Budgets

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Last updated: 2021-09-29

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Knit directory: emlr_obs_analysis/

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Rmd	1245ec8	jens-daniel-mueller	2021-09-21	rebuildt with global MLR cleaning

---

1 Global

1.1 Read files

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = "v_12")

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = "v_22")

Version_IDs <- c(Version_IDs_1, Version_IDs_2)
rm(Version_IDs_1, Version_IDs_2)

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,
    target_global_rmse_max = as.character(params_local$target_global_rmse_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_1201"
[1] "v_1202"
[1] "v_1203"
[1] "v_1204"
[1] "v_2201"
[1] "v_2202"
[1] "v_2203"
[1] "v_2204"

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 Thresholds

global_bias_rel_max <- 10
regional_bias_rel_max <- 20

global_bias_rel_max

[1] 10

regional_bias_rel_max

[1] 20

1.3 Individual cases

1.3.1 Absoulte values

dcant_budget_global_all %>%
  ggplot(aes(period, dcant, col=target_global_rmse_max)) +
  geom_point() +
  facet_grid(. ~ data_source)

Past versions of cases_absolute_global-1.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

1.3.2 Biases

dcant_budget_global_bias_all %>%
  ggplot(aes(period, dcant_bias, col=target_global_rmse_max)) +
  geom_hline(yintercept = 0) +
  geom_point()

Past versions of cases_bias_global-1.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

dcant_budget_global_bias_all %>%
  ggplot(aes(period, dcant_bias_rel, col=target_global_rmse_max)) +
  geom_hline(yintercept = 0) +
  geom_point()

Past versions of cases_bias_global-2.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

ensemble_target_global_rmse_max_global <- dcant_budget_global_bias_all %>% 
  filter(dcant_bias_rel < global_bias_rel_max) %>% 
  count(target_global_rmse_max) %>% 
  filter(n == 2) %>% 
  pull(target_global_rmse_max)

1.4 Ensemble

dcant_budget_global_ensemble <- dcant_budget_global_all %>% 
  filter(data_source %in% c("mod", "obs"),
         target_global_rmse_max %in% ensemble_target_global_rmse_max_global) %>% 
  group_by(data_source, period) %>% 
  summarise(dcant_mean = mean(dcant),
            dcant_sd = sd(dcant),
            dcant_range = max(dcant)- min(dcant)) %>% 
  ungroup()

1.4.1 Mean

dcant_budget_global_ensemble %>%
  ggplot(aes(period, dcant_mean)) +
  geom_pointrange(aes(ymax = dcant_mean + dcant_sd,
                      ymin = dcant_mean - dcant_sd),
                  shape = 21) +
  facet_grid(. ~ data_source)

Past versions of ensemble_mean_global-1.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

1.4.2 Mean bias

dcant_budget_global_ensemble_bias <- full_join(
  dcant_budget_global_ensemble %>%
    filter(data_source == "mod") %>% 
    select(period, dcant_mean, dcant_sd),
  dcant_budget_global_all %>%
    filter(data_source == "mod_truth",
           target_global_rmse_max == "2") %>% 
    select(period, dcant)
)

dcant_budget_global_ensemble_bias <- dcant_budget_global_ensemble_bias %>% 
  mutate(dcant_mean_bias = dcant_mean - dcant,
         dcant_mean_bias_rel = 100 * dcant_mean_bias / dcant)

dcant_budget_global_ensemble_bias %>%
  ggplot(aes(period, dcant_mean_bias)) +
  geom_hline(yintercept = 0) +
  geom_point()

Past versions of ensemble_mean_bias_global-1.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

dcant_budget_global_ensemble_bias %>%
  ggplot(aes(period, dcant_mean_bias_rel)) +
  geom_hline(yintercept = 0) +
  geom_point()

Past versions of ensemble_mean_bias_global-2.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

1.4.3 SD vs bias

dcant_budget_global_ensemble_bias %>% 
  ggplot(aes(dcant_mean_bias, dcant_sd, col = period)) +
  geom_point()

Past versions of ensemble_sd_vs_bias_global-1.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

1.5 Vertical patterns

1.5.1 Absoulte values

dcant_budget_global_all_depth %>%
  filter(target_global_rmse_max %in% ensemble_target_global_rmse_max_global) %>% 
  group_by(data_source) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~  ggplot(data = .x,
              aes(dcant, target_global_rmse_max, fill=period)) +
      geom_vline(xintercept = 0) +
      geom_col(position = "dodge") +
      scale_fill_brewer(palette = "Dark2") +
      facet_grid(inv_depth ~ .) +
      labs(title = paste("data_source:", unique(.x$data_source)))
  )

[[1]]

Past versions of cases_absolute_depth_global-1.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

[[2]]

Past versions of cases_absolute_depth_global-2.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

[[3]]

Past versions of cases_absolute_depth_global-3.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

1.5.2 Biases

dcant_budget_global_bias_all_depth %>%
  filter(target_global_rmse_max %in% ensemble_target_global_rmse_max_global) %>%
  ggplot(aes(dcant_bias, target_global_rmse_max, fill = period)) +
  geom_vline(xintercept = 0) +
  geom_col(position = "dodge") +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ .)

Past versions of cases_bias_depth_global-1.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

dcant_budget_global_bias_all_depth %>%
  filter(target_global_rmse_max %in% ensemble_target_global_rmse_max_global) %>%
  ggplot(aes(dcant_bias_rel, target_global_rmse_max, fill = period)) +
  geom_vline(xintercept = 0) +
  geom_col(position = "dodge") +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ .)

Past versions of cases_bias_rel_depth_global-1.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

rm(dcant_budget_global_all,
   dcant_budget_global_all_depth,
   dcant_budget_global_bias_all,
   dcant_budget_global_bias_all_depth,
   dcant_budget_global_ensemble,
   dcant_budget_global_ensemble_bias,
   params_local_all)

2 Regional

2.1 Read files

# 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_bias <-
    read_csv(paste(path_version_data,
                   "dcant_budget_basin_AIP_bias.csv",
                   sep = ""))
  
  dcant_budget_basin_AIP <- bind_rows(dcant_budget_basin_AIP,
                                      dcant_budget_basin_AIP_mod_truth)
  
  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)
  
  params_local <-
    read_rds(paste(path_version_data,
                   "params_local.rds",
                   sep = ""))
  
  params_local <- bind_cols(
    Version_ID = i_Version_IDs,
    target_global_rmse_max = as.character(params_local$target_global_rmse_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_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("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_1201"
[1] "v_1202"
[1] "v_1203"
[1] "v_1204"
[1] "v_2201"
[1] "v_2202"
[1] "v_2203"
[1] "v_2204"

rm(
  dcant_budget_basin_AIP,
  dcant_budget_basin_AIP_bias,
  dcant_budget_basin_AIP_mod_truth,
  params_local,
  tref
)

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_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)

2.2 Individual cases

2.2.1 Absoulte values

dcant_budget_basin_AIP_all %>%
  ggplot(aes(period, dcant, col=target_global_rmse_max)) +
  geom_point() +
  facet_grid(basin_AIP ~ data_source)

Past versions of cases_absolute-1.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

2.2.2 Biases

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

Past versions of cases_bias-1.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

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

Past versions of cases_bias-2.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

dcant_budget_basin_AIP_bias_all %>% 
  filter(dcant_bias_rel < regional_bias_rel_max) %>% 
  distinct(period, target_global_rmse_max, basin_AIP)

# A tibble: 24 × 3
   basin_AIP target_global_rmse_max period     
   <chr>     <chr>                  <chr>      
 1 Indian    50                     1994 - 2004
 2 Pacific   50                     1994 - 2004
 3 Atlantic  50                     1994 - 2004
 4 Indian    30                     1994 - 2004
 5 Pacific   30                     1994 - 2004
 6 Atlantic  30                     1994 - 2004
 7 Indian    20                     1994 - 2004
 8 Pacific   20                     1994 - 2004
 9 Atlantic  20                     1994 - 2004
10 Indian    10                     1994 - 2004
# … with 14 more rows

ensemble_target_global_rmse_max_regional <- dcant_budget_basin_AIP_bias_all %>% 
  filter(dcant_bias_rel < regional_bias_rel_max) %>% 
  count(target_global_rmse_max) %>% 
  filter(n == 6) %>% 
  pull(target_global_rmse_max)

2.3 Ensemble

dcant_budget_basin_AIP_ensemble <- dcant_budget_basin_AIP_all %>% 
  filter(data_source %in% c("mod", "obs"),
         target_global_rmse_max %in% ensemble_target_global_rmse_max_global,
         target_global_rmse_max %in% ensemble_target_global_rmse_max_regional) %>% 
  group_by(basin_AIP, data_source, period) %>% 
  summarise(dcant_mean = mean(dcant),
            dcant_sd = sd(dcant),
            dcant_range = max(dcant)- min(dcant)) %>% 
  ungroup()

2.3.1 Mean

dcant_budget_basin_AIP_ensemble %>%
  ggplot(aes(period, dcant_mean, col=basin_AIP)) +
  geom_pointrange(aes(ymax = dcant_mean + dcant_sd,
                      ymin = dcant_mean - dcant_sd),
                  shape = 21) +
  facet_grid(. ~ data_source)

Past versions of ensemble_mean-1.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

2.3.2 Mean bias

dcant_budget_basin_AIP_ensemble_bias <- full_join(
  dcant_budget_basin_AIP_ensemble %>%
    filter(data_source == "mod") %>% 
    select(basin_AIP, period, dcant_mean, dcant_sd),
  dcant_budget_basin_AIP_all %>%
    filter(data_source == "mod_truth",
           target_global_rmse_max == "2") %>% 
    select(basin_AIP, period, dcant)
)

dcant_budget_basin_AIP_ensemble_bias <- dcant_budget_basin_AIP_ensemble_bias %>% 
  mutate(dcant_mean_bias = dcant_mean - dcant)

dcant_budget_basin_AIP_ensemble_bias %>%
  ggplot(aes(period, dcant_mean_bias, col = basin_AIP)) +
  geom_hline(yintercept = 0) +
  geom_point()

Past versions of ensemble_mean_bias-1.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

2.3.3 SD vs bias

dcant_budget_basin_AIP_ensemble_bias %>% 
  ggplot(aes(dcant_mean_bias, dcant_sd, col=basin_AIP)) +
  geom_point() +
  facet_grid(. ~ period)

Past versions of ensemble_sd_vs_bias-1.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

2.4 Vertical patterns

2.4.1 Absoulte values

unique(
dcant_budget_basin_AIP_all_depth$target_global_rmse_max)

[1] "50" "30" "20" "10"

dcant_budget_basin_AIP_all_depth %>%
  filter(
    target_global_rmse_max %in% ensemble_target_global_rmse_max_global,
    target_global_rmse_max %in% ensemble_target_global_rmse_max_regional
  ) %>%
  group_by(data_source) %>%
  group_split() %>%
  head(1) %>%
  map(
    ~  ggplot(data = .x,
              aes(dcant, target_global_rmse_max, fill = basin_AIP)) +
      geom_vline(xintercept = 0) +
      geom_col() +
      scale_fill_brewer(palette = "Dark2") +
      facet_grid(inv_depth ~ period) +
      labs(title = paste("data_source:", unique(.x$data_source)))
  )

[[1]]

Past versions of cases_absolute_depth-1.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

2.4.2 Biases

dcant_budget_basin_AIP_bias_all_depth %>%
  filter(
    target_global_rmse_max %in% ensemble_target_global_rmse_max_global,
    target_global_rmse_max %in% ensemble_target_global_rmse_max_regional
  ) %>%
  ggplot(aes(dcant_bias, target_global_rmse_max, fill = basin_AIP)) +
  geom_vline(xintercept = 0) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ period)

Past versions of cases_bias_depth-1.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

dcant_budget_basin_AIP_bias_all_depth %>%
  filter(
    target_global_rmse_max %in% ensemble_target_global_rmse_max_global,
    target_global_rmse_max %in% ensemble_target_global_rmse_max_regional
  ) %>%
  ggplot(aes(dcant_bias_rel, target_global_rmse_max, fill = basin_AIP)) +
  geom_vline(xintercept = 0) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ period)

Past versions of cases_bias_rel_depth-1.png

Version	Author	Date
63911d0	jens-daniel-mueller	2021-09-21

Session information

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