Global sections
Jens Daniel Müller and Donghe Zhu
21 January, 2021
Last updated: 2021-01-21
Checks: 7 0
Knit directory: emlr_mod_v_XXX/
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1 Data sources
Following Cant mean values per grid cell are used:
1.1 This study
Results from this study are referred to as JDM.
cant_JDM <-
read_csv(paste(path_version_data,
"cant_3d.csv",
sep = ""))
cant_sd <- cant_JDM %>%
select(lon,
lat,
depth,
basin,
basin_AIP,
eras,
cant_sd,
cant_pos_sd,
gamma,
gamma_slab)
cant_JDM <- cant_JDM %>%
select(lon,
lat,
depth,
basin_AIP,
eras,
cant,
cant_pos)1.2 Model Cant
“True” Cant fields directly inferred from the model output are referred to as M.
cant_M <-
read_csv(paste(path_version_data,
"cant_M.csv", sep = ""))
cant_M <- cant_M %>%
select(lon,
lat,
depth,
basin_AIP,
eras,
cant,
cant_pos)1.3 Join data sets
Cant fields are merged, and differences calculate per grid cell and per eras.
# add estimate label
cant_long <- bind_rows(cant_JDM %>% mutate(estimate = "JDM"),
cant_M %>% mutate(estimate = "M"))
# pivot to wide format
cant_wide <- cant_long %>%
pivot_wider(names_from = estimate, values_from = cant:cant_pos) %>%
drop_na()
# calculate offset
cant_wide <- cant_wide %>%
mutate(
cant_pos_offset = cant_pos_JDM - cant_pos_M,
cant_offset = cant_JDM - cant_M,
estimate = "JDM - M"
)
# join with SD deviation of Cant across all (currently 10) MLR models for each grid cell
cant_wide <- left_join(cant_wide, cant_sd)2 Global sections
2.1 Cant - positive only
for (i_eras in unique(cant_long$eras)) {
for (i_estimate in unique(cant_long$estimate)) {
print(
p_section_global(
df = cant_long %>% filter(estimate == i_estimate, eras == i_eras),
var = "cant_pos",
subtitle_text = paste("Estimate:", i_estimate, " | Eras:", i_eras)
)
)
}
print(
p_section_global(
df = cant_wide %>% filter(eras == i_eras),
var = "cant_pos_offset",
col = "divergent",
subtitle_text = paste("Estimate: JDM - M | Eras:", i_eras)
)
)
}
2.2 Cant - all
for (i_eras in unique(cant_long$eras)) {
for (i_estimate in unique(cant_long$estimate)) {
print(
p_section_global(
df = cant_long %>% filter(estimate == i_estimate, eras == i_eras),
var = "cant",
col = "divergent",
subtitle_text = paste("Estimate:", i_estimate, " | Eras:", i_eras)
)
)
}
print(
p_section_global(
df = cant_wide %>% filter(eras == i_eras),
var = "cant_offset",
col = "divergent",
subtitle_text = paste("Estimate: JDM - M | Eras:", i_eras)
)
)
}
3 Model quality check
3.1 Cant - positive only
cant_wide %>%
ggplot(aes(cant_pos_sd, cant_pos_offset)) +
geom_bin2d(binwidth = 5) +
scale_fill_viridis_c(option = "magma",
direction = -1,
trans = "log10") +
facet_grid(eras ~ basin_AIP) +
labs(title = "The offset vs sd of positive Cant across models")
| Version | Author | Date |
|---|---|---|
| 1f3e5b6 | jens-daniel-mueller | 2021-01-20 |
3.2 Cant - all
cant_wide %>%
ggplot(aes(cant_sd, cant_offset)) +
geom_bin2d(binwidth = 5) +
scale_fill_viridis_c(option = "magma",
direction = -1,
trans = "log10") +
facet_grid(eras ~ basin_AIP) +
labs(title = "The offset vs sd of Cant across models")
| Version | Author | Date |
|---|---|---|
| 1f3e5b6 | jens-daniel-mueller | 2021-01-20 |
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 marelac_2.1.10 shape_1.4.5 scales_1.1.1
[5] metR_0.9.0 scico_1.2.0 patchwork_1.1.1 collapse_1.5.0
[9] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
[13] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.3
[17] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.2 viridisLite_0.3.0 jsonlite_1.7.2
[4] here_1.0.1 modelr_0.1.8 assertthat_0.2.1
[7] blob_1.2.1 cellranger_1.1.0 yaml_2.2.1
[10] pillar_1.4.7 backports_1.1.10 lattice_0.20-41
[13] glue_1.4.2 RcppEigen_0.3.3.9.1 digest_0.6.27
[16] promises_1.1.1 checkmate_2.0.0 rvest_0.3.6
[19] colorspace_2.0-0 htmltools_0.5.0 httpuv_1.5.4
[22] Matrix_1.2-18 pkgconfig_2.0.3 broom_0.7.3
[25] seacarb_3.2.15 haven_2.3.1 whisker_0.4
[28] later_1.1.0.1 git2r_0.27.1 farver_2.0.3
[31] generics_0.1.0 ellipsis_0.3.1 withr_2.3.0
[34] cli_2.2.0 magrittr_2.0.1 crayon_1.3.4
[37] readxl_1.3.1 evaluate_0.14 fs_1.5.0
[40] fansi_0.4.1 xml2_1.3.2 RcppArmadillo_0.10.1.2.2
[43] oce_1.2-0 tools_4.0.3 data.table_1.13.6
[46] hms_0.5.3 lifecycle_0.2.0 munsell_0.5.0
[49] reprex_0.3.0 gsw_1.0-5 isoband_0.2.3
[52] compiler_4.0.3 rlang_0.4.10 grid_4.0.3
[55] rstudioapi_0.13 labeling_0.4.2 rmarkdown_2.5
[58] testthat_3.0.1 gtable_0.3.0 DBI_1.1.0
[61] R6_2.5.0 lubridate_1.7.9 knitr_1.30
[64] rprojroot_2.0.2 stringi_1.5.3 parallel_4.0.3
[67] Rcpp_1.0.5 vctrs_0.3.6 dbplyr_1.4.4
[70] tidyselect_1.1.0 xfun_0.20