Analysis of Cant estimates
Jens Daniel Müller
14 August, 2020
Last updated: 2020-08-14
Checks: 7 0
Knit directory: Cant_eMLR/
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library(tidyverse)
library(lubridate)1 Data sources
1.1 Sabine 2004
Cant_94 <- read_csv(here::here("data/GLODAPv1_1/_summarized_files",
"Cant_94.csv"))
Cant_94_inv <-
read_csv(here::here("data/GLODAPv1_1/_summarized_files",
"Cant_94_inv.csv"))1.2 Gruber 2019
Cant_07 <- read_csv(here::here("data/Gruber_2019/_summarized_files",
"Cant_07.csv"))
Cant_07_inv <-
read_csv(here::here("data/Gruber_2019/_summarized_files",
"Cant_07_inv.csv"))2 Merge data sets
Cant_94_inv <- Cant_94_inv %>%
select(-cant_inv) %>%
rename(cant_inv = cant_inv_pos)
Cant_inv <- full_join(Cant_07_inv %>% mutate(estimate = "G19"),
Cant_94_inv %>% mutate(estimate = "S04"))3 Inventory maps
Cant_inv %>%
ggplot() +
geom_raster(data = landmask %>% filter(region == "land"),
aes(lon, lat),
fill = "grey80") +
geom_raster(aes(lon, lat, fill = cant_inv)) +
coord_quickmap(expand = 0) +
scale_fill_viridis_c() +
facet_wrap( ~ estimate, ncol = 1) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
)
4 Relative inventory ratios
Cant_inv_wide <- Cant_inv %>%
pivot_wider(values_from = cant_inv, names_from = estimate)
Cant_inv_wide <- Cant_inv_wide %>%
drop_na() %>%
mutate(G19_rel = G19 / sum(G19),
S04_rel = S04 / sum(S04),
cant_ratio = G19 / S04,
cant_ratio_rel = G19_rel / S04_rel)Cant_inv_wide %>%
filter(cant_ratio_rel < 10,
cant_ratio_rel > 0.1) %>%
ggplot() +
geom_raster(data = landmask %>% filter(region == "land",
lat <= 65,
lat >= -80),
aes(lon, lat),
fill = "grey80") +
geom_contour_filled(aes(lon, lat, z = log10(cant_ratio_rel))) +
coord_quickmap(expand = 0) +
scale_fill_brewer(palette = "RdBu", direction = -1) +
labs(title = "Cant inventory distribution | 1994-2007 vs preind-1994",
subtitle = "Ratio of relative contributions to total inventory") +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
legend.title = element_blank()
)
5 Relative inventories
Cant_inv_rel <- Cant_inv_wide %>%
pivot_longer(G19_rel:S04_rel, values_to = "cant_inv_rel", names_to = "estimate")
Cant_inv_rel %>%
ggplot() +
geom_raster(data = landmask %>% filter(region == "land"),
aes(lon, lat),
fill = "grey80") +
geom_raster(aes(lon, lat, fill = cant_inv_rel)) +
coord_quickmap(expand = 0) +
scale_fill_viridis_c() +
facet_wrap( ~ estimate, ncol = 1) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
)
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] lubridate_1.7.9 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0
[5] purrr_0.3.4 readr_1.3.1 tidyr_1.1.0 tibble_3.0.3
[9] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 here_0.1 assertthat_0.2.1 rprojroot_1.3-2
[5] digest_0.6.25 R6_2.4.1 cellranger_1.1.0 backports_1.1.8
[9] reprex_0.3.0 evaluate_0.14 httr_1.4.2 pillar_1.4.6
[13] rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11 whisker_0.4
[17] blob_1.2.1 rmarkdown_2.3 labeling_0.3 munsell_0.5.0
[21] broom_0.7.0 compiler_4.0.2 httpuv_1.5.4 modelr_0.1.8
[25] xfun_0.16 pkgconfig_2.0.3 htmltools_0.5.0 tidyselect_1.1.0
[29] fansi_0.4.1 viridisLite_0.3.0 crayon_1.3.4 dbplyr_1.4.4
[33] withr_2.2.0 later_1.1.0.1 grid_4.0.2 jsonlite_1.7.0
[37] gtable_0.3.0 lifecycle_0.2.0 DBI_1.1.0 git2r_0.27.1
[41] magrittr_1.5 scales_1.1.1 cli_2.0.2 stringi_1.4.6
[45] farver_2.0.3 fs_1.4.2 promises_1.1.1 xml2_1.3.2
[49] ellipsis_0.3.1 generics_0.0.2 vctrs_0.3.2 RColorBrewer_1.1-2
[53] tools_4.0.2 glue_1.4.1 hms_0.5.3 yaml_2.2.1
[57] colorspace_1.4-1 rvest_0.3.6 isoband_0.2.2 knitr_1.29
[61] haven_2.3.1