Basics
Jens Daniel Müller
15 July, 2022
Last updated: 2022-07-15
Checks: 7 0
Knit directory: emlr_obs_analysis/analysis/
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1 Read files
GLODAP_preprocessed <-
read_csv(
paste(
path_preprocessing,
"GLODAPv2.2021_preprocessed.csv",
sep = ""
)
)# land sea mask
landseamask <-
read_csv(paste(path_files,
"land_sea_mask_WOA18.csv",
sep = ""))2 Time series histogram
time_histo <- GLODAP_preprocessed %>%
drop_na() %>%
mutate(version = if_else(cruise <1000, "Gruber et al. (2019)",
"New observations"),
version = if_else(cruise %in% c(1041, 1042), "Gruber et al. (2019)", version)) %>%
count(year, version)
GLODAP_preprocessed %>%
drop_na() %>%
mutate(version = if_else(cruise <1000, "Gruber et al. (2019)",
"New observations"),
version = if_else(cruise %in% c(1041, 1042), "Gruber et al. (2019)", version)) %>%
count(version)# A tibble: 2 × 2
version n
<chr> <int>
1 Gruber et al. (2019) 203878
2 New observations 92136p_time_histo_G19 <-
time_histo %>%
filter(version == "Gruber et al. (2019)") %>%
ggplot() +
geom_col(aes(year, n, fill = version),
col = "grey20") +
scale_fill_manual(values = c("grey70"),
name = "") +
scale_x_continuous(breaks = seq(1900, 2100, 5),
limits = c(1981, 2021)) +
scale_y_continuous(limits = c(0, max(time_histo$n) + 500)) +
coord_cartesian(expand = 0) +
labs(title = "Observations per year") +
theme_classic() +
theme(axis.title = element_blank())
p_time_histo_all <-
time_histo %>%
mutate(version = fct_rev(version)) %>%
ggplot() +
geom_col(aes(year, n, fill = version),
col = "grey20") +
scale_fill_manual(values = c("darkgoldenrod1", "grey70"),
name = "") +
scale_x_continuous(breaks = seq(1900, 2100, 5),
limits = c(1981, 2021)) +
scale_y_continuous(limits = c(0, max(time_histo$n) + 500)) +
coord_cartesian(expand = 0) +
labs(title = "Observations per year") +
theme_classic() +
theme(axis.title = element_blank())
p_time_histo_G19
p_time_histo_all
# ggsave(plot = p_time_histo_G19,
# path = here::here("output/publication"),
# filename = "time_histo_G19.png",
# height = 2,
# width = 10)
ggsave(plot = p_time_histo_all,
path = here::here("output/publication"),
filename = "FigS_coverage_time_series.png",
height = 4,
width = 10)
rm(
p_time_histo_G19,
p_time_histo_all
)time_histo <- GLODAP_preprocessed %>%
filter(year >= 1989,
year <= 2020) %>%
count(year, basin_AIP)
p_time_histo_basin <-
time_histo %>%
ggplot() +
geom_col(aes(year, n, fill = basin_AIP)) +
scale_fill_brewer(palette = "Dark2", name = "Ocean\nbasin") +
scale_x_continuous(breaks = seq(1900, 2100, 5)) +
scale_y_continuous(limits = c(0, NA), expand = c(0,0)) +
labs(y = "Observations per year") +
theme(axis.title.x = element_blank())
p_time_histo_basin
# ggsave(plot = p_time_histo_basin,
# path = here::here("output/publication"),
# filename = "FigS_time_series_observations.png",
# height = 2,
# width = 10)
rm(p_time_histo_basin)3 Basin maps
3.1 MLR basins
MLR_basins_in <- c("1", "2", "AIP", "SO_2", "5", "SO_AIP", "SO")
basinmask <- basinmask %>%
filter(MLR_basins %in% MLR_basins_in)
basinmask <- basinmask %>%
group_by(MLR_basins) %>%
mutate(basin = as.character(as.numeric(as.factor(basin)))) %>%
ungroup()
basin_maps <-
map +
geom_raster(data = basinmask,
aes(lon, lat, fill = basin)) +
scale_fill_brewer(palette = "Dark2", guide = "none") +
facet_wrap( ~ MLR_basins) +
theme(axis.text = element_blank(),
axis.ticks = element_blank())
basin_maps
ggsave(plot = basin_maps,
path = here::here("output/publication"),
filename = "FigS_basin_masks.png",
height = 5,
width = 10)3.2 5 basins
MLR_basins_in <- c("5")
basinmask <- basinmask %>%
filter(MLR_basins %in% MLR_basins_in)
basinmask <- basinmask %>%
group_by(MLR_basins) %>%
mutate(basin = as.character(as.numeric(as.factor(basin)))) %>%
ungroup()
basinmask <- basinmask %>%
mutate(
basin = fct_recode(
basin,
"N. Pacific" = "3",
"S. Pacific" = "5",
"N. Atlantic" = "2",
"S. Atlantic" = "4",
"Indian" = "1"
)
)
basinmask <- basinmask %>%
mutate(basin = fct_relevel(
basin,
"N. Pacific",
"S. Pacific",
"N. Atlantic",
"S. Atlantic",
"Indian"
))
basin_maps <-
map +
geom_raster(data = basinmask,
aes(lon, lat, fill = basin)) +
scale_fill_brewer(palette = "Paired", guide = "none") +
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
legend.title = element_blank())
basin_maps
# ggsave(plot = basin_maps,
# path = here::here("output/publication"),
# filename = "FigS_basin_mask_5.png",
# height = 5,
# width = 10)3.3 Area scaling
mapped_ocean_mask <- full_join(
landseamask %>%
filter(region == "ocean") %>%
select(lon, lat),
basinmask %>%
select(lon, lat) %>%
mutate(mapped_ocean = "1")
) %>%
mutate(mapped_ocean = replace_na(mapped_ocean, 0))
map +
geom_raster(data = mapped_ocean_mask,
aes(lon, lat, fill = mapped_ocean)) +
scale_fill_brewer(palette = "Set1") +
theme(
axis.text = element_blank(),
axis.ticks = element_blank()
)
mapped_ocean_mask %>%
mutate(surface_area = earth_surf(lat, lon)) %>%
group_by(mapped_ocean) %>%
summarise(surface_area = sum(surface_area)) %>%
ungroup() %>%
mutate(surface_area_ratio = surface_area / lead(surface_area))# A tibble: 2 × 3
mapped_ocean surface_area surface_area_ratio
<chr> <dbl> <dbl>
1 0 4.97e12 0.0149
2 1 3.33e14 NA 4 coverage maps all
GLODAP_era_grid <- GLODAP_preprocessed %>%
mutate(era = cut(year,
c(1989, 2000, 2010, 2021),
labels = c("1989 - 1999", "2000 - 2009", "2010 - 2020"),
right = FALSE)) %>%
group_by(lon, lat, era) %>%
summarise(year_max = max(year),
year_min = min(year)) %>%
ungroup() %>%
drop_na()
coverage_map <-
map +
geom_tile(data = GLODAP_era_grid,
aes(lon, lat,
fill = "X")) +
scale_fill_brewer(palette = "Dark2", guide = "none") +
facet_wrap(~ era, ncol = 2) +
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank())
coverage_map
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3
Matrix products: default
BLAS: /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/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] colorspace_2.0-2 marelac_2.1.10 shape_1.4.6 ggforce_0.3.3
[5] metR_0.11.0 scico_1.3.0 patchwork_1.1.1 collapse_1.7.0
[9] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[13] readr_2.1.1 tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5
[17] tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 bit64_4.0.5 lubridate_1.8.0 gsw_1.0-6
[5] RColorBrewer_1.1-2 httr_1.4.2 rprojroot_2.0.2 tools_4.1.2
[9] backports_1.4.1 bslib_0.3.1 utf8_1.2.2 R6_2.5.1
[13] DBI_1.1.2 withr_2.4.3 tidyselect_1.1.1 processx_3.5.2
[17] bit_4.0.4 compiler_4.1.2 git2r_0.29.0 cli_3.1.1
[21] rvest_1.0.2 xml2_1.3.3 labeling_0.4.2 sass_0.4.0
[25] scales_1.1.1 checkmate_2.0.0 SolveSAPHE_2.1.0 callr_3.7.0
[29] digest_0.6.29 rmarkdown_2.11 oce_1.5-0 pkgconfig_2.0.3
[33] htmltools_0.5.2 highr_0.9 dbplyr_2.1.1 fastmap_1.1.0
[37] rlang_1.0.2 readxl_1.3.1 rstudioapi_0.13 jquerylib_0.1.4
[41] generics_0.1.1 farver_2.1.0 jsonlite_1.7.3 vroom_1.5.7
[45] magrittr_2.0.1 Rcpp_1.0.8 munsell_0.5.0 fansi_1.0.2
[49] lifecycle_1.0.1 stringi_1.7.6 whisker_0.4 yaml_2.2.1
[53] MASS_7.3-55 grid_4.1.2 parallel_4.1.2 promises_1.2.0.1
[57] crayon_1.4.2 haven_2.4.3 hms_1.1.1 seacarb_3.3.0
[61] knitr_1.37 ps_1.6.0 pillar_1.6.4 reprex_2.0.1
[65] glue_1.6.0 evaluate_0.14 getPass_0.2-2 data.table_1.14.2
[69] modelr_0.1.8 vctrs_0.3.8 tzdb_0.2.0 tweenr_1.0.2
[73] httpuv_1.6.5 cellranger_1.1.0 gtable_0.3.0 polyclip_1.10-0
[77] assertthat_0.2.1 xfun_0.29 broom_0.7.11 later_1.3.0
[81] ellipsis_0.3.2 here_1.0.1