Column inventories
Jens Daniel Müller
13 January, 2022
Last updated: 2022-01-13
Checks: 7 0
Knit directory: emlr_obs_analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 269809e | jens-daniel-mueller | 2022-01-12 | Build site. |
| html | 1696b98 | jens-daniel-mueller | 2022-01-11 | Build site. |
| html | 570e738 | jens-daniel-mueller | 2022-01-10 | Build site. |
| html | 9bf6789 | jens-daniel-mueller | 2022-01-10 | Build site. |
| html | 7e0a36b | jens-daniel-mueller | 2021-11-21 | Build site. |
| html | e505a4b | jens-daniel-mueller | 2021-11-09 | Build site. |
| html | 66ec048 | jens-daniel-mueller | 2021-11-04 | Build site. |
| Rmd | 3eaddfc | jens-daniel-mueller | 2021-11-04 | compared G19 and this study directly |
| html | 71920de | jens-daniel-mueller | 2021-11-04 | Build site. |
| html | f7c3da2 | jens-daniel-mueller | 2021-11-03 | Build site. |
| Rmd | 582b24e | jens-daniel-mueller | 2021-11-03 | rebuild with G19 runs |
1 Read files
# identify required version IDs
Version_IDs <-
list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = "v_G0")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_inv <-
read_csv(paste(path_version_data,
"dcant_inv.csv",
sep = ""))
dcant_inv_mod_truth <-
read_csv(paste(path_version_data,
"dcant_inv_mod_truth.csv",
sep = "")) %>%
filter(method == "total") %>%
select(-method)
dcant_inv_bias <-
read_csv(paste(path_version_data,
"dcant_inv_bias.csv",
sep = "")) %>%
mutate(Version_ID = i_Version_IDs)
dcant_inv <- bind_rows(dcant_inv,
dcant_inv_mod_truth) %>%
mutate(Version_ID = i_Version_IDs)
dcant_budget_lat_grid <-
read_csv(paste(path_version_data,
"dcant_budget_lat_grid.csv",
sep = "")) %>%
mutate(Version_ID = i_Version_IDs)
dcant_budget_lon_grid <-
read_csv(paste(path_version_data,
"dcant_budget_lon_grid.csv",
sep = "")) %>%
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,
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_inv_all")) {
dcant_inv_all <- bind_rows(dcant_inv_all, dcant_inv)
}
if (!exists("dcant_inv_all")) {
dcant_inv_all <- dcant_inv
}
if (exists("dcant_inv_bias_all")) {
dcant_inv_bias_all <- bind_rows(dcant_inv_bias_all, dcant_inv_bias)
}
if (!exists("dcant_inv_bias_all")) {
dcant_inv_bias_all <- dcant_inv_bias
}
if (exists("dcant_budget_lat_grid_all")) {
dcant_budget_lat_grid_all <- bind_rows(dcant_budget_lat_grid_all, dcant_budget_lat_grid)
}
if (!exists("dcant_budget_lat_grid_all")) {
dcant_budget_lat_grid_all <- dcant_budget_lat_grid
}
if (exists("dcant_budget_lon_grid_all")) {
dcant_budget_lon_grid_all <- bind_rows(dcant_budget_lon_grid_all, dcant_budget_lon_grid)
}
if (!exists("dcant_budget_lon_grid_all")) {
dcant_budget_lon_grid_all <- dcant_budget_lon_grid
}
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_G001"
[1] "v_G002"
[1] "v_G003"
[1] "v_G004"
[1] "v_G005"
[1] "v_G006"rm(dcant_inv,
dcant_inv_bias,
dcant_inv_mod_truth,
dcant_budget_lat_grid,
dcant_budget_lon_grid,
params_local,
tref)
# params_local_all <-
# params_local_all %>%
# mutate(period = factor(period, c("1994 - 2004", "2004 - 2014", "1994 - 2014")))
dcant_inv_all <- full_join(dcant_inv_all,
params_local_all)
dcant_inv_bias_all <- full_join(dcant_inv_bias_all,
params_local_all)
dcant_budget_lat_grid_all <- full_join(dcant_budget_lat_grid_all,
params_local_all)
dcant_budget_lon_grid_all <- full_join(dcant_budget_lon_grid_all,
params_local_all)dcant_inv_all <- dcant_inv_all %>%
filter(inv_depth == params_global$inventory_depth_standard)
dcant_budget_lat_grid_all <- dcant_budget_lat_grid_all %>%
filter(inv_depth == params_global$inventory_depth_standard)
dcant_budget_lon_grid_all <- dcant_budget_lon_grid_all %>%
filter(inv_depth == params_global$inventory_depth_standard)dcant_budget_lat_grid_all <- dcant_budget_lat_grid_all %>%
pivot_wider(names_from = estimate,
values_from = value) %>%
filter(method == "total")
dcant_budget_lon_grid_all <- dcant_budget_lon_grid_all %>%
pivot_wider(names_from = estimate,
values_from = value) %>%
filter(method == "total")dcant_inv_all_G19 <- read_csv(paste0(path_preprocessing,
"G19_dcant_inv_all.csv"))2 Individual cases
2.1 Absoulte values
dcant_inv_join <- bind_rows(
dcant_inv_all_G19 %>%
filter(Version_ID %in% c("01", "04", "05", "07")) %>%
mutate(
MLR_basins = recode(
Version_ID,
"01" = "2",
"04" = "SO_AIP",
"05" = "2+1",
"07" = "5"
)
) %>%
select(lon, lat, dcant = dcant_pos, MLR_basins) %>%
mutate(source = "Gruber 2019"),
dcant_inv_all %>%
filter(data_source %in% c("obs")) %>%
select(lon, lat, dcant, MLR_basins) %>%
mutate(source = "This study")
)
dcant_inv_join <- dcant_inv_join %>%
mutate(dcant = dcant * 10 / 13)dcant_inv_join %>%
p_map_cant_inv(var = "dcant") +
facet_grid(MLR_basins ~ source) +
theme(axis.text = element_blank(),
axis.ticks = element_blank())
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3
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 viridisLite_0.3.0
[4] jsonlite_1.7.1 modelr_0.1.8 bslib_0.2.5.1
[7] assertthat_0.2.1 highr_0.8 blob_1.2.1
[10] cellranger_1.1.0 yaml_2.2.1 pillar_1.6.2
[13] backports_1.1.10 lattice_0.20-41 glue_1.4.2
[16] RcppEigen_0.3.3.7.0 digest_0.6.27 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