Analysis of Cant estimates - 3d vis
Jens Daniel Müller
14 October, 2020
Last updated: 2020-10-14
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Knit directory: Cant_eMLR/
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library(tidyverse)
library(patchwork)
library(scico)
library(scales)
library(metR)
library(marelac)
library(kableExtra)
library(threejs)1 Data sources
Cant estimates from this study:
- Inventories (lat, lon)
Cant_inv <-
read_csv(here::here("data/mapping/_summarized_files",
"Cant_inv.csv"))2 Color scale
For ease of comparison with Gruber et al (2019) we adapt their color scale, including the ranges and breaks applied in various types of visualizations.
rgb2hex <- function(r, g, b)
rgb(r, g, b, maxColorValue = 100)
cols = c(rgb2hex(95, 95, 95),
rgb2hex(0, 0, 95),
rgb2hex(100, 0, 0),
rgb2hex(100, 100, 0))
Gruber_rainbow <- colorRampPalette(cols)
rm(rgb2hex, cols)3 Cant - positive
In a first series of plots we explore the distribution of Cant, taking only positive estimates into account (positive here refers to the mean Cant estimate across 10 eMLR model predictions available for each grid cell). Negative values were set to zero before calculating mean sections and inventories.
3.1 Inventory
3.1.1 Map
Column inventory of positive Cant between the surface and 3000m water depth per horizontal grid cell (lat x lon).
breaks <- c(seq(0,16,2),Inf)
breaks_n <- length(breaks) - 1
Cant_inv <- Cant_inv %>%
mutate(cant_inv_pos_int = cut(cant_inv_pos,
breaks,
right = FALSE)) %>%
mutate(eras = factor(eras, c("JGOFS_GO", "GO_new")))
Cant_inv %>%
filter(eras == "JGOFS_GO") %>%
ggplot() +
geom_raster(data = landmask,
aes(lon, lat), fill = "grey30") +
geom_raster(aes(lon, lat, fill = cant_inv_pos_int)) +
coord_quickmap(expand = 0) +
scale_fill_manual(values = Gruber_rainbow(breaks_n),
name = "Cant") +
theme(axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
legend.position = "bottom")
3.1.2 Interactive Globe
x <- Cant_inv %>%
filter(eras == "JGOFS_GO") %>%
select(lon, lat, cant_inv_pos) %>%
mutate(cant_col = as.character(cut(
cant_inv_pos,
breaks,
Gruber_rainbow(breaks_n)
)))
globejs(
lat = x$lat,
long = x$lon,
color = x$cant_col,
val = 0,
pointsize = 2,
bg = "white",
rotationlong = -0.8,
rotationlat = 0.3
)rm(x)
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_Germany.1252 LC_CTYPE=English_Germany.1252
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C
[5] LC_TIME=English_Germany.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] threejs_0.3.3 igraph_1.2.5 kableExtra_1.1.0 marelac_2.1.10
[5] shape_1.4.4 metR_0.7.0 scales_1.1.1 scico_1.2.0
[9] patchwork_1.0.1 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0
[13] purrr_0.3.4 readr_1.3.1 tidyr_1.1.0 tibble_3.0.3
[17] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.2 jsonlite_1.7.0 viridisLite_0.3.0 here_0.1
[5] modelr_0.1.8 assertthat_0.2.1 blob_1.2.1 cellranger_1.1.0
[9] yaml_2.2.1 pillar_1.4.6 backports_1.1.8 glue_1.4.1
[13] digest_0.6.25 promises_1.1.1 checkmate_2.0.0 rvest_0.3.6
[17] colorspace_1.4-1 htmltools_0.5.0 httpuv_1.5.4 pkgconfig_2.0.3
[21] broom_0.7.0 seacarb_3.2.13 haven_2.3.1 webshot_0.5.2
[25] whisker_0.4 later_1.1.0.1 git2r_0.27.1 farver_2.0.3
[29] generics_0.0.2 ellipsis_0.3.1 withr_2.2.0 cli_2.0.2
[33] magrittr_1.5 crayon_1.3.4 readxl_1.3.1 evaluate_0.14
[37] fs_1.4.2 fansi_0.4.1 xml2_1.3.2 oce_1.2-0
[41] tools_4.0.2 data.table_1.13.0 hms_0.5.3 lifecycle_0.2.0
[45] munsell_0.5.0 reprex_0.3.0 gsw_1.0-5 compiler_4.0.2
[49] rlang_0.4.7 grid_4.0.2 rstudioapi_0.11 htmlwidgets_1.5.1
[53] crosstalk_1.1.0.1 labeling_0.3 base64enc_0.1-3 rmarkdown_2.3
[57] testthat_2.3.2 gtable_0.3.0 DBI_1.1.0 R6_2.4.1
[61] lubridate_1.7.9 knitr_1.30 rprojroot_1.3-2 stringi_1.4.6
[65] Rcpp_1.0.5 vctrs_0.3.2 dbplyr_1.4.4 tidyselect_1.1.0
[69] xfun_0.16