04"
[1] "v_2c05"
[1] "v_2c06"
[1] "v_2c07"
rm(
  dcant_budget_basin_AIP,
  dcant_budget_basin_AIP_bias,
  dcant_budget_basin_AIP_mod_truth,
  dcant_slab_budget,
  dcant_slab_budget_bias
)

dcant_budget_basin_AIP_all <- full_join(dcant_budget_basin_AIP_all,
                                        params_local_all)

dcant_budget_basin_AIP_bias_all <-
  full_join(dcant_budget_basin_AIP_bias_all,
            params_local_all)

dcant_slab_budget_all <- full_join(dcant_slab_budget_all,
                                        params_local_all)

dcant_slab_budget_bias_all <-
  full_join(dcant_slab_budget_bias_all,
            params_local_all)
dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP_all %>%
  filter(estimate == "dcant", 
         method == "total") %>% 
  select(-c(estimate, method)) %>% 
  rename(dcant = value)

dcant_budget_basin_AIP_all_depth <- dcant_budget_basin_AIP_all

dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP_all %>%
  filter(inv_depth == params_global$inventory_depth_standard)

dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias_all %>%
  filter(estimate == "dcant") %>% 
  select(-c(estimate))

dcant_budget_basin_AIP_bias_all_depth <- dcant_budget_basin_AIP_bias_all

dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias_all %>%
  filter(inv_depth == params_global$inventory_depth_standard)

2 Global

2.1 Individual cases

2.1.1 Absoulte values

legend_title = expression(atop(Delta * C[ant],
                               (mu * mol ~ kg ^ {
                                 -1
                               })))

dcant_budget_global_all %>%
  ggplot(aes(period, dcant, col = cstar_deep_sd)) +
  geom_jitter(width = 0.05, height = 0) +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(. ~ data_source) +
  ylim(0,NA) +
  theme(axis.text.x = element_text(angle = 45, hjust=1),
        axis.title.x = element_blank())

Version Author Date
e9138fd jens-daniel-mueller 2021-11-10
3a1c6c5 jens-daniel-mueller 2021-11-09
4f8de7a jens-daniel-mueller 2021-11-09
e412cf9 jens-daniel-mueller 2021-11-09
e505a4b jens-daniel-mueller 2021-11-09

2.1.2 Biases

dcant_budget_global_bias_all %>%
  ggplot(aes(period, dcant_bias, col = cstar_deep_sd)) +
  geom_hline(yintercept = 0) +
  scale_color_brewer(palette = "Dark2", name = "basin\nseparation") +
  labs(y = expression(atop(Delta * C[ant] ~ bias,
                               (mu * mol ~ kg ^ {-1})))) +
  geom_point()

Version Author Date
e9138fd jens-daniel-mueller 2021-11-10
3a1c6c5 jens-daniel-mueller 2021-11-09
4f8de7a jens-daniel-mueller 2021-11-09
e412cf9 jens-daniel-mueller 2021-11-09
e505a4b jens-daniel-mueller 2021-11-09

3 Regional

3.1 Individual cases

3.1.1 Absoulte values

dcant_budget_basin_AIP_all %>%
  ggplot(aes(period, dcant, col = cstar_deep_sd)) +
  geom_jitter(width = 0.05, height = 0) +
  scale_color_brewer(palette = "Dark2", name = "basin\nseparation") +
  facet_grid(basin_AIP ~ data_source) +
  ylim(0,NA) +
  theme(axis.text.x = element_text(angle = 45, hjust=1),
        axis.title.x = element_blank())

Version Author Date
e9138fd jens-daniel-mueller 2021-11-10
3a1c6c5 jens-daniel-mueller 2021-11-09
4f8de7a jens-daniel-mueller 2021-11-09
e412cf9 jens-daniel-mueller 2021-11-09
e505a4b jens-daniel-mueller 2021-11-09

3.1.2 Biases

dcant_budget_basin_AIP_bias_all %>%
  ggplot(aes(period, dcant_bias, col=cstar_deep_sd)) +
  geom_hline(yintercept = 0) +
  geom_point() +
  facet_grid(basin_AIP ~ .)

Version Author Date
e9138fd jens-daniel-mueller 2021-11-10
3a1c6c5 jens-daniel-mueller 2021-11-09
4f8de7a jens-daniel-mueller 2021-11-09
e412cf9 jens-daniel-mueller 2021-11-09
e505a4b jens-daniel-mueller 2021-11-09

3.2 Slab budgets

3.2.1 Absolute values

dcant_slab_budget_all %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>% 
  ggplot(aes(cstar_deep_sd, dcant, fill = gamma_slab)) +
  geom_hline(yintercept = 0, col = "red") +
  geom_col() +
  scale_fill_scico_d(direction = -1) +
  facet_grid(basin_AIP ~ period)

Version Author Date
e9138fd jens-daniel-mueller 2021-11-10
3a1c6c5 jens-daniel-mueller 2021-11-09
4f8de7a jens-daniel-mueller 2021-11-09
e412cf9 jens-daniel-mueller 2021-11-09
dcant_slab_budget_all %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  ggplot(aes(cstar_deep_sd, dcant, fill = gamma_slab)) +
  geom_hline(yintercept = 0) +
  geom_col() +
  scale_fill_scico_d(direction = -1) +
  facet_grid(gamma_slab ~ basin_AIP)

Version Author Date
e9138fd jens-daniel-mueller 2021-11-10
3a1c6c5 jens-daniel-mueller 2021-11-09
4f8de7a jens-daniel-mueller 2021-11-09
e412cf9 jens-daniel-mueller 2021-11-09

3.2.2 Bias

dcant_slab_budget_bias_all %>%
  filter(period != "1994 - 2014") %>%
  ggplot(aes(gamma_slab, dcant_bias, fill = gamma_slab)) +
  geom_col() +
  coord_flip() +
  scale_x_discrete(limits = rev) +
  scale_fill_scico_d(direction = -1) +
  facet_grid(basin_AIP ~ cstar_deep_sd)

Version Author Date
e9138fd jens-daniel-mueller 2021-11-10
3a1c6c5 jens-daniel-mueller 2021-11-09
4f8de7a jens-daniel-mueller 2021-11-09
e412cf9 jens-daniel-mueller 2021-11-09

3.2.3 Spread

dcant_slab_budget_all %>%
  filter(period != "1994 - 2014",
         data_source != "mod_truth") %>%
  group_by(data_source, basin_AIP, gamma_slab, period) %>%
  summarise(dcant_range = max(dcant) - min(dcant)) %>%
  ungroup() %>%
  ggplot(aes(gamma_slab, dcant_range, fill = gamma_slab)) +
  geom_col() +
  coord_flip() +
  scale_x_discrete(limits = rev) +
  scale_fill_scico_d(direction = -1) +
  facet_grid(basin_AIP ~ data_source)

Version Author Date
e9138fd jens-daniel-mueller 2021-11-10
3a1c6c5 jens-daniel-mueller 2021-11-09
4f8de7a jens-daniel-mueller 2021-11-09
e412cf9 jens-daniel-mueller 2021-11-09

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] ggforce_0.3.3   metR_0.9.0      scico_1.2.0     patchwork_1.1.1
 [5] collapse_1.5.0  forcats_0.5.0   stringr_1.4.0   dplyr_1.0.5    
 [9] purrr_0.3.4     readr_1.4.0     tidyr_1.1.3     tibble_3.1.3   
[13] ggplot2_3.3.5   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2               sass_0.4.0               jsonlite_1.7.1          
 [4] modelr_0.1.8             bslib_0.2.5.1            assertthat_0.2.1        
 [7] highr_0.8                blob_1.2.1               cellranger_1.1.0        
[10] yaml_2.2.1               pillar_1.6.2             backports_1.1.10        
[13] lattice_0.20-41          glue_1.4.2               RcppEigen_0.3.3.7.0     
[16] digest_0.6.27            RColorBrewer_1.1-2       promises_1.1.1          
[19] polyclip_1.10-0          checkmate_2.0.0          rvest_0.3.6             
[22] colorspace_2.0-2         htmltools_0.5.1.1        httpuv_1.5.4            
[25] Matrix_1.2-18            pkgconfig_2.0.3          broom_0.7.9             
[28] haven_2.3.1              scales_1.1.1             tweenr_1.0.2            
[31] whisker_0.4              later_1.2.0              git2r_0.27.1            
[34] farver_2.0.3             generics_0.1.0           ellipsis_0.3.2          
[37] withr_2.3.0              cli_3.0.1                magrittr_1.5            
[40] crayon_1.3.4             readxl_1.3.1             evaluate_0.14           
[43] fs_1.5.0                 fansi_0.4.1              MASS_7.3-53             
[46] xml2_1.3.2               RcppArmadillo_0.10.1.2.0 tools_4.0.3             
[49] data.table_1.14.0        hms_0.5.3                lifecycle_1.0.0         
[52] munsell_0.5.0            reprex_0.3.0             compiler_4.0.3          
[55] jquerylib_0.1.4          rlang_0.4.11             grid_4.0.3              
[58] rstudioapi_0.13          labeling_0.4.2           rmarkdown_2.10          
[61] gtable_0.3.0             DBI_1.1.0                R6_2.5.0                
[64] lubridate_1.7.9          knitr_1.33               utf8_1.1.4              
[67] rprojroot_2.0.2          stringi_1.5.3            parallel_4.0.3          
[70] Rcpp_1.0.5               vctrs_0.3.8              dbplyr_1.4.4            
[73] tidyselect_1.1.0         xfun_0.25